options-spread-conviction-engine

Options Spread Conviction Engine Multi-regime options spread scoring using technical indicators and IV term structure analysis. Install Overview This engine analyzes any ticker and scores seven options strategies across two categories: Vertical Spreads (Directional) | Strategy | Type | Philosophy | Ideal Setup | |----------|------|------------|-------------| | bull put | Credit | Mean Reversion | Bullish trend + oversold dip | | bear call | Credit | Mean Reversion | Bearish trend + overbought rip | | bull call | Debit | Breakout | Strong bullish momentum | | bear put | Debit | Breakout | Stro…

, ticker.upper()):\n raise ValueError(f\"Invalid ticker: {ticker}\")\n df = yf.download(ticker, period=period, interval=interval, progress=False)\n return df\n\n# After\ndef fetch_ohlcv(ticker: str, period: str = \"2y\", interval: str = \"1d\") -> pd.DataFrame:\n # Validate ticker\n if not re.match(r'^[A-Z]{1,5}

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

, ticker.upper()):\n raise ValueError(f\"Invalid ticker format: {ticker}\")\n \n # Validate period\n valid_periods = {\"1d\", \"5d\", \"1mo\", \"3mo\", \"6mo\", \"1y\", \"2y\", \"5y\", \"10y\", \"ytd\", \"max\"}\n if period not in valid_periods:\n raise ValueError(f\"Invalid period: {period}. Must be one of {valid_periods}\")\n \n # Validate interval\n valid_intervals = {\"1m\", \"2m\", \"5m\", \"15m\", \"30m\", \"60m\", \"90m\", \"1h\", \"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"}\n if interval not in valid_intervals:\n raise ValueError(f\"Invalid interval: {interval}\")\n \n # Check period/interval compatibility\n if interval in {\"1m\", \"2m\", \"5m\", \"15m\", \"30m\", \"60m\", \"90m\"} and period not in {\"1d\", \"5d\", \"1mo\"}:\n raise ValueError(f\"Intraday intervals only available for periods \u003c= 1mo\")\n \n df = yf.download(ticker, period=period, interval=interval, progress=False)\n if df.empty:\n raise DataFetchError(f\"No data returned for {ticker} with period={period}, interval={interval}\")\n \n return df\n```\n\n### 5.4 Missing Timeout Handling\n\nAll yfinance calls lack timeout parameters:\n\n```python\n# Current (chain_analyzer.py:185)\nvix = yf.Ticker(self.vix_ticker)\nhist = vix.history(start=start_date, end=end_date)\n\n# Should be\ndef fetch_with_timeout(ticker, timeout: float = 30.0):\n \"\"\"Fetch data with timeout protection.\"\"\"\n import signal\n \n class TimeoutError(Exception):\n pass\n \n def handler(signum, frame):\n raise TimeoutError(f\"Data fetch exceeded {timeout}s\")\n \n # Set alarm (Unix only; Windows needs different approach)\n signal.signal(signal.SIGALRM, handler)\n signal.alarm(int(timeout))\n \n try:\n tk = yf.Ticker(ticker)\n hist = tk.history(start=start_date, end=end_date)\n return hist\n finally:\n signal.alarm(0) # Cancel alarm\n```\n\n---\n\n## 6. Performance Issues\n\n### 6.1 Inefficient Loops\n\n| File | Lines | Issue | Optimization |\n|------|-------|-------|--------------|\n| `leg_optimizer.py` | 585-785 | Nested loops over options chain with repeated calculations | Pre-calculate valid options, use list comprehensions |\n| `backtest_validator.py` | 165-225 | Walk-forward loop creates new DataFrame for each date | Vectorized operations or caching |\n| `options_math.py` | 185-215 | Monte Carlo loop could be vectorized | Use numpy broadcasting |\n\n### 6.2 Redundant API Calls\n\n| File | Lines | Issue | Solution |\n|------|-------|-------|----------|\n| `regime_detector.py` | 95-125 | VIX data fetched separately from stock data | Batch data fetches |\n| `quant_scanner.py` | 385-395, 405-415 | Quote fetched, then chain fetched separately | Unified fetch operation |\n| `vol_forecaster.py` | 95-115 | Returns fetched separately per ticker | Cache per session |\n\n### 6.3 No Caching of Expensive Computations\n\n```python\n# Add to options_math.py or create caching module\nfrom functools import lru_cache\nimport hashlib\n\nclass ComputationCache:\n \"\"\"Disk-backed cache for expensive computations.\"\"\"\n \n def __init__(self, cache_dir: str = \".cache\"):\n self.cache_dir = Path(cache_dir)\n self.cache_dir.mkdir(exist_ok=True)\n \n def _get_key(self, func_name: str, args: tuple, kwargs: dict) -> str:\n \"\"\"Generate cache key from function arguments.\"\"\"\n key_str = f\"{func_name}:{args}:{sorted(kwargs.items())}\"\n return hashlib.md5(key_str.encode()).hexdigest()\n \n def get_or_compute(self, func, *args, ttl: int = 3600, **kwargs):\n \"\"\"Get cached result or compute and cache.\"\"\"\n key = self._get_key(func.__name__, args, kwargs)\n cache_file = self.cache_dir / f\"{key}.pkl\"\n \n # Check cache\n if cache_file.exists():\n import time\n if time.time() - cache_file.stat().st_mtime \u003c ttl:\n with open(cache_file, 'rb') as f:\n return pickle.load(f)\n \n # Compute and cache\n result = func(*args, **kwargs)\n with open(cache_file, 'wb') as f:\n pickle.dump(result, f)\n \n return result\n\n# Usage\nfrom options_math import ProbabilityCalculator\n\ncache = ComputationCache()\ncalc = ProbabilityCalculator()\n\n# Cached GARCH fitting\nresult = cache.get_or_compute(\n calc.fit_garch, \n returns_series, \n ttl=86400 # Cache for 1 day\n)\n```\n\n### 6.4 Memory Leaks\n\n| File | Lines | Issue | Fix |\n|------|-------|-------|-----|\n| `quant_scanner.py` | 145-155 | `_vix_cache` and `_cache_date` could grow unbounded | Use LRU cache with maxsize |\n| `leg_optimizer.py` | 125-130 | `historical_vols` dict never cleared | Clear after use or use TTL |\n\n---\n\n## 7. Type Safety Issues\n\n### 7.1 Missing Type Hints\n\n**File-by-file coverage:**\n\n| File | Lines | Missing Type Hints | % Coverage |\n|------|-------|-------------------|------------|\n| `quant_scanner.py` | 423 | 180 lines | ~57% |\n| `spread_conviction_engine.py` | 1795 | 650 lines | ~64% |\n| `leg_optimizer.py` | 1454 | 580 lines | ~60% |\n| `multi_leg_strategies.py` | 1688 | 720 lines | ~57% |\n| `options_math.py` | 700 | 280 lines | ~60% |\n| `chain_analyzer.py` | 400 | 120 lines | ~70% |\n\n**Priority fixes:**\n\n```python\n# Before (common pattern in codebase)\ndef calculate_strategy_metrics(self, strategy, iv=0.25):\n ...\n\n# After \ndef calculate_strategy_metrics(\n self, \n strategy: MultiLegStrategy, \n iv: float = 0.25\n) -> MultiLegStrategy:\n \"\"\"Calculate all metrics for a strategy.\n \n Args:\n strategy: The strategy to analyze\n iv: Implied volatility estimate (annualized, decimal)\n \n Returns:\n Strategy with populated metrics\n \n Raises:\n ValueError: If strategy has no legs\n \"\"\"\n ...\n```\n\n### 7.2 Using `Any` When Specific Type Possible\n\n| File | Lines | Current | Should Be |\n|------|-------|---------|-----------|\n| `quant_scanner.py` | 195 | `-> Dict[str, Any]` | `-> Dict[str, Union[str, float, Dict]]` |\n| `quantitative_integration.py` | 145 | `existing_positions: List[Dict]` | `List[PositionCorrelation]` |\n| `backtest_validator.py` | 55 | `conviction_engine: Any` | `ConvictionEngine` (Protocol) |\n\n### 7.3 Incorrect Type Hints\n\n| File | Lines | Issue | Fix |\n|------|-------|-------|-----|\n| `options_math.py` | 95 | `n_sims: int = 100000` OK, but docstring says default 10000 | Align docstring |\n| `position_sizer.py` | 85 | `pop: float = 0.0` but should be constrained to [0,1] | Use `Annotated[float, Interval(ge=0, le=1)]` with pydantic |\n\n---\n\n## 8. Documentation Issues\n\n### 8.1 Missing Docstrings\n\n| File | Function/Class | Missing Elements |\n|------|----------------|------------------|\n| `quant_scanner.py` | `QuantConvictionEngine._estimate_pop()` | All documentation |\n| `quant_scanner.py` | `QuantConvictionEngine._is_regime_favorable()` | Args, returns |\n| `leg_optimizer.py` | `LegOptimizer.optimize_iron_condors()` | Detailed parameters |\n| `multi_leg_strategies.py` | `score_iron_condor()` | Algorithm explanation |\n\n### 8.2 Outdated Docstrings\n\n| File | Line(s) | Issue |\n|------|---------|-------|\n| `spread_conviction_engine.py` | 45-55 | Version says 2.0.0 but constants still use v1.x values |\n| `backtest_validator.py` | 75-85 | References \"White (2000)\" but implementation differs |\n\n### 8.3 Unclear Parameter Descriptions\n\n```python\n# Before (leg_optimizer.py)\ndef calculate_strategy_metrics(self, strategy: MultiLegStrategy,\n iv: float = 0.25) -> MultiLegStrategy:\n \"\"\"Calculate all metrics for a strategy\"\"\"\n\n# After\ndef calculate_strategy_metrics(\n self, \n strategy: MultiLegStrategy,\n iv: float = 0.25\n) -> MultiLegStrategy:\n \"\"\"Calculate P&L metrics, POP, Greeks, and account fit for a strategy.\n \n Args:\n strategy: MultiLegStrategy with populated legs. Must have at least\n 2 legs for spreads, 4 for iron condors.\n iv: Implied volatility estimate for probability calculations.\n Should be annualized decimal (e.g., 0.25 for 25% IV).\n Used for Black-Scholes POP calculation.\n \n Returns:\n The same strategy object with populated fields:\n - max_profit: Maximum possible profit in dollars\n - max_loss: Maximum possible loss in dollars (always >= 0)\n - breakevens: List of breakeven underlying prices\n - pop: Probability of profit [0, 1]\n - expected_value: Risk-adjusted expected P&L\n \n Raises:\n ValueError: If strategy has no legs or invalid construction\n \"\"\"\n```\n\n---\n\n## 9. Refactoring Priority Queue\n\n### P0 (Critical) — Must Fix Immediately\n\n1. **Consolidate Kelly Criterion Implementations**\n - **Effort:** 4 hours\n - **Risk:** Current state risks inconsistent position sizing decisions\n - **Action:** Create `position_sizing/kelly.py`, migrate all implementations\n\n2. **Merge Duplicate QuantConvictionEngine Classes**\n - **Effort:** 6 hours\n - **Risk:** Users get different behavior depending on import path\n - **Action:** Audit both implementations, merge features, deprecate one\n\n3. **Add Input Validation to Public APIs**\n - **Effort:** 3 hours\n - **Risk:** Silent failures produce incorrect trading recommendations\n - **Action:** Add validation layer to all user-facing entry points\n\n### P1 (High) — Fix This Week\n\n4. **Replace Bare Except Clauses**\n - **Effort:** 2 hours\n - **Action:** Audit all `except:` and `except Exception:` patterns\n\n5. **Extract Strategy Pattern for Calculators**\n - **Effort:** 8 hours\n - **Action:** Create `strategies/` package with base class and implementations\n\n6. **Add Type Hints to Public Interfaces**\n - **Effort:** 6 hours\n - **Action:** Focus on entry points: `analyse()`, `calculate_position()`, `scan_ticker()`\n\n7. **Create Data Provider Abstraction**\n - **Effort:** 5 hours\n - **Action:** Extract yfinance dependencies behind protocol\n\n### P2 (Medium) — Fix Next Sprint\n\n8. **Consolidate Constants**\n - **Effort:** 3 hours\n - **Action:** Single `constants.py` module with all magic numbers\n\n9. **Implement Proper Caching Layer**\n - **Effort:** 6 hours\n - **Action:** Disk-backed LRU cache for expensive computations\n\n10. **Extract Scoring Tables to Configuration**\n - **Effort:** 4 hours\n - **Action:** JSON/YAML config for RSI/MACD/%B scoring tables\n\n11. **Add Timeout Handling to Network Calls**\n - **Effort:** 3 hours\n - **Action:** Wrapper for all yfinance calls\n\n### P3 (Low) — Nice to Have\n\n12. **Full Type Hint Coverage**\n - **Effort:** 12 hours\n\n13. **Comprehensive Docstring Updates**\n - **Effort:** 8 hours\n\n14. **Performance Optimization (Vectorization)**\n - **Effort:** 10 hours\n\n---\n\n## 10. Consolidation Opportunities\n\n### 10.1 Files That Should Be Merged\n\n| Files | Merge Target | Rationale |\n|-------|--------------|-----------|\n| `position_sizer.py` + `enhanced_kelly.py` + `options_math.py` (Kelly parts) | `position_sizing/` package | Single responsibility: position sizing |\n| `quant_scanner.py` + `quantitative_integration.py` | `scanner/quantitative.py` | Single unified scanner |\n| `spread_conviction_engine.py` + `multi_leg_strategies.py` | `conviction/` package | Conviction scoring strategies |\n| `calculator.py` + `options_math.py` (strategy parts) | `strategies/calculators.py` | Strategy P&L calculations |\n\n### 10.2 New Module Structure Recommended\n\n```\noptions-spread-conviction-engine/\n├── __init__.py\n├── cli.py # Entry points\n├── config/\n│ ├── __init__.py\n│ ├── constants.py # All magic numbers\n│ └── scoring_tables.py # JSON scoring configurations\n├── conviction/\n│ ├── __init__.py\n│ ├── base.py # ConvictionEngine ABC\n│ ├── vertical_spreads.py # Current spread_conviction_engine\n│ ├── multi_leg.py # Current multi_leg_strategies\n│ └── quantitative.py # QuantConvictionEngine\n├── data/\n│ ├── __init__.py\n│ ├── protocols.py # Provider protocols\n│ ├── yfinance_provider.py # Yahoo Finance implementation\n│ └── cache.py # Caching layer\n├── position_sizing/\n│ ├── __init__.py\n│ ├── kelly.py # Unified Kelly implementation\n│ ├── sizer.py # Position sizing logic\n│ └── constraints.py # Account constraint validation\n├── strategies/\n│ ├── __init__.py\n│ ├── base.py # StrategyCalculator ABC\n│ ├── registry.py # Strategy lookup\n│ ├── vertical_spreads.py # Put/call credit/debit calculators\n│ ├── iron_condor.py # IC calculator\n│ └── butterfly.py # Butterfly calculator\n├── analysis/\n│ ├── __init__.py\n│ ├── regime.py # Regime detection\n│ ├── volatility.py # Vol forecasting\n│ └── backtest.py # Validation framework\n└── utils/\n ├── __init__.py\n ├── math_helpers.py\n └── validation.py\n```\n\n### 10.3 Shared Utilities to Extract\n\n| Utility | Current Location(s) | New Location |\n|---------|---------------------|--------------|\n| Ticker validation regex | `quant_scanner.py`, `spread_conviction_engine.py` | `utils/validation.py` |\n| Rate limiting | `chain_analyzer.py`, `vol_forecaster.py` | `data/rate_limiter.py` |\n| Risk-free rate | Multiple files | `config/constants.py` |\n| IV floor validation | `leg_optimizer.py`, `options_math.py` | `utils/validation.py` |\n| Account constraint checks | `options_math.py`, `leg_optimizer.py` | `position_sizing/constraints.py` |\n\n---\n\n## 11. Specific Code Examples\n\n### 11.1 Before/After: Position Sizing Consolidation\n\n**Before (3 files, inconsistent):**\n\n```python\n# position_sizer.py\ndef calculate_position(account_value, max_loss_per_spread, ...):\n full_kelly = kelly_criterion(pop, win_amount, max_loss_per_spread)\n # ... 60 more lines\n\n# enhanced_kelly.py \nclass EnhancedKellySizer:\n def kelly_criterion(self, win_prob, win_amount, loss_amount):\n odds = win_amount / loss_amount\n kelly = (win_prob * odds - loss_prob) / odds\n return kelly, edge\n # ... different calculation!\n\n# options_math.py\ndef kelly_position_size(pop, max_profit, max_loss, ...):\n f_full = kelly_fraction(pop, b)\n # ... different API entirely\n```\n\n**After (unified):**\n\n```python\n# position_sizing/kelly.py\nfrom dataclasses import dataclass\nfrom enum import Enum, auto\nfrom typing import Optional\nimport numpy as np\n\nclass KellyVariant(Enum):\n \"\"\"Kelly fraction variants for risk management.\"\"\"\n FULL = (1.0, \"aggressive\")\n HALF = (0.5, \"moderate\")\n QUARTER = (0.25, \"conservative\")\n EIGHTH = (0.125, \"very_conservative\")\n \n def __init__(self, multiplier: float, description: str):\n self.multiplier = multiplier\n self.description = description\n\n@dataclass(frozen=True)\nclass TradeParameters:\n \"\"\"Validated trade parameters for Kelly calculation.\"\"\"\n win_probability: float\n win_amount: float\n loss_amount: float\n \n def __post_init__(self):\n if not 0 \u003c= self.win_probability \u003c= 1:\n raise ValueError(f\"win_probability must be in [0,1]\")\n if self.win_amount \u003c= 0:\n raise ValueError(f\"win_amount must be positive\")\n if self.loss_amount \u003c= 0:\n raise ValueError(f\"loss_amount must be positive\")\n \n @property\n def odds(self) -> float:\n \"\"\"Calculate odds ratio (b in Kelly formula).\"\"\"\n return self.win_amount / self.loss_amount\n \n @property\n def expected_value(self) -> float:\n \"\"\"Calculate expected value of trade.\"\"\"\n q = 1 - self.win_probability\n return self.win_probability * self.win_amount - q * self.loss_amount\n \n @property\n def edge(self) -> float:\n \"\"\"Edge as percentage of risk.\"\"\"\n return self.expected_value / self.loss_amount\n\n@dataclass(frozen=True)\nclass KellyResult:\n \"\"\"Result of Kelly criterion calculation.\"\"\"\n raw_fraction: float # f* (can be negative)\n adjusted_fraction: float # After variant and cap\n variant_used: KellyVariant\n max_fraction_cap: float\n edge: float\n expected_value: float\n is_favorable: bool # True if raw_fraction > 0\n \n def get_contracts(self, account_value: float, \n max_risk_per_trade: float) -> int:\n \"\"\"Calculate integer contract count.\"\"\"\n risk_dollars = self.adjusted_fraction * account_value\n risk_dollars = min(risk_dollars, max_risk_per_trade)\n return max(0, int(risk_dollars // self.loss_amount))\n\nclass KellyCriterion:\n \"\"\"Unified Kelly criterion calculator.\"\"\"\n \n DEFAULT_MAX_FRACTION = 0.25\n \n @classmethod\n def calculate(\n cls,\n params: TradeParameters,\n variant: KellyVariant = KellyVariant.HALF,\n max_fraction: Optional[float] = None,\n existing_correlation: float = 0.0\n ) -> KellyResult:\n \"\"\"Calculate Kelly fraction with full validation.\n \n Args:\n params: Validated trade parameters\n variant: Kelly fraction variant (default: Half Kelly)\n max_fraction: Hard cap on fraction (default: 0.25)\n existing_correlation: Correlation with existing positions [0,1]\n \n Returns:\n KellyResult with all calculations\n \"\"\"\n max_fraction = max_fraction or cls.DEFAULT_MAX_FRACTION\n \n # Raw Kelly: f* = (p*b - q) / b\n q = 1 - params.win_probability\n b = params.odds\n f_star = (params.win_probability * b - q) / b if b > 0 else 0.0\n \n # Apply correlation adjustment\n correlation_factor = 1 - (existing_correlation * 0.5)\n f_adjusted = f_star * variant.multiplier * correlation_factor\n \n # Apply hard cap\n f_adjusted = max(0.0, min(f_adjusted, max_fraction))\n \n return KellyResult(\n raw_fraction=f_star,\n adjusted_fraction=f_adjusted,\n variant_used=variant,\n max_fraction_cap=max_fraction,\n edge=params.edge,\n expected_value=params.expected_value,\n is_favorable=f_star > 0\n )\n\n# Legacy compatibility functions\ndef kelly_criterion(pop: float, win_amount: float, loss_amount: float) -> float:\n \"\"\"Legacy compatibility: returns raw Kelly fraction only.\"\"\"\n params = TradeParameters(win_probability=pop, \n win_amount=win_amount, \n loss_amount=loss_amount)\n result = KellyCriterion.calculate(params, variant=KellyVariant.FULL)\n return result.raw_fraction\n```\n\n### 11.2 Before/After: Error Handling\n\n**Before:**\n\n```python\n# quant_scanner.py (lines 185-200)\ndef _estimate_current_iv(self, ticker: str, forecaster: VolatilityForecaster) -> float:\n try:\n fetcher = ChainFetcher(rate_limit_delay=0.1)\n quote = fetcher.fetch_quote(ticker)\n if quote and 'impliedVolatility' in quote:\n return quote['impliedVolatility'] * 100\n except:\n pass\n \n if forecaster._garch_result:\n rv = forecaster._garch_result.fitted_vol.iloc[-1]\n return rv + 2.5\n \n return 20.0\n```\n\n**After:**\n\n```python\n# quant_scanner.py\ndef _estimate_current_iv(\n self, \n ticker: str, \n forecaster: VolatilityForecaster,\n logger: Optional[logging.Logger] = None\n) -> float:\n \"\"\"Estimate current implied volatility with fallback chain.\n \n Tries multiple estimation methods in order:\n 1. Live options quote IV (most accurate)\n 2. GARCH forecast + typical VRP (2-3%)\n 3. Default assumption (20%)\n \n Args:\n ticker: Stock symbol\n forecaster: Volatility forecaster with potential GARCH fit\n logger: Optional logger for debugging\n \n Returns:\n Estimated IV as percentage (e.g., 25.0 for 25%)\n \"\"\"\n log = logger or logging.getLogger(__name__)\n \n # Attempt 1: Live options quote\n try:\n fetcher = ChainFetcher(rate_limit_delay=0.1)\n quote = fetcher.fetch_quote(ticker)\n if quote and quote.get('impliedVolatility'):\n iv = quote['impliedVolatility'] * 100\n log.debug(f\"IV from options quote for {ticker}: {iv:.1f}%\")\n return iv\n log.debug(f\"No IV in quote for {ticker}\")\n except DataFetchError as e:\n log.warning(f\"Failed to fetch options quote for {ticker}: {e}\")\n except Exception as e:\n log.error(f\"Unexpected error fetching IV for {ticker}: {e}\")\n \n # Attempt 2: GARCH forecast + typical VRP\n if forecaster._garch_result is not None:\n try:\n rv = forecaster._garch_result.fitted_vol.iloc[-1]\n typical_vrp = 2.5 # Historical average VRP\n estimated_iv = rv + typical_vrp\n log.debug(f\"IV from GARCH+VRP for {ticker}: {estimated_iv:.1f}%\")\n return estimated_iv\n except (AttributeError, IndexError) as e:\n log.warning(f\"GARCH result incomplete for {ticker}: {e}\")\n \n # Attempt 3: Default assumption\n default_iv = 20.0\n log.warning(f\"Using default IV for {ticker}: {default_iv}%\")\n return default_iv\n```\n\n---\n\n## 12. Summary\n\nThe Options Spread Conviction Engine is a sophisticated quantitative trading tool with solid mathematical underpinnings. The main issues are:\n\n1. **Accumulated Technical Debt:** Multiple development phases have left duplicate implementations scattered across files\n2. **Inconsistent Interfaces:** Similar classes with different APIs create confusion\n3. **Insufficient Abstraction:** Strategy calculations embedded in if/elif chains rather than proper strategy pattern\n4. **Error Handling Gaps:** Silent failures and bare except clauses create maintenance risk\n\nThe recommended refactoring focuses on:\n1. **Consolidation** (P0) — Merge duplicate Kelly and engine implementations\n2. **Abstraction** (P1) — Extract strategy pattern and data providers\n3. **Validation** (P1) — Add proper input validation and error handling\n4. **Type Safety** (P2) — Comprehensive type hints for maintainability\n\nEstimated total effort: **80-110 hours** for full refactoring, but **16 hours** for critical P0 fixes that would significantly improve code health.\n\n---\n\n*Report generated by OpenClaw Subagent* \n*Review based on codebase state as of February 13, 2026*\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":38637,"content_sha256":"a374e6e8b879071401d5a586a4785c8d35aab32168edbc0e6869003ff4bea0c3"},{"filename":"data/ndx100_tickers.txt","content":"AAPL\nMSFT\nAMZN\nNVDA\nGOOGL\nGOOG\nMETA\nTSLA\nAVGO\nPEP\nCOST\nCSCO\nADBE\nTXN\nCMCSA\nAMD\nNFLX\nQCOM\nHON\nINTU\nAMGN\nSBUX\nISRG\nMDLZ\nGILD\nADI\nVRTX\nMU\nBKNG\nLRCX\nREGN\nPANW\nSNPS\nKLAC\nCSX\nASML\nMRNA\nCDNS\nLULU\nMAR\nKDP\nKHC\nABNB\nNXPI\nADP\nCTAS\nORLY\nMELI\nCRWD\nMRVL\nDXCM\nMNST\nAZN\nPYPL\nFTNT\nAEP\nTEAM\nDASH\nROST\nFAST\nDDOG\nODFL\nKDP\nZS\nEA\nBIDU\nLCID\nJD\nEBAY\nCTSH\nXEL\nNTES\nMCHP\nWBD\nRIVN\nSIRI\nALGN\nILMN\nENPH\nZM\nMTCH\nDOCU\nOKTA\nPCAR\nCPRT\nVRSK\nSGEN\nPAYX\nSWKS\nANSS\nSPLK\nTCOM\nDLTR\nFANG\nCHTR\nCTVA\nBIIB\nFOXA\nEXC\nRPRX\nAAL\nWBA\n","content_type":"text/plain; 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charset=utf-8","language":null,"size":4029,"content_sha256":"2c5ab7170575a3c0deddd233420413f469c03b557e418264f245ec09c2cb0bb5"},{"filename":"data/test_tickers.txt","content":"AAPL\nMSFT\nNVDA\n","content_type":"text/plain; charset=utf-8","language":null,"size":15,"content_sha256":"6f403f461a91181cbd62a9534bcdc70cb8cd0ea3f852bf760185f3b90d1e8f81"},{"filename":"MULTI_LEG_REPORT.md","content":"# Options Spread Conviction Engine — Multi-Leg Extension Report\n\n## 1. Files Modified/Created\n\n### New File Created\n**`/home/linuxbrew/.openclaw/workspace/skills/options-spread-conviction-engine/scripts/multi_leg_strategies.py`**\n\nA complete new module (1500+ lines) implementing:\n- `MultiLegStrategyType` enum (iron_condor, butterfly, calendar)\n- Component weight definitions for each strategy\n- IV Rank computation via Bollinger Bandwidth percentile\n- Squeeze detection for butterflies\n- IV Term Structure analysis from live options chains\n- Neutrality scoring (shared across strategies)\n- Strategy-specific strike calculation\n- Full analysis pipeline (`analyse_multi_leg()`)\n- Report formatting (`print_multi_leg_report()`)\n\n### Modified Files\n\n1. **`/home/linuxbrew/.openclaw/workspace/skills/options-spread-conviction-engine/scripts/spread_conviction_engine.py`**\n - Added `__version__ = \"2.0.0\"` module variable\n - Updated docstring to document v2.0.0 additions\n - Refactored `main()` to route strategies:\n - Vertical spreads (bull_put, bear_call, bull_call, bear_put) → existing `analyse()`\n - Multi-leg strategies (iron_condor, butterfly, calendar) → new `analyse_multi_leg()`\n - Updated argument parser with all 7 strategies\n - Enhanced `--help` output with strategy categorization\n\n2. **`/home/linuxbrew/.openclaw/workspace/skills/options-spread-conviction-engine/SKILL.md`**\n - Updated version to 2.0.0\n - Added documentation for all three multi-leg strategies\n - Documented scoring weights for each strategy\n - Added IV Rank approximation methodology\n - Added IV Term Structure data source explanation\n - Updated usage examples\n - Added limitations and assumptions section\n\n---\n\n## 2. New Strategy Detection Logic\n\n### Iron Condor (Credit / Premium Selling)\n\n**Philosophy:** Sell rich premiums in range-bound, high-volatility environments.\n\n**Detection Triggers:**\n| Signal | Threshold | Weight |\n|--------|-----------|--------|\n| IV Rank (BBW %) | >70 = rich premiums | 25 pts |\n| RSI Neutrality | 40-60 = no momentum | 20 pts |\n| ADX Range-Bound | \u003c25 = weak trend | 20 pts |\n| Price Centering | %B near 0.50 | 20 pts |\n| MACD Neutrality | Histogram near zero | 15 pts |\n\n**Strike Calculation:**\n- Uses 1-sigma (midpoint of SMA and BB edge) for short strikes\n- Uses 2-sigma (BB band edges) for long strikes (wings)\n- Example: With price $681, short put at ~$685, long put at ~$680, short call at ~$695, long call at ~$700\n\n**Output:**\n- `max_profit_zone`: Width between short strikes ($685-$695)\n- `wing_width`: Distance from short to long strikes ($5)\n- All 4 strikes with descriptive rationale\n\n### Butterfly (Debit / Pinning Play)\n\n**Philosophy:** Profit from volatility compression when price is pinned near middle strike.\n\n**Detection Triggers:**\n| Signal | Threshold | Weight |\n|--------|-----------|--------|\n| BB Squeeze | Percentile \u003c25 = compression | 30 pts |\n| RSI Neutrality | 45-55 = dead-center | 25 pts |\n| ADX Weakness | \u003c20 = no trend | 20 pts |\n| Price Centering | %B at 0.50 | 15 pts |\n| MACD Flatness | Histogram near zero | 10 pts |\n\n**Special Logic:**\n- Squeeze duration bonus: >5 bars = +5 pts, >10 bars = +10 pts\n- Tighter thresholds than iron condor (stricter neutrality required)\n\n**Strike Calculation:**\n- Middle strike at SMA (rounded)\n- Wings equidistant from center\n- Example: With price $681, lower wing at $685, center at $690, upper wing at $695\n\n**Output:**\n- `max_profit_price`: Middle strike where max profit occurs\n- `profit_zone`: Approximate breakeven range\n- All 3 strikes with wing width\n\n### Calendar Spread (Debit / Theta Harvesting)\n\n**Philosophy:** Harvest theta decay differential when front-month IV exceeds back-month IV.\n\n**Detection Triggers:**\n| Signal | Threshold | Weight |\n|--------|-----------|--------|\n| IV Term Structure | Front IV > Back IV by >5% | 30 pts |\n| Price Stability | Low recent BBW | 20 pts |\n| RSI Neutrality | No directional bias | 20 pts |\n| ADX Moderate | 18-25 = structure without trend | 15 pts |\n| MACD Neutrality | No acceleration | 15 pts |\n\n**IV Data Sources:**\n1. **Primary:** Live ATM call IV from Yahoo Finance options chains\n2. **Fallback:** Historical volatility term structure (HV 10-day vs HV 30-day)\n\n**Strike Calculation:**\n- ATM strike rounded to standard intervals\n- Front expiry: nearest available\n- Back expiry: 25+ days after front\n\n**Output:**\n- `iv_differential_pct`: (Front IV - Back IV) / Back IV × 100\n- `theta_advantage`: Human-readable description of edge\n- `is_inverted`: Boolean flag if term structure is inverted\n\n---\n\n## 3. Example Outputs\n\n### Iron Condor — SPY (WATCH Tier)\n\n```\n================================================================================\n CONVICTION REPORT: SPY (v2.0.0)\n Strategy: Iron Condor (Credit)\n================================================================================\n Price: $681.27\n Quality: HIGH\n Conviction: 31.8 / 100\n Action Tier: WAIT\n--------------------------------------------------------------------------------\n Strategy: Iron Condor (Credit) (Premium Selling / Range-Bound)\n Ideal Setup: IV Rank >70, RSI neutral (40-60), price centered in range, ADX \u003c25\n Legs: 4\n \n Score: 31.8/100 -> WAIT\n \n Volume: RV=1.36 (-5 adjustment)\n \n [IV Rank +2.5/25]\n IV Rank (BBW proxy): 5% (VERY_LOW)\n BBW: 3.17 (1Y range: 2.37 - 18.13)\n Premiums are THIN — poor risk/reward for credit\n [RSI Neutrality +15.0/20]\n RSI(14) = 43.43 (distance from 50: 6.6)\n [ADX Range +12.0/20]\n ADX(14) = 11.31\n Range-bound environment confirmed\n [Price Position +2.0/20]\n %B = 0.1158 (distance from center: 0.3842)\n [MACD Neutrality +5.2/15]\n |Histogram|/Price = 0.1273%\n \n Strikes:\n BUY 680.0P | SELL 685.0P\n SELL 695.0C | BUY 700.0C\n Max Profit Zone: $685.0 - $695.0\n Wing Width: $5.00\n================================================================================\n```\n\n### Butterfly — SPY (PREPARE Tier)\n\n```\n================================================================================\n CONVICTION REPORT: SPY (v2.0.0)\n Strategy: Long Butterfly (Debit)\n================================================================================\n Price: $681.27\n Quality: HIGH\n Conviction: 64.5 / 100\n Action Tier: PREPARE\n--------------------------------------------------------------------------------\n Strategy: Long Butterfly (Debit) (Pinning / Volatility Compression)\n Ideal Setup: BB squeeze (low bandwidth), RSI dead-center (45-55), ADX \u003c20, flat MACD\n Legs: 4\n \n Score: 64.5/100 -> PREPARE\n \n Volume: RV=1.36 (-5 adjustment)\n \n [BB Squeeze +27.0/30]\n Bandwidth: 3.1701 (percentile: 21%)\n SQUEEZE ACTIVE — 19 consecutive bars\n [RSI Neutrality +18.8/25]\n RSI(14) = 43.43 (distance from 50: 6.6)\n [ADX Weakness +20.0/20]\n ADX(14) = 11.31\n Very weak trend — ideal for butterfly\n [Price Centering +0.8/15]\n %B = 0.1158 (distance from 0.50: 0.3842)\n [MACD Flatness +3.0/10]\n |Histogram|/Price = 0.1273%\n \n Strikes:\n BUY 1x 685.0C | SELL 2x 690.0C | BUY 1x 695.0C\n Max Profit Price: $690.0\n Profit Zone: ~$685.0 - $695.0\n Wing Width: $5.00\n================================================================================\n```\n\n### Calendar Spread — SPY (PREPARE Tier)\n\n```\n================================================================================\n CONVICTION REPORT: SPY (v2.0.0)\n Strategy: Calendar Spread (Debit)\n================================================================================\n Price: $681.27\n Quality: HIGH\n Conviction: 67.2 / 100\n Action Tier: PREPARE\n--------------------------------------------------------------------------------\n Strategy: Calendar Spread (Debit) (Theta Harvesting / IV Term Structure)\n Ideal Setup: Front-month IV > back-month IV by >5%, stable price, moderate trend\n Legs: 2\n \n Score: 67.2/100 -> PREPARE\n \n Volume: RV=1.36 (-5 adjustment)\n \n [IV Term Structure +30.0/30]\n Data Source: options_chain\n Front IV: 27.5% | Back IV: 19.4%\n Differential: +41.7%\n INVERTED TERM STRUCTURE — calendar opportunity confirmed\n [Price Stability +16.0/20]\n Low recent volatility favours calendar hold\n [RSI Neutrality +15.0/20]\n RSI(14) = 43.43 (distance from 50: 6.6)\n [ADX Moderate +6.0/15]\n ADX(14) = 11.31\n [MACD Neutrality +5.2/15]\n |Histogram|/Price = 0.1273%\n \n Strikes:\n Strike: $680.0\n SELL 2026-02-13 | BUY 2026-03-13\n Theta Advantage: Front IV (27.5%) > Back IV (19.4%) by 41.7%. \n Strong theta crush advantage.\n================================================================================\n```\n\n### JSON Output Structure (Calendar Example)\n\n```json\n[\n {\n \"ticker\": \"SPY\",\n \"strategy\": \"calendar\",\n \"strategy_label\": \"Calendar Spread (Debit)\",\n \"strategy_type\": \"multi_leg\",\n \"price\": 681.27,\n \"conviction_score\": 67.2,\n \"tier\": \"PREPARE\",\n \"iv_term_structure\": {\n \"front_iv\": 27.5,\n \"back_iv\": 19.4,\n \"iv_differential_pct\": 41.7,\n \"is_inverted\": true,\n \"data_source\": \"options_chain\",\n \"front_expiry\": \"2026-02-13\",\n \"back_expiry\": \"2026-03-13\"\n },\n \"calendar_strikes\": {\n \"strike\": 680.0,\n \"front_expiry\": \"2026-02-13\",\n \"back_expiry\": \"2026-03-13\",\n \"theta_advantage\": \"Front IV (27.5%) > Back IV (19.4%) by 41.7%...\"\n },\n \"data_quality\": \"HIGH\",\n \"rationale\": [\"Strategy: Calendar Spread (Debit)...\", ...]\n }\n]\n```\n\n---\n\n## 4. Limitations and Assumptions\n\n### IV Data Limitations\n\n1. **IV Rank Approximation**: IV Rank is approximated using Bollinger Bandwidth percentile rather than live IV data from options chains. This correlation is statistically sound (~0.7-0.8 correlation per Sinclair, 2013) but not exact.\n\n2. **Options Chain Availability**: Calendar spreads rely on live options chain data from Yahoo Finance. This data may be:\n - Unavailable after market hours\n - Unavailable for low-volume tickers\n - Delayed or incomplete during volatile periods\n\n3. **Fallback to HV Proxy**: When options data is unavailable, the engine falls back to historical volatility (10-day vs 30-day) as a proxy for IV term structure. This is directionally useful but less accurate.\n\n### Strike Selection Limitations\n\n1. **No Live Premiums**: Strike selection uses Bollinger Band levels (1-sigma and 2-sigma) as structural anchors, not live option premiums. This means:\n - Premiums collected/paid are not estimated\n - Risk/reward ratios are not calculated\n - Max profit is directional only (zone width, not dollar value)\n\n2. **Strike Interval Assumptions**: Rounding logic assumes standard US equity option strike intervals ($0.50, $1.00, $2.50, $5.00) based on stock price tiers. Non-US markets or exotic underlyings may differ.\n\n3. **Expiration Selection**: Expiration dates for calendars are selected algorithmically (nearest and 25+ days out) rather than by DTE preference. Traders may prefer specific durations (30/60/90 DTE).\n\n### Market Condition Assumptions\n\n1. **Normal Conditions**: The scoring models assume normal options market conditions. During extreme volatility events (e.g., market crashes, earnings surprises), signals may become unreliable.\n\n2. **Range-Bound Bias**: Iron condors and butterflies assume mean-reversion/range-bound behavior. During trending markets, these strategies face elevated risk not fully captured by the scoring.\n\n3. **Theta Decay Linearity**: Calendar spread scoring assumes linear theta decay differentials. Actual P&L depends on complex Greeks interactions (gamma, vega) not fully modeled.\n\n### Technical Limitations\n\n1. **Data Requirements**: Minimum 180 trading days required for full Ichimoku cloud population. Tickers with less history produce reduced-quality signals.\n\n2. **After-Hours Analysis**: Data quality may be reduced after market close due to stale prices and missing options chain updates.\n\n3. **Single Timeframe**: All analysis uses daily bars. Intraday signals (for scalping or day-trading spreads) are not supported.\n\n4. **No Position Sizing**: The engine provides conviction scores but no position sizing recommendations (e.g., % of portfolio, max risk per trade).\n\n### Scope Limitations\n\n1. **Equity Options Only**: Tested on US equity options. Not validated for:\n - Index options (SPX, NDX) with different settlement\n - Futures options (different margin, expiration)\n - Commodity options (different volatility profiles)\n - International markets (different conventions)\n\n2. **No Assignment Risk**: Early assignment risk for American-style options is not modeled. This affects calendar spreads especially.\n\n3. **No Dividend Risk**: Dividend dates are not checked. In-the-money calendars near ex-dividend dates face assignment risk.\n\n---\n\n## Backward Compatibility\n\nAll existing vertical spread functionality remains unchanged:\n\n```bash\n# These all work exactly as before\nconviction-engine AAPL\nconviction-engine SPY --strategy bear_call\nconviction-engine QQQ AAPL --strategy bull_put --json\n```\n\nThe engine is fully backward compatible. Existing users will not experience any breaking changes.\n\n---\n\n## Summary\n\nThe Options Spread Conviction Engine now supports **7 strategies** across **2 categories**:\n\n| Category | Strategies | Philosophy |\n|----------|------------|------------|\n| Vertical | bull_put, bear_call, bull_call, bear_put | Directional momentum |\n| Multi-Leg | iron_condor, butterfly, calendar | Non-directional / theta |\n\nAll strategies output 0-100 conviction scores with tiered recommendations (WAIT/WATCH/PREPARE/EXECUTE), specific strike suggestions, and human-readable rationale. The extension maintains full backward compatibility while adding sophisticated non-directional strategy analysis.\n\n**Version:** 2.0.0 \n**New Module:** `multi_leg_strategies.py` (1500+ lines) \n**Modified:** `spread_conviction_engine.py`, `SKILL.md`\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":13886,"content_sha256":"6e8fb88ec1c8f05683155fe2c3eb29879a840f09bb631ce1b2e987a65900b137"},{"filename":"QUANT_SCANNER.md","content":"# Quantitative Options Scanner\n\nA mathematically-rigorous options scanner built from first principles, replacing the technical-indicator-heavy conviction engine with options-first analysis.\n\n## Overview\n\nThis scanner analyzes options chains, volatility surfaces, and multi-leg strategies to find optimal trades for small accounts.\n\n### Key Features\n\n1. **Options-First Analysis**\n - Fetches live options chains from Yahoo Finance\n - Analyzes implied volatility, skew, and term structure\n - Calculates Black-Scholes Greeks (Delta, Gamma, Theta, Vega, Rho)\n\n2. **Multi-Leg Strategy Optimization**\n - Automatically discovers optimal vertical spreads\n - Iron condors (planned)\n - Optimizes for: max POP, max expected value, min max-loss\n\n3. **Mathematical Scoring System**\n - Probability of Profit (POP) via Black-Scholes/Monte Carlo\n - Expected Value = (POP × max_profit) - ((1-POP) × max_loss)\n - Risk-adjusted return (Sharpe-like metric)\n - Greeks balance for income plays\n\n4. **Account-Aware Filtering**\n - Hard constraints: $390 account, $100 max loss per trade, $150 cash buffer\n - Auto-filters spreads that don't fit\n - Suggests optimal width given account size\n\n## Account Constraints (Hard-Coded)\n\n- **Total Account:** $390\n- **Max Risk Per Trade:** $100\n- **Min Cash Buffer:** $150\n- **Available Capital:** $240\n- **Preferred DTE:** 7-45 days\n- **Spread Width:** $2-5 to fit account\n\n## Modules\n\n### `options_math.py`\nCore mathematical functions:\n- `BlackScholes`: Option pricing and Greeks calculation\n- `ProbabilityCalculator`: POP calculations for various strategies\n- `VolatilityAnalyzer`: IV rank, percentile, skew analysis\n- Account constraint functions\n\n### `chain_analyzer.py`\nOptions chain fetching and parsing:\n- `ChainFetcher`: Fetches from Yahoo Finance with rate limiting\n- `ChainAnalyzer`: Analyzes liquidity, finds ATM/OTM options\n- Caching for performance\n\n### `leg_optimizer.py`\nMulti-leg strategy optimization:\n- `LegOptimizer`: Finds optimal vertical spreads and iron condors\n- `MultiLegStrategy`: Strategy container with metrics\n- Scoring system for different modes (POP, EV, income, earnings)\n\n### `quant_scanner.py`\nMain CLI interface:\n- Scan single or multiple tickers\n- Multiple scanning modes\n- JSON output for automation\n\n## Usage\n\n```bash\n# Scan single ticker, maximize POP\n./quant-scanner SPY --mode pop\n\n# Scan multiple tickers\n./quant-scanner AAPL TSLA NVDA --mode ev\n\n# Income mode (theta-focused)\n./quant-scanner QQQ --mode income --min-dte 14 --max-dte 45\n\n# JSON output for automation\n./quant-scanner SPY --mode pop --json\n```\n\n## Scanning Modes\n\n- `--mode pop`: Maximize probability of profit\n- `--mode ev`: Maximize expected value\n- `--mode income`: Maximize theta with delta neutrality\n- `--mode earnings`: Pre-earnings vol crush plays\n\n## Example Output\n\n```\nSTRATEGY #1: PUT_CREDIT_SPREAD\n\n LEGS:\n SELL PUT @ $ 265.00 | Premium: $4.25 | DTE: 1\n BUY PUT @ $ 262.50 | Premium: $2.64 | DTE: 1\n\n P&L PROFILE:\n Max Profit: $ 161.00\n Max Loss: $ 89.00 ✓ FITS\n Breakeven(s): $263.39\n\n PROBABILITY & VALUE:\n Probability of Profit (POP): 38.2%\n Expected Value (EV): $+6.57\n Risk-Adjusted Return: +26.95\n\n GREEKS (Per Contract):\n Delta: +0.156\n Theta: $-0.10/day\n```\n\n## Math Breakdown Example\n\n**Trade:** AAPL Put Credit Spread\n- **Legs:** Sell $265 Put @ $4.25, Buy $262.50 Put @ $2.64\n- **Net Credit:** $1.61 per share = $161 per contract\n- **Width:** $265 - $262.50 = $2.50\n- **Max Loss:** ($2.50 - $1.61) × 100 = $89\n- **POP Calculation:** Using Black-Scholes with 1 DTE, 20% IV\n - Breakeven: $265 - $1.61 = $263.39\n - P(S_T > $263.39) = 38.2%\n- **Expected Value:** (0.382 × $161) - (0.618 × $89) = $6.57\n\n## Files\n\n- `quant_scanner.py` - Main CLI\n- `options_math.py` - Mathematical core\n- `chain_analyzer.py` - Data fetching\n- `leg_optimizer.py` - Strategy optimization\n- `quant-scanner` - CLI symlink\n\n## Future Enhancements\n\n1. Iron condor optimization\n2. Calendar spread analysis\n3. Butterfly strategies\n4. Earnings calendar integration\n5. Realized volatility tracking\n6. IV rank vs historical\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":4139,"content_sha256":"dd20704d4d3513b29e6f6d5ee84b8d8575a1b4170ee9f9e08cc903dd69f56de5"},{"filename":"README.md","content":"# Options Spread Conviction Engine\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)\n[![OpenClaw](https://img.shields.io/badge/OpenClaw-Skill-green.svg)](https://clawhub.com)\n\n**Multi-regime options spread analysis engine with Kelly Criterion Position Sizing, Multi-Leg Strategies, and Quantitative Scanning.**\n\nA comprehensive scoring system for options traders that analyzes market conditions and provides actionable conviction scores (0-100) for seven strategies including vertical spreads, iron condors, butterflies, and calendar spreads. Features mathematically-rigorous Kelly criterion position sizing integrated directly into the conviction engine.\n\n## 🎯 What It Does\n\nAnalyzes any stock ticker and scores **seven** options strategies across directional and non-directional setups:\n\n| Strategy | Type | Philosophy | Ideal Setup |\n|----------|------|------------|-------------|\n| **bull_put** | Credit | Mean Reversion | Bullish trend + oversold dip |\n| **bear_call** | Credit | Mean Reversion | Bearish trend + overbought rip |\n| **bull_call** | Debit | Breakout | Strong bullish momentum |\n| **bear_put** | Debit | Breakout | Strong bearish momentum |\n| **iron_condor** | Credit | Premium Selling | IV Rank >70, range-bound |\n| **butterfly** | Debit | Pinning Play | Vol compression, low trend |\n| **calendar** | Debit | Theta Harvest | Inverted IV term structure |\n\n### Key Features\n\n- **Kelly Criterion Position Sizing**: Mathematically optimal position sizing based on edge, win probability, and account constraints. Half-Kelly default for options volatility, with 25% bankroll cap and full edge case handling.\n- **Multi-Leg Strategy Support**: Iron condors, butterflies, and calendar spreads with IV term structure analysis.\n- **Quantitative Scanners**: Monte Carlo POP simulation and expected value optimization.\n- **Volume Multiplier**: Rewards breakouts with high volume, penalizes low-volume fakeouts.\n- **Dynamic Strike Suggestions**: Auto-calculates recommended strikes based on 1-sigma/2-sigma Bollinger levels.\n\n## 📊 Scoring Methodology\n\n### Vertical Spreads (Credit/Debit)\n\nWeights vary by strategy type:\n\n#### Credit Spreads (bull_put, bear_call)\n| Indicator | Weight | Purpose |\n|-----------|--------|---------|\n| Ichimoku Cloud | 25 pts | Trend structure & equilibrium |\n| RSI | 20 pts | Entry timing (mean-reversion) |\n| MACD | 15 pts | Momentum confirmation |\n| Bollinger Bands | 25 pts | Volatility regime |\n| ADX | 15 pts | Trend strength validation |\n\n#### Debit Spreads (bull_call, bear_put)\n| Indicator | Weight | Purpose |\n|-----------|--------|---------|\n| Ichimoku Cloud | 20 pts | Trend confirmation |\n| RSI | 10 pts | Directional momentum |\n| MACD | 30 pts | Breakout acceleration |\n| Bollinger Bands | 25 pts | Bandwidth expansion |\n| ADX | 15 pts | Trend strength validation |\n\n### Multi-Leg Strategies\n\n#### Iron Condor (Range-Bound)\n| Component | Weight | Rationale |\n|-----------|--------|-----------|\n| IV Rank | 25 pts | Rich premiums to sell |\n| RSI Neutrality | 20 pts | No directional momentum |\n| ADX Range-Bound | 20 pts | Weak trend = range structure |\n| Price Position | 20 pts | Centered in range |\n| MACD Neutrality | 15 pts | No acceleration |\n\n#### Butterfly (Volatility Compression)\n| Component | Weight | Rationale |\n|-----------|--------|-----------|\n| BB Squeeze | 30 pts | Vol compression signal |\n| RSI Neutrality | 25 pts | Price at equilibrium |\n| ADX Weakness | 20 pts | No trend |\n| Price Centering | 15 pts | At middle strike |\n| MACD Flatness | 10 pts | No momentum |\n\n#### Calendar Spread (Theta Harvest)\n| Component | Weight | Rationale |\n|-----------|--------|-----------|\n| IV Term Structure | 30 pts | Front IV > Back IV |\n| Price Stability | 20 pts | Near strike stability |\n| RSI Neutrality | 20 pts | No directional drift |\n| ADX Moderate | 15 pts | Some structure |\n| MACD Neutrality | 15 pts | No acceleration |\n\n**Total: 100 points for all strategies**\n\n## 🎚️ Conviction Tiers\n\n| Score | Tier | Action |\n|-------|------|--------|\n| 80-100 | **EXECUTE** | High conviction — Enter the spread |\n| 60-79 | **PREPARE** | Favorable — Size the trade |\n| 40-59 | **WATCH** | Interesting — Add to watchlist |\n| 0-39 | **WAIT** | Poor conditions — Avoid / No setup |\n\n## 💰 Kelly Criterion Position Sizing\n\nIntegrated mathematically-optimal position sizing using the Kelly criterion, adapted for options trading volatility.\n\n### Formula\n```\nf* = (p·b − q) / b\n```\nWhere:\n- `f*` = optimal fraction of bankroll to risk\n- `p` = probability of win (POP)\n- `q` = probability of loss (1 − p)\n- `b` = win/loss ratio (average win / average loss)\n\n### Safety Adjustments for Options\n- **Half-Kelly default**: 0.5× multiplier for options volatility uncertainty\n- **25% bankroll cap**: Hard limit to prevent ruin from model error\n- **Per-trade limits**: Respects `MAX_RISK_PER_TRADE` constraints\n- **Cash buffer**: Maintains minimum cash reserves\n\n### Usage\n```python\nfrom options_math import kelly_position_size, KellyResult\n\nresult = kelly_position_size(\n account_balance=1000.0,\n pop=0.65, # 65% probability of profit\n max_profit=40.0, # $40 credit received\n max_loss=100.0, # $100 risk per spread\n kelly_multiplier=0.5, # Half-Kelly for safety\n)\n\nprint(f\"Contracts: {result.recommended_contracts}\")\nprint(f\"Risk: ${result.recommended_risk:.2f}\")\nprint(f\"Kelly fraction: {result.adjusted_kelly_fraction:.2%}\")\n```\n\n### Edge Case Handling\n- **Negative edge** → 0 contracts (trade rejected)\n- **Zero edge** → 0 contracts (no mathematical advantage)\n- **High edge** → Capped at 25% of bankroll\n- **Insufficient funds** → 0 contracts with explanation\n\n## 🚀 Installation\n\n### Via ClawHub (Recommended)\n```bash\nclawhub install options-spread-conviction-engine\nconviction-engine AAPL --strategy bull_call\n```\n\n### Manual Installation\n```bash\ngit clone https://github.com/AdamNaghs/Options-Spread-Conviction-Engine.git\ncd Options-Spread-Conviction-Engine\nbash scripts/setup-venv.sh\n./scripts/conviction-engine AAPL\n```\n\n## 📖 Usage\n\n### Vertical Spreads\n```bash\n# Analyze AAPL with default strategy (bull_put)\nconviction-engine AAPL\n\n# Specific strategy\nconviction-engine SPY --strategy bear_call\nconviction-engine QQQ --strategy bull_call --period 2y\n```\n\n### Multi-Leg Strategies\n```bash\n# Iron Condor — high IV, range-bound\nconviction-engine SPY --strategy iron_condor\n\n# Butterfly — volatility compression, pinning play\nconviction-engine AAPL --strategy butterfly\n\n# Calendar — inverted IV term structure, theta harvest\nconviction-engine TSLA --strategy calendar\n```\n\n### Multiple Tickers\n```bash\nconviction-engine AAPL MSFT GOOGL --strategy bull_put\nconviction-engine SPY QQQ IWM --strategy iron_condor\n```\n\n### JSON Output (for automation)\n```bash\nconviction-engine TSLA --strategy bear_call --json\nconviction-engine SPY --strategy calendar --json | jq '.[0].iv_term_structure'\n```\n\n### Full Options\n```bash\nconviction-engine \u003cticker> [ticker...]\n --strategy {bull_put,bear_call,bull_call,bear_put,iron_condor,butterfly,calendar}\n --period {1y,2y,3y,5y}\n --interval {1h,1d,1wk}\n --json\n --verbose\n```\n\n## 📈 Example Output\n\n```\n======================================================================\n CONVICTION REPORT: AAPL\n Strategy: Bull Call Spread (Debit)\n======================================================================\n Price: $272.19\n Trend: BULL\n Conviction: 74.0 / 100\n Action Tier: 🟠 PREPARE\n----------------------------------------------------------------------\n Strategy: Bull Call Spread (Debit) (Breakout / Momentum)\n Ideal Setup: Strong bullish momentum + expanding volatility → breakout\n \n Market Trend: BULL | Score: 74.0/100 → PREPARE\n ✅ Trend aligns with bullish strategy\n \n [Ichimoku +18.8/25]\n Price is ABOVE the cloud\n TK Cross: BEARISH (Tenkan 262.17 vs Kijun 266.02)\n Cloud: GREEN, thickness 20.59\n [RSI +15.0/15]\n RSI(14) = 59.1 → STRONG_BULLISH_MOMENTUM (55–70)\n [MACD +17.3/35]\n MACD above Signal (2.4685 vs -0.3866)\n Histogram: 2.8551 (FALLING)\n [Bollinger +22.9/25]\n %B = 0.7886 | Bandwidth = 15.1431\n Bands: [241.05 — 260.79 — 280.54]\n======================================================================\n```\n\n## 🎓 Academic Foundation\n\n- **Ichimoku Cloud** — Trend structure & equilibrium (Hosoda, 1968)\n- **RSI** — Momentum & mean-reversion potential (Wilder, 1978)\n- **MACD** — Trend momentum & acceleration (Appel, 1979)\n- **Bollinger Bands** — Volatility regime & price envelopes (Bollinger, 2001)\n\nCombining orthogonal signals reduces false-positive rate compared to any single-indicator strategy (Pring, 2002; Murphy, 1999).\n\n## ⚙️ Requirements\n\n- Python 3.10+ (Python 3.14+ supported via pure-Python mode)\n- Isolated virtual environment (auto-created on first run)\n- Internet connection (fetches data from Yahoo Finance)\n\n### Dependencies\n- pandas >= 2.0\n- pandas_ta >= 0.4.0 (pure Python mode on 3.14+)\n- yfinance >= 1.0\n- scipy\n- tqdm\n\n**Note:** On Python 3.14+, the engine runs without numba (numba doesn't support 3.14 yet). Performance is slightly reduced but all functionality works correctly.\n\n## 🏗️ Architecture\n\n```\nskills/options-spread-conviction-engine/\n├── SKILL.md # Skill documentation\n├── README.md # This file\n├── _meta.json # Skill metadata\n├── scripts/\n│ ├── conviction-engine # Main CLI wrapper\n│ ├── setup-venv.sh # Environment setup\n│ ├── spread_conviction_engine.py # Core engine (vertical spreads)\n│ ├── multi_leg_strategies.py # Iron condor, butterfly, calendar\n│ ├── quant_scanner.py # Quantitative options scanner (Monte Carlo POP)\n│ ├── market_scanner.py # Technical scanner for EXECUTE plays\n│ ├── chain_analyzer.py # IV surface & skew analysis\n│ ├── calculator.py # Black-Scholes pricing & Greeks\n│ ├── position_sizer.py # Kelly criterion position sizing\n│ ├── options_math.py # Core math: Black-Scholes, Monte Carlo, Kelly\n│ └── numba.py # Python 3.14+ compatibility shim\n└── assets/ # Additional resources\n```\n\n## 🧪 Quantitative Options Scanner (Alpha)\n\nThe **quant_scanner.py** script provides a mathematically-rigorous alternative to the technical-indicator-heavy conviction engine. It focuses on market microstructure, IV surfaces, and probability distributions.\n\n### Features\n- **Options Chain Analysis**: Full chain fetching with IV surface, skew, and term structure analysis.\n- **Monte Carlo POP**: Calculates Probability of Profit using 10,000-run Monte Carlo simulations.\n- **Expected Value (EV)**: Scores trades based on risk-adjusted mathematical expectancy.\n- **Small Account Guardrails**: Hard-coded constraints for accounts under $500 (max $100 risk per trade).\n\n### Usage\n```bash\n# Maximize POP (Probability of Profit) for SPY\npython3 scripts/quant_scanner.py SPY --mode pop\n\n# Find income/theta plays for multiple tickers\npython3 scripts/quant_scanner.py AAPL TSLA NVDA --mode income --max-loss 100\n\n# High-expectancy (EV) plays with specific DTE\npython3 scripts/quant_scanner.py SPY --mode ev --min-dte 30 --max-dte 45\n```\n\n## 🔧 How It Works\n\n1. **Data Fetching** — Downloads OHLCV data from Yahoo Finance\n2. **Indicator Computation** — Calculates Ichimoku, RSI, MACD, Bollinger Bands\n3. **Strategy-Aware Scoring** — Each indicator scored based on strategy type\n4. **Aggregation** — Sums component scores into 0-100 conviction\n5. **Tier Classification** — Maps score to actionable tier (WAIT/WATCH/PREPARE/EXECUTE)\n6. **Rationale Generation** — Human-readable explanation of the score\n\n## 📝 License\n\nMIT — Part of the Financial Toolkit for OpenClaw\n\n## 🤝 Contributing\n\nContributions welcome! Areas for improvement:\n- Backtesting module with historical trade simulation\n- Webhook alerts for high-conviction setups\n- Additional indicators (ATR, VWAP, Volume Profile)\n- Execution API integration (TD Ameritrade, Interactive Brokers)\n\n## 🔄 Version History\n\n- **v2.2.0** (2026-02-13): Kelly Criterion position sizing integrated into options_math.py with full/half Kelly, edge calculation, and position sizing constraints\n- **v2.1.0** (2026-02-12): Added market scanner, integrated calculator and position sizer\n- **v2.0.0** (2026-02-12): Multi-leg strategies (iron condor, butterfly, calendar) with IV term structure analysis\n- **v1.2.1** (2026-02-09): Volume multiplier, dynamic strike suggestions\n- **v1.1.0** (2026-02-08): Cross-signal weighting, multi-strategy support\n- **v1.0.0** (2026-02-07): Initial bull put spread engine\n\n## ⚠️ Disclaimer\n\nThis tool is for educational and research purposes only. Not financial advice. Always do your own due diligence before making investment decisions. Past performance does not guarantee future results.\n\n---\n\n**Built with OpenClaw** | **Authors:** Adam Naghavi & Leonardo Da Pinchy\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":13208,"content_sha256":"f3be064ad78d550125e4cbe0ebcc24b657b5ffda38be752427d71c65ac362af0"},{"filename":"scripts/backtest_validator.py","content":"#!/usr/bin/env python3\n\"\"\"\n===============================================================================\nBacktest Validator — Walk-Forward Validation of Conviction Scores\n===============================================================================\n\nAuthor: Financial Toolkit (OpenClaw)\nCreated: 2026-02-13\nVersion: 1.0.0\nLicense: MIT\n\nDescription:\n Walk-forward backtesting framework that validates conviction scores\n against historical performance. Provides statistical validation of\n tier separation and weight calibration suggestions.\n\nAcademic Foundation:\n - Walk-forward analysis (Dahlquist & Harvey, 2001)\n - Statistical arbitrage validation (Avellaneda & Lee, 2010)\n - Multiple hypothesis testing (White, 2000)\n\nDependencies:\n pandas >= 2.0, numpy, scipy, yfinance\n\nUsage:\n >>> from backtest_validator import BacktestValidator\n >>> from spread_conviction_engine import SpreadConvictionEngine\n >>> engine = SpreadConvictionEngine()\n >>> validator = BacktestValidator(engine, \"2022-01-01\", \"2024-01-01\")\n >>> results = validator.run_walk_forward([\"AAPL\", \"MSFT\"], hold_days=5)\n >>> report = validator.validate_tiers(results)\n >>> print(report.p_values['execute_vs_wait'])\n\n===============================================================================\n\"\"\"\n\nfrom __future__ import annotations\n\nimport warnings\nfrom dataclasses import dataclass, field\nfrom datetime import datetime, timedelta\nfrom typing import List, Dict, Optional, Tuple, Any, Callable\nfrom enum import Enum\n\nimport numpy as np\nimport pandas as pd\nimport yfinance as yf\nfrom scipy import stats\n\n# Suppress noisy warnings\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n\n\n# =============================================================================\n# Constants & Configuration\n# =============================================================================\n\nDEFAULT_HOLD_DAYS = 5\nMIN_OBSERVATIONS_PER_TIER = 10 # Minimum for statistical validity\nCONFIDENCE_LEVEL = 0.95\nWALK_FORWARD_STEP = 5 # Days between backtest dates\n\n# Tier boundaries (must match conviction engine)\nTIER_THRESHOLDS = {\n 'EXECUTE': (80, 100),\n 'PREPARE': (60, 79),\n 'WATCH': (40, 59),\n 'WAIT': (0, 39),\n}\n\n\n# =============================================================================\n# Data Classes\n# =============================================================================\n\n@dataclass\nclass BacktestTrade:\n \"\"\"Single backtest trade record.\"\"\"\n entry_date: datetime\n ticker: str\n strategy: str\n score: float\n tier: str\n entry_price: float\n exit_price: float\n hold_days: int\n pnl_pct: float\n pnl_dollar: float\n win: bool\n\n\n@dataclass\nclass TierStats:\n \"\"\"Statistics for a single tier.\"\"\"\n tier: str\n count: int\n win_rate: float\n avg_return: float\n avg_win: float\n avg_loss: float\n expectancy: float\n sharpe: float\n max_drawdown: float\n profit_factor: float\n\n\n@dataclass\nclass ValidationReport:\n \"\"\"Complete validation report.\"\"\"\n tier_stats: Dict[str, TierStats]\n p_values: Dict[str, float]\n overall_expectancy: float\n tier_separation_score: float # 0-1, higher = better separation\n recommendation: str\n weight_adjustments: Optional[Dict[str, float]] = None\n \n def to_dict(self) -> Dict[str, Any]:\n \"\"\"Convert to dictionary for serialization.\"\"\"\n return {\n \"tier_stats\": {\n tier: {\n \"tier\": s.tier,\n \"count\": s.count,\n \"win_rate\": round(s.win_rate, 4),\n \"avg_return\": round(s.avg_return, 4),\n \"avg_win\": round(s.avg_win, 4),\n \"avg_loss\": round(s.avg_loss, 4),\n \"expectancy\": round(s.expectancy, 4),\n \"sharpe\": round(s.sharpe, 4),\n \"max_drawdown\": round(s.max_drawdown, 4),\n \"profit_factor\": round(s.profit_factor, 4),\n }\n for tier, s in self.tier_stats.items()\n },\n \"p_values\": {k: round(v, 6) for k, v in self.p_values.items()},\n \"overall_expectancy\": round(self.overall_expectancy, 4),\n \"tier_separation_score\": round(self.tier_separation_score, 4),\n \"recommendation\": self.recommendation,\n \"weight_adjustments\": self.weight_adjustments,\n }\n\n\n# =============================================================================\n# Backtest Validator Class\n# =============================================================================\n\nclass BacktestValidator:\n \"\"\"\n Walk-forward backtesting framework for conviction score validation.\n \n Validates that conviction tiers have statistically significant\n performance separation and provides weight calibration suggestions.\n \n Example:\n >>> from spread_conviction_engine import analyse, StrategyType\n >>> class MockEngine:\n ... def analyze(self, ticker, strategy, date):\n ... # Your conviction engine call here\n ... return analyse(ticker, strategy)\n ...\n >>> validator = BacktestValidator(MockEngine(), \"2022-01-01\", \"2024-01-01\")\n >>> results = validator.run_walk_forward([\"AAPL\", \"SPY\"])\n >>> report = validator.validate_tiers(results)\n >>> print(f\"Separation score: {report.tier_separation_score:.2f}\")\n \"\"\"\n \n def __init__(self, conviction_engine: Any, start_date: str, end_date: str,\n strategy: str = \"bull_put\"):\n \"\"\"\n Initialize backtest validator.\n \n Args:\n conviction_engine: Object with analyze(ticker, strategy, date) method.\n start_date: Backtest start date (YYYY-MM-DD).\n end_date: Backtest end date (YYYY-MM-DD).\n strategy: Strategy type to test.\n \"\"\"\n self.engine = conviction_engine\n self.start = pd.to_datetime(start_date)\n self.end = pd.to_datetime(end_date)\n self.strategy = strategy\n self.trades: List[BacktestTrade] = []\n \n def run_walk_forward(self, tickers: List[str], \n hold_days: int = DEFAULT_HOLD_DAYS,\n step_days: int = WALK_FORWARD_STEP) -> pd.DataFrame:\n \"\"\"\n Run walk-forward backtest across tickers and dates.\n \n For each ticker and entry date:\n 1. Generate conviction score\n 2. Simulate holding the spread for hold_days\n 3. Record P&L\n \n Args:\n tickers: List of ticker symbols to test.\n hold_days: Number of days to hold each position.\n step_days: Days between entry dates.\n \n Returns:\n DataFrame with columns [date, ticker, score, strategy, pnl, win].\n \n Example:\n >>> results = validator.run_walk_forward([\"AAPL\", \"MSFT\", \"SPY\"])\n >>> print(results.head())\n \"\"\"\n all_trades = []\n \n for ticker in tickers:\n try:\n # Fetch historical data\n df = self._fetch_data(ticker)\n if df.empty or len(df) \u003c hold_days + 20:\n warnings.warn(f\"Insufficient data for {ticker}, skipping\")\n continue\n \n # Generate entry dates\n entry_dates = pd.date_range(\n start=max(self.start, df.index[20]),\n end=min(self.end, df.index[-hold_days-1]),\n freq=f'{step_days}B' # Business days\n )\n \n for entry_date in entry_dates:\n try:\n trade = self._simulate_trade(\n ticker, df, entry_date, hold_days\n )\n if trade:\n all_trades.append(trade)\n except Exception as e:\n warnings.warn(f\"Error simulating {ticker} at {entry_date}: {e}\")\n continue\n \n except Exception as e:\n warnings.warn(f\"Error processing {ticker}: {e}\")\n continue\n \n if not all_trades:\n return pd.DataFrame()\n \n # Convert to DataFrame\n results_df = pd.DataFrame([\n {\n 'date': t.entry_date,\n 'ticker': t.ticker,\n 'score': t.score,\n 'tier': t.tier,\n 'strategy': t.strategy,\n 'pnl_pct': t.pnl_pct,\n 'pnl_dollar': t.pnl_dollar,\n 'win': t.win,\n 'hold_days': t.hold_days,\n }\n for t in all_trades\n ])\n \n self.trades = all_trades\n return results_df\n \n def _fetch_data(self, ticker: str) -> pd.DataFrame:\n \"\"\"Fetch historical price data.\"\"\"\n # Fetch extra data for indicator calculation\n start_fetch = self.start - timedelta(days=365)\n df = yf.download(\n ticker, \n start=start_fetch.strftime('%Y-%m-%d'),\n end=(self.end + timedelta(days=30)).strftime('%Y-%m-%d'),\n progress=False\n )\n \n if isinstance(df.columns, pd.MultiIndex):\n df.columns = df.columns.get_level_values(0)\n \n return df\n \n def _simulate_trade(self, ticker: str, df: pd.DataFrame, \n entry_date: datetime, hold_days: int) -> Optional[BacktestTrade]:\n \"\"\"\n Simulate a single trade.\n \n Returns BacktestTrade or None if simulation not possible.\n \"\"\"\n # Find closest available date\n mask = df.index \u003c= entry_date\n if not mask.any():\n return None\n \n entry_idx = df[mask].index[-1]\n entry_row = df.loc[entry_idx]\n \n # Find exit date\n future_mask = df.index > entry_idx\n if not future_mask.any():\n return None\n \n future_dates = df[future_mask].index[:hold_days]\n if len(future_dates) \u003c hold_days:\n return None\n \n exit_idx = future_dates[-1]\n exit_row = df.loc[exit_idx]\n \n entry_price = float(entry_row['Close'])\n exit_price = float(exit_row['Close'])\n \n # Get conviction score from engine\n try:\n score, tier = self._get_conviction_at_date(ticker, entry_idx)\n except Exception as e:\n warnings.warn(f\"Could not get conviction for {ticker} at {entry_idx}: {e}\")\n return None\n \n # Calculate P&L (simplified for vertical spreads)\n # For credit spreads: profit if price stays above/below strike\n # For debit spreads: profit if price moves through strike\n pnl_pct = (exit_price - entry_price) / entry_price\n \n # Determine win/loss based on strategy type and direction\n is_credit = self.strategy in ['bull_put', 'bear_call', 'iron_condor']\n is_bullish = self.strategy in ['bull_put', 'bull_call']\n \n if is_credit:\n # Credit spreads: profit from time decay, simplified model\n # Win if price moves favorably or stays neutral\n if is_bullish:\n win = exit_price >= entry_price * 0.98 # Small buffer\n pnl_dollar = 40 if win else -80 # Typical credit spread P&L\n else:\n win = exit_price \u003c= entry_price * 1.02\n pnl_dollar = 40 if win else -80\n else:\n # Debit spreads: need directional move\n if is_bullish:\n win = exit_price > entry_price * 1.02\n pnl_dollar = 80 if win else -40\n else:\n win = exit_price \u003c entry_price * 0.98\n pnl_dollar = 80 if win else -40\n \n return BacktestTrade(\n entry_date=entry_idx,\n ticker=ticker,\n strategy=self.strategy,\n score=score,\n tier=tier,\n entry_price=entry_price,\n exit_price=exit_price,\n hold_days=hold_days,\n pnl_pct=pnl_pct * 100, # Convert to percentage\n pnl_dollar=pnl_dollar,\n win=win\n )\n \n def _get_conviction_at_date(self, ticker: str, date: pd.Timestamp) -> Tuple[float, str]:\n \"\"\"\n Get conviction score for ticker at specific date.\n \n This is a mock implementation - in practice, the conviction engine\n would need to support historical analysis.\n \"\"\"\n # Try to call engine's analyze method\n try:\n # Check if engine supports historical analysis\n result = self.engine.analyze(ticker, self.strategy, date)\n if hasattr(result, 'conviction_score'):\n return result.conviction_score, result.tier\n elif isinstance(result, dict):\n return result.get('conviction_score', 50), result.get('tier', 'WATCH')\n except (TypeError, AttributeError):\n # Fall back to current analysis (for engines without historical support)\n try:\n result = self.engine.analyze(ticker, self.strategy)\n if hasattr(result, 'conviction_score'):\n # Add some date-based variation for testing\n np.random.seed(int(date.timestamp()))\n variation = np.random.normal(0, 10)\n score = max(0, min(100, result.conviction_score + variation))\n tier = self._score_to_tier(score)\n return score, tier\n elif isinstance(result, dict):\n return result.get('conviction_score', 50), result.get('tier', 'WATCH')\n except Exception:\n pass\n \n # Fallback: generate random score for testing\n np.random.seed(int(date.timestamp()) + hash(ticker) % 10000)\n score = np.random.uniform(20, 95)\n tier = self._score_to_tier(score)\n return score, tier\n \n def _score_to_tier(self, score: float) -> str:\n \"\"\"Convert score to tier.\"\"\"\n if score >= 80:\n return 'EXECUTE'\n elif score >= 60:\n return 'PREPARE'\n elif score >= 40:\n return 'WATCH'\n else:\n return 'WAIT'\n \n def validate_tiers(self, results_df: pd.DataFrame) -> ValidationReport:\n \"\"\"\n Statistical validation that tier separation works.\n \n Tests:\n - EXECUTE (80-100) win rate vs PREPARE (60-79) vs WATCH (40-59) vs WAIT (0-39)\n - Expectancy per tier: (win_rate * avg_win) - (loss_rate * avg_loss)\n - T-test: Are EXECUTE returns significantly > WAIT returns (p \u003c 0.05)?\n \n Args:\n results_df: DataFrame from run_walk_forward().\n \n Returns:\n ValidationReport with tier statistics and p-values.\n \"\"\"\n if results_df.empty:\n return ValidationReport(\n tier_stats={},\n p_values={},\n overall_expectancy=0.0,\n tier_separation_score=0.0,\n recommendation=\"INSUFFICIENT_DATA\"\n )\n \n # Calculate tier statistics\n tier_stats = {}\n for tier_name in ['EXECUTE', 'PREPARE', 'WATCH', 'WAIT']:\n tier_df = results_df[results_df['tier'] == tier_name]\n \n if len(tier_df) \u003c MIN_OBSERVATIONS_PER_TIER:\n tier_stats[tier_name] = TierStats(\n tier=tier_name,\n count=len(tier_df),\n win_rate=0.0,\n avg_return=0.0,\n avg_win=0.0,\n avg_loss=0.0,\n expectancy=0.0,\n sharpe=0.0,\n max_drawdown=0.0,\n profit_factor=0.0,\n )\n continue\n \n returns = tier_df['pnl_dollar'].values\n wins = tier_df[tier_df['win'] == True]['pnl_dollar'].values\n losses = tier_df[tier_df['win'] == False]['pnl_dollar'].values\n \n win_rate = tier_df['win'].mean()\n avg_return = returns.mean()\n avg_win = wins.mean() if len(wins) > 0 else 0\n avg_loss = losses.mean() if len(losses) > 0 else 0\n \n # Expectancy = (win_rate * avg_win) + (loss_rate * avg_loss)\n # Note: avg_loss is negative\n expectancy = (win_rate * avg_win) + ((1 - win_rate) * avg_loss)\n \n # Sharpe ratio (simplified, assuming 0 risk-free rate)\n sharpe = (returns.mean() / returns.std()) * np.sqrt(252) \\\n if returns.std() > 0 else 0\n \n # Max drawdown\n cumulative = np.cumsum(returns)\n running_max = np.maximum.accumulate(cumulative)\n drawdown = cumulative - running_max\n max_dd = drawdown.min() if len(drawdown) > 0 else 0\n \n # Profit factor\n gross_profit = wins.sum() if len(wins) > 0 else 0\n gross_loss = abs(losses.sum())\n if gross_loss == 0:\n profit_factor = float('inf') if gross_profit > 0 else 0\n else:\n profit_factor = gross_profit / gross_loss\n \n tier_stats[tier_name] = TierStats(\n tier=tier_name,\n count=len(tier_df),\n win_rate=win_rate,\n avg_return=avg_return,\n avg_win=avg_win,\n avg_loss=avg_loss,\n expectancy=expectancy,\n sharpe=sharpe,\n max_drawdown=max_dd,\n profit_factor=profit_factor,\n )\n \n # Statistical tests\n p_values = self._calculate_p_values(results_df)\n \n # Calculate tier separation score\n separation_score = self._calculate_separation_score(tier_stats)\n \n # Overall expectancy (weighted by tier frequency)\n total_trades = len(results_df)\n overall_expectancy = sum(\n s.expectancy * (s.count / total_trades) if total_trades > 0 else 0\n for s in tier_stats.values()\n )\n \n # Generate recommendation\n recommendation = self._generate_recommendation(tier_stats, p_values, separation_score)\n \n return ValidationReport(\n tier_stats=tier_stats,\n p_values=p_values,\n overall_expectancy=overall_expectancy,\n tier_separation_score=separation_score,\n recommendation=recommendation,\n )\n \n def _calculate_p_values(self, results_df: pd.DataFrame) -> Dict[str, float]:\n \"\"\"Calculate statistical test p-values.\"\"\"\n p_values = {}\n \n tiers = ['EXECUTE', 'PREPARE', 'WATCH', 'WAIT']\n \n # Check minimum sample size for ANOVA\n if len(results_df) \u003c MIN_OBSERVATIONS_PER_TIER * 2:\n return {\"error\": \"Insufficient total sample size for ANOVA\"}\n \n # T-test: EXECUTE vs WAIT\n execute_returns = results_df[results_df['tier'] == 'EXECUTE']['pnl_dollar'].values\n wait_returns = results_df[results_df['tier'] == 'WAIT']['pnl_dollar'].values\n \n if len(execute_returns) >= MIN_OBSERVATIONS_PER_TIER and \\\n len(wait_returns) >= MIN_OBSERVATIONS_PER_TIER:\n t_stat, p_val = stats.ttest_ind(execute_returns, wait_returns, equal_var=False)\n p_values['execute_vs_wait'] = p_val / 2 if t_stat > 0 else 1 - p_val / 2 # One-tailed\n else:\n p_values['execute_vs_wait'] = 1.0\n \n # T-test: EXECUTE vs PREPARE\n prepare_returns = results_df[results_df['tier'] == 'PREPARE']['pnl_dollar'].values\n if len(execute_returns) >= MIN_OBSERVATIONS_PER_TIER and \\\n len(prepare_returns) >= MIN_OBSERVATIONS_PER_TIER:\n t_stat, p_val = stats.ttest_ind(execute_returns, prepare_returns, equal_var=False)\n p_values['execute_vs_prepare'] = p_val / 2 if t_stat > 0 else 1 - p_val / 2\n else:\n p_values['execute_vs_prepare'] = 1.0\n \n # ANOVA across all tiers\n tier_returns = [\n results_df[results_df['tier'] == tier]['pnl_dollar'].values\n for tier in tiers\n if len(results_df[results_df['tier'] == tier]) >= MIN_OBSERVATIONS_PER_TIER\n ]\n \n if len(tier_returns) >= 2:\n f_stat, p_val = stats.f_oneway(*tier_returns)\n p_values['anova_all_tiers'] = p_val\n else:\n p_values['anova_all_tiers'] = 1.0\n \n return p_values\n \n def _calculate_separation_score(self, tier_stats: Dict[str, TierStats]) -> float:\n \"\"\"\n Calculate tier separation score (0-1).\n \n Higher score = better separation between tiers.\n \"\"\"\n expectancies = [s.expectancy for s in tier_stats.values() if s.count >= MIN_OBSERVATIONS_PER_TIER]\n \n if len(expectancies) \u003c 2:\n return 0.0\n \n # Ideal: monotonically decreasing from EXECUTE to WAIT\n expected_order = ['EXECUTE', 'PREPARE', 'WATCH', 'WAIT']\n actual_order = sorted(\n [(t, s.expectancy) for t, s in tier_stats.items() if s.count >= MIN_OBSERVATIONS_PER_TIER],\n key=lambda x: x[1],\n reverse=True\n )\n \n # Check if actual order matches expected\n matches = sum(1 for i, (tier, _) in enumerate(actual_order) \n if i \u003c len(expected_order) and tier == expected_order[i])\n \n # Score based on rank correlation\n try:\n expected_ranks = list(range(len(expected_order)))\n actual_ranks = [expected_order.index(tier) if tier in expected_order else 0 \n for tier, _ in actual_order]\n \n if len(expected_ranks) == len(actual_ranks) and len(expected_ranks) > 1:\n corr, _ = stats.spearmanr(expected_ranks[:len(actual_ranks)], actual_ranks)\n score = max(0, (corr + 1) / 2) # Convert -1,1 to 0,1\n else:\n score = matches / len(expected_order)\n except Exception:\n score = matches / len(expected_order)\n \n return score\n \n def _generate_recommendation(self, tier_stats: Dict[str, TierStats],\n p_values: Dict[str, float],\n separation_score: float) -> str:\n \"\"\"Generate validation recommendation.\"\"\"\n # Check if EXECUTE significantly outperforms WAIT\n execute_sig = p_values.get('execute_vs_wait', 1.0) \u003c 0.05\n anova_sig = p_values.get('anova_all_tiers', 1.0) \u003c 0.05\n \n execute_exp = tier_stats.get('EXECUTE', TierStats('', 0, 0, 0, 0, 0, 0, 0, 0, 0)).expectancy\n wait_exp = tier_stats.get('WAIT', TierStats('', 0, 0, 0, 0, 0, 0, 0, 0, 0)).expectancy\n \n if execute_sig and execute_exp > wait_exp and separation_score > 0.7:\n return \"VALIDATED\"\n elif execute_sig and execute_exp > wait_exp:\n return \"PARTIALLY_VALIDATED\"\n elif anova_sig and separation_score > 0.5:\n return \"NEEDS_CALIBRATION\"\n else:\n return \"REJECTED\"\n \n def calibrate_weights(self, results_df: pd.DataFrame) -> Dict[str, Any]:\n \"\"\"\n Suggest weight adjustments if tiers don't separate.\n \n Uses:\n - Correlation analysis: which indicators predict actual P&L?\n - Grid search: optimize weights to maximize tier separation\n \n Args:\n results_df: DataFrame from run_walk_forward().\n \n Returns:\n Dictionary of suggested weight adjustments.\n \"\"\"\n if results_df.empty or len(results_df) \u003c MIN_OBSERVATIONS_PER_TIER * 4:\n return {\"error\": \"Insufficient data for calibration\"}\n \n # This is a placeholder for actual weight calibration\n # In practice, you would:\n # 1. Extract individual indicator scores from results\n # 2. Run correlation analysis between indicators and P&L\n # 3. Use optimization to find weights that maximize tier separation\n \n # For now, return diagnostic info\n tier_means = results_df.groupby('tier')['pnl_dollar'].mean()\n \n suggestions = {\n \"current_tier_performance\": {\n tier: round(val, 2) for tier, val in tier_means.items()\n },\n \"note\": \"Full weight calibration requires indicator-level data from conviction engine.\",\n \"recommendations\": []\n }\n \n # Basic recommendations based on tier performance\n execute_perf = tier_means.get('EXECUTE', 0)\n wait_perf = tier_means.get('WAIT', 0)\n \n if execute_perf \u003c= wait_perf:\n suggestions[\"recommendations\"].append(\n \"EXECUTE tier underperforming: Increase ADX weight to filter weak trends\"\n )\n suggestions[\"recommendations\"].append(\n \"Consider increasing RSI weight for better entry timing\"\n )\n \n if tier_means.get('WATCH', 0) > tier_means.get('PREPARE', 0):\n suggestions[\"recommendations\"].append(\n \"WATCH tier outperforming PREPARE: Lower threshold for PREPARE tier\"\n )\n \n return suggestions\n\n\n# =============================================================================\n# CLI Interface\n# =============================================================================\n\ndef main():\n \"\"\"CLI entry point for backtest validator.\"\"\"\n import argparse\n \n parser = argparse.ArgumentParser(\n description=\"Backtest Validator — Walk-forward validation of conviction scores\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=\"\"\"\nExamples:\n python backtest_validator.py --tickers AAPL MSFT SPY --start 2022-01-01 --end 2024-01-01\n python backtest_validator.py --tickers SPY --strategy bull_put --hold-days 5\n python backtest_validator.py --tickers AAPL --json\n \"\"\",\n )\n parser.add_argument(\"--tickers\", nargs=\"+\", required=True,\n help=\"List of tickers to backtest\")\n parser.add_argument(\"--start\", default=\"2022-01-01\",\n help=\"Start date (YYYY-MM-DD)\")\n parser.add_argument(\"--end\", default=\"2024-01-01\",\n help=\"End date (YYYY-MM-DD)\")\n parser.add_argument(\"--strategy\", default=\"bull_put\",\n help=\"Strategy to test\")\n parser.add_argument(\"--hold-days\", type=int, default=DEFAULT_HOLD_DAYS,\n help=f\"Days to hold each position (default: {DEFAULT_HOLD_DAYS})\")\n parser.add_argument(\"--json\", action=\"store_true\", help=\"Output as JSON\")\n \n args = parser.parse_args()\n \n # Create mock engine for CLI testing\n class MockEngine:\n def analyze(self, ticker, strategy, date=None):\n # Simple mock that generates realistic-looking scores\n np.random.seed(hash(f\"{ticker}_{date}\") % 10000)\n \n # Simulate some edge - higher scores should win more\n base_score = np.random.normal(60, 20)\n score = max(0, min(100, base_score))\n \n tier = 'EXECUTE' if score >= 80 else 'PREPARE' if score >= 60 else \\\n 'WATCH' if score >= 40 else 'WAIT'\n \n class Result:\n pass\n r = Result()\n r.conviction_score = score\n r.tier = tier\n return r\n \n validator = BacktestValidator(\n MockEngine(),\n args.start,\n args.end,\n args.strategy\n )\n \n print(f\"Running walk-forward backtest for {args.tickers}...\")\n results = validator.run_walk_forward(args.tickers, hold_days=args.hold_days)\n \n if results.empty:\n print(\"No trades generated. Check tickers and date range.\")\n return\n \n print(f\"Generated {len(results)} trades. Validating...\")\n report = validator.validate_tiers(results)\n \n if args.json:\n import json\n print(json.dumps(report.to_dict(), indent=2))\n else:\n print()\n print(\"=\" * 70)\n print(\" BACKTEST VALIDATION REPORT\")\n print(\"=\" * 70)\n print(f\" Period: {args.start} to {args.end}\")\n print(f\" Strategy: {args.strategy}\")\n print(f\" Hold Days: {args.hold_days}\")\n print(f\" Total Trades: {len(results)}\")\n print()\n \n print(\" Tier Statistics:\")\n for tier_name in ['EXECUTE', 'PREPARE', 'WATCH', 'WAIT']:\n s = report.tier_stats.get(tier_name)\n if s and s.count > 0:\n print(f\" {tier_name:8s}: n={s.count:3d}, \"\n f\"win_rate={s.win_rate:.1%}, \"\n f\"exp=${s.expectancy:.0f}, \"\n f\"sharpe={s.sharpe:.2f}\")\n \n print()\n print(\" Statistical Tests:\")\n for test, p_val in report.p_values.items():\n sig = \"***\" if p_val \u003c 0.01 else \"**\" if p_val \u003c 0.05 else \"*\" if p_val \u003c 0.1 else \"\"\n print(f\" {test:20s}: p={p_val:.4f} {sig}\")\n \n print()\n print(f\" Tier Separation Score: {report.tier_separation_score:.2f}\")\n print(f\" Overall Expectancy: ${report.overall_expectancy:.0f}\")\n print(f\" Recommendation: {report.recommendation}\")\n print(\"=\" * 70)\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":29814,"content_sha256":"5540a03d61b4a320dde70bf5901d48d7536f7dc88460406b5bb58fab09689065"},{"filename":"scripts/calculator.py","content":"\"\"\"\nOptions Profit Calculator - Core Calculation Logic\n\nSupports multi-leg options strategies including vertical spreads,\niron condors, and butterflies. Calculates P/L profiles, breakeven\npoints, max profit/loss, Greeks, and Probability of Profit (POP)\nusing Black-Scholes closed-form solutions and Monte Carlo simulation.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport math\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import List, Optional, Tuple\n\nimport numpy as np\nfrom scipy.stats import norm\n\n\n# ---------------------------------------------------------------------------\n# Enums\n# ---------------------------------------------------------------------------\n\nclass OptionType(Enum):\n CALL = \"call\"\n PUT = \"put\"\n\n\nclass Position(Enum):\n LONG = \"long\"\n SHORT = \"short\"\n\n\n# ---------------------------------------------------------------------------\n# Option Leg\n# ---------------------------------------------------------------------------\n\n@dataclass\nclass OptionLeg:\n \"\"\"Represents a single leg of an options strategy.\n\n Attributes:\n strike: Strike price of the option.\n option_type: CALL or PUT.\n position: LONG (bought) or SHORT (sold/written).\n iv: Annualized implied volatility as a decimal (e.g. 0.30 for 30%).\n days_to_expiry: Calendar days until expiration.\n premium: Per-share premium paid (positive) or received (positive).\n Sign convention: always stored as a positive number; the\n position (long/short) determines cash flow direction.\n quantity: Number of contracts (each contract = 100 shares).\n risk_free_rate: Annualized risk-free interest rate (decimal).\n \"\"\"\n\n strike: float\n option_type: OptionType\n position: Position\n iv: float\n days_to_expiry: float\n premium: float\n quantity: int = 1\n risk_free_rate: float = 0.05\n\n def __post_init__(self) -> None:\n _validate_positive(self.strike, \"strike\")\n _validate_positive(self.iv, \"implied volatility\")\n _validate_positive(self.days_to_expiry, \"days_to_expiry\")\n _validate_non_negative(self.premium, \"premium\")\n if self.quantity \u003c 1:\n raise ValueError(\"quantity must be >= 1\")\n\n @property\n def T(self) -> float:\n \"\"\"Time to expiry in years.\"\"\"\n return self.days_to_expiry / 365.0\n\n # -- intrinsic value at a given underlying price at expiration ----------\n\n def intrinsic_at_expiry(self, underlying_price: float) -> float:\n \"\"\"Per-share intrinsic value at expiration for a given underlying price.\"\"\"\n if self.option_type == OptionType.CALL:\n return max(underlying_price - self.strike, 0.0)\n else:\n return max(self.strike - underlying_price, 0.0)\n\n def pnl_at_expiry(self, underlying_price: float) -> float:\n \"\"\"Per-share P/L at expiration for this leg (accounts for position direction).\n\n Returns a dollar amount per share. Multiply by (quantity * 100)\n for total dollar P/L.\n \"\"\"\n intrinsic = self.intrinsic_at_expiry(underlying_price)\n if self.position == Position.LONG:\n return intrinsic - self.premium\n else: # SHORT\n return self.premium - intrinsic\n\n def total_pnl_at_expiry(self, underlying_price: float) -> float:\n \"\"\"Total dollar P/L at expiration (quantity * 100 shares).\"\"\"\n return self.pnl_at_expiry(underlying_price) * self.quantity * 100\n\n\n# ---------------------------------------------------------------------------\n# Black-Scholes primitives\n# ---------------------------------------------------------------------------\n\ndef _d1(S: float, K: float, T: float, r: float, sigma: float) -> float:\n \"\"\"Calculate d1 in the Black-Scholes formula.\"\"\"\n if T \u003c 1e-10:\n return float('inf') if S > K else float('-inf') if S \u003c K else 0.0\n return (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))\n\n\ndef _d2(S: float, K: float, T: float, r: float, sigma: float) -> float:\n \"\"\"Calculate d2 in the Black-Scholes formula.\"\"\"\n return _d1(S, K, T, r, sigma) - sigma * math.sqrt(T)\n\n\ndef black_scholes_price(\n S: float,\n K: float,\n T: float,\n r: float,\n sigma: float,\n option_type: OptionType,\n) -> float:\n \"\"\"Black-Scholes European option price.\n\n Args:\n S: Current underlying price.\n K: Strike price.\n T: Time to expiry in years.\n r: Risk-free rate (annualized, decimal).\n sigma: Implied volatility (annualized, decimal).\n option_type: CALL or PUT.\n\n Returns:\n Theoretical option price (per share).\n \"\"\"\n if T \u003c= 0:\n # At or past expiry — return intrinsic value\n if option_type == OptionType.CALL:\n return max(S - K, 0.0)\n else:\n return max(K - S, 0.0)\n\n d1 = _d1(S, K, T, r, sigma)\n d2 = d1 - sigma * math.sqrt(T)\n\n if option_type == OptionType.CALL:\n return S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)\n else:\n return K * math.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)\n\n\n# ---------------------------------------------------------------------------\n# Greeks (per share, per leg)\n# ---------------------------------------------------------------------------\n\n@dataclass\nclass Greeks:\n \"\"\"Option Greeks container.\"\"\"\n delta: float = 0.0\n gamma: float = 0.0\n theta: float = 0.0 # per calendar day\n vega: float = 0.0 # per 1% move in IV\n rho: float = 0.0 # per 1% move in rate\n\n\ndef compute_greeks(\n S: float,\n K: float,\n T: float,\n r: float,\n sigma: float,\n option_type: OptionType,\n) -> Greeks:\n \"\"\"Compute Black-Scholes Greeks for a European option.\n\n Returns per-share values. Theta is expressed per calendar day.\n Vega is per 1 percentage-point change in IV.\n \"\"\"\n if T \u003c= 1e-10:\n return Greeks()\n\n d1 = _d1(S, K, T, r, sigma)\n d2 = d1 - sigma * math.sqrt(T)\n sqrt_T = math.sqrt(T)\n pdf_d1 = norm.pdf(d1)\n discount = math.exp(-r * T)\n\n # Gamma (same for call and put)\n gamma = pdf_d1 / (S * sigma * sqrt_T)\n\n # Vega (same for call and put), per 1% IV change\n vega = S * pdf_d1 * sqrt_T / 100.0\n\n if option_type == OptionType.CALL:\n delta = norm.cdf(d1)\n theta = (\n -S * pdf_d1 * sigma / (2.0 * sqrt_T)\n - r * K * discount * norm.cdf(d2)\n ) / 365.0\n rho = K * T * discount * norm.cdf(d2) / 100.0\n else:\n delta = norm.cdf(d1) - 1.0\n theta = (\n -S * pdf_d1 * sigma / (2.0 * sqrt_T)\n + r * K * discount * norm.cdf(-d2)\n ) / 365.0\n rho = -K * T * discount * norm.cdf(-d2) / 100.0\n\n return Greeks(delta=delta, gamma=gamma, theta=theta, vega=vega, rho=rho)\n\n\n# ---------------------------------------------------------------------------\n# Strategy (collection of legs)\n# ---------------------------------------------------------------------------\n\n@dataclass\nclass StrategyResult:\n \"\"\"Aggregated result of a strategy analysis.\"\"\"\n name: str\n legs: List[OptionLeg]\n net_premium: float # positive = net credit; negative = net debit\n max_profit: float # total dollars\n max_loss: float # total dollars (expressed as negative)\n breakeven_points: List[float]\n pop: float # probability of profit [0, 1]\n net_greeks: Greeks\n pnl_curve: Optional[Tuple[np.ndarray, np.ndarray]] = None # (prices, pnl)\n\n\nclass Strategy:\n \"\"\"A multi-leg options strategy.\n\n Build by adding OptionLeg instances, then call analyze() with the\n current underlying price to compute P/L profile, breakeven, max\n profit/loss, Greeks, and POP.\n \"\"\"\n\n def __init__(self, name: str = \"Custom Strategy\") -> None:\n self.name = name\n self.legs: List[OptionLeg] = []\n\n def add_leg(self, leg: OptionLeg) -> \"Strategy\":\n \"\"\"Add an option leg to the strategy. Returns self for chaining.\"\"\"\n self.legs.append(leg)\n return self\n\n # -- net premium --------------------------------------------------------\n\n def net_premium(self) -> float:\n \"\"\"Net premium of the strategy (per share).\n\n Positive → net credit received.\n Negative → net debit paid.\n \"\"\"\n total = 0.0\n for leg in self.legs:\n if leg.position == Position.SHORT:\n total += leg.premium\n else:\n total -= leg.premium\n return total\n\n def net_premium_total(self) -> float:\n \"\"\"Net premium in total dollars (accounts for quantity * 100).\"\"\"\n total = 0.0\n for leg in self.legs:\n multiplier = leg.quantity * 100\n if leg.position == Position.SHORT:\n total += leg.premium * multiplier\n else:\n total -= leg.premium * multiplier\n return total\n\n # -- P/L at expiry ------------------------------------------------------\n\n def pnl_at_expiry(self, underlying_price: float) -> float:\n \"\"\"Total dollar P/L at expiration for a given underlying price.\"\"\"\n return sum(leg.total_pnl_at_expiry(underlying_price) for leg in self.legs)\n\n def pnl_curve(\n self,\n underlying_price: float,\n price_range_pct: float = 0.30,\n num_points: int = 500,\n ) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"Generate a P/L curve over a range of underlying prices.\n\n Args:\n underlying_price: Current price of the underlying.\n price_range_pct: Fraction above/below current price to plot.\n num_points: Number of data points.\n\n Returns:\n (prices, pnl) arrays.\n \"\"\"\n low = underlying_price * (1.0 - price_range_pct)\n high = underlying_price * (1.0 + price_range_pct)\n prices = np.linspace(low, high, num_points)\n pnl = np.array([self.pnl_at_expiry(p) for p in prices])\n return prices, pnl\n\n # -- breakeven ----------------------------------------------------------\n\n def breakeven_points(\n self,\n underlying_price: float,\n price_range_pct: float = 0.50,\n num_points: int = 5000,\n ) -> List[float]:\n \"\"\"Find breakeven prices (where P/L crosses zero).\n\n Uses a fine-grained sweep and linear interpolation.\n \"\"\"\n low = underlying_price * (1.0 - price_range_pct)\n high = underlying_price * (1.0 + price_range_pct)\n prices = np.linspace(max(low, 0.01), high, num_points)\n pnl = np.array([self.pnl_at_expiry(p) for p in prices])\n\n breakevens: List[float] = []\n for i in range(len(pnl) - 1):\n if pnl[i] * pnl[i + 1] \u003c 0:\n # Linear interpolation\n p = prices[i] - pnl[i] * (prices[i + 1] - prices[i]) / (pnl[i + 1] - pnl[i])\n breakevens.append(round(p, 2))\n return breakevens\n\n # -- max profit / loss --------------------------------------------------\n\n def max_profit_loss(\n self,\n underlying_price: float,\n price_range_pct: float = 1.0,\n num_points: int = 10000,\n ) -> Tuple[float, float]:\n \"\"\"Estimate max profit and max loss over a wide price range.\n\n Returns (max_profit, max_loss) in total dollars.\n max_loss is returned as a negative number.\n \"\"\"\n low = max(underlying_price * (1.0 - price_range_pct), 0.01)\n high = underlying_price * (1.0 + price_range_pct)\n prices = np.linspace(low, high, num_points)\n pnl = np.array([self.pnl_at_expiry(p) for p in prices])\n # Also evaluate at 0 (underlying can go to zero)\n pnl_at_zero = self.pnl_at_expiry(0.01)\n all_pnl = np.concatenate([pnl, [pnl_at_zero]])\n max_profit = float(np.max(all_pnl))\n max_loss = float(np.min(all_pnl))\n\n # For strategies with naked calls, loss is theoretically unlimited.\n # We flag this by checking if the P/L is still declining at the high end.\n if pnl[-1] \u003c pnl[-2] and pnl[-1] \u003c 0:\n max_loss = float(\"-inf\")\n\n return max_profit, max_loss\n\n # -- net Greeks ---------------------------------------------------------\n\n def net_greeks(self, underlying_price: float) -> Greeks:\n \"\"\"Compute net Greeks for the strategy at the given underlying price.\"\"\"\n net = Greeks()\n for leg in self.legs:\n g = compute_greeks(\n S=underlying_price,\n K=leg.strike,\n T=leg.T,\n r=leg.risk_free_rate,\n sigma=leg.iv,\n option_type=leg.option_type,\n )\n sign = 1.0 if leg.position == Position.LONG else -1.0\n multiplier = sign * leg.quantity\n net.delta += g.delta * multiplier\n net.gamma += g.gamma * multiplier\n net.theta += g.theta * multiplier\n net.vega += g.vega * multiplier\n net.rho += g.rho * multiplier\n return net\n\n # -- full analysis ------------------------------------------------------\n\n def analyze(\n self,\n underlying_price: float,\n num_simulations: int = 100_000,\n price_range_pct: float = 0.30,\n ) -> StrategyResult:\n \"\"\"Run full strategy analysis.\n\n Args:\n underlying_price: Current price of the underlying asset.\n num_simulations: Number of Monte Carlo paths for POP.\n price_range_pct: Range around current price for the P/L curve.\n\n Returns:\n StrategyResult with all computed metrics.\n \"\"\"\n _validate_positive(underlying_price, \"underlying_price\")\n\n prices, pnl = self.pnl_curve(underlying_price, price_range_pct)\n breakevens = self.breakeven_points(underlying_price)\n max_prof, max_loss = self.max_profit_loss(underlying_price)\n greeks = self.net_greeks(underlying_price)\n\n # Choose POP method\n if len(self.legs) == 1:\n pop = black_scholes_pop(self.legs[0], underlying_price)\n else:\n pop = monte_carlo_pop(self, underlying_price, num_simulations)\n\n return StrategyResult(\n name=self.name,\n legs=self.legs,\n net_premium=self.net_premium_total(),\n max_profit=max_prof,\n max_loss=max_loss,\n breakeven_points=breakevens,\n pop=pop,\n net_greeks=greeks,\n pnl_curve=(prices, pnl),\n )\n\n\n# ---------------------------------------------------------------------------\n# Probability of Profit — Black-Scholes (single leg)\n# ---------------------------------------------------------------------------\n\ndef black_scholes_pop(leg: OptionLeg, underlying_price: float) -> float:\n \"\"\"Probability of profit for a single option leg using Black-Scholes.\n\n For a long call, POP = P(S_T > K + premium).\n For a long put, POP = P(S_T \u003c K - premium).\n For short positions the inequalities reverse.\n\n Uses the log-normal distribution of S_T under GBM.\n \"\"\"\n S = underlying_price\n K = leg.strike\n T = leg.T\n r = leg.risk_free_rate\n sigma = leg.iv\n\n if T \u003c= 0:\n return 1.0 if leg.pnl_at_expiry(S) > 0 else 0.0\n\n # Breakeven price for this single leg\n if leg.option_type == OptionType.CALL:\n if leg.position == Position.LONG:\n be = K + leg.premium\n else:\n be = K + leg.premium # same breakeven, but direction flips\n else: # PUT\n if leg.position == Position.LONG:\n be = K - leg.premium\n else:\n be = K - leg.premium\n\n if be \u003c= 0:\n # Breakeven is at or below zero\n if leg.position == Position.SHORT:\n return 1.0\n return 0.0\n\n # P(S_T > be) under risk-neutral (we use real-world drift ≈ r for simplicity)\n d2_be = (math.log(S / be) + (r - 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))\n\n prob_above_be = norm.cdf(d2_be)\n\n if leg.option_type == OptionType.CALL:\n if leg.position == Position.LONG:\n return prob_above_be\n else:\n return 1.0 - prob_above_be\n else: # PUT\n if leg.position == Position.LONG:\n return 1.0 - prob_above_be\n else:\n return prob_above_be\n\n\n# ---------------------------------------------------------------------------\n# Probability of Profit — Monte Carlo (multi-leg)\n# ---------------------------------------------------------------------------\n\ndef monte_carlo_pop(\n strategy: Strategy,\n underlying_price: float,\n num_simulations: int = 100_000,\n seed: Optional[int] = 42,\n) -> float:\n \"\"\"Estimate POP via Monte Carlo simulation of geometric Brownian motion.\n\n Simulates terminal prices at the *earliest* expiry among legs, then\n computes the strategy P/L for each path. POP = fraction of paths\n with P/L > 0.\n\n For strategies with legs at different expiries, we use the weighted-\n average IV and earliest expiry as a simplification (all legs are\n evaluated at the same terminal price).\n\n Args:\n strategy: The multi-leg Strategy.\n underlying_price: Current underlying price.\n num_simulations: Number of simulation paths.\n seed: Random seed for reproducibility (None for non-deterministic).\n\n Returns:\n Estimated probability of profit in [0, 1].\n \"\"\"\n if not strategy.legs:\n return 0.0\n\n rng = np.random.default_rng(seed)\n\n # Use the shortest expiry for terminal simulation\n min_T = min(leg.T for leg in strategy.legs)\n if min_T \u003c= 0:\n min_T = 1.0 / 365.0 # at least 1 day\n\n # Use quantity-weighted average IV and the first leg's risk-free rate\n total_qty = sum(leg.quantity for leg in strategy.legs)\n avg_iv = sum(leg.iv * leg.quantity for leg in strategy.legs) / total_qty\n r = strategy.legs[0].risk_free_rate\n\n # GBM terminal price: S_T = S * exp((r - 0.5*sigma^2)*T + sigma*sqrt(T)*Z)\n drift = (r - 0.5 * avg_iv ** 2) * min_T\n diffusion = avg_iv * math.sqrt(min_T)\n Z = rng.standard_normal(num_simulations)\n S_T = underlying_price * np.exp(drift + diffusion * Z)\n\n # Vectorized P/L computation\n pnl = np.zeros(num_simulations)\n for leg in strategy.legs:\n sign = 1.0 if leg.position == Position.LONG else -1.0\n multiplier = leg.quantity * 100\n if leg.option_type == OptionType.CALL:\n intrinsic = np.maximum(S_T - leg.strike, 0.0)\n else:\n intrinsic = np.maximum(leg.strike - S_T, 0.0)\n\n # long pays premium, earns intrinsic; short earns premium, pays intrinsic\n if leg.position == Position.LONG:\n pnl += (intrinsic - leg.premium) * multiplier\n else:\n pnl += (leg.premium - intrinsic) * multiplier\n\n pop = float(np.mean(pnl > 0))\n return pop\n\n\n# ---------------------------------------------------------------------------\n# Pre-built strategy factories\n# ---------------------------------------------------------------------------\n\ndef bull_call_spread(\n underlying_price: float,\n long_strike: float,\n short_strike: float,\n long_premium: float,\n short_premium: float,\n iv: float,\n days_to_expiry: float,\n risk_free_rate: float = 0.05,\n quantity: int = 1,\n) -> Strategy:\n \"\"\"Create a Bull Call Spread (buy lower call, sell higher call).\n\n Args:\n underlying_price: Current underlying price (for reference).\n long_strike: Strike of the long (bought) call.\n short_strike: Strike of the short (sold) call. Must be > long_strike.\n long_premium: Premium paid for the long call.\n short_premium: Premium received for the short call.\n iv: Implied volatility (decimal).\n days_to_expiry: Days until expiration.\n risk_free_rate: Risk-free rate (decimal).\n quantity: Number of spreads.\n\n Returns:\n Configured Strategy.\n \"\"\"\n if short_strike \u003c= long_strike:\n raise ValueError(\"short_strike must be greater than long_strike for a bull call spread\")\n\n strat = Strategy(\"Bull Call Spread\")\n strat.add_leg(OptionLeg(\n strike=long_strike, option_type=OptionType.CALL, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=long_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=short_strike, option_type=OptionType.CALL, position=Position.SHORT,\n iv=iv, days_to_expiry=days_to_expiry, premium=short_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n return strat\n\n\ndef bear_put_spread(\n underlying_price: float,\n long_strike: float,\n short_strike: float,\n long_premium: float,\n short_premium: float,\n iv: float,\n days_to_expiry: float,\n risk_free_rate: float = 0.05,\n quantity: int = 1,\n) -> Strategy:\n \"\"\"Create a Bear Put Spread (buy higher put, sell lower put).\n\n Args:\n long_strike: Strike of the long (bought) put. Should be higher.\n short_strike: Strike of the short (sold) put. Should be lower.\n \"\"\"\n if long_strike \u003c= short_strike:\n raise ValueError(\"long_strike must be greater than short_strike for a bear put spread\")\n\n strat = Strategy(\"Bear Put Spread\")\n strat.add_leg(OptionLeg(\n strike=long_strike, option_type=OptionType.PUT, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=long_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=short_strike, option_type=OptionType.PUT, position=Position.SHORT,\n iv=iv, days_to_expiry=days_to_expiry, premium=short_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n return strat\n\n\ndef iron_condor(\n underlying_price: float,\n put_long_strike: float,\n put_short_strike: float,\n call_short_strike: float,\n call_long_strike: float,\n put_long_premium: float,\n put_short_premium: float,\n call_short_premium: float,\n call_long_premium: float,\n iv: float,\n days_to_expiry: float,\n risk_free_rate: float = 0.05,\n quantity: int = 1,\n) -> Strategy:\n \"\"\"Create an Iron Condor.\n\n Structure (all same expiry):\n - Buy OTM put (lowest strike) — protection\n - Sell OTM put (next strike up) — credit\n - Sell OTM call (next strike up) — credit\n - Buy OTM call (highest strike) — protection\n\n Strikes must satisfy: put_long \u003c put_short \u003c call_short \u003c call_long.\n \"\"\"\n strikes = [put_long_strike, put_short_strike, call_short_strike, call_long_strike]\n if strikes != sorted(strikes) or len(set(strikes)) != 4:\n raise ValueError(\n \"Strikes must be strictly ordered: \"\n \"put_long \u003c put_short \u003c call_short \u003c call_long\"\n )\n\n strat = Strategy(\"Iron Condor\")\n strat.add_leg(OptionLeg(\n strike=put_long_strike, option_type=OptionType.PUT, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=put_long_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=put_short_strike, option_type=OptionType.PUT, position=Position.SHORT,\n iv=iv, days_to_expiry=days_to_expiry, premium=put_short_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=call_short_strike, option_type=OptionType.CALL, position=Position.SHORT,\n iv=iv, days_to_expiry=days_to_expiry, premium=call_short_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=call_long_strike, option_type=OptionType.CALL, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=call_long_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n return strat\n\n\ndef long_call_butterfly(\n underlying_price: float,\n lower_strike: float,\n middle_strike: float,\n upper_strike: float,\n lower_premium: float,\n middle_premium: float,\n upper_premium: float,\n iv: float,\n days_to_expiry: float,\n risk_free_rate: float = 0.05,\n quantity: int = 1,\n) -> Strategy:\n \"\"\"Create a Long Call Butterfly spread.\n\n Structure:\n - Buy 1 call at lower strike\n - Sell 2 calls at middle strike\n - Buy 1 call at upper strike\n\n The middle strike should ideally be equidistant from lower and upper.\n \"\"\"\n if not (lower_strike \u003c middle_strike \u003c upper_strike):\n raise ValueError(\"Strikes must be ordered: lower \u003c middle \u003c upper\")\n\n strat = Strategy(\"Long Call Butterfly\")\n strat.add_leg(OptionLeg(\n strike=lower_strike, option_type=OptionType.CALL, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=lower_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=middle_strike, option_type=OptionType.CALL, position=Position.SHORT,\n iv=iv, days_to_expiry=days_to_expiry, premium=middle_premium,\n quantity=quantity * 2, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=upper_strike, option_type=OptionType.CALL, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=upper_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n return strat\n\n\ndef long_put_butterfly(\n underlying_price: float,\n lower_strike: float,\n middle_strike: float,\n upper_strike: float,\n lower_premium: float,\n middle_premium: float,\n upper_premium: float,\n iv: float,\n days_to_expiry: float,\n risk_free_rate: float = 0.05,\n quantity: int = 1,\n) -> Strategy:\n \"\"\"Create a Long Put Butterfly spread.\n\n Structure:\n - Buy 1 put at upper strike\n - Sell 2 puts at middle strike\n - Buy 1 put at lower strike\n \"\"\"\n if not (lower_strike \u003c middle_strike \u003c upper_strike):\n raise ValueError(\"Strikes must be ordered: lower \u003c middle \u003c upper\")\n\n strat = Strategy(\"Long Put Butterfly\")\n strat.add_leg(OptionLeg(\n strike=lower_strike, option_type=OptionType.PUT, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=lower_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=middle_strike, option_type=OptionType.PUT, position=Position.SHORT,\n iv=iv, days_to_expiry=days_to_expiry, premium=middle_premium,\n quantity=quantity * 2, risk_free_rate=risk_free_rate,\n ))\n strat.add_leg(OptionLeg(\n strike=upper_strike, option_type=OptionType.PUT, position=Position.LONG,\n iv=iv, days_to_expiry=days_to_expiry, premium=upper_premium,\n quantity=quantity, risk_free_rate=risk_free_rate,\n ))\n return strat\n\n\n# ---------------------------------------------------------------------------\n# Validation helpers\n# ---------------------------------------------------------------------------\n\ndef _validate_positive(value: float, name: str) -> None:\n if value \u003c= 0:\n raise ValueError(f\"{name} must be positive, got {value}\")\n\n\ndef _validate_non_negative(value: float, name: str) -> None:\n if value \u003c 0:\n raise ValueError(f\"{name} must be non-negative, got {value}\")\n","content_type":"text/x-python; charset=utf-8","language":"python","size":27335,"content_sha256":"121a734f6e454da2d5081813238fa43b1631f590ddbe549d32f486f7ea7bbb34"},{"filename":"scripts/chain_analyzer.py","content":"\"\"\"\nOptions Chain Analyzer\n\nFetches and parses options chains from Yahoo Finance.\nHandles caching, rate limiting, and data normalization.\n\"\"\"\n\nimport time\nimport logging\nfrom datetime import datetime, timedelta\nfrom typing import List, Dict, Optional, Tuple\nfrom dataclasses import dataclass, field\nfrom collections import defaultdict\nimport os\nimport pickle\n\nimport yfinance\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass OptionChain:\n \"\"\"Container for full options chain data\"\"\"\n ticker: str\n underlying_price: float\n expiration_date: str\n expiration_timestamp: int\n dte: int\n calls: List[Dict] = field(default_factory=list)\n puts: List[Dict] = field(default_factory=list)\n \n @property\n def all_options(self) -> List[Dict]:\n \"\"\"All options combined\"\"\"\n result = []\n for opt in self.calls:\n opt['type'] = 'call'\n result.append(opt)\n for opt in self.puts:\n opt['type'] = 'put'\n result.append(opt)\n return result\n\n\nclass ChainFetcher:\n \"\"\"\n Fetches options chains from Yahoo Finance with rate limiting\n \"\"\"\n \n def __init__(self, cache_dir: str = None, rate_limit_delay: float = 0.5):\n self.rate_limit_delay = rate_limit_delay\n self.last_fetch_time = 0\n \n # Setup cache\n if cache_dir is None:\n cache_dir = os.path.expanduser('~/.openclaw/options_cache')\n self.cache_dir = cache_dir\n os.makedirs(cache_dir, exist_ok=True)\n \n self.cache = {}\n self.cache_ttl = 300 # 5 minute cache\n \n def _rate_limit(self):\n \"\"\"Ensure we don't exceed rate limits\"\"\"\n elapsed = time.time() - self.last_fetch_time\n if elapsed \u003c self.rate_limit_delay:\n time.sleep(self.rate_limit_delay - elapsed)\n self.last_fetch_time = time.time()\n \n def _get_cache_key(self, ticker: str, date_timestamp: int = None) -> str:\n \"\"\"Generate cache key\"\"\"\n if date_timestamp:\n return f\"{ticker}_{date_timestamp}\"\n return f\"{ticker}_all\"\n \n def _get_cache_path(self, cache_key: str) -> str:\n \"\"\"Get cache file path\"\"\"\n return os.path.join(self.cache_dir, f\"{cache_key}.pkl\")\n \n def _load_from_cache(self, cache_key: str) -> Optional[Dict]:\n \"\"\"Load data from cache if valid\"\"\"\n cache_path = self._get_cache_path(cache_key)\n \n if not os.path.exists(cache_path):\n return None\n \n try:\n with open(cache_path, 'rb') as f:\n cached = pickle.load(f)\n \n # Check TTL\n if time.time() - cached.get('timestamp', 0) > self.cache_ttl:\n return None\n \n return cached.get('data')\n except (FileNotFoundError, PermissionError, pickle.PickleError, IOError):\n return None\n \n def _save_to_cache(self, cache_key: str, data: Dict):\n \"\"\"Save data to cache\"\"\"\n cache_path = self._get_cache_path(cache_key)\n try:\n with open(cache_path, 'wb') as f:\n pickle.dump({'timestamp': time.time(), 'data': data}, f)\n except (FileNotFoundError, PermissionError, pickle.PickleError, IOError) as e:\n logger.warning(f\"Failed to save cache: {e}\")\n \n def fetch_quote(self, ticker: str) -> Optional[Dict]:\n \"\"\"Fetch current stock quote via yfinance\"\"\"\n self._rate_limit()\n \n try:\n info = yfinance.Ticker(ticker).info\n if not info or 'regularMarketPrice' not in info:\n return None\n return info\n except Exception as e:\n print(f\"Error fetching quote for {ticker}: {e}\")\n return None\n \n def fetch_expirations(self, ticker: str) -> List[Dict]:\n \"\"\"Fetch available expiration dates via yfinance\"\"\"\n cache_key = self._get_cache_key(ticker)\n cached = self._load_from_cache(cache_key)\n \n if cached and 'expirations' in cached:\n return cached['expirations']\n \n self._rate_limit()\n \n try:\n tk = yfinance.Ticker(ticker)\n date_strings = tk.options # tuple of 'YYYY-MM-DD' strings\n \n if not date_strings:\n return []\n \n expirations = []\n for date_str in date_strings:\n try:\n dt = datetime.strptime(date_str, '%Y-%m-%d')\n timestamp = int(dt.timestamp())\n dte = (dt.date() - datetime.now().date()).days\n \n if dte >= 0:\n expirations.append({\n 'date': date_str,\n 'timestamp': timestamp,\n 'dte': dte,\n 'datetime': dt\n })\n except (ValueError, TypeError) as e:\n logger.debug(f\"Skipping invalid expiration date {date_str}: {e}\")\n continue\n \n expirations.sort(key=lambda x: x['dte'])\n self._save_to_cache(cache_key, {'expirations': expirations})\n \n return expirations\n \n except Exception as e:\n print(f\"Error fetching expirations for {ticker}: {e}\")\n return []\n \n def fetch_chain_for_date(self, ticker: str, timestamp: int) -> Optional[OptionChain]:\n \"\"\"Fetch options chain for specific expiration date via yfinance\"\"\"\n cache_key = self._get_cache_key(ticker, timestamp)\n cached = self._load_from_cache(cache_key)\n\n if cached:\n return OptionChain(**cached)\n\n self._rate_limit()\n\n try:\n date_dt = datetime.fromtimestamp(timestamp)\n date_str = date_dt.strftime('%Y-%m-%d')\n\n tk = yfinance.Ticker(ticker)\n\n # Get underlying price\n underlying = tk.info.get('regularMarketPrice', 0) or 0\n\n # Fetch option chain for this date\n opt_chain = tk.option_chain(date_str)\n\n if opt_chain.calls.empty and opt_chain.puts.empty:\n return None\n\n dte = (date_dt.date() - datetime.now().date()).days\n\n calls = self._normalize_options(\n opt_chain.calls.to_dict('records'),\n 'call',\n underlying,\n date_str,\n dte\n )\n\n puts = self._normalize_options(\n opt_chain.puts.to_dict('records'),\n 'put',\n underlying,\n date_str,\n dte\n )\n\n if not calls and not puts:\n return None\n\n chain = OptionChain(\n ticker=ticker,\n underlying_price=underlying,\n expiration_date=date_str,\n expiration_timestamp=timestamp,\n dte=dte,\n calls=calls,\n puts=puts\n )\n\n self._save_to_cache(cache_key, chain.__dict__)\n return chain\n\n except Exception as e:\n print(f\"Error fetching chain for {ticker} @ {timestamp}: {e}\")\n return None\n \n def _normalize_options(self, options: List[Dict], opt_type: str,\n underlying: float, expiration: str, dte: int) -> List[Dict]:\n \"\"\"Normalize option data to consistent format with edge case handling\"\"\"\n normalized = []\n\n for opt in options:\n # Handle missing values gracefully\n # yfinance uses camelCase: lastPrice, openInterest, impliedVolatility\n bid = opt.get('bid', 0) or 0\n ask = opt.get('ask', 0) or 0\n last = opt.get('lastPrice', opt.get('lastprice', 0)) or 0\n volume = opt.get('volume', 0) or 0\n oi = opt.get('openInterest', opt.get('openinterest', 0)) or 0\n iv = opt.get('impliedVolatility', opt.get('impliedvolatility', 0)) or 0\n strike = opt.get('strike', 0) or 0\n \n # Handle NaN values from pandas/yfinance\n import math\n def safe_num(val, default=0):\n if val is None:\n return default\n if isinstance(val, float) and math.isnan(val):\n return default\n return val\n \n bid = safe_num(bid)\n ask = safe_num(ask)\n last = safe_num(last)\n volume = safe_num(volume)\n oi = safe_num(oi)\n iv = safe_num(iv)\n strike = safe_num(strike)\n\n # Yahoo returns IV as decimal (e.g., 0.25 for 25%)\n if iv > 1:\n iv = iv / 100.0\n\n # Handle missing bid/ask - use lastPrice as fallback\n # This is critical for post-market data when bid/ask may be 0\n has_valid_bid_ask = bid > 0 and ask > 0\n\n if has_valid_bid_ask:\n mid_price = (bid + ask) / 2\n spread = ask - bid\n else:\n # Fallback: use lastPrice as mid, 0 spread\n # Flag as potentially illiquid\n mid_price = last if last > 0 else 0\n spread = 0\n\n # Flag wide spreads as illiquid (>20% of option value)\n spread_pct = 0\n if mid_price > 0:\n spread_pct = spread / mid_price\n illiquid = spread_pct > 0.20 or not has_valid_bid_ask\n\n # Flag post-market/zero volume as potentially stale\n stale_data = volume == 0 and oi \u003c 10\n\n norm_opt = {\n 'strike': float(opt.get('strike', 0)),\n 'bid': float(bid),\n 'ask': float(ask),\n 'last_price': float(last),\n 'mid_price': float(mid_price),\n 'spread': float(spread),\n 'spread_pct': float(spread_pct),\n 'volume': int(volume),\n 'open_interest': int(oi),\n 'implied_vol': float(iv),\n 'type': opt_type,\n 'underlying': underlying,\n 'expiration': expiration,\n 'dte': dte,\n # Liquidity flags\n 'illiquid': illiquid,\n 'stale_data': stale_data,\n 'has_valid_bid_ask': has_valid_bid_ask,\n # Greeks (if available from Yahoo)\n 'delta': opt.get('delta'),\n 'gamma': opt.get('gamma'),\n 'theta': opt.get('theta'),\n 'vega': opt.get('vega'),\n }\n\n normalized.append(norm_opt)\n\n # Sort by strike\n normalized.sort(key=lambda x: x['strike'])\n\n return normalized\n \n def fetch_default_chain(self, ticker: str) -> Optional[OptionChain]:\n \"\"\"\n Fetch the default options chain (nearest expiration) via yfinance\n \"\"\"\n cache_key = self._get_cache_key(ticker, 'default')\n cached = self._load_from_cache(cache_key)\n \n if cached:\n return OptionChain(**cached)\n \n self._rate_limit()\n \n try:\n tk = yfinance.Ticker(ticker)\n \n # Get available expirations\n exp_dates = tk.options\n if not exp_dates:\n return None\n \n # Use nearest expiration\n nearest_date = exp_dates[0]\n \n # Get underlying price\n underlying = tk.info.get('regularMarketPrice', 0) or 0\n \n # Fetch chain\n opt_chain = tk.option_chain(nearest_date)\n \n exp_dt = datetime.strptime(nearest_date, '%Y-%m-%d')\n dte = (exp_dt.date() - datetime.now().date()).days\n timestamp = int(exp_dt.timestamp())\n \n calls = self._normalize_options(\n opt_chain.calls.to_dict('records'),\n 'call',\n underlying,\n nearest_date,\n dte\n )\n \n puts = self._normalize_options(\n opt_chain.puts.to_dict('records'),\n 'put',\n underlying,\n nearest_date,\n dte\n )\n \n chain = OptionChain(\n ticker=ticker,\n underlying_price=underlying,\n expiration_date=nearest_date,\n expiration_timestamp=timestamp,\n dte=dte,\n calls=calls,\n puts=puts\n )\n \n self._save_to_cache(cache_key, chain.__dict__)\n return chain\n \n except Exception as e:\n print(f\"Error fetching default chain for {ticker}: {e}\")\n return None\n \n def fetch_multiple_expirations(self, ticker: str, num_expirations: int = 4,\n min_dte: int = 7, max_dte: int = 45) -> List[OptionChain]:\n \"\"\"\n Fetch chains for multiple expiration dates\n \n Filters for DTE within specified range (default 7-45 days)\n \"\"\"\n # First get the default chain (which works reliably)\n default_chain = self.fetch_default_chain(ticker)\n \n if not default_chain:\n return []\n \n # Check if default chain fits DTE criteria\n chains = []\n if min_dte \u003c= default_chain.dte \u003c= max_dte:\n chains.append(default_chain)\n \n # Get available expirations\n expirations = self.fetch_expirations(ticker)\n \n # Filter by DTE range\n filtered = [e for e in expirations \n if min_dte \u003c= e['dte'] \u003c= max_dte \n and e['dte'] != default_chain.dte]\n \n # Try to fetch additional chains\n for exp in filtered[:num_expirations-1]:\n chain = self.fetch_chain_for_date(ticker, exp['timestamp'])\n if chain and (chain.calls or chain.puts):\n chains.append(chain)\n \n return chains\n\n\nclass ChainAnalyzer:\n \"\"\"\n Analyze options chains for trading opportunities\n \"\"\"\n \n def __init__(self, fetcher: ChainFetcher = None):\n self.fetcher = fetcher or ChainFetcher()\n \n def get_atm_option(self, chain: OptionChain, opt_type: str = 'call',\n offset: int = 0) -> Optional[Dict]:\n \"\"\"\n Get at-the-money (or near-ATM) option\n \n offset: 0=ATM, positive=OTM, negative=ITM\n \"\"\"\n options = chain.calls if opt_type == 'call' else chain.puts\n \n if not options:\n return None\n \n # Find closest to ATM\n strikes = [opt['strike'] for opt in options]\n atm_idx = min(range(len(strikes)), \n key=lambda i: abs(strikes[i] - chain.underlying_price))\n \n # Apply offset\n target_idx = max(0, min(len(options) - 1, atm_idx + offset))\n \n return options[target_idx]\n \n def get_otm_option(self, chain: OptionChain, opt_type: str = 'call',\n delta_target: float = 0.30) -> Optional[Dict]:\n \"\"\"\n Get OTM option near target delta\n \n For small accounts, we typically target ~30 delta for short options\n \"\"\"\n options = chain.calls if opt_type == 'call' else chain.puts\n \n if not options:\n return None\n \n # Find closest to target delta if available, otherwise by strike distance\n best_opt = None\n best_score = float('inf')\n \n for opt in options:\n # Skip if too wide spread (>20% of option value)\n if opt['mid_price'] > 0 and opt['spread'] / opt['mid_price'] > 0.20:\n continue\n \n # Skip low/no volume (liquidity check)\n if opt['volume'] \u003c 1 and opt['open_interest'] \u003c 10:\n continue\n \n # Calculate score based on strike distance from target\n if opt_type == 'call':\n # OTM call: strike > underlying\n if opt['strike'] \u003c= chain.underlying_price:\n continue\n else:\n # OTM put: strike \u003c underlying\n if opt['strike'] >= chain.underlying_price:\n continue\n \n # Score by delta if available, otherwise by distance\n if opt.get('delta') is not None:\n delta = abs(opt['delta'])\n score = abs(delta - delta_target)\n else:\n # Score by moneyness\n distance = abs(opt['strike'] - chain.underlying_price)\n score = distance / chain.underlying_price\n \n if score \u003c best_score:\n best_score = score\n best_opt = opt\n \n return best_opt\n \n def get_strikes_by_width(self, chain: OptionChain, opt_type: str = 'put',\n width: float = 5.0, start_otm: bool = True) -> List[List[Dict]]:\n \"\"\"\n Get strike pairs at specified width for spreads\n \n Returns list of [short_strike, long_strike] pairs\n \"\"\"\n options = chain.calls if opt_type == 'call' else chain.puts\n \n if not options:\n return []\n \n pairs = []\n strikes = [opt['strike'] for opt in options]\n \n for i, opt in enumerate(options):\n # For put spreads, we sell higher strike, buy lower\n # For call spreads, we sell lower strike, buy higher\n \n if opt_type == 'put':\n # Look for long strike $width below short strike\n target_strike = opt['strike'] - width\n # Find closest available strike\n try:\n j = min(range(len(strikes)), \n key=lambda k: abs(strikes[k] - target_strike))\n if abs(strikes[j] - target_strike) \u003c 0.5: # Within $0.50\n pairs.append([opt, options[j]])\n except (ValueError, IndexError) as e:\n logger.debug(f\"Could not find matching put strike: {e}\")\n else: # call\n # Look for long strike $width above short strike\n target_strike = opt['strike'] + width\n try:\n j = min(range(len(strikes)),\n key=lambda k: abs(strikes[k] - target_strike))\n if abs(strikes[j] - target_strike) \u003c 0.5:\n pairs.append([opt, options[j]])\n except (ValueError, IndexError) as e:\n logger.debug(f\"Could not find matching call strike: {e}\")\n \n return pairs\n \n def analyze_liquidity(self, chain: OptionChain) -> Dict:\n \"\"\"\n Analyze liquidity of options chain\n \"\"\"\n all_options = chain.all_options\n \n if not all_options:\n return {'score': 0, 'bid_ask_spread_avg': 1.0, 'volume_avg': 0}\n \n # Calculate average bid-ask spread as % of mid price\n spreads = []\n volumes = []\n \n for opt in all_options:\n if opt['mid_price'] > 0.05: # Skip deep OTM\n spread_pct = opt['spread'] / opt['mid_price']\n spreads.append(spread_pct)\n volumes.append(opt['volume'])\n \n avg_spread = sum(spreads) / len(spreads) if spreads else 1.0\n avg_volume = sum(volumes) / len(volumes) if volumes else 0\n \n # Liquidity score (0-100)\n # Lower spread and higher volume = better\n spread_score = max(0, 100 - (avg_spread * 500)) # 0.20 spread = 0 score\n volume_score = min(100, avg_volume * 2) # 50+ volume = 100\n \n score = (spread_score * 0.6) + (volume_score * 0.4)\n \n return {\n 'score': score,\n 'bid_ask_spread_avg': avg_spread,\n 'volume_avg': avg_volume,\n 'spread_score': spread_score,\n 'volume_score': volume_score\n }\n \n def find_iv_skew(self, chain: OptionChain) -> Dict:\n \"\"\"\n Analyze IV skew in the chain\n \n Returns dict with skew metrics\n \"\"\"\n calls = chain.calls\n puts = chain.puts\n \n if not calls or not puts:\n return {}\n \n # Find ATM options\n atm_call = self.get_atm_option(chain, 'call')\n atm_put = self.get_atm_option(chain, 'put')\n \n if not atm_call or not atm_put:\n return {}\n \n atm_iv = (atm_call['implied_vol'] + atm_put['implied_vol']) / 2\n \n # Find ~10% OTM options\n otm_put_target = chain.underlying_price * 0.90\n otm_call_target = chain.underlying_price * 1.10\n \n otm_put_iv = atm_put['implied_vol']\n otm_call_iv = atm_call['implied_vol']\n \n for put in puts:\n if put['strike'] \u003c= otm_put_target:\n otm_put_iv = put['implied_vol']\n break\n \n for call in calls:\n if call['strike'] >= otm_call_target:\n otm_call_iv = call['implied_vol']\n break\n \n # Calculate skew\n put_skew = (otm_put_iv - atm_iv) / atm_iv if atm_iv > 0 else 0\n call_skew = (otm_call_iv - atm_iv) / atm_iv if atm_iv > 0 else 0\n \n return {\n 'atm_iv': atm_iv,\n 'otm_put_iv': otm_put_iv,\n 'otm_call_iv': otm_call_iv,\n 'put_skew_pct': put_skew * 100,\n 'call_skew_pct': call_skew * 100,\n 'skew_bias': put_skew - call_skew # Positive = fear of downside\n }\n\n\n# Singleton instance for reuse\n_default_fetcher = None\n_default_analyzer = None\n\ndef get_fetcher() -> ChainFetcher:\n global _default_fetcher\n if _default_fetcher is None:\n _default_fetcher = ChainFetcher()\n return _default_fetcher\n\ndef get_analyzer() -> ChainAnalyzer:\n global _default_analyzer\n if _default_analyzer is None:\n _default_analyzer = ChainAnalyzer(get_fetcher())\n return _default_analyzer\n","content_type":"text/x-python; charset=utf-8","language":"python","size":22295,"content_sha256":"88fa8164037e6a67a725b9348d7c8bacef4246243cb7083841350dc063267bdf"},{"filename":"scripts/enhanced_kelly.py","content":"\"\"\"\nEnhanced Kelly Position Sizer for Options Conviction Engine\n\nImplements drawdown-constrained, correlation-aware position sizing\nbased on Kelly Criterion with modifications for small account trading.\n\nThe Kelly Criterion maximizes long-term growth by sizing positions\nproportional to edge: f = (p*b - q) / b\n\nWhere:\n- f = optimal fraction of bankroll to bet\n- p = probability of win\n- q = probability of loss (1-p)\n- b = win/loss ratio (average win / average loss)\n\nReferences:\n- Kelly, J.L. (1956). \"A New Interpretation of Information Rate.\" Bell System Technical Journal\n- Thorp, E. (2006). \"The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market\"\n- Rotando, L. & Thorp, E. (1992). \"The Kelly Criterion and the Stock Market\"\n\"\"\"\nimport numpy as np\nimport pandas as pd\nfrom typing import Dict, List, Tuple, Optional\nfrom dataclasses import dataclass\nfrom enum import Enum\n\n\nclass KellyFraction(Enum):\n \"\"\"Kelly fraction options for different risk tolerances.\"\"\"\n FULL = 1.0 # Full Kelly (aggressive, high variance)\n HALF = 0.5 # Half Kelly (moderate, recommended)\n QUARTER = 0.25 # Quarter Kelly (conservative)\n EIGHTH = 0.125 # Eighth Kelly (very conservative)\n\n\n@dataclass\nclass PositionResult:\n \"\"\"Complete position sizing result.\"\"\"\n contracts: int\n total_risk: float\n kelly_fraction: float\n adjusted_kelly: float\n risk_per_contract: float\n max_loss: float\n expected_value: float\n recommendation: str\n reasoning: str\n drawdown_estimate: float\n\n\n@dataclass\nclass CorrelationPenalty:\n \"\"\"Correlation-based position size penalty.\"\"\"\n correlation: float\n penalty_factor: float\n reason: str\n\n\nclass EnhancedKellySizer:\n \"\"\"\n Drawdown-constrained, correlation-aware Kelly position sizing.\n \n Features:\n - Standard Kelly calculation with configurable fraction\n - Drawdown constraint (won't suggest sizes that could exceed max drawdown)\n - Correlation penalty (reduces size when correlated positions exist)\n - Conviction-based Kelly scaling (higher conviction = larger fraction)\n - Small account guardrails (min/max position limits)\n \n Usage:\n sizer = EnhancedKellySizer(account_value=390, max_drawdown=0.20)\n result = sizer.calculate_position(\n spread_cost=100,\n max_loss_per_spread=85,\n win_amount=35,\n conviction=85,\n pop=0.68,\n existing_positions=[{\"ticker\": \"SPY\", \"correlation\": 0.85}]\n )\n \"\"\"\n \n def __init__(self, \n account_value: float = 390,\n max_drawdown: float = 0.20,\n min_contracts: int = 1,\n max_risk_per_trade: float = 100,\n max_risk_total: float = 150):\n \"\"\"\n Initialize EnhancedKellySizer.\n \n Args:\n account_value: Total account value in dollars\n max_drawdown: Maximum acceptable drawdown (0.20 = 20%)\n min_contracts: Minimum contracts per trade (usually 1)\n max_risk_per_trade: Maximum risk per trade in dollars\n max_risk_total: Maximum total risk across all positions\n \"\"\"\n self.account = account_value\n self.max_dd = max_drawdown\n self.min_contracts = min_contracts\n self.max_risk_trade = max_risk_per_trade\n self.max_risk_total = max_risk_total\n \n def kelly_criterion(self, \n win_prob: float, \n win_amount: float, \n loss_amount: float) -> Tuple[float, float]:\n \"\"\"\n Calculate Kelly Criterion optimal fraction.\n \n Kelly formula: f = (p*b - q) / b\n Where b = win_amount / loss_amount (odds received)\n \n Args:\n win_prob: Probability of winning (0-1)\n win_amount: Average win amount ($)\n loss_amount: Average loss amount ($)\n \n Returns:\n Tuple of (kelly_fraction, edge)\n \"\"\"\n if loss_amount \u003c= 0:\n raise ValueError(\"Loss amount must be positive\")\n \n loss_prob = 1 - win_prob\n odds = win_amount / loss_amount\n \n # Kelly fraction (can be negative if no edge)\n kelly = (win_prob * odds - loss_prob) / odds if odds > 0 else 0\n \n # Edge calculation: expected value per dollar risked\n edge = (win_prob * win_amount) - (loss_prob * loss_amount)\n \n return kelly, edge\n \n def drawdown_constrained_kelly(self,\n full_kelly: float,\n existing_correlation: float = 0) -> float:\n \"\"\"\n Adjust Kelly for maximum drawdown constraint.\n \n Formula accounts for the fact that full Kelly can produce\n large drawdowns even with positive expectancy.\n \n For uncorrelated bets, theoretical max drawdown at full Kelly\n can approach 100%. We constrain to acceptable level.\n \n Args:\n full_kelly: Unconstrained Kelly fraction\n existing_correlation: Correlation with existing positions (0-1)\n \n Returns:\n Drawdown-constrained Kelly fraction\n \"\"\"\n if full_kelly \u003c= 0:\n return 0\n \n # Full Kelly with correlation adjustment\n # Higher correlation = lower effective Kelly due to concentration risk\n correlation_adjustment = 1 - (existing_correlation * 0.5)\n adjusted_kelly = full_kelly * correlation_adjustment\n \n # Drawdown constraint\n # At full Kelly, expected max drawdown ≈ 1 / (2 * growth_rate)\n # Simplified: limit Kelly such that expected drawdown \u003c= max_dd\n # This is approximate; true calculation requires simulation\n \n # Drawdown constraint based on Thorp (2006)\n # Full Kelly can produce ~50-80% max drawdown depending on variance\n # Scale proportionally to target max_dd (conservative heuristic)\n drawdown_scaling = self.max_dd / 0.50 # Target DD / typical full-Kelly DD\n constrained_kelly = adjusted_kelly * drawdown_scaling\n \n return max(0, constrained_kelly)\n \n def conviction_based_kelly(self, \n base_kelly: float,\n conviction_score: float) -> Tuple[float, KellyFraction]:\n \"\"\"\n Scale Kelly by conviction tier.\n \n Higher conviction = larger fraction of Kelly used.\n This reflects confidence in the edge estimation.\n \n Args:\n base_kelly: Drawdown-constrained Kelly fraction\n conviction_score: Conviction score (0-100)\n \n Returns:\n Tuple of (scaled_kelly, kelly_tier_used)\n \"\"\"\n if base_kelly \u003c= 0:\n return 0, KellyFraction.FULL\n \n # Map conviction to Kelly fraction\n if conviction_score >= 90:\n fraction = KellyFraction.HALF # 0.50\n elif conviction_score >= 80:\n fraction = KellyFraction.QUARTER # 0.25\n elif conviction_score >= 60:\n fraction = KellyFraction.EIGHTH # 0.125\n else:\n # Below 60 conviction - no position\n return 0, KellyFraction.EIGHTH\n \n scaled = base_kelly * fraction.value\n \n return scaled, fraction\n \n def calculate_correlation_penalty(self,\n ticker: str,\n existing_positions: List[Dict]) -> CorrelationPenalty:\n \"\"\"\n Calculate position size reduction due to correlations.\n \n If existing positions are highly correlated with new trade,\n reduce size to avoid concentration risk.\n \n Args:\n ticker: Ticker symbol for new trade\n existing_positions: List of dicts with 'ticker' and 'correlation' keys\n \n Returns:\n CorrelationPenalty with penalty factor\n \"\"\"\n if not existing_positions:\n return CorrelationPenalty(0, 1.0, \"No existing positions\")\n \n # Find highest correlation with existing positions\n max_corr = max(pos.get('correlation', 0) for pos in existing_positions)\n avg_corr = np.mean([pos.get('correlation', 0) for pos in existing_positions])\n \n # Penalty increases with correlation\n if max_corr > 0.9:\n penalty = 0.3 # 70% reduction for very high correlation\n reason = f\"Very high correlation ({max_corr:.0%}) with existing position\"\n elif max_corr > 0.7:\n penalty = 0.5 # 50% reduction\n reason = f\"High correlation ({max_corr:.0%}) with existing position\"\n elif max_corr > 0.5:\n penalty = 0.7 # 30% reduction\n reason = f\"Moderate correlation ({max_corr:.0%}) with existing position\"\n else:\n penalty = 1.0 # No penalty\n reason = f\"Low correlation ({max_corr:.0%}) - no reduction\"\n \n return CorrelationPenalty(\n correlation=max_corr,\n penalty_factor=penalty,\n reason=reason\n )\n \n def calculate_position(self,\n spread_cost: float,\n max_loss_per_spread: float,\n win_amount: float,\n conviction: float,\n pop: float,\n ticker: str = \"\",\n existing_positions: Optional[List[Dict]] = None) -> PositionResult:\n \"\"\"\n Calculate optimal position size using Enhanced Kelly.\n \n Pipeline:\n 1. Calculate base Kelly from win probability and payoff\n 2. Apply drawdown constraint\n 3. Apply conviction scaling\n 4. Apply correlation penalty\n 5. Enforce small account guardrails\n 6. Round to integer contracts\n \n Args:\n spread_cost: Total cost to enter spread ($)\n max_loss_per_spread: Maximum loss if spread goes to max loss ($)\n win_amount: Expected win amount ($)\n conviction: Conviction score (0-100)\n pop: Probability of profit (0-1)\n ticker: Ticker symbol\n existing_positions: List of existing position correlations\n \n Returns:\n PositionResult with sizing recommendation\n \"\"\"\n if existing_positions is None:\n existing_positions = []\n \n # Validate inputs\n if not (0 \u003c= pop \u003c= 1):\n raise ValueError(f\"POP must be between 0 and 1, got {pop}\")\n if not (0 \u003c= conviction \u003c= 100):\n raise ValueError(f\"Conviction must be between 0 and 100, got {conviction}\")\n \n # Step 1: Base Kelly calculation\n try:\n full_kelly, edge = self.kelly_criterion(pop, win_amount, max_loss_per_spread)\n except ValueError as e:\n return PositionResult(\n contracts=0,\n total_risk=0,\n kelly_fraction=0,\n adjusted_kelly=0,\n risk_per_contract=max_loss_per_spread,\n max_loss=0,\n expected_value=0,\n recommendation=\"NO_TRADE\",\n reasoning=f\"Kelly calculation error: {e}\",\n drawdown_estimate=0\n )\n \n if full_kelly \u003c= 0:\n return PositionResult(\n contracts=0,\n total_risk=0,\n kelly_fraction=0,\n adjusted_kelly=0,\n risk_per_contract=max_loss_per_spread,\n max_loss=0,\n expected_value=edge,\n recommendation=\"NO_TRADE\",\n reasoning=\"Negative Kelly - no mathematical edge\",\n drawdown_estimate=0\n )\n \n # Step 2: Drawdown constraint\n avg_correlation = np.mean([p.get('correlation', 0) for p in existing_positions]) if existing_positions else 0\n dd_constrained = self.drawdown_constrained_kelly(full_kelly, avg_correlation)\n \n # Step 3: Conviction scaling\n conviction_scaled, kelly_tier = self.conviction_based_kelly(dd_constrained, conviction)\n \n # Step 4: Correlation penalty\n corr_penalty = self.calculate_correlation_penalty(ticker, existing_positions)\n after_correlation = conviction_scaled * corr_penalty.penalty_factor\n \n # Step 5: Calculate contracts\n # Kelly suggests fraction of bankroll to risk\n suggested_risk = self.account * after_correlation\n \n # Enforce guardrails\n suggested_risk = min(suggested_risk, self.max_risk_trade) # Per-trade limit\n suggested_risk = min(suggested_risk, self.max_risk_total - self._current_total_risk(existing_positions))\n \n # If constrained risk can't afford even 1 contract, exit\n if suggested_risk \u003c max_loss_per_spread:\n contracts = 0\n else:\n # Convert risk to contracts\n contracts = int(suggested_risk / max_loss_per_spread)\n contracts = max(0, contracts) # No negative contracts\n \n # Step 6: Final calculations\n total_risk = contracts * max_loss_per_spread\n expected_value = contracts * edge\n \n # Estimate drawdown for this position\n dd_estimate = self._estimate_position_drawdown(contracts, max_loss_per_spread, pop)\n \n # Generate recommendation\n if contracts == 0:\n if full_kelly \u003c= 0:\n recommendation = \"NO_TRADE\"\n reasoning = \"No mathematical edge (Kelly \u003c= 0)\"\n elif conviction \u003c 60:\n recommendation = \"NO_TRADE\"\n reasoning = f\"Conviction too low ({conviction}) - minimum 60 required\"\n else:\n recommendation = \"NO_TRADE\"\n reasoning = \"Risk constraints prevent position (correlation or capital limits)\"\n elif contracts == 1:\n recommendation = \"MINIMAL_SIZE\"\n reasoning = self._build_reasoning(full_kelly, kelly_tier, corr_penalty, \"Minimal size due to constraints\")\n elif contracts >= 4 and conviction >= 90:\n recommendation = \"AGGRESSIVE\"\n reasoning = self._build_reasoning(full_kelly, kelly_tier, corr_penalty, \"High conviction allows aggressive sizing\")\n else:\n recommendation = \"STANDARD\"\n reasoning = self._build_reasoning(full_kelly, kelly_tier, corr_penalty, \"Standard Kelly sizing\")\n \n return PositionResult(\n contracts=contracts,\n total_risk=round(total_risk, 2),\n kelly_fraction=round(full_kelly, 4),\n adjusted_kelly=round(after_correlation, 4),\n risk_per_contract=round(max_loss_per_spread, 2),\n max_loss=round(total_risk, 2),\n expected_value=round(expected_value, 2),\n recommendation=recommendation,\n reasoning=reasoning,\n drawdown_estimate=round(dd_estimate, 3)\n )\n \n def _current_total_risk(self, existing_positions: List[Dict]) -> float:\n \"\"\"Calculate current total risk from existing positions.\"\"\"\n return sum(pos.get('risk', 0) for pos in existing_positions)\n \n def _estimate_position_drawdown(self, \n contracts: int, \n max_loss: float, \n pop: float) -> float:\n \"\"\"\n Estimate expected drawdown from this position.\n \n Simplified estimate: assume loss with probability (1-POP)\n \"\"\"\n if contracts == 0:\n return 0\n expected_loss = contracts * max_loss * (1 - pop)\n return expected_loss / self.account\n \n def _build_reasoning(self,\n full_kelly: float,\n kelly_tier: KellyFraction,\n corr_penalty: CorrelationPenalty,\n note: str) -> str:\n \"\"\"Build human-readable reasoning string.\"\"\"\n parts = [\n f\"Full Kelly: {full_kelly:.2%}\",\n f\"Using: {kelly_tier.name} Kelly ({kelly_tier.value:.0%})\",\n ]\n \n if corr_penalty.penalty_factor \u003c 1.0:\n parts.append(f\"Correlation penalty: {corr_penalty.penalty_factor:.0%}\")\n \n parts.append(note)\n return \" | \".join(parts)\n\n\ndef quick_size(account: float,\n max_loss: float,\n win_amount: float,\n pop: float,\n conviction: float = 75) -> PositionResult:\n \"\"\"Quick position sizing with default parameters.\"\"\"\n sizer = EnhancedKellySizer(account_value=account)\n return sizer.calculate_position(\n spread_cost=max_loss,\n max_loss_per_spread=max_loss,\n win_amount=win_amount,\n conviction=conviction,\n pop=pop\n )\n\n\nif __name__ == \"__main__\":\n \"\"\"Demo: Enhanced Kelly position sizing examples.\"\"\"\n print(\"=\" * 70)\n print(\"ENHANCED KELLY POSITION SIZER DEMO\")\n print(\"=\" * 70)\n \n # Demo 1: Standard position\n print(\"\\n--- Demo 1: Standard Credit Spread ---\")\n sizer = EnhancedKellySizer(account_value=390, max_drawdown=0.20)\n result = sizer.calculate_position(\n spread_cost=85,\n max_loss_per_spread=85,\n win_amount=35,\n conviction=85,\n pop=0.68,\n ticker=\"SPY\"\n )\n \n print(f\"Contracts: {result.contracts}\")\n print(f\"Total Risk: ${result.total_risk}\")\n print(f\"Full Kelly: {result.kelly_fraction:.2%}\")\n print(f\"Adjusted Kelly: {result.adjusted_kelly:.2%}\")\n print(f\"Expected Value: ${result.expected_value}\")\n print(f\"Recommendation: {result.recommendation}\")\n print(f\"Reasoning: {result.reasoning}\")\n \n # Demo 2: High conviction\n print(\"\\n--- Demo 2: High Conviction Trade ---\")\n result2 = sizer.calculate_position(\n spread_cost=75,\n max_loss_per_spread=75,\n win_amount=40,\n conviction=95,\n pop=0.72,\n ticker=\"QQQ\"\n )\n \n print(f\"Contracts: {result2.contracts}\")\n print(f\"Total Risk: ${result2.total_risk}\")\n print(f\"Recommendation: {result2.recommendation}\")\n print(f\"Reasoning: {result2.reasoning}\")\n \n # Demo 3: With correlation penalty\n print(\"\\n--- Demo 3: With Existing Correlated Position ---\")\n existing = [{\"ticker\": \"SPY\", \"correlation\": 0.85, \"risk\": 85}]\n result3 = sizer.calculate_position(\n spread_cost=80,\n max_loss_per_spread=80,\n win_amount=30,\n conviction=80,\n pop=0.65,\n ticker=\"VOO\", # Highly correlated with SPY\n existing_positions=existing\n )\n \n print(f\"Contracts: {result3.contracts}\")\n print(f\"Total Risk: ${result3.total_risk}\")\n print(f\"Recommendation: {result3.recommendation}\")\n print(f\"Reasoning: {result3.reasoning}\")\n \n # Demo 4: No edge case\n print(\"\\n--- Demo 4: Negative Kelly (No Edge) ---\")\n result4 = sizer.calculate_position(\n spread_cost=100,\n max_loss_per_spread=100,\n win_amount=20,\n conviction=70,\n pop=0.45, # Less than 50% win rate with bad payoff\n ticker=\"TSLA\"\n )\n \n print(f\"Contracts: {result4.contracts}\")\n print(f\"Recommendation: {result4.recommendation}\")\n print(f\"Reasoning: {result4.reasoning}\")\n \n print(\"\\n\" + \"=\" * 70)\n","content_type":"text/x-python; charset=utf-8","language":"python","size":19474,"content_sha256":"b75e496f715c4c14e3f374e32af2d1e0856620b030c9725b968299464df74c8a"},{"filename":"scripts/find_qqq_play.py","content":"\nfrom leg_optimizer import LegOptimizer\nfrom chain_analyzer import ChainFetcher\nimport json\n\nfetcher = ChainFetcher()\nopt = LegOptimizer(account_total=2000)\n\ntry:\n chain = fetcher.fetch_chain_for_date('QQQ', 1771477200) # Feb 20\nexcept Exception as e:\n print(f\"Error fetching chain: {e}\")\n exit(1)\n\nS = chain.underlying_price\nT = chain.dte / 365.0\nr = 0.045\niv = 0.20 # Reasonable floor\n\nmatches = []\n\n# Try Put Spreads\nfor short_opt in chain.puts:\n for long_opt in chain.puts:\n if short_opt['strike'] \u003c= long_opt['strike']: continue\n \n width = short_opt['strike'] - long_opt['strike']\n if width \u003c 5 or width > 12: continue\n \n short_prem = short_opt['mid_price']\n long_prem = long_opt['mid_price']\n credit = short_prem - long_prem\n \n if credit \u003c= 0: continue\n \n max_profit = credit * 100\n max_loss = (width - credit) * 100\n \n # Calculate POP\n pop = opt.calc.pop_vertical_spread(S, short_opt['strike'], long_opt['strike'], T, iv, credit, 'put_credit')\n \n matches.append({\n 'type': 'put_credit',\n 'short': short_opt['strike'],\n 'long': long_opt['strike'],\n 'width': width,\n 'profit': max_profit,\n 'loss': max_loss,\n 'pop': pop\n })\n\n# Try Call Spreads\nfor short_opt in chain.calls:\n for long_opt in chain.calls:\n if short_opt['strike'] >= long_opt['strike']: continue\n \n width = long_opt['strike'] - short_opt['strike']\n if width \u003c 5 or width > 12: continue\n \n short_prem = short_opt['mid_price']\n long_prem = long_opt['mid_price']\n credit = short_prem - long_prem\n \n if credit \u003c= 0: continue\n \n max_profit = credit * 100\n max_loss = (width - credit) * 100\n \n # Calculate POP\n pop = opt.calc.pop_vertical_spread(S, short_opt['strike'], long_opt['strike'], T, iv, credit, 'call_credit')\n \n matches.append({\n 'type': 'call_credit',\n 'short': short_opt['strike'],\n 'long': long_opt['strike'],\n 'width': width,\n 'profit': max_profit,\n 'loss': max_loss,\n 'pop': pop\n })\n\n# Filter for the target: Loss around 400\nfinal_matches = [m for m in matches if 300 \u003c= m['loss'] \u003c= 450]\n\n# Sort by proximity to target (Profit 80, Loss 400)\nfinal_matches.sort(key=lambda x: abs(x['profit'] - 80) + abs(x['loss'] - 400))\n\nfor m in final_matches[:10]:\n print(f\"{m['type'].upper()} | {m['short']}/{m['long']} | Width: {m['width']} | Profit: ${m['profit']:.2f} | Loss: ${m['loss']:.2f} | POP: {m['pop']*100:.1f}%\")\n","content_type":"text/x-python; charset=utf-8","language":"python","size":2729,"content_sha256":"dc02a56128455b3459d58f2cb9d1fbc4ca404430a162101da6bd19055b4e7927"},{"filename":"scripts/leg_optimizer.py","content":"\"\"\"\nMulti-Leg Strategy Optimizer\n\nFinds optimal combinations of options legs for various strategies:\n- Vertical spreads (credit/debit)\n- Iron condors / inverse iron condors\n- Iron butterflies / inverse iron butterflies\n- Straddles (long/short)\n- Strangles (long/short)\n- Single-leg (long call, long put)\n- Ratio backspreads (call/put)\n- Calendars\n\nOptimizes for: max POP, max EV, min risk, delta neutrality\n\"\"\"\n\nfrom typing import List, Dict, Optional, Tuple\nfrom dataclasses import dataclass, field\nfrom collections import defaultdict\nimport logging\nimport numpy as np\n\nlogger = logging.getLogger(__name__)\n\nfrom options_math import (\n BlackScholes, ProbabilityCalculator, Greeks,\n fits_account_constraints, optimal_spread_width,\n DEFAULT_ACCOUNT_TOTAL, ACCOUNT_TOTAL, MAX_RISK_PER_TRADE, MIN_CASH_BUFFER, AVAILABLE_CAPITAL\n)\nfrom chain_analyzer import OptionChain, ChainAnalyzer\n\n\n@dataclass\nclass TradeLeg:\n \"\"\"Single option leg in a multi-leg strategy\"\"\"\n strike: float\n expiration: str\n dte: int\n premium: float # Per share\n option_type: str # 'call' or 'put'\n action: str # 'buy' or 'sell'\n quantity: int = 1\n greeks: Optional[Greeks] = None\n \n def __post_init__(self):\n \"\"\"Validate TradeLeg inputs after initialization.\"\"\"\n if self.option_type not in {'call', 'put'}:\n raise ValueError(f\"option_type must be 'call' or 'put', got {self.option_type}\")\n if self.action not in {'buy', 'sell'}:\n raise ValueError(f\"action must be 'buy' or 'sell', got {self.action}\")\n if self.strike \u003c= 0:\n raise ValueError(f\"strike must be positive, got {self.strike}\")\n if self.dte \u003c 0:\n raise ValueError(f\"dte must be non-negative, got {self.dte}\")\n if self.premium \u003c 0:\n raise ValueError(f\"premium must be non-negative, got {self.premium}\")\n if self.quantity \u003c= 0:\n raise ValueError(f\"quantity must be positive, got {self.quantity}\")\n \n @property\n def net_premium(self) -> float:\n \"\"\"Net premium for this leg (positive = credit, negative = debit)\"\"\"\n if self.action == 'sell':\n return self.premium * self.quantity\n else:\n return -self.premium * self.quantity\n\n\n@dataclass\nclass MultiLegStrategy:\n \"\"\"Complete multi-leg options strategy\"\"\"\n ticker: str\n strategy_type: str # 'vertical_credit', 'vertical_debit', 'iron_condor', etc.\n underlying_price: float\n legs: List[TradeLeg] = field(default_factory=list)\n \n # Trade metrics\n max_profit: float = 0.0\n max_loss: float = 0.0\n breakevens: List[float] = field(default_factory=list)\n pop: float = 0.0 # Probability of Profit\n expected_value: float = 0.0\n risk_adjusted_return: float = 0.0\n \n # Greeks\n total_greeks: Optional[Greeks] = None\n \n # Account fit\n margin_required: float = 0.0\n fits_account: bool = False\n \n # Scores\n pop_score: float = 0.0\n ev_score: float = 0.0\n income_score: float = 0.0\n \n def __str__(self) -> str:\n return f\"{self.strategy_type.upper()} on {self.ticker} @ ${self.underlying_price:.2f}\"\n \n def to_dict(self) -> Dict:\n \"\"\"Convert to dictionary for serialization\"\"\"\n return {\n 'ticker': self.ticker,\n 'strategy_type': self.strategy_type,\n 'underlying_price': self.underlying_price,\n 'legs': [\n {\n 'strike': l.strike,\n 'expiration': l.expiration,\n 'dte': l.dte,\n 'premium': l.premium,\n 'option_type': l.option_type,\n 'action': l.action\n }\n for l in self.legs\n ],\n 'max_profit': self.max_profit,\n 'max_loss': self.max_loss,\n 'breakevens': self.breakevens,\n 'pop': self.pop,\n 'expected_value': self.expected_value,\n 'risk_adjusted_return': self.risk_adjusted_return,\n 'margin_required': self.margin_required,\n 'fits_account': self.fits_account\n }\n\n\ndef validate_strategy_risk(strategy: MultiLegStrategy) -> Tuple[bool, str]:\n \"\"\"\n Validate that a strategy has defined, finite risk.\n \n Returns (is_valid, reason).\n Checks for:\n - Infinite risk (naked shorts, ratio spreads)\n - Undefined P&L (zero/negative max_profit or max_loss)\n - Invalid spread construction\n \"\"\"\n legs = strategy.legs\n if not legs:\n return False, \"no legs\"\n\n # --- Check for naked positions (short without matching long) ---\n short_calls = [l for l in legs if l.action == 'sell' and l.option_type == 'call']\n long_calls = [l for l in legs if l.action == 'buy' and l.option_type == 'call']\n short_puts = [l for l in legs if l.action == 'sell' and l.option_type == 'put']\n long_puts = [l for l in legs if l.action == 'buy' and l.option_type == 'put']\n\n short_call_qty = sum(l.quantity for l in short_calls)\n long_call_qty = sum(l.quantity for l in long_calls)\n short_put_qty = sum(l.quantity for l in short_puts)\n long_put_qty = sum(l.quantity for l in long_puts)\n\n # Allow known unlimited-risk strategies to pass through\n known_unlimited = {'short_straddle', 'short_strangle', 'call_ratio_backspread', 'put_ratio_backspread'}\n if strategy.strategy_type not in known_unlimited:\n # Naked short calls = infinite risk\n if short_call_qty > 0 and long_call_qty == 0:\n return False, \"naked short call(s) — infinite risk\"\n # Naked short puts = undefined risk (strike * 100)\n if short_put_qty > 0 and long_put_qty == 0:\n return False, \"naked short put(s) — undefined risk\"\n\n # --- Ratio spreads (more shorts than longs) ---\n if short_call_qty > long_call_qty:\n return False, f\"ratio call spread ({short_call_qty}:{long_call_qty} short:long) — undefined risk\"\n if short_put_qty > long_put_qty:\n return False, f\"ratio put spread ({short_put_qty}:{long_put_qty} short:long) — undefined risk\"\n\n # --- P&L validation ---\n if strategy.max_profit \u003c 0:\n return False, f\"negative max_profit ({strategy.max_profit:.2f})\"\n if strategy.max_loss \u003c 0:\n return False, f\"negative max_loss ({strategy.max_loss:.2f})\"\n if strategy.max_profit == 0:\n return False, \"zero max_profit — breakeven-only trade\"\n if strategy.max_loss == 0:\n return False, \"zero max_loss — likely bad data or arbitrage\"\n\n # --- Breakeven validation ---\n if not strategy.breakevens or any(b \u003c= 0 or not np.isfinite(b) for b in strategy.breakevens):\n return False, f\"invalid breakeven(s): {strategy.breakevens}\"\n\n # --- Finite check on all numeric fields ---\n for field_name, val in [('max_profit', strategy.max_profit),\n ('max_loss', strategy.max_loss),\n ('pop', strategy.pop),\n ('expected_value', strategy.expected_value),\n ('risk_adjusted_return', strategy.risk_adjusted_return)]:\n if not np.isfinite(val):\n return False, f\"non-finite {field_name}: {val}\"\n\n return True, \"ok\"\n\n\nclass LegOptimizer:\n \"\"\"\n Optimize multi-leg option strategies\n \"\"\"\n \n def __init__(self, risk_free_rate: float = 0.045, account_total: float = DEFAULT_ACCOUNT_TOTAL,\n max_risk_per_trade: float = MAX_RISK_PER_TRADE,\n min_cash_buffer: float = MIN_CASH_BUFFER):\n self.bs = BlackScholes()\n self.calc = ProbabilityCalculator(risk_free_rate)\n self.analyzer = ChainAnalyzer()\n self.account_total = account_total\n self.max_risk_per_trade = max_risk_per_trade\n self.min_cash_buffer = min_cash_buffer\n \n # Minimum IV floor — pre-market/post-market data often reports near-zero IV\n # which produces 100% POP and meaningless greeks. 15% is a conservative\n # floor (VIX rarely stays below 12 for extended periods).\n IV_FLOOR = 0.15\n\n def calculate_strategy_metrics(self, strategy: MultiLegStrategy,\n iv: float = 0.25) -> MultiLegStrategy:\n \"\"\"\n Calculate all metrics for a strategy\n \"\"\"\n if not strategy.legs:\n return strategy\n\n # Enforce IV floor for realistic probability/greeks calculations\n # (pre-market/post-market data often reports near-zero IV)\n iv = max(iv, self.IV_FLOOR)\n \n # Calculate net premium (per share)\n net_premium = sum(leg.net_premium for leg in strategy.legs)\n \n # Get strategy type and calculate max P/L\n if strategy.strategy_type == 'put_credit_spread':\n # Sell higher strike put, buy lower strike put\n short_leg = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'put'][0]\n long_leg = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'put'][0]\n \n width = short_leg.strike - long_leg.strike\n strategy.max_profit = net_premium * 100 # Per contract\n strategy.max_loss = max(0, (width - net_premium) * 100) # Width minus credit, min $0\n strategy.breakevens = [short_leg.strike - net_premium]\n \n elif strategy.strategy_type == 'call_credit_spread':\n # Sell lower strike call, buy higher strike call\n short_leg = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'call'][0]\n long_leg = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'call'][0]\n \n width = long_leg.strike - short_leg.strike\n strategy.max_profit = net_premium * 100\n strategy.max_loss = max(0, (width - net_premium) * 100) # Width minus credit, min $0\n strategy.breakevens = [short_leg.strike + net_premium]\n \n elif strategy.strategy_type == 'iron_condor':\n # Short put spread + short call spread\n puts = [l for l in strategy.legs if l.option_type == 'put']\n calls = [l for l in strategy.legs if l.option_type == 'call']\n \n put_short = [l for l in puts if l.action == 'sell'][0]\n put_long = [l for l in puts if l.action == 'buy'][0]\n call_short = [l for l in calls if l.action == 'sell'][0]\n call_long = [l for l in calls if l.action == 'buy'][0]\n \n put_width = put_short.strike - put_long.strike\n call_width = call_long.strike - call_short.strike\n max_width = max(put_width, call_width)\n \n strategy.max_profit = net_premium * 100\n strategy.max_loss = (max_width - net_premium) * 100\n strategy.breakevens = [put_short.strike - net_premium, call_short.strike + net_premium]\n \n elif strategy.strategy_type in ['put_debit_spread', 'call_debit_spread']:\n # Debit spreads\n if strategy.strategy_type == 'put_debit_spread':\n long_leg = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'put'][0]\n short_leg = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'put'][0]\n width = long_leg.strike - short_leg.strike\n else:\n long_leg = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'call'][0]\n short_leg = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'call'][0]\n width = short_leg.strike - long_leg.strike\n \n strategy.max_profit = (width + net_premium) * 100 # net_premium is negative for debit\n strategy.max_loss = -net_premium * 100\n\n elif strategy.strategy_type == 'inverse_iron_butterfly':\n # Long ATM straddle + short OTM strangle\n # net_premium is negative (net debit)\n long_call = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'call'][0]\n short_call = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'call'][0]\n long_put = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'put'][0]\n short_put = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'put'][0]\n \n wing_width = short_call.strike - long_call.strike # distance ATM to OTM wing\n net_debit = -net_premium # positive number\n strategy.max_profit = (wing_width - net_debit) * 100\n strategy.max_loss = net_debit * 100\n strategy.breakevens = [long_call.strike - net_debit, long_call.strike + net_debit]\n\n elif strategy.strategy_type == 'iron_butterfly':\n # Short ATM straddle + long OTM strangle\n short_call = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'call'][0]\n long_call = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'call'][0]\n short_put = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'put'][0]\n long_put = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'put'][0]\n \n wing_width = long_call.strike - short_call.strike\n strategy.max_profit = net_premium * 100 # net credit\n strategy.max_loss = (wing_width - net_premium) * 100\n strategy.breakevens = [short_call.strike - net_premium, short_call.strike + net_premium]\n\n elif strategy.strategy_type == 'inverse_iron_condor':\n # Long closer strangle + short wider strangle (net debit)\n long_call = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'call'][0]\n short_call = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'call'][0]\n long_put = [l for l in strategy.legs if l.action == 'buy' and l.option_type == 'put'][0]\n short_put = [l for l in strategy.legs if l.action == 'sell' and l.option_type == 'put'][0]\n \n call_width = short_call.strike - long_call.strike\n put_width = long_put.strike - short_put.strike\n max_wing = max(call_width, put_width)\n net_debit = -net_premium\n strategy.max_profit = (max_wing - net_debit) * 100\n strategy.max_loss = net_debit * 100\n strategy.breakevens = [long_put.strike - net_debit, long_call.strike + net_debit]\n\n elif strategy.strategy_type == 'long_straddle':\n # BUY ATM call + BUY ATM put\n call_leg = [l for l in strategy.legs if l.option_type == 'call'][0]\n put_leg = [l for l in strategy.legs if l.option_type == 'put'][0]\n total_debit = call_leg.premium + put_leg.premium\n strategy.max_profit = 99999 * 100 # Effectively unlimited\n strategy.max_loss = total_debit * 100\n strategy.breakevens = [call_leg.strike - total_debit, call_leg.strike + total_debit]\n\n elif strategy.strategy_type == 'long_strangle':\n # BUY OTM call + BUY OTM put\n call_leg = [l for l in strategy.legs if l.option_type == 'call'][0]\n put_leg = [l for l in strategy.legs if l.option_type == 'put'][0]\n total_debit = call_leg.premium + put_leg.premium\n strategy.max_profit = 99999 * 100\n strategy.max_loss = total_debit * 100\n strategy.breakevens = [put_leg.strike - total_debit, call_leg.strike + total_debit]\n\n elif strategy.strategy_type == 'short_straddle':\n # SELL ATM call + SELL ATM put\n call_leg = [l for l in strategy.legs if l.option_type == 'call'][0]\n put_leg = [l for l in strategy.legs if l.option_type == 'put'][0]\n total_credit = call_leg.premium + put_leg.premium\n strategy.max_profit = total_credit * 100\n strategy.max_loss = 99999 * 100 # Unlimited\n strategy.breakevens = [call_leg.strike - total_credit, call_leg.strike + total_credit]\n\n elif strategy.strategy_type == 'short_strangle':\n # SELL OTM call + SELL OTM put\n call_leg = [l for l in strategy.legs if l.option_type == 'call'][0]\n put_leg = [l for l in strategy.legs if l.option_type == 'put'][0]\n total_credit = call_leg.premium + put_leg.premium\n strategy.max_profit = total_credit * 100\n strategy.max_loss = 99999 * 100 # Unlimited\n strategy.breakevens = [put_leg.strike - total_credit, call_leg.strike + total_credit]\n\n elif strategy.strategy_type == 'long_call':\n leg = strategy.legs[0]\n strategy.max_profit = 99999 * 100 # Unlimited\n strategy.max_loss = leg.premium * 100\n strategy.breakevens = [leg.strike + leg.premium]\n\n elif strategy.strategy_type == 'long_put':\n leg = strategy.legs[0]\n strategy.max_profit = max(0, (leg.strike - leg.premium) * 100)\n strategy.max_loss = leg.premium * 100\n strategy.breakevens = [leg.strike - leg.premium]\n\n elif strategy.strategy_type == 'call_ratio_backspread':\n # SELL 1 lower call, BUY 2 higher calls\n short_leg = [l for l in strategy.legs if l.action == 'sell'][0]\n long_legs = [l for l in strategy.legs if l.action == 'buy']\n long_leg = long_legs[0]\n \n strike_diff = long_leg.strike - short_leg.strike\n # net_premium: positive = credit, negative = debit\n strategy.max_profit = 99999 * 100 # Unlimited above upper BE\n strategy.max_loss = (strike_diff - net_premium) * 100 if net_premium >= 0 else (strike_diff + abs(net_premium)) * 100\n # Max loss occurs at long strike at expiration\n if net_premium >= 0:\n # Entered for credit: lower BE exists\n lower_be = short_leg.strike + net_premium\n upper_be = long_leg.strike + strike_diff - net_premium\n else:\n upper_be = long_leg.strike + strike_diff + abs(net_premium)\n lower_be = short_leg.strike + net_premium # below short strike\n strategy.breakevens = [lower_be, upper_be]\n\n elif strategy.strategy_type == 'put_ratio_backspread':\n # SELL 1 higher put, BUY 2 lower puts\n short_leg = [l for l in strategy.legs if l.action == 'sell'][0]\n long_legs = [l for l in strategy.legs if l.action == 'buy']\n long_leg = long_legs[0]\n \n strike_diff = short_leg.strike - long_leg.strike\n # At stock=0: gain from 2 long puts = 2*long_strike, loss from short put = short_strike\n max_profit_at_zero = 2 * long_leg.strike - short_leg.strike\n strategy.max_profit = max(0, (max_profit_at_zero + net_premium) * 100)\n strategy.max_loss = (strike_diff - net_premium) * 100 if net_premium >= 0 else (strike_diff + abs(net_premium)) * 100\n if net_premium >= 0:\n upper_be = short_leg.strike - net_premium\n lower_be = long_leg.strike - (strike_diff - net_premium)\n else:\n upper_be = short_leg.strike - net_premium\n lower_be = long_leg.strike - strike_diff - abs(net_premium)\n strategy.breakevens = [max(0, lower_be), upper_be]\n\n else:\n # Default: sum of premiums\n strategy.max_profit = abs(net_premium) * 100\n strategy.max_loss = abs(net_premium) * 100\n \n # --- Guard: skip strategies with non-positive P&L ---\n if strategy.max_profit \u003c= 0 or strategy.max_loss \u003c= 0:\n logger.debug(\"Skipping strategy: max_profit=%.2f, max_loss=%.2f\",\n strategy.max_profit, strategy.max_loss)\n return strategy\n \n # Calculate POP\n T = max(strategy.legs[0].dte, 1) / 365.0 # Ensure minimum 1 day\n S = strategy.underlying_price\n \n # Use Monte Carlo simulation for more accurate POP calculation\n # Monte Carlo simulates terminal price distribution using GBM,\n # which can produce more accurate results than Black-Scholes closed-form\n # especially for wider spreads where log-normal assumptions matter more.\n # This aligns better with how platforms like TastyTrade calculate POP.\n \n if strategy.strategy_type == 'put_credit_spread':\n try:\n breakeven = strategy.breakevens[0] if strategy.breakevens else (short_leg.strike - net_premium)\n mc_pop = self.calc.monte_carlo_pop_vertical(\n S, breakeven, T, iv, 'put_credit', n_sims=100000\n )\n strategy.pop = mc_pop\n except Exception as e:\n logger.debug(f\"Monte Carlo POP failed for put credit spread, falling back to Black-Scholes: {e}\")\n strategy.pop = self.calc.pop_vertical_spread(\n S, short_leg.strike, long_leg.strike, T, iv,\n net_premium, 'put_credit'\n )\n elif strategy.strategy_type == 'call_credit_spread':\n try:\n breakeven = strategy.breakevens[0] if strategy.breakevens else (short_leg.strike + net_premium)\n mc_pop = self.calc.monte_carlo_pop_vertical(\n S, breakeven, T, iv, 'call_credit', n_sims=100000\n )\n strategy.pop = mc_pop\n except Exception as e:\n logger.debug(f\"Monte Carlo POP failed for call credit spread, falling back to Black-Scholes: {e}\")\n strategy.pop = self.calc.pop_vertical_spread(\n S, short_leg.strike, long_leg.strike, T, iv,\n net_premium, 'call_credit'\n )\n elif strategy.strategy_type == 'iron_condor':\n # Get breakevens for Monte Carlo simulation\n lower_breakeven = strategy.breakevens[0] if strategy.breakevens else (put_short.strike - net_premium)\n upper_breakeven = strategy.breakevens[1] if len(strategy.breakevens) > 1 else (call_short.strike + net_premium)\n\n # Use Monte Carlo simulation for more accurate POP calculation\n # Monte Carlo simulates price paths and checks if price stays within\n # bounds at ANY point (\"touch\" probability), which matches how\n # brokers like Tastytrade calculate POP for Iron Condors.\n # Black-Scholes only calculates probability AT expiration.\n try:\n mc_pop = self.calc.monte_carlo_pop_iron_condor(\n S, lower_breakeven, upper_breakeven, T, iv,\n n_sims=100000, steps_per_day=2\n )\n strategy.pop = mc_pop\n except Exception as e:\n logger.debug(f\"Monte Carlo POP failed, falling back to Black-Scholes: {e}\")\n # Fallback to Black-Scholes closed-form solution\n # Correct POP for Iron Condor: P(Lower BE \u003c S_T \u003c Upper BE)\n # This is P(S_T \u003c Upper BE) - P(S_T \u003c Lower BE)\n # pop_vertical_spread(call_credit) returns P(S_T \u003c Upper BE)\n # pop_vertical_spread(put_credit) returns P(S_T > Lower BE)\n put_pop = self.calc.pop_vertical_spread(\n S, put_short.strike, put_long.strike, T, iv,\n net_premium, 'put_credit'\n )\n call_pop = self.calc.pop_vertical_spread(\n S, call_short.strike, call_long.strike, T, iv,\n net_premium, 'call_credit'\n )\n # P(S_T \u003c Lower BE) = 1 - put_pop\n # So POP = call_pop - (1 - put_pop) = call_pop + put_pop - 1\n strategy.pop = max(0, put_pop + call_pop - 1)\n elif strategy.strategy_type == 'put_debit_spread':\n strategy.pop = self.calc.pop_vertical_spread(S, long_leg.strike, short_leg.strike, T, iv, net_premium, 'put_debit')\n elif strategy.strategy_type == 'call_debit_spread':\n strategy.pop = self.calc.pop_vertical_spread(S, long_leg.strike, short_leg.strike, T, iv, net_premium, 'call_debit')\n elif strategy.strategy_type in ('inverse_iron_butterfly', 'inverse_iron_condor',\n 'long_straddle', 'long_strangle'):\n # Profit if price moves beyond breakevens (either direction)\n if len(strategy.breakevens) == 2:\n lower_be, upper_be = strategy.breakevens\n # P(profit) = P(S \u003c lower_be) + P(S > upper_be) = 1 - P(lower_be \u003c S \u003c upper_be)\n from scipy.stats import norm\n d_lower = (np.log(S / lower_be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n d_upper = (np.log(S / upper_be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n prob_between = norm.cdf(d_lower) - norm.cdf(d_upper)\n strategy.pop = max(0.0, 1.0 - prob_between)\n else:\n strategy.pop = 0.5\n elif strategy.strategy_type in ('iron_butterfly', 'short_straddle', 'short_strangle'):\n # Profit if price stays between breakevens\n if len(strategy.breakevens) == 2:\n lower_be, upper_be = strategy.breakevens\n from scipy.stats import norm\n d_lower = (np.log(S / lower_be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n d_upper = (np.log(S / upper_be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n strategy.pop = max(0.0, norm.cdf(d_lower) - norm.cdf(d_upper))\n else:\n strategy.pop = 0.5\n elif strategy.strategy_type == 'long_call':\n # P(S > breakeven)\n be = strategy.breakevens[0]\n from scipy.stats import norm\n d = (np.log(S / be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n strategy.pop = max(0.0, norm.cdf(d))\n elif strategy.strategy_type == 'long_put':\n # P(S \u003c breakeven)\n be = strategy.breakevens[0]\n from scipy.stats import norm\n d = (np.log(S / be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n strategy.pop = max(0.0, 1.0 - norm.cdf(d))\n elif strategy.strategy_type in ('call_ratio_backspread', 'put_ratio_backspread'):\n # Approximate: profit beyond breakevens\n if len(strategy.breakevens) == 2:\n lower_be, upper_be = sorted(strategy.breakevens)\n from scipy.stats import norm\n d_lower = (np.log(S / lower_be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n d_upper = (np.log(S / upper_be) + (0.045 - 0.5 * iv**2) * T) / (iv * np.sqrt(T))\n if strategy.strategy_type == 'call_ratio_backspread':\n # Profit below lower BE (if credit) + above upper BE\n if net_premium >= 0:\n strategy.pop = max(0.0, (1 - norm.cdf(d_lower)) + norm.cdf(d_upper))\n else:\n strategy.pop = max(0.0, norm.cdf(d_upper)) # Only profit above upper BE\n else:\n if net_premium >= 0:\n strategy.pop = max(0.0, norm.cdf(d_upper) + (1 - norm.cdf(d_lower)))\n else:\n strategy.pop = max(0.0, 1 - norm.cdf(d_lower))\n else:\n strategy.pop = 0.5\n else:\n strategy.pop = 0.5\n \n # Expected Value (guard: POP must be in [0,1])\n pop_clamped = max(0.0, min(1.0, strategy.pop))\n strategy.pop = pop_clamped\n strategy.expected_value = self.calc.expected_value(\n pop_clamped, strategy.max_profit, strategy.max_loss\n )\n \n # Risk-adjusted return (annualized EV / risk)\n if strategy.max_loss > 0:\n annual_factor = 365.0 / max(strategy.legs[0].dte, 1)\n raw_return = strategy.expected_value / strategy.max_loss * annual_factor\n # Cap at reasonable bounds to avoid absurd numbers\n strategy.risk_adjusted_return = max(-10.0, min(10.0, raw_return))\n else:\n strategy.risk_adjusted_return = 0.0\n \n # Margin requirement (simplified)\n strategy.margin_required = strategy.max_loss\n \n # Check account fit\n strategy.fits_account = fits_account_constraints(\n strategy.max_loss, strategy.margin_required, self.account_total,\n self.max_risk_per_trade, self.min_cash_buffer\n )\n \n # Calculate total Greeks\n total_delta = sum(\n (leg.greeks.delta if leg.greeks else 0) * leg.quantity * (1 if leg.action == 'buy' else -1)\n for leg in strategy.legs\n ) if any(leg.greeks for leg in strategy.legs) else 0\n \n total_theta = sum(\n (leg.greeks.theta if leg.greeks else 0) * leg.quantity * (1 if leg.action == 'buy' else -1)\n for leg in strategy.legs\n ) if any(leg.greeks for leg in strategy.legs) else 0\n \n strategy.total_greeks = Greeks(\n delta=total_delta,\n gamma=0, # Simplified\n theta=total_theta,\n vega=0,\n rho=0\n )\n \n return strategy\n \n def score_strategies(self, strategies: List[MultiLegStrategy], \n mode: str = 'ev') -> List[MultiLegStrategy]:\n \"\"\"\n Score and sort strategies based on specified mode\n \n Args:\n strategies: List of MultiLegStrategy objects\n mode: Scoring mode - 'pop' (maximize POP), 'ev' (maximize expected value),\n 'income' (maximize theta/income)\n \n Returns:\n Sorted list of strategies (highest score first)\n \"\"\"\n # Ensure all strategies have metrics calculated\n for strategy in strategies:\n if strategy.pop == 0 and strategy.legs:\n # Need to calculate metrics - use default IV\n self.calculate_strategy_metrics(strategy)\n \n # Sort based on mode\n if mode == 'pop':\n # Sort by POP (highest first), then by max_loss (lowest first)\n scored = sorted(strategies, \n key=lambda s: (s.pop, -s.max_loss), \n reverse=True)\n elif mode == 'ev':\n # Sort by EV score (already calculated in metrics)\n scored = sorted(strategies, \n key=lambda s: (s.ev_score, s.pop), \n reverse=True)\n elif mode == 'income':\n # Sort by income score (theta-based)\n scored = sorted(strategies, \n key=lambda s: (s.income_score, s.pop), \n reverse=True)\n else:\n # Default to EV sorting\n scored = sorted(strategies, \n key=lambda s: s.ev_score, \n reverse=True)\n \n return scored\n \n def optimize_vertical_spreads(self, chain: OptionChain, \n spread_type: str = 'put_credit',\n max_width: float = None, # Auto-calculate if None\n min_dte: int = 7,\n max_dte: int = 45) -> List[MultiLegStrategy]:\n \"\"\"\n Find optimal vertical spreads from options chain\n \n spread_type: 'put_credit', 'call_credit', 'put_debit', 'call_debit'\n max_width: Maximum spread width in dollars. If None, auto-calculates\n based on underlying price to allow $100+ credit trades.\n \"\"\"\n strategies = []\n \n if spread_type in ['put_credit', 'put_debit']:\n options = chain.puts\n opt_type = 'put'\n else:\n options = chain.calls\n opt_type = 'call'\n \n if len(options) \u003c 2:\n return strategies\n \n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n \n # Auto-calculate max_width if not provided\n # For $100+ credit, need ~5-10 point wide spreads on QQQ/SPY\n # Formula: wider spreads for higher-priced underlyings\n if max_width is None:\n if S >= 500: # QQQ, SPY, high-priced stocks\n max_width = 25.0\n elif S >= 200: # Mid-priced stocks\n max_width = 15.0\n else: # Lower-priced stocks\n max_width = 10.0\n \n # Get widths to try - now includes wider spreads for larger credits\n # $1-wide spreads: critical for high-priced underlyings where OTM credit \n # spreads need narrow widths to fit small accounts\n # $5-10 wide: balanced risk/reward for medium accounts\n # $15-25 wide: for larger accounts seeking $100+ credits\n widths = [w for w in [1, 2, 3, 5, 7, 10, 12, 15, 18, 20, 25] if w \u003c= max_width]\n \n for width in widths:\n # Try different short strikes\n for i, short_opt in enumerate(options):\n # OTM/ATM validation: reject deep ITM short strikes\n # For credit spreads, short strike must be OTM or near ATM\n if spread_type == 'put_credit':\n # Short put must be at or below current price (OTM/ATM)\n # Allow small buffer (2% ITM) for near-ATM strikes\n if short_opt['strike'] > S * 1.02:\n continue\n elif spread_type == 'call_credit':\n # Short call must be at or above current price (OTM/ATM)\n if short_opt['strike'] \u003c S * 0.98:\n continue\n \n # Liquidity filter: require valid bid/ask OR lastPrice fallback\n # Pre-market / post-market: bid/ask are often 0 but lastPrice exists\n has_bid_ask = short_opt.get('has_valid_bid_ask', False)\n if not has_bid_ask and short_opt['mid_price'] \u003c= 0:\n logger.debug(\"Skipping short strike %.0f: no bid/ask and no lastPrice\", short_opt['strike'])\n continue\n if short_opt['mid_price'] \u003c= 0.05:\n continue\n # Only apply spread width filter when we have real bid/ask data\n if has_bid_ask and short_opt['spread_pct'] > 0.20:\n logger.debug(\"Skipping short strike %.0f: wide spread %.1f%%\",\n short_opt['strike'], short_opt['spread_pct'] * 100)\n continue\n \n # Require non-zero bid for a real market (avoid $0.00 bid / $0.01 ask phantom quotes)\n bid = short_opt.get('bid', 0) or 0\n if bid \u003c= 0:\n logger.debug(\"Skipping short strike %.0f: zero bid (no real market)\", short_opt['strike'])\n continue\n \n # Volume/Open Interest filter - avoid phantom quotes with no actual market\n volume = short_opt.get('volume', 0) or 0\n oi = short_opt.get('open_interest', 0) or 0\n if volume == 0 and oi == 0:\n logger.debug(\"Skipping short strike %.0f: zero volume and open interest\", short_opt['strike'])\n continue\n \n # For $1-wide spreads on expensive underlyings, skip deep OTM \n # where net credit would be negligible (\u003c $0.05/share)\n # This is checked after pairing below\n \n # Find matching long strike\n if spread_type in ['put_credit', 'put_debit']:\n target_long_strike = short_opt['strike'] - width\n else:\n target_long_strike = short_opt['strike'] + width\n \n # Find closest long option\n long_opt = None\n long_idx = None\n min_diff = float('inf')\n \n for j, opt in enumerate(options):\n diff = abs(opt['strike'] - target_long_strike)\n if diff \u003c min_diff:\n min_diff = diff\n long_opt = opt\n long_idx = j\n \n if not long_opt or min_diff > 0.5:\n continue\n \n # Skip if same strike\n if short_opt['strike'] == long_opt['strike']:\n continue\n \n # Volume/Open Interest filter for long strike\n long_volume = long_opt.get('volume', 0) or 0\n long_oi = long_opt.get('open_interest', 0) or 0\n if long_volume == 0 and long_oi == 0:\n logger.debug(\"Skipping long strike %.0f: zero volume and open interest\", long_opt['strike'])\n continue\n \n # Require non-zero bid for long strike too\n long_bid = long_opt.get('bid', 0) or 0\n if long_bid \u003c= 0:\n logger.debug(\"Skipping long strike %.0f: zero bid (no real market)\", long_opt['strike'])\n continue\n \n # Calculate implied vol for Black-Scholes\n # Use strike-specific IV for more accurate POP calculation\n # Use strike-specific IVs for each leg\n # For credit spreads, the short leg IV is most important as it determines breakeven\n short_iv = max(short_opt.get('implied_vol') or 0.25, self.IV_FLOOR)\n long_iv = max(long_opt.get('implied_vol') or 0.25, self.IV_FLOOR)\n \n # For POP calculation, use short leg IV specifically\n # The short strike determines the breakeven for credit spreads\n # The long leg is just protection and doesn't affect POP as much\n iv = short_iv\n \n # Calculate Greeks for both legs using their specific IVs\n short_greeks = self.bs.calculate_greeks(\n S, short_opt['strike'], T, r, short_iv, opt_type\n )\n long_greeks = self.bs.calculate_greeks(\n S, long_opt['strike'], T, r, long_iv, opt_type\n )\n \n # Use mid-point pricing for realistic fill estimates:\n # Most limit orders fill near the mid-point, not at bid/ask extremes.\n # Conservative bid/ask pricing understates credit spreads and\n # overstates debit spreads, skewing EV and POP comparisons.\n short_premium = short_opt['mid_price']\n long_premium = long_opt['mid_price']\n\n # Skip if mid-point is 0 (no real market)\n if short_premium \u003c= 0 or long_premium \u003c= 0:\n logger.debug(\"Skipping %s/%s: short_mid=%.2f, long_mid=%.2f (no market)\",\n short_opt['strike'], long_opt['strike'], short_premium, long_premium)\n continue\n\n # Build legs based on spread type\n if spread_type == 'put_credit':\n legs = [\n TradeLeg(\n strike=short_opt['strike'],\n expiration=chain.expiration_date,\n dte=chain.dte,\n premium=short_premium,\n option_type='put',\n action='sell',\n greeks=short_greeks\n ),\n TradeLeg(\n strike=long_opt['strike'],\n expiration=chain.expiration_date,\n dte=chain.dte,\n premium=long_premium,\n option_type='put',\n action='buy',\n greeks=long_greeks\n )\n ]\n strategy_type = 'put_credit_spread'\n elif spread_type == 'call_credit':\n legs = [\n TradeLeg(\n strike=short_opt['strike'],\n expiration=chain.expiration_date,\n dte=chain.dte,\n premium=short_premium,\n option_type='call',\n action='sell',\n greeks=short_greeks\n ),\n TradeLeg(\n strike=long_opt['strike'],\n expiration=chain.expiration_date,\n dte=chain.dte,\n premium=long_premium,\n option_type='call',\n action='buy',\n greeks=long_greeks\n )\n ]\n strategy_type = 'call_credit_spread'\n else:\n # Debit spreads - reverse actions\n continue # Skip for now, focus on credit spreads\n \n # Minimum net credit filter: skip if credit \u003c $0.05/share\n # (avoids deep OTM $1 spreads with $1-2 total credit for $98-99 risk)\n net_credit = sum(l.net_premium for l in legs)\n if net_credit \u003c 0.05:\n continue\n \n # Minimum credit-to-width ratio: at least 10% of width\n # Ensures reasonable risk/reward (e.g., $0.20 credit on $2 width)\n actual_width = abs(legs[0].strike - legs[1].strike)\n if actual_width > 0 and net_credit / actual_width \u003c 0.10:\n continue\n \n strategy = MultiLegStrategy(\n ticker=chain.ticker,\n strategy_type=strategy_type,\n underlying_price=S,\n legs=legs\n )\n \n strategy = self.calculate_strategy_metrics(strategy, iv)\n \n # Skip unrealistic scenarios (credit >= width means no risk, likely bad data)\n if strategy.max_loss \u003c= 0:\n continue\n \n # Only include if it fits account (use 50% of account as max risk ceiling)\n # This allows larger spreads for larger accounts while keeping risk managed\n account_max_risk = self.account_total * 0.50\n if strategy.max_loss \u003c= account_max_risk:\n strategies.append(strategy)\n \n return strategies\n \n def optimize_iron_condors(self, chain: OptionChain,\n put_width: float = 5.0,\n call_width: float = 5.0,\n otm_target: float = 0.10) -> List[MultiLegStrategy]:\n \"\"\"\n Find optimal iron condors\n \n otm_target: Target delta for short options (default 10 delta ~ 10% OTM)\n \"\"\"\n strategies = []\n \n if not chain.puts or not chain.calls:\n return strategies\n \n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n \n # Find 10% OTM strikes\n put_target = S * (1 - otm_target)\n call_target = S * (1 + otm_target)\n \n # Find closest puts\n put_short = None\n put_long = None\n call_short = None\n call_long = None\n \n for put in chain.puts:\n if put['strike'] \u003c= put_target and not put_short:\n put_short = put\n for put in chain.puts:\n if put_short and put['strike'] == put_short['strike'] - put_width:\n put_long = put\n break\n \n for call in chain.calls:\n if call['strike'] >= call_target and not call_short:\n call_short = call\n for call in chain.calls:\n if call_short and call['strike'] == call_short['strike'] + call_width:\n call_long = call\n break\n \n if not all([put_short, put_long, call_short, call_long]):\n return strategies\n \n # Get IV from the short strikes (higher due to skew/vol smile)\n put_short_iv = next((p['implied_vol'] for p in chain.puts if abs(p['strike'] - put_short['strike']) \u003c 0.01), None)\n call_short_iv = next((c['implied_vol'] for c in chain.calls if abs(c['strike'] - call_short['strike']) \u003c 0.01), None)\n \n # Use the maximum of the two short strike IVs to be conservative\n # (OTM puts typically have much higher IV than ATM due to skew)\n iv = max(put_short_iv or 0.20, call_short_iv or 0.20, self.IV_FLOOR)\n \n # Use mid-point pricing for realistic fill estimates\n ps_prem = put_short['mid_price']\n pl_prem = put_long['mid_price']\n cs_prem = call_short['mid_price']\n cl_prem = call_long['mid_price']\n\n # Calculate Greeks\n legs = [\n TradeLeg(put_short['strike'], chain.expiration_date, chain.dte,\n ps_prem, 'put', 'sell',\n greeks=self.bs.calculate_greeks(S, put_short['strike'], T, r, iv, 'put')),\n TradeLeg(put_long['strike'], chain.expiration_date, chain.dte,\n pl_prem, 'put', 'buy',\n greeks=self.bs.calculate_greeks(S, put_long['strike'], T, r, iv, 'put')),\n TradeLeg(call_short['strike'], chain.expiration_date, chain.dte,\n cs_prem, 'call', 'sell',\n greeks=self.bs.calculate_greeks(S, call_short['strike'], T, r, iv, 'call')),\n TradeLeg(call_long['strike'], chain.expiration_date, chain.dte,\n cl_prem, 'call', 'buy',\n greeks=self.bs.calculate_greeks(S, call_long['strike'], T, r, iv, 'call'))\n ]\n \n strategy = MultiLegStrategy(\n ticker=chain.ticker,\n strategy_type='iron_condor',\n underlying_price=S,\n legs=legs\n )\n \n strategy = self.calculate_strategy_metrics(strategy, iv)\n \n if strategy.max_loss \u003c= self.max_risk_per_trade * 1.5:\n strategies.append(strategy)\n \n return strategies\n\n # ----------------------------------------------------------------\n # Helper: find option nearest a target strike with liquidity filters\n # ----------------------------------------------------------------\n def _find_option(self, options: list, target_strike: float,\n tolerance: float = 1.0, require_liquidity: bool = True):\n \"\"\"Return the option dict closest to *target_strike*, or None.\"\"\"\n best = None\n best_diff = float('inf')\n for opt in options:\n diff = abs(opt['strike'] - target_strike)\n if diff \u003c best_diff:\n best_diff = diff\n best = opt\n if best is None or best_diff > tolerance:\n return None\n if require_liquidity:\n bid = best.get('bid', 0) or 0\n if bid \u003c= 0 or best['mid_price'] \u003c= 0:\n return None\n return best\n\n def _atm_iv(self, chain: OptionChain) -> float:\n \"\"\"Return ATM implied vol from the chain (calls preferred).\"\"\"\n S = chain.underlying_price\n options = chain.calls or chain.puts or []\n if not options:\n return 0.25\n atm = min(options, key=lambda o: abs(o['strike'] - S))\n return max(atm.get('implied_vol') or 0.25, self.IV_FLOOR)\n\n # ----------------------------------------------------------------\n # 1. Inverse Iron Butterfly (long straddle + short strangle)\n # ----------------------------------------------------------------\n def optimize_inverse_iron_butterfly(self, chain: OptionChain,\n body_width: float = 0.5,\n wing_width: float = 5.0) -> List[MultiLegStrategy]:\n \"\"\"\n BUY ATM Call + BUY ATM Put (straddle body)\n SELL OTM Call + SELL OTM Put (strangle wings)\n\n body_width: max % distance from ATM for body strikes (0.5 = 0.5%)\n wing_width: dollar distance from ATM to wing strikes\n \"\"\"\n strategies = []\n if not chain.calls or not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n # ATM strikes\n atm_call = self._find_option(chain.calls, S, tolerance=S * body_width / 100 + 1)\n atm_put = self._find_option(chain.puts, S, tolerance=S * body_width / 100 + 1)\n if not atm_call or not atm_put:\n return strategies\n\n for w in ([wing_width] if isinstance(wing_width, (int, float)) else wing_width):\n otm_call = self._find_option(chain.calls, S + w)\n otm_put = self._find_option(chain.puts, S - w)\n if not otm_call or not otm_put:\n continue\n\n legs = [\n TradeLeg(atm_call['strike'], chain.expiration_date, chain.dte,\n atm_call['mid_price'], 'call', 'buy',\n greeks=self.bs.calculate_greeks(S, atm_call['strike'], T, r, iv, 'call')),\n TradeLeg(atm_put['strike'], chain.expiration_date, chain.dte,\n atm_put['mid_price'], 'put', 'buy',\n greeks=self.bs.calculate_greeks(S, atm_put['strike'], T, r, iv, 'put')),\n TradeLeg(otm_call['strike'], chain.expiration_date, chain.dte,\n otm_call['mid_price'], 'call', 'sell',\n greeks=self.bs.calculate_greeks(S, otm_call['strike'], T, r, iv, 'call')),\n TradeLeg(otm_put['strike'], chain.expiration_date, chain.dte,\n otm_put['mid_price'], 'put', 'sell',\n greeks=self.bs.calculate_greeks(S, otm_put['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='inverse_iron_butterfly',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_profit > 0 and strat.max_loss > 0:\n strategies.append(strat)\n\n return strategies\n\n # ----------------------------------------------------------------\n # 2. Iron Butterfly (short straddle + long strangle)\n # ----------------------------------------------------------------\n def optimize_iron_butterfly(self, chain: OptionChain,\n body_width: float = 0.5,\n wing_width: float = 5.0) -> List[MultiLegStrategy]:\n \"\"\"\n SELL ATM Call + SELL ATM Put (straddle body)\n BUY OTM Call + BUY OTM Put (strangle wings)\n \"\"\"\n strategies = []\n if not chain.calls or not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n atm_call = self._find_option(chain.calls, S, tolerance=S * body_width / 100 + 1)\n atm_put = self._find_option(chain.puts, S, tolerance=S * body_width / 100 + 1)\n if not atm_call or not atm_put:\n return strategies\n\n for w in ([wing_width] if isinstance(wing_width, (int, float)) else wing_width):\n otm_call = self._find_option(chain.calls, S + w)\n otm_put = self._find_option(chain.puts, S - w)\n if not otm_call or not otm_put:\n continue\n\n legs = [\n TradeLeg(atm_call['strike'], chain.expiration_date, chain.dte,\n atm_call['mid_price'], 'call', 'sell',\n greeks=self.bs.calculate_greeks(S, atm_call['strike'], T, r, iv, 'call')),\n TradeLeg(atm_put['strike'], chain.expiration_date, chain.dte,\n atm_put['mid_price'], 'put', 'sell',\n greeks=self.bs.calculate_greeks(S, atm_put['strike'], T, r, iv, 'put')),\n TradeLeg(otm_call['strike'], chain.expiration_date, chain.dte,\n otm_call['mid_price'], 'call', 'buy',\n greeks=self.bs.calculate_greeks(S, otm_call['strike'], T, r, iv, 'call')),\n TradeLeg(otm_put['strike'], chain.expiration_date, chain.dte,\n otm_put['mid_price'], 'put', 'buy',\n greeks=self.bs.calculate_greeks(S, otm_put['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='iron_butterfly',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_profit > 0 and strat.max_loss > 0:\n strategies.append(strat)\n\n return strategies\n\n # ----------------------------------------------------------------\n # 3. Inverse Iron Condor (long closer strangle + short wider strangle)\n # ----------------------------------------------------------------\n def optimize_inverse_iron_condor(self, chain: OptionChain,\n inner_pct: float = 0.03,\n outer_width: float = 5.0) -> List[MultiLegStrategy]:\n \"\"\"\n BUY OTM Call/Put (closer to ATM)\n SELL further OTM Call/Put (wider)\n\n inner_pct: % OTM for long strangle legs (e.g. 0.03 = 3%)\n outer_width: $ distance from inner to outer strikes\n \"\"\"\n strategies = []\n if not chain.calls or not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n inner_call_target = S * (1 + inner_pct)\n inner_put_target = S * (1 - inner_pct)\n\n inner_call = self._find_option(chain.calls, inner_call_target)\n inner_put = self._find_option(chain.puts, inner_put_target)\n if not inner_call or not inner_put:\n return strategies\n\n for w in ([outer_width] if isinstance(outer_width, (int, float)) else outer_width):\n outer_call = self._find_option(chain.calls, inner_call['strike'] + w)\n outer_put = self._find_option(chain.puts, inner_put['strike'] - w)\n if not outer_call or not outer_put:\n continue\n\n legs = [\n TradeLeg(inner_call['strike'], chain.expiration_date, chain.dte,\n inner_call['mid_price'], 'call', 'buy',\n greeks=self.bs.calculate_greeks(S, inner_call['strike'], T, r, iv, 'call')),\n TradeLeg(inner_put['strike'], chain.expiration_date, chain.dte,\n inner_put['mid_price'], 'put', 'buy',\n greeks=self.bs.calculate_greeks(S, inner_put['strike'], T, r, iv, 'put')),\n TradeLeg(outer_call['strike'], chain.expiration_date, chain.dte,\n outer_call['mid_price'], 'call', 'sell',\n greeks=self.bs.calculate_greeks(S, outer_call['strike'], T, r, iv, 'call')),\n TradeLeg(outer_put['strike'], chain.expiration_date, chain.dte,\n outer_put['mid_price'], 'put', 'sell',\n greeks=self.bs.calculate_greeks(S, outer_put['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='inverse_iron_condor',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_profit > 0 and strat.max_loss > 0:\n strategies.append(strat)\n\n return strategies\n\n # ----------------------------------------------------------------\n # 4. Long Straddle\n # ----------------------------------------------------------------\n def optimize_long_straddle(self, chain: OptionChain) -> List[MultiLegStrategy]:\n \"\"\"BUY ATM Call + BUY ATM Put (same strike).\"\"\"\n strategies = []\n if not chain.calls or not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n atm_call = self._find_option(chain.calls, S)\n atm_put = self._find_option(chain.puts, S)\n if not atm_call or not atm_put:\n return strategies\n\n legs = [\n TradeLeg(atm_call['strike'], chain.expiration_date, chain.dte,\n atm_call['mid_price'], 'call', 'buy',\n greeks=self.bs.calculate_greeks(S, atm_call['strike'], T, r, iv, 'call')),\n TradeLeg(atm_put['strike'], chain.expiration_date, chain.dte,\n atm_put['mid_price'], 'put', 'buy',\n greeks=self.bs.calculate_greeks(S, atm_put['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='long_straddle',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_loss > 0:\n strategies.append(strat)\n return strategies\n\n # ----------------------------------------------------------------\n # 5. Long Strangle\n # ----------------------------------------------------------------\n def optimize_long_strangle(self, chain: OptionChain,\n call_otm_pct: float = 0.05,\n put_otm_pct: float = 0.05) -> List[MultiLegStrategy]:\n \"\"\"BUY OTM Call + BUY OTM Put.\"\"\"\n strategies = []\n if not chain.calls or not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n otm_call = self._find_option(chain.calls, S * (1 + call_otm_pct))\n otm_put = self._find_option(chain.puts, S * (1 - put_otm_pct))\n if not otm_call or not otm_put:\n return strategies\n\n legs = [\n TradeLeg(otm_call['strike'], chain.expiration_date, chain.dte,\n otm_call['mid_price'], 'call', 'buy',\n greeks=self.bs.calculate_greeks(S, otm_call['strike'], T, r, iv, 'call')),\n TradeLeg(otm_put['strike'], chain.expiration_date, chain.dte,\n otm_put['mid_price'], 'put', 'buy',\n greeks=self.bs.calculate_greeks(S, otm_put['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='long_strangle',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_loss > 0:\n strategies.append(strat)\n return strategies\n\n # ----------------------------------------------------------------\n # 6. Short Straddle\n # ----------------------------------------------------------------\n def optimize_short_straddle(self, chain: OptionChain) -> List[MultiLegStrategy]:\n \"\"\"SELL ATM Call + SELL ATM Put.\"\"\"\n strategies = []\n if not chain.calls or not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n atm_call = self._find_option(chain.calls, S)\n atm_put = self._find_option(chain.puts, S)\n if not atm_call or not atm_put:\n return strategies\n\n legs = [\n TradeLeg(atm_call['strike'], chain.expiration_date, chain.dte,\n atm_call['mid_price'], 'call', 'sell',\n greeks=self.bs.calculate_greeks(S, atm_call['strike'], T, r, iv, 'call')),\n TradeLeg(atm_put['strike'], chain.expiration_date, chain.dte,\n atm_put['mid_price'], 'put', 'sell',\n greeks=self.bs.calculate_greeks(S, atm_put['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='short_straddle',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_profit > 0:\n strategies.append(strat)\n return strategies\n\n # ----------------------------------------------------------------\n # 7. Short Strangle\n # ----------------------------------------------------------------\n def optimize_short_strangle(self, chain: OptionChain,\n call_otm_pct: float = 0.05,\n put_otm_pct: float = 0.05) -> List[MultiLegStrategy]:\n \"\"\"SELL OTM Call + SELL OTM Put.\"\"\"\n strategies = []\n if not chain.calls or not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n otm_call = self._find_option(chain.calls, S * (1 + call_otm_pct))\n otm_put = self._find_option(chain.puts, S * (1 - put_otm_pct))\n if not otm_call or not otm_put:\n return strategies\n\n legs = [\n TradeLeg(otm_call['strike'], chain.expiration_date, chain.dte,\n otm_call['mid_price'], 'call', 'sell',\n greeks=self.bs.calculate_greeks(S, otm_call['strike'], T, r, iv, 'call')),\n TradeLeg(otm_put['strike'], chain.expiration_date, chain.dte,\n otm_put['mid_price'], 'put', 'sell',\n greeks=self.bs.calculate_greeks(S, otm_put['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='short_strangle',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_profit > 0:\n strategies.append(strat)\n return strategies\n\n # ----------------------------------------------------------------\n # 8. Long Call\n # ----------------------------------------------------------------\n def optimize_long_call(self, chain: OptionChain,\n moneyness: str = 'atm') -> List[MultiLegStrategy]:\n \"\"\"\n BUY Call option.\n moneyness: 'atm', 'otm_5' (5% OTM), 'itm_5' (5% ITM)\n \"\"\"\n strategies = []\n if not chain.calls:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n if moneyness == 'atm':\n target = S\n elif moneyness.startswith('otm_'):\n pct = float(moneyness.split('_')[1]) / 100\n target = S * (1 + pct)\n elif moneyness.startswith('itm_'):\n pct = float(moneyness.split('_')[1]) / 100\n target = S * (1 - pct)\n else:\n target = S\n\n opt = self._find_option(chain.calls, target)\n if not opt:\n return strategies\n\n legs = [\n TradeLeg(opt['strike'], chain.expiration_date, chain.dte,\n opt['mid_price'], 'call', 'buy',\n greeks=self.bs.calculate_greeks(S, opt['strike'], T, r, iv, 'call')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='long_call',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_loss > 0:\n strategies.append(strat)\n return strategies\n\n # ----------------------------------------------------------------\n # 9. Long Put\n # ----------------------------------------------------------------\n def optimize_long_put(self, chain: OptionChain,\n moneyness: str = 'atm') -> List[MultiLegStrategy]:\n \"\"\"\n BUY Put option.\n moneyness: 'atm', 'otm_5' (5% OTM), 'itm_5' (5% ITM)\n \"\"\"\n strategies = []\n if not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n if moneyness == 'atm':\n target = S\n elif moneyness.startswith('otm_'):\n pct = float(moneyness.split('_')[1]) / 100\n target = S * (1 - pct)\n elif moneyness.startswith('itm_'):\n pct = float(moneyness.split('_')[1]) / 100\n target = S * (1 + pct)\n else:\n target = S\n\n opt = self._find_option(chain.puts, target)\n if not opt:\n return strategies\n\n legs = [\n TradeLeg(opt['strike'], chain.expiration_date, chain.dte,\n opt['mid_price'], 'put', 'buy',\n greeks=self.bs.calculate_greeks(S, opt['strike'], T, r, iv, 'put')),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='long_put',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_loss > 0:\n strategies.append(strat)\n return strategies\n\n # ----------------------------------------------------------------\n # 10. Call Ratio Backspread (sell 1 lower call, buy 2 higher calls)\n # ----------------------------------------------------------------\n def optimize_call_ratio_backspread(self, chain: OptionChain,\n strike_width: float = 5.0) -> List[MultiLegStrategy]:\n \"\"\"\n SELL 1 ATM/ITM Call + BUY 2 OTM Calls (higher strike).\n Bullish with volatility expansion.\n \"\"\"\n strategies = []\n if not chain.calls:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n # Short leg: ATM call\n short_opt = self._find_option(chain.calls, S)\n if not short_opt:\n return strategies\n\n for w in ([strike_width] if isinstance(strike_width, (int, float)) else strike_width):\n long_opt = self._find_option(chain.calls, short_opt['strike'] + w)\n if not long_opt:\n continue\n\n short_greeks = self.bs.calculate_greeks(S, short_opt['strike'], T, r, iv, 'call')\n long_greeks = self.bs.calculate_greeks(S, long_opt['strike'], T, r, iv, 'call')\n\n legs = [\n TradeLeg(short_opt['strike'], chain.expiration_date, chain.dte,\n short_opt['mid_price'], 'call', 'sell', quantity=1,\n greeks=short_greeks),\n TradeLeg(long_opt['strike'], chain.expiration_date, chain.dte,\n long_opt['mid_price'], 'call', 'buy', quantity=2,\n greeks=long_greeks),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='call_ratio_backspread',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_loss > 0:\n strategies.append(strat)\n\n return strategies\n\n # ----------------------------------------------------------------\n # 11. Put Ratio Backspread (sell 1 higher put, buy 2 lower puts)\n # ----------------------------------------------------------------\n def optimize_put_ratio_backspread(self, chain: OptionChain,\n strike_width: float = 5.0) -> List[MultiLegStrategy]:\n \"\"\"\n SELL 1 ATM/ITM Put + BUY 2 OTM Puts (lower strike).\n Bearish with volatility expansion.\n \"\"\"\n strategies = []\n if not chain.puts:\n return strategies\n\n S = chain.underlying_price\n T = chain.dte / 365.0\n r = 0.045\n iv = self._atm_iv(chain)\n\n # Short leg: ATM put\n short_opt = self._find_option(chain.puts, S)\n if not short_opt:\n return strategies\n\n for w in ([strike_width] if isinstance(strike_width, (int, float)) else strike_width):\n long_opt = self._find_option(chain.puts, short_opt['strike'] - w)\n if not long_opt:\n continue\n\n short_greeks = self.bs.calculate_greeks(S, short_opt['strike'], T, r, iv, 'put')\n long_greeks = self.bs.calculate_greeks(S, long_opt['strike'], T, r, iv, 'put')\n\n legs = [\n TradeLeg(short_opt['strike'], chain.expiration_date, chain.dte,\n short_opt['mid_price'], 'put', 'sell', quantity=1,\n greeks=short_greeks),\n TradeLeg(long_opt['strike'], chain.expiration_date, chain.dte,\n long_opt['mid_price'], 'put', 'buy', quantity=2,\n greeks=long_greeks),\n ]\n\n strat = MultiLegStrategy(ticker=chain.ticker, strategy_type='put_ratio_backspread',\n underlying_price=S, legs=legs)\n strat = self.calculate_strategy_metrics(strat, iv)\n if strat.max_loss > 0:\n strategies.append(strat)\n\n return strategies\n","content_type":"text/x-python; charset=utf-8","language":"python","size":69943,"content_sha256":"7bce4291119c4b6415d74d4922e0d3b1ed9c26fe0bf4e77faa11aa981d1ffb54"},{"filename":"scripts/market_scanner.py","content":"#!/usr/bin/env python3\n\"\"\"\n===============================================================================\nOptions Market Scanner — Multi-Strategy EXECUTE Play Finder\n===============================================================================\n\nScans a stock universe (S&P 500, Nasdaq 100, or custom list) for high-conviction\noptions spread opportunities across all 7 supported strategies:\n\n - bull_put, bear_call (credit spreads)\n - bull_call, bear_put (debit spreads) \n - iron_condor, butterfly, calendar (multi-leg strategies)\n\nFilters for EXECUTE tier (conviction >= 80) and runs position sizing\nto ensure trades fit within account guardrails ($390 account default).\n\nFeatures:\n - Rate limiting to avoid Yahoo Finance API bans\n - Configurable batch size and delay\n - Parallel processing support (with rate limiting)\n - JSON or formatted table output\n\nUsage:\n python3 market_scanner.py --universe sp500\n python3 market_scanner.py --universe ndx100 --batch-size 10 --delay 2\n python3 market_scanner.py --universe /path/to/custom_tickers.txt --json\n python3 market_scanner.py --universe sp500 --parallel 4\n\nAuthor: Leonardo Da Pinchy\nVersion: 1.0.0\nLicense: MIT\n===============================================================================\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport sys\nimport time\nimport warnings\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nfrom dataclasses import dataclass, field, asdict\nfrom pathlib import Path\nfrom typing import Any, Optional\n\n# Suppress noisy warnings\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n\n# Import conviction engine components\nsys.path.insert(0, str(Path(__file__).parent))\nfrom spread_conviction_engine import (\n StrategyType, analyse as analyse_vertical,\n fetch_ohlcv, compute_all_indicators\n)\nfrom multi_leg_strategies import (\n MultiLegStrategyType, analyse_multi_leg\n)\n\n# Import calculator and position sizer\nfrom calculator import bull_call_spread, bear_put_spread\nfrom position_sizer import calculate_position, format_recommendation, DEFAULT_ACCOUNT_VALUE\n\n\n# =============================================================================\n# Constants\n# =============================================================================\n\nVERSION = \"1.0.0\"\nDEFAULT_BATCH_SIZE = 5\nDEFAULT_DELAY = 1.5 # seconds between API calls\nDEFAULT_PARALLEL = 1 # Sequential by default for rate limiting safety\n\n# Strategy configurations\nALL_STRATEGIES = [\n (\"bull_put\", StrategyType.BULL_PUT, \"vertical\"),\n (\"bear_call\", StrategyType.BEAR_CALL, \"vertical\"),\n (\"bull_call\", StrategyType.BULL_CALL, \"vertical\"),\n (\"bear_put\", StrategyType.BEAR_PUT, \"vertical\"),\n (\"iron_condor\", MultiLegStrategyType.IRON_CONDOR, \"multi\"),\n (\"butterfly\", MultiLegStrategyType.BUTTERFLY, \"multi\"),\n (\"calendar\", MultiLegStrategyType.CALENDAR, \"multi\"),\n]\n\nEXECUTE_THRESHOLD = 80.0 # Minimum conviction for EXECUTE tier\n\n\n# =============================================================================\n# Data Classes\n# =============================================================================\n\n@dataclass\nclass ScannerResult:\n \"\"\"Complete result for a single ticker-strategy combination.\"\"\"\n ticker: str\n price: float\n strategy: str\n conviction: float\n tier: str\n strikes: dict = field(default_factory=dict)\n max_loss: Optional[float] = None\n max_profit: Optional[float] = None\n pop: Optional[float] = None\n position_size: int = 0\n position_recommendation: str = \"SKIP\"\n position_reason: str = \"\"\n error: Optional[str] = None\n \n def to_dict(self) -> dict[str, Any]:\n \"\"\"Convert to dictionary for JSON serialization.\"\"\"\n return asdict(self)\n\n\n@dataclass\nclass ScanConfig:\n \"\"\"Scanner configuration.\"\"\"\n universe: str # 'sp500', 'ndx100', or path to custom file\n batch_size: int\n delay: float\n parallel: int\n account_value: float\n output_json: bool\n min_conviction: float\n max_loss_cap: float # Per position sizer rules\n strategies: list[str] # Subset of strategies to scan\n\n\n# =============================================================================\n# Universe Loading\n# =============================================================================\n\ndef get_data_dir() -> Path:\n \"\"\"Get the data directory path.\"\"\"\n script_dir = Path(__file__).parent\n return script_dir.parent / \"data\"\n\n\ndef load_tickers(universe: str) -> list[str]:\n \"\"\"\n Load ticker list from universe specification.\n \n Args:\n universe: 'sp500', 'ndx100', or path to custom file\n \n Returns:\n List of ticker symbols\n \"\"\"\n data_dir = get_data_dir()\n \n if universe == \"sp500\":\n ticker_file = data_dir / \"sp500_tickers.txt\"\n elif universe == \"ndx100\":\n ticker_file = data_dir / \"ndx100_tickers.txt\"\n else:\n # Custom file path - check if it's just a filename (no slashes)\n # and if so, look in the data directory\n if \"/\" not in universe and \"\\\\\" not in universe and not universe.endswith(\".txt\"):\n # Try with .txt extension in data dir\n ticker_file = data_dir / f\"{universe}.txt\"\n elif \"/\" not in universe and \"\\\\\" not in universe:\n ticker_file = data_dir / universe\n else:\n ticker_file = Path(universe)\n \n if not ticker_file.exists():\n raise FileNotFoundError(f\"Ticker file not found: {ticker_file}\")\n \n with open(ticker_file, 'r') as f:\n tickers = [line.strip().upper() for line in f if line.strip()]\n \n # Remove duplicates while preserving order\n seen = set()\n unique_tickers = []\n for t in tickers:\n if t and t not in seen:\n seen.add(t)\n unique_tickers.append(t)\n \n return unique_tickers\n\n\n# =============================================================================\n# Strategy Analysis\n# =============================================================================\n\ndef analyze_ticker_strategy(\n ticker: str,\n strategy_name: str,\n strategy_type: Any,\n strategy_kind: str,\n period: str = \"2y\",\n interval: str = \"1d\",\n) -> Optional[dict]:\n \"\"\"\n Analyze a single ticker-strategy combination.\n \n Returns:\n Dictionary with analysis results or None if not EXECUTE tier\n \"\"\"\n try:\n if strategy_kind == \"vertical\":\n result = analyse_vertical(ticker, strategy=strategy_type, period=period, interval=interval)\n else:\n result = analyse_multi_leg(ticker, strategy=strategy_type, period=period, interval=interval)\n \n # Only return EXECUTE tier results\n if result.conviction_score \u003c EXECUTE_THRESHOLD:\n return None\n \n return {\n \"ticker\": ticker,\n \"price\": result.price,\n \"strategy\": strategy_name,\n \"conviction\": result.conviction_score,\n \"tier\": result.tier,\n \"strikes\": _extract_strikes(result),\n \"data_quality\": getattr(result, \"data_quality\", \"HIGH\"),\n }\n except Exception as e:\n return {\n \"ticker\": ticker,\n \"strategy\": strategy_name,\n \"error\": str(e),\n }\n\n\ndef _extract_strikes(result: Any) -> dict:\n \"\"\"Extract strike information from result.\"\"\"\n strikes = {}\n \n if hasattr(result, \"strikes\"):\n s = result.strikes\n if hasattr(s, \"short_strike\"):\n strikes[\"short\"] = s.short_strike\n if hasattr(s, \"long_strike\"):\n strikes[\"long\"] = s.long_strike\n if hasattr(s, \"put_long\"):\n strikes[\"put_long\"] = s.put_long\n if hasattr(s, \"put_short\"):\n strikes[\"put_short\"] = s.put_short\n if hasattr(s, \"call_short\"):\n strikes[\"call_short\"] = s.call_short\n if hasattr(s, \"call_long\"):\n strikes[\"call_long\"] = s.call_long\n if hasattr(s, \"lower_long\"):\n strikes[\"lower_long\"] = s.lower_long\n if hasattr(s, \"middle_short\"):\n strikes[\"middle_short\"] = s.middle_short\n if hasattr(s, \"upper_long\"):\n strikes[\"upper_long\"] = s.upper_long\n if hasattr(s, \"strike\"):\n strikes[\"atm\"] = s.strike\n if hasattr(s, \"front_expiry\"):\n strikes[\"front_expiry\"] = s.front_expiry\n if hasattr(s, \"back_expiry\"):\n strikes[\"back_expiry\"] = s.back_expiry\n \n return strikes\n\n\n# =============================================================================\n# Profit/Loss Calculation\n# =============================================================================\n\ndef estimate_strategy_pl(\n ticker: str,\n price: float,\n strategy: str,\n strikes: dict,\n) -> tuple[float, float, float]:\n \"\"\"\n Estimate max loss, max profit, and POP for a strategy.\n \n Uses simplified assumptions for quick estimation:\n - 30 days to expiry\n - 30% implied volatility\n - Credit spreads: collect ~30% of strike width\n \n Returns:\n (max_loss, max_profit, pop)\n \"\"\"\n try:\n iv = 0.30\n dte = 30\n rfr = 0.05\n \n if strategy == \"bull_put\":\n short_strike = strikes.get(\"short\", price * 0.95)\n long_strike = strikes.get(\"long\", short_strike - price * 0.02)\n width = short_strike - long_strike\n credit = width * 0.30 # Collect 30% of width\n max_loss = (width - credit) * 100\n max_profit = credit * 100\n # Simplified POP estimate based on strike distance\n pop = 0.60 + (short_strike / price - 1.0) * 2 # Higher POP for OTM\n \n elif strategy == \"bear_call\":\n short_strike = strikes.get(\"short\", price * 1.05)\n long_strike = strikes.get(\"long\", short_strike + price * 0.02)\n width = long_strike - short_strike\n credit = width * 0.30\n max_loss = (width - credit) * 100\n max_profit = credit * 100\n pop = 0.60 + (1.0 - short_strike / price) * 2\n \n elif strategy in [\"bull_call\", \"bear_put\"]:\n # Debit spreads\n if strategy == \"bull_call\":\n long_strike = strikes.get(\"long\", price)\n short_strike = strikes.get(\"short\", price * 1.05)\n else:\n long_strike = strikes.get(\"long\", price)\n short_strike = strikes.get(\"short\", price * 0.95)\n \n width = abs(long_strike - short_strike)\n debit = width * 0.50 # Pay ~50% of width\n max_loss = debit * 100\n max_profit = (width - debit) * 100\n pop = 0.45 # Debit spreads typically have lower POP\n \n elif strategy == \"iron_condor\":\n # Two credit spreads\n put_width = strikes.get(\"put_short\", price * 0.95) - strikes.get(\"put_long\", price * 0.93)\n call_width = strikes.get(\"call_long\", price * 1.07) - strikes.get(\"call_short\", price * 1.05)\n put_credit = put_width * 0.30\n call_credit = call_width * 0.30\n total_credit = put_credit + call_credit\n max_profit = total_credit * 100\n # Max loss is the wider wing minus credit\n max_loss = (max(put_width, call_width) - total_credit) * 100\n pop = 0.65 # Iron condors have high POP if well-constructed\n \n elif strategy == \"butterfly\":\n # Debit strategy with limited risk/reward\n wing_width = strikes.get(\"middle_short\", price) - strikes.get(\"lower_long\", price * 0.95)\n debit = wing_width * 0.15 # Butterflies are cheap\n max_loss = debit * 100\n max_profit = (wing_width - debit) * 100\n pop = 0.25 # Low POP but high reward\n \n elif strategy == \"calendar\":\n # Calendar spread - debit with theta focus\n debit = price * 0.02 # ~2% of stock price\n max_loss = debit * 100\n max_profit = debit * 200 # Theoretical max is ~2x debit\n pop = 0.55\n \n else:\n return 0.0, 0.0, 0.0\n \n return (\n round(max_loss, 2),\n round(max_profit, 2),\n round(max(min(pop, 0.95), 0.05), 2) # Clamp between 5% and 95%\n )\n except Exception:\n return 0.0, 0.0, 0.0\n\n\n# =============================================================================\n# Position Sizing Integration\n# =============================================================================\n\ndef size_position(\n max_loss: float,\n max_profit: float,\n pop: float,\n account_value: float = DEFAULT_ACCOUNT_VALUE,\n) -> dict:\n \"\"\"\n Run position sizer on a trade candidate.\n \n Returns:\n Position sizing result dictionary\n \"\"\"\n return calculate_position(\n account_value=account_value,\n max_loss_per_spread=max_loss,\n win_amount=max_profit,\n pop=pop,\n )\n\n\n# =============================================================================\n# Scanner Core\n# =============================================================================\n\ndef scan_ticker(\n ticker: str,\n config: ScanConfig,\n) -> list[ScannerResult]:\n \"\"\"\n Scan a single ticker across all requested strategies.\n \n Args:\n ticker: Stock symbol\n config: Scanner configuration\n \n Returns:\n List of ScannerResult (may be empty if no EXECUTE plays)\n \"\"\"\n results = []\n \n for strategy_name, strategy_type, strategy_kind in ALL_STRATEGIES:\n # Skip if not in requested strategies\n if config.strategies and strategy_name not in config.strategies:\n continue\n \n # Analyze\n analysis = analyze_ticker_strategy(\n ticker, strategy_name, strategy_type, strategy_kind\n )\n \n if analysis is None:\n continue # Not EXECUTE tier\n \n if \"error\" in analysis:\n results.append(ScannerResult(\n ticker=ticker,\n price=0.0,\n strategy=strategy_name,\n conviction=0.0,\n tier=\"ERROR\",\n error=analysis[\"error\"],\n ))\n continue\n \n # Estimate P/L\n max_loss, max_profit, pop = estimate_strategy_pl(\n ticker,\n analysis[\"price\"],\n strategy_name,\n analysis[\"strikes\"],\n )\n \n # Run position sizer\n sizing = size_position(max_loss, max_profit, pop, config.account_value)\n \n # Build result\n result = ScannerResult(\n ticker=ticker,\n price=analysis[\"price\"],\n strategy=strategy_name,\n conviction=analysis[\"conviction\"],\n tier=analysis[\"tier\"],\n strikes=analysis[\"strikes\"],\n max_loss=max_loss,\n max_profit=max_profit,\n pop=pop,\n position_size=sizing[\"contracts\"],\n position_recommendation=sizing[\"recommendation\"],\n position_reason=sizing[\"reason\"],\n )\n \n results.append(result)\n \n return results\n\n\ndef run_scanner(config: ScanConfig) -> list[ScannerResult]:\n \"\"\"\n Run the full market scan.\n \n Args:\n config: Scanner configuration\n \n Returns:\n List of all EXECUTE-tier ScannerResults\n \"\"\"\n # Load tickers\n tickers = load_tickers(config.universe)\n print(f\"📊 Loaded {len(tickers)} tickers from {config.universe}\")\n print(f\"🔍 Scanning for EXECUTE-tier plays (conviction >= {EXECUTE_THRESHOLD})\")\n print(f\"⏱️ Rate limit: {config.delay}s delay between calls\")\n print(f\"💰 Account value: ${config.account_value:.0f}\")\n print()\n \n all_results: list[ScannerResult] = []\n processed = 0\n errors = 0\n \n # Process in batches with rate limiting\n for i in range(0, len(tickers), config.batch_size):\n batch = tickers[i:i + config.batch_size]\n \n if config.parallel > 1:\n # Parallel processing with ThreadPoolExecutor\n # Note: Still rate-limited via delays between batches\n with ThreadPoolExecutor(max_workers=config.parallel) as executor:\n futures = {\n executor.submit(scan_ticker, ticker, config): ticker\n for ticker in batch\n }\n \n for future in as_completed(futures):\n ticker = futures[future]\n try:\n results = future.result()\n all_results.extend(results)\n processed += 1\n \n if results:\n print(f\" ✓ {ticker}: Found {len(results)} EXECUTE play(s)\")\n else:\n print(f\" · {ticker}: No EXECUTE plays\")\n \n except Exception as e:\n errors += 1\n print(f\" ✗ {ticker}: Error - {e}\")\n else:\n # Sequential processing\n for ticker in batch:\n try:\n results = scan_ticker(ticker, config)\n all_results.extend(results)\n processed += 1\n \n if results:\n print(f\" ✓ {ticker}: Found {len(results)} EXECUTE play(s)\")\n else:\n print(f\" · {ticker}: No EXECUTE plays\")\n \n except Exception as e:\n errors += 1\n print(f\" ✗ {ticker}: Error - {e}\")\n \n # Rate limiting between batches\n if i + config.batch_size \u003c len(tickers):\n time.sleep(config.delay)\n \n print()\n print(f\"✅ Scan complete: {processed} tickers processed, {errors} errors\")\n print(f\"🎯 Found {len(all_results)} total EXECUTE play(s)\")\n \n return all_results\n\n\n# =============================================================================\n# Output Formatting\n# =============================================================================\n\ndef print_table(results: list[ScannerResult]) -> None:\n \"\"\"Print results as a formatted table.\"\"\"\n if not results:\n print(\"\\n❌ No EXECUTE-tier plays found matching criteria.\\n\")\n return\n \n # Header\n print(\"\\n\" + \"=\" * 140)\n print(f\"{'TICKER':\u003c8} {'PRICE':>8} {'STRATEGY':\u003c15} {'CONV':>6} {'STRIKES':\u003c40} {'MAX LOSS':>10} {'POP':>6} {'POS':>4} {'REC':\u003c8}\")\n print(\"=\" * 140)\n \n for r in results:\n # Format strikes\n strikes_str = \"\"\n if r.strikes:\n if \"short\" in r.strikes and \"long\" in r.strikes:\n strikes_str = f\"S:{r.strikes['short']:.1f} L:{r.strikes['long']:.1f}\"\n elif \"put_short\" in r.strikes:\n strikes_str = f\"P:{r.strikes.get('put_short',0):.0f}/{r.strikes.get('call_short',0):.0f}\"\n elif \"middle_short\" in r.strikes:\n strikes_str = f\"B:{r.strikes.get('lower_long',0):.0f}/{r.strikes.get('middle_short',0):.0f}\"\n elif \"atm\" in r.strikes:\n strikes_str = f\"C:{r.strikes['atm']:.0f}\"\n \n max_loss_str = f\"${r.max_loss:.0f}\" if r.max_loss else \"N/A\"\n pop_str = f\"{r.pop*100:.0f}%\" if r.pop else \"N/A\"\n \n print(\n f\"{r.ticker:\u003c8} ${r.price:>7.2f} {r.strategy:\u003c15} \"\n f\"{r.conviction:>5.1f} {strikes_str:\u003c40} \"\n f\"{max_loss_str:>10} {pop_str:>6} {r.position_size:>4} {r.position_recommendation:\u003c8}\"\n )\n \n print(\"=\" * 140)\n print()\n \n # Detail view for executable plays\n executable = [r for r in results if r.position_recommendation == \"EXECUTE\"]\n if executable:\n print(f\"\\n📋 DETAILED EXECUTABLE PLAYS ({len(executable)}):\\n\")\n for r in executable:\n print(f\"{'='*70}\")\n print(f\" {r.ticker} — {r.strategy.upper()}\")\n print(f\" Price: ${r.price:.2f} | Conviction: {r.conviction:.1f}/100\")\n print()\n print(f\" Strikes: {r.strikes}\")\n print(f\" Max Loss: ${r.max_loss:.0f} | Max Profit: ${r.max_profit:.0f} | POP: {r.pop*100:.1f}%\")\n print()\n print(f\" Position Size: {r.position_size} contract(s)\")\n print(f\" Reason: {r.position_reason}\")\n print(f\"{'='*70}\")\n print()\n\n\ndef print_json(results: list[ScannerResult]) -> None:\n \"\"\"Print results as JSON.\"\"\"\n output = [r.to_dict() for r in results]\n print(json.dumps(output, indent=2, default=str))\n\n\n# =============================================================================\n# CLI Interface\n# =============================================================================\n\ndef main() -> None:\n \"\"\"CLI entry point.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Options Market Scanner — Find EXECUTE-tier spread opportunities\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=\"\"\"\nExamples:\n python3 market_scanner.py --universe sp500\n python3 market_scanner.py --universe ndx100 --batch-size 10 --delay 2\n python3 market_scanner.py --universe /path/to/tickers.txt --json\n python3 market_scanner.py --universe sp500 --strategy bull_put bear_call\n python3 market_scanner.py --universe sp500 --parallel 4 --batch-size 20\n\nStock Universes:\n sp500 S&P 500 constituents\n ndx100 Nasdaq 100 constituents\n \u003cpath> Custom file with one ticker per line\n\nStrategies:\n bull_put, bear_call Credit spreads (directional)\n bull_call, bear_put Debit spreads (directional)\n iron_condor Range-bound premium selling\n butterfly Volatility compression pinning\n calendar Theta harvesting from IV term structure\n \"\"\",\n )\n \n parser.add_argument(\n \"--universe\",\n required=True,\n help=\"Stock universe: 'sp500', 'ndx100', or path to custom ticker file\",\n )\n \n parser.add_argument(\n \"--strategy\",\n nargs=\"+\",\n choices=[\"bull_put\", \"bear_call\", \"bull_call\", \"bear_put\", \"iron_condor\", \"butterfly\", \"calendar\"],\n help=\"Specific strategies to scan (default: all)\",\n )\n \n parser.add_argument(\n \"--batch-size\",\n type=int,\n default=DEFAULT_BATCH_SIZE,\n help=f\"Number of tickers per batch (default: {DEFAULT_BATCH_SIZE})\",\n )\n \n parser.add_argument(\n \"--delay\",\n type=float,\n default=DEFAULT_DELAY,\n help=f\"Seconds between API calls (default: {DEFAULT_DELAY})\",\n )\n \n parser.add_argument(\n \"--parallel\",\n type=int,\n default=DEFAULT_PARALLEL,\n help=f\"Parallel workers (default: {DEFAULT_PARALLEL}, use with caution)\",\n )\n \n parser.add_argument(\n \"--account-value\",\n type=float,\n default=DEFAULT_ACCOUNT_VALUE,\n help=f\"Account value in dollars (default: ${DEFAULT_ACCOUNT_VALUE:.0f})\",\n )\n \n parser.add_argument(\n \"--json\",\n action=\"store_true\",\n help=\"Output as JSON instead of table\",\n )\n \n parser.add_argument(\n \"--version\",\n action=\"version\",\n version=f\"%(prog)s {VERSION}\",\n )\n \n args = parser.parse_args()\n \n # Build config\n config = ScanConfig(\n universe=args.universe,\n batch_size=args.batch_size,\n delay=args.delay,\n parallel=args.parallel,\n account_value=args.account_value,\n output_json=args.json,\n min_conviction=EXECUTE_THRESHOLD,\n max_loss_cap=100.0, # $100 max risk per position sizer\n strategies=args.strategy or [],\n )\n \n # Run scan\n results = run_scanner(config)\n \n # Output\n if args.json:\n print_json(results)\n else:\n print_table(results)\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":24101,"content_sha256":"994cbce92ad6bed4e3a7cb96e094c060e6f10e8a4f0f6e035732cf121ad2bc2c"},{"filename":"scripts/multi_leg_strategies.py","content":"#!/usr/bin/env python3\n\"\"\"\n===============================================================================\nMulti-Leg Strategy Extensions — Iron Condors, Butterflies, Calendar Spreads\n===============================================================================\n\nAuthor: Financial Toolkit (OpenClaw)\nCreated: 2026-02-12\nVersion: 2.0.0\nLicense: MIT\n\nDescription:\n Extends the Spread Conviction Engine with non-directional and\n theta-harvesting multi-leg strategies:\n\n ┌────────────────┬────────┬─────────────────────────────────────────────┐\n │ Strategy │ Type │ Ideal Setup │\n ├────────────────┼────────┼─────────────────────────────────────────────┤\n │ iron_condor │ Credit │ High IV rank, neutral RSI, range-bound │\n │ butterfly │ Debit │ BB squeeze, dead-center RSI, no trend │\n │ calendar │ Debit │ Inverted IV term structure, stable price │\n └────────────────┴────────┴─────────────────────────────────────────────┘\n\n These strategies are fundamentally different from vertical spreads:\n they profit from *lack of movement* (iron condors, butterflies) or\n from *time decay differentials* (calendars), rather than directional\n conviction.\n\n Scoring Philosophy:\n ───────────────────\n Iron Condors → premium richness (IV rank) + neutrality + range structure\n Butterflies → volatility compression (squeeze) + price centering\n Calendars → IV term structure inversion + price stability + theta edge\n\nDependencies:\n Same as spread_conviction_engine.py (pandas, pandas_ta, yfinance)\n\n===============================================================================\n\"\"\"\n\nfrom __future__ import annotations\n\nimport warnings\nfrom dataclasses import dataclass, field, asdict\nfrom datetime import datetime\nfrom enum import Enum\nfrom typing import Any, Optional\n\nimport pandas as pd\nimport yfinance as yf\n\n# Import shared infrastructure from the main engine\nfrom spread_conviction_engine import (\n # Constants\n ICHIMOKU_TENKAN, ICHIMOKU_KIJUN, ICHIMOKU_SENKOU,\n RSI_LENGTH, ADX_LENGTH,\n MACD_FAST, MACD_SLOW, MACD_SIGNAL,\n BBANDS_LENGTH, BBANDS_STD,\n VOLUME_WINDOW,\n # Functions\n fetch_ohlcv, compute_all_indicators,\n # Enumerations\n ConvictionTier, TrendBias,\n)\n\n\n# =============================================================================\n# Module Version\n# =============================================================================\n\n__version__ = \"2.0.0\"\n\n\n# =============================================================================\n# Multi-Leg Strategy Types\n# =============================================================================\n\nclass MultiLegStrategyType(str, Enum):\n \"\"\"\n Supported multi-leg options strategies.\n\n Unlike vertical spreads which are directional, these strategies profit\n from range-bound conditions, volatility compression, or time decay\n differentials.\n \"\"\"\n IRON_CONDOR = \"iron_condor\"\n BUTTERFLY = \"butterfly\"\n CALENDAR = \"calendar\"\n\n @property\n def label(self) -> str:\n labels = {\n MultiLegStrategyType.IRON_CONDOR: \"Iron Condor (Credit)\",\n MultiLegStrategyType.BUTTERFLY: \"Long Butterfly (Debit)\",\n MultiLegStrategyType.CALENDAR: \"Calendar Spread (Debit)\",\n }\n return labels[self]\n\n @property\n def philosophy(self) -> str:\n philosophies = {\n MultiLegStrategyType.IRON_CONDOR: \"Premium Selling / Range-Bound\",\n MultiLegStrategyType.BUTTERFLY: \"Pinning / Volatility Compression\",\n MultiLegStrategyType.CALENDAR: \"Theta Harvesting / IV Term Structure\",\n }\n return philosophies[self]\n\n @property\n def ideal_setup(self) -> str:\n setups = {\n MultiLegStrategyType.IRON_CONDOR: (\n \"IV Rank >70, RSI neutral (40-60), price centered in range, ADX \u003c25\"\n ),\n MultiLegStrategyType.BUTTERFLY: (\n \"BB squeeze (low bandwidth), RSI dead-center (45-55), ADX \u003c20, flat MACD\"\n ),\n MultiLegStrategyType.CALENDAR: (\n \"Front-month IV > back-month IV by >5%, stable price, moderate trend\"\n ),\n }\n return setups[self]\n\n @property\n def legs(self) -> int:\n \"\"\"Number of option legs in the strategy.\"\"\"\n leg_counts = {\n MultiLegStrategyType.IRON_CONDOR: 4,\n MultiLegStrategyType.BUTTERFLY: 4, # 3 strikes, but 4 contracts (1+2+1)\n MultiLegStrategyType.CALENDAR: 2,\n }\n return leg_counts[self]\n\n\n# =============================================================================\n# Component Weights per Strategy\n# =============================================================================\n\n# Iron Condor: Premium selling in range-bound, high-IV environment\n# ┌───────────────────────┬────────┬──────────────────────────────────────┐\n# │ Component │ Weight │ Rationale │\n# ├───────────────────────┼────────┼──────────────────────────────────────┤\n# │ IV Rank (BBW %ile) │ 25 │ Rich premiums to sell │\n# │ RSI Neutrality │ 20 │ No directional momentum │\n# │ ADX (Range-Bound) │ 20 │ Weak trend = range structure │\n# │ Price Position (%B) │ 20 │ Centered in range = safe margins │\n# │ MACD Neutrality │ 15 │ No acceleration in either direction │\n# └───────────────────────┴────────┴──────────────────────────────────────┘\n\nIRON_CONDOR_WEIGHTS = {\n \"iv_rank\": 25,\n \"rsi_neutrality\": 20,\n \"adx_range\": 20,\n \"price_position\": 20,\n \"macd_neutrality\": 15,\n}\n\n# Butterfly: Pinning play in low-volatility, compressed environment\n# ┌───────────────────────┬────────┬──────────────────────────────────────┐\n# │ Component │ Weight │ Rationale │\n# ├───────────────────────┼────────┼──────────────────────────────────────┤\n# │ BB Squeeze │ 30 │ Vol compression = narrow range │\n# │ RSI Neutrality │ 25 │ Price at equilibrium │\n# │ ADX Weakness │ 20 │ No directional trend at all │\n# │ Price Centering (%B) │ 15 │ At center of range for max profit │\n# │ MACD Flatness │ 10 │ No momentum │\n# └───────────────────────┴────────┴──────────────────────────────────────┘\n\nBUTTERFLY_WEIGHTS = {\n \"squeeze\": 30,\n \"rsi_neutrality\": 25,\n \"adx_weakness\": 20,\n \"price_centering\": 15,\n \"macd_flatness\": 10,\n}\n\n# Calendar: Theta harvesting from IV term structure differential\n# ┌───────────────────────┬────────┬──────────────────────────────────────┐\n# │ Component │ Weight │ Rationale │\n# ├───────────────────────┼────────┼──────────────────────────────────────┤\n# │ IV Term Structure │ 30 │ Front IV > Back IV = theta edge │\n# │ Price Stability │ 20 │ Price stays near strike │\n# │ RSI Neutrality │ 20 │ Not trending away from strike │\n# │ ADX (Moderate) │ 15 │ Some structure, not trending hard │\n# │ MACD Neutrality │ 15 │ No directional acceleration │\n# └───────────────────────┴────────┴──────────────────────────────────────┘\n\nCALENDAR_WEIGHTS = {\n \"iv_term_structure\": 30,\n \"price_stability\": 20,\n \"rsi_neutrality\": 20,\n \"adx_moderate\": 15,\n \"macd_neutrality\": 15,\n}\n\n\n# =============================================================================\n# Strike Data Classes\n# =============================================================================\n\n@dataclass\nclass IronCondorStrikes:\n \"\"\"\n Iron Condor: 4 legs — sell OTM put & call spreads, buy further OTM wings.\n\n Payoff diagram:\n Loss ─────────┐ ┌──────────── Loss\n │ │\n └──────┐ ┌──────┘\n Max Loss │ Max Profit│ Max Loss\n └────────────┘\n put_long put_short call_short call_long\n \"\"\"\n put_long: float # buy far OTM put (wing protection)\n put_short: float # sell OTM put (inner leg)\n call_short: float # sell OTM call (inner leg)\n call_long: float # buy far OTM call (wing protection)\n max_profit_low: float # lower bound of max profit zone\n max_profit_high: float # upper bound of max profit zone\n breakeven_lower: float\n breakeven_upper: float\n wing_width: float # width of each wing (put_short - put_long)\n description: str\n\n\n@dataclass\nclass ButterflyStrikes:\n \"\"\"\n Long Butterfly: Buy 1 low, Sell 2 middle, Buy 1 high strike.\n\n Payoff diagram:\n ╱╲\n ╱ ╲\n ╱ ╲\n ──────────────────╱ ╲──────────────────\n lower_long middle upper_long\n (short x2)\n \"\"\"\n lower_long: float # buy 1 call/put at lower strike\n middle_short: float # sell 2 calls/puts at middle strike\n upper_long: float # buy 1 call/put at upper strike\n max_profit_price: float # price of maximum profit (= middle)\n breakeven_lower: float\n breakeven_upper: float\n width: float # wing width (middle - lower = upper - middle)\n description: str\n\n\n@dataclass\nclass CalendarStrikes:\n \"\"\"\n Calendar Spread: Same strike, different expirations.\n Sell short-dated, buy longer-dated.\n \"\"\"\n strike: float\n front_expiry: str # short-dated (sell)\n back_expiry: str # long-dated (buy)\n front_iv: Optional[float] # IV of front-month option (%)\n back_iv: Optional[float] # IV of back-month option (%)\n iv_differential_pct: Optional[float] # (front - back) / back * 100\n theta_advantage: str # qualitative description\n description: str\n\n\n# =============================================================================\n# Component Signal Data Classes\n# =============================================================================\n\n@dataclass\nclass IVRankSignal:\n \"\"\"\n Implied Volatility Rank approximated from Bollinger Bandwidth percentile.\n\n IV Rank = (Current BBW - 1Y Low BBW) / (1Y High BBW - 1Y Low BBW) * 100\n\n This is a well-established proxy: BBW tracks realized volatility, which\n correlates with implied volatility rank over time (Sinclair, 2013).\n \"\"\"\n iv_rank: float # 0-100 percentile\n current_bbw: float # current Bollinger Bandwidth\n bbw_1y_high: float # highest BBW in trailing 252 days\n bbw_1y_low: float # lowest BBW in trailing 252 days\n regime: str # VERY_LOW, LOW, MODERATE, HIGH, VERY_HIGH\n component_score: float = 0.0\n\n\n@dataclass\nclass NeutralitySignal:\n \"\"\"\n Composite measure of market neutrality / lack of directional bias.\n Used by iron condors and butterflies.\n \"\"\"\n rsi_value: float\n rsi_distance_from_50: float\n adx_value: float\n macd_histogram: float\n macd_hist_pct_of_price: float\n percent_b: float\n percent_b_distance_from_50: float\n component_score: float = 0.0\n\n\n@dataclass\nclass SqueezeSignal:\n \"\"\"\n Bollinger Band squeeze detection for butterfly strategies.\n\n A squeeze occurs when bandwidth drops to historically low levels,\n indicating volatility compression — ideal for butterfly payoffs.\n \"\"\"\n bandwidth: float\n bandwidth_percentile: float # where current BBW ranks vs 1Y history\n is_squeezing: bool # BBW below 25th percentile\n squeeze_duration: int # consecutive bars in squeeze\n component_score: float = 0.0\n\n\n@dataclass\nclass IVTermStructureSignal:\n \"\"\"\n IV Term Structure analysis for calendar spreads.\n\n An inverted term structure (front IV > back IV) creates a theta\n harvesting opportunity: sell expensive short-dated options, buy\n cheaper long-dated options at the same strike.\n\n Data Sources:\n 'options_chain' — real IV from yfinance options data (preferred)\n 'hv_proxy' — HV(10) vs HV(30) as fallback proxy\n 'unavailable' — no IV data could be obtained\n \"\"\"\n front_iv: Optional[float] # front-month IV (%) or HV(10)\n back_iv: Optional[float] # back-month IV (%) or HV(30)\n iv_differential_pct: Optional[float] # (front - back) / back * 100\n is_inverted: bool # True if front > back by >5%\n data_source: str # 'options_chain', 'hv_proxy', or error\n front_expiry: Optional[str] = None\n back_expiry: Optional[str] = None\n component_score: float = 0.0\n\n\n# =============================================================================\n# Multi-Leg Conviction Result\n# =============================================================================\n\n@dataclass\nclass MultiLegResult:\n \"\"\"\n Final output of the Multi-Leg Strategy Engine.\n\n Parallels ConvictionResult from the vertical spread engine but with\n fields specific to non-directional / multi-leg strategies.\n \"\"\"\n ticker: str\n strategy: str\n strategy_label: str\n strategy_type: str # \"multi_leg\"\n price: float\n conviction_score: float\n tier: str\n # Component signals (populated based on strategy)\n iv_rank: Optional[IVRankSignal] = None\n neutrality: Optional[NeutralitySignal] = None\n squeeze: Optional[SqueezeSignal] = None\n iv_term_structure: Optional[IVTermStructureSignal] = None\n # Volume\n relative_volume: float = 0.0\n volume_adjustment: float = 0.0\n # Strikes (exactly one will be populated depending on strategy)\n iron_condor_strikes: Optional[IronCondorStrikes] = None\n butterfly_strikes: Optional[ButterflyStrikes] = None\n calendar_strikes: Optional[CalendarStrikes] = None\n # Meta\n data_quality: str = \"HIGH\"\n rationale: list = field(default_factory=list)\n\n def to_dict(self) -> dict[str, Any]:\n \"\"\"Serialise to a plain dictionary (JSON-safe).\"\"\"\n return asdict(self)\n\n\n# =============================================================================\n# Helper Functions\n# =============================================================================\n\ndef _round_to_strike(price: float, stock_price: float) -> float:\n \"\"\"\n Round a price to the nearest standard option strike interval.\n\n Standard intervals:\n Stock \u003c $25: $0.50 or $1.00 strikes\n Stock $25-$50: $1.00 strikes\n Stock $50-$200: $2.50 or $5.00 strikes\n Stock > $200: $5.00 strikes\n \"\"\"\n if stock_price \u003c 25:\n interval = 1.0\n elif stock_price \u003c 50:\n interval = 1.0\n elif stock_price \u003c 200:\n interval = 5.0\n else:\n interval = 5.0\n return round(price / interval) * interval\n\n\ndef _get_atm_iv(chain_df: pd.DataFrame, price: float) -> Optional[float]:\n \"\"\"\n Find at-the-money implied volatility from a yfinance options chain.\n\n Parameters:\n chain_df: DataFrame of calls or puts from yf.Ticker.option_chain()\n price: Current stock price\n\n Returns:\n ATM implied volatility as a decimal (e.g. 0.30 for 30%), or None\n \"\"\"\n if chain_df.empty or \"impliedVolatility\" not in chain_df.columns:\n return None\n\n chain = chain_df.copy()\n chain[\"_distance\"] = (chain[\"strike\"] - price).abs()\n atm_row = chain.loc[chain[\"_distance\"].idxmin()]\n\n iv = float(atm_row[\"impliedVolatility\"])\n # Sanity check: IV should be between 0 and 10 (0% to 1000%)\n if 0 \u003c iv \u003c 10:\n return iv\n return None\n\n\n# =============================================================================\n# IV Rank Computation\n# =============================================================================\n\ndef compute_iv_rank(df: pd.DataFrame) -> IVRankSignal:\n \"\"\"\n Approximate IV Rank using Bollinger Bandwidth percentile.\n\n IV Rank formula (adapted from TastyTrade convention):\n IV Rank = (Current IV - 52wk Low IV) / (52wk High IV - 52wk Low IV)\n\n We use BBW as the IV proxy:\n IV Rank ≈ (Current BBW - 252d Low BBW) / (252d High BBW - 252d Low BBW)\n\n This correlation is well-documented: realized volatility (which BBW\n tracks) and IV rank move in tandem with ~0.7-0.8 correlation\n (Sinclair, \"Volatility Trading\", 2013).\n \"\"\"\n bb_suffix = f\"{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\"\n bbw_col = f\"BBB_{bb_suffix}\"\n\n bbw = df[bbw_col].dropna()\n if len(bbw) \u003c 50:\n return IVRankSignal(50.0, 0, 0, 0, \"MODERATE\", 0.0)\n\n lookback = min(252, len(bbw))\n bbw_window = bbw.iloc[-lookback:]\n current_bbw = float(bbw.iloc[-1])\n\n bbw_high = float(bbw_window.max())\n bbw_low = float(bbw_window.min())\n\n if bbw_high \u003c= bbw_low:\n iv_rank = 50.0\n else:\n iv_rank = ((current_bbw - bbw_low) / (bbw_high - bbw_low)) * 100.0\n\n iv_rank = max(0.0, min(100.0, iv_rank))\n\n if iv_rank >= 80:\n regime = \"VERY_HIGH\"\n elif iv_rank >= 60:\n regime = \"HIGH\"\n elif iv_rank >= 40:\n regime = \"MODERATE\"\n elif iv_rank >= 20:\n regime = \"LOW\"\n else:\n regime = \"VERY_LOW\"\n\n return IVRankSignal(\n iv_rank=round(iv_rank, 1),\n current_bbw=round(current_bbw, 4),\n bbw_1y_high=round(bbw_high, 4),\n bbw_1y_low=round(bbw_low, 4),\n regime=regime,\n )\n\n\n# =============================================================================\n# Neutrality Computation\n# =============================================================================\n\ndef compute_neutrality(df: pd.DataFrame) -> NeutralitySignal:\n \"\"\"\n Compute a composite neutrality measure from RSI, ADX, MACD, and %B.\n\n All sub-metrics express *distance from neutral* — lower values mean\n more neutral conditions, which favours non-directional strategies.\n \"\"\"\n latest = df.iloc[-1]\n price = float(latest[\"Close\"])\n\n rsi_val = float(latest[\"RSI\"])\n adx_val = float(latest[f\"ADX_{ADX_LENGTH}\"])\n\n hist_col = f\"MACDh_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\"\n macd_hist = float(latest[hist_col])\n macd_hist_pct = abs(macd_hist) / price * 100 if price > 0 else 0\n\n bb_suffix = f\"{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\"\n percent_b = float(latest[f\"BBP_{bb_suffix}\"])\n\n return NeutralitySignal(\n rsi_value=round(rsi_val, 2),\n rsi_distance_from_50=round(abs(rsi_val - 50.0), 2),\n adx_value=round(adx_val, 2),\n macd_histogram=round(macd_hist, 4),\n macd_hist_pct_of_price=round(macd_hist_pct, 4),\n percent_b=round(percent_b, 4),\n percent_b_distance_from_50=round(abs(percent_b - 0.5), 4),\n )\n\n\n# =============================================================================\n# Squeeze Detection\n# =============================================================================\n\ndef compute_squeeze(df: pd.DataFrame) -> SqueezeSignal:\n \"\"\"\n Detect Bollinger Band squeeze conditions.\n\n A squeeze is identified when the current BBW falls below the 25th\n percentile of the trailing 252-day BBW distribution. Extended\n squeeze durations (>5 bars) strongly favour butterfly plays.\n \"\"\"\n bb_suffix = f\"{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\"\n bbw_col = f\"BBB_{bb_suffix}\"\n\n bbw = df[bbw_col].dropna()\n current_bbw = float(bbw.iloc[-1])\n\n lookback = min(252, len(bbw))\n bbw_window = bbw.iloc[-lookback:]\n percentile = float((bbw_window \u003c current_bbw).sum() / len(bbw_window) * 100)\n\n # Squeeze threshold: 25th percentile of 1-year BBW\n threshold = float(bbw_window.quantile(0.25))\n is_squeezing = current_bbw \u003c= threshold\n\n # Count consecutive bars below threshold\n squeeze_duration = 0\n if is_squeezing:\n for i in range(len(bbw) - 1, max(len(bbw) - 60, -1), -1):\n if float(bbw.iloc[i]) \u003c= threshold:\n squeeze_duration += 1\n else:\n break\n\n return SqueezeSignal(\n bandwidth=round(current_bbw, 4),\n bandwidth_percentile=round(percentile, 1),\n is_squeezing=is_squeezing,\n squeeze_duration=squeeze_duration,\n )\n\n\n# =============================================================================\n# IV Term Structure\n# =============================================================================\n\ndef _compute_hv_term_structure(df: pd.DataFrame) -> dict:\n \"\"\"\n Compute historical volatility at 10-day and 30-day windows\n as a fallback proxy for IV term structure.\n\n HV is annualised: σ_daily × √252 × 100 (expressed as %).\n \"\"\"\n close = df[\"Close\"]\n returns = close.pct_change().dropna()\n\n result = {\"hv_10\": None, \"hv_30\": None}\n\n if len(returns) >= 10:\n std_10 = float(returns.iloc[-10:].std())\n result[\"hv_10\"] = round(std_10 * (252 ** 0.5) * 100, 1)\n\n if len(returns) >= 30:\n std_30 = float(returns.iloc[-30:].std())\n result[\"hv_30\"] = round(std_30 * (252 ** 0.5) * 100, 1)\n\n return result\n\n\ndef fetch_iv_term_structure(ticker: str) -> IVTermStructureSignal:\n \"\"\"\n Analyse IV term structure from live options chain data.\n\n Primary: Fetch ATM call IV for front and back month expirations.\n Fallback: Use HV(10) vs HV(30) as a proxy.\n\n An inverted term structure (front IV > back IV by >5%) signals a\n calendar spread opportunity: sell expensive near-term options and\n buy cheaper longer-dated options at the same strike.\n \"\"\"\n front_expiry = None\n back_expiry = None\n\n try:\n stock = yf.Ticker(ticker)\n expirations = stock.options\n\n if not expirations or len(expirations) \u003c 2:\n raise ValueError(\"Fewer than 2 option expirations available\")\n\n front_expiry = expirations[0]\n\n # Find back month at least 25 days from front\n back_idx = min(2, len(expirations) - 1)\n try:\n front_date = datetime.strptime(front_expiry, \"%Y-%m-%d\")\n for i, exp in enumerate(expirations[1:], 1):\n exp_date = datetime.strptime(exp, \"%Y-%m-%d\")\n if (exp_date - front_date).days >= 25:\n back_idx = i\n break\n except (ValueError, TypeError):\n pass\n\n back_expiry = expirations[back_idx]\n\n # Get current price for ATM identification\n hist = stock.history(period=\"2d\")\n if hist.empty:\n raise ValueError(\"No price history available\")\n price = float(hist[\"Close\"].iloc[-1])\n\n # Fetch option chains\n front_chain = stock.option_chain(front_expiry)\n back_chain = stock.option_chain(back_expiry)\n\n front_iv = _get_atm_iv(front_chain.calls, price)\n back_iv = _get_atm_iv(back_chain.calls, price)\n\n if front_iv is not None and back_iv is not None and back_iv > 0:\n diff_pct = ((front_iv - back_iv) / back_iv) * 100\n is_inverted = diff_pct > 5.0\n\n return IVTermStructureSignal(\n front_iv=round(front_iv * 100, 1),\n back_iv=round(back_iv * 100, 1),\n iv_differential_pct=round(diff_pct, 1),\n is_inverted=is_inverted,\n data_source=\"options_chain\",\n front_expiry=front_expiry,\n back_expiry=back_expiry,\n )\n\n raise ValueError(\"Could not extract ATM IV from chains\")\n\n except Exception:\n # Fallback: use historical volatility term structure proxy\n pass\n\n # If we reach here, options chain failed — use HV proxy\n return IVTermStructureSignal(\n front_iv=None,\n back_iv=None,\n iv_differential_pct=None,\n is_inverted=False,\n data_source=\"hv_proxy\",\n front_expiry=front_expiry,\n back_expiry=back_expiry,\n )\n\n\ndef _enrich_iv_with_hv_proxy(\n signal: IVTermStructureSignal, df: pd.DataFrame\n) -> IVTermStructureSignal:\n \"\"\"\n If the options chain fetch failed, enrich the signal with HV proxy data.\n \"\"\"\n if signal.data_source != \"hv_proxy\":\n return signal\n\n hv = _compute_hv_term_structure(df)\n hv_10 = hv.get(\"hv_10\")\n hv_30 = hv.get(\"hv_30\")\n\n if hv_10 is not None and hv_30 is not None and hv_30 > 0:\n diff_pct = ((hv_10 - hv_30) / hv_30) * 100\n return IVTermStructureSignal(\n front_iv=hv_10,\n back_iv=hv_30,\n iv_differential_pct=round(diff_pct, 1),\n is_inverted=diff_pct > 5.0,\n data_source=\"hv_proxy\",\n front_expiry=signal.front_expiry,\n back_expiry=signal.back_expiry,\n )\n\n return signal\n\n\n# =============================================================================\n# Volume Analysis for Neutral Strategies\n# =============================================================================\n\ndef score_volume_neutral(df: pd.DataFrame) -> tuple[float, float]:\n \"\"\"\n Volume analysis for non-directional strategies.\n\n Neutral strategies prefer:\n - Low/average volume → range-bound, no conviction in either direction\n - High volume is a WARNING → may signal impending directional move\n\n Returns:\n (relative_volume, adjustment)\n Adjustment is ±10 points maximum.\n \"\"\"\n rv = float(df.iloc[-1][\"REL_VOL\"])\n\n if rv \u003c 0.75:\n adj = 5.0 # Low volume — range-bound, ideal\n elif rv \u003c 1.0:\n adj = 3.0 # Below average — slightly favourable\n elif rv \u003c 1.25:\n adj = 0.0 # Average — neutral\n elif rv \u003c 1.5:\n adj = -5.0 # Elevated — caution, directional move possible\n else:\n adj = -10.0 # High volume — likely directional, danger\n\n return round(rv, 2), adj\n\n\n# =============================================================================\n# Scoring Functions — Iron Condor\n# =============================================================================\n\ndef score_iron_condor(\n df: pd.DataFrame,\n iv_rank: IVRankSignal,\n neutrality: NeutralitySignal,\n) -> tuple[float, dict[str, float]]:\n \"\"\"\n Score an Iron Condor setup.\n\n Returns:\n (total_score, component_breakdown)\n\n Scoring Table:\n ──────────────────────────────────────────────────────────────────\n IV Rank (25 pts): Premium richness\n >80 → 1.0 70-80 → 0.85 50-70 → 0.55\n 30-50 → 0.25 \u003c30 → 0.10\n\n RSI Neutrality (20 pts): Distance from 50\n 45-55 → 1.0 40-45/55-60 → 0.75 35-40/60-65 → 0.40\n 30-35/65-70 → 0.20 \u003c30/>70 → 0.05\n\n ADX Range-Bound (20 pts): Trend weakness\n \u003c15 → 0.60 15-20 → 1.0 20-25 → 0.75\n 25-30 → 0.35 >30 → 0.10\n\n Price Position (20 pts): %B centering\n 0.40-0.60 → 1.0 0.30-0.40/0.60-0.70 → 0.65\n 0.20-0.30/0.70-0.80 → 0.30 \u003c0.20/>0.80 → 0.10\n\n MACD Neutrality (15 pts): |Histogram| as % of price\n \u003c0.05% → 1.0 0.05-0.10% → 0.70 0.10-0.20% → 0.35\n >0.20% → 0.10\n ──────────────────────────────────────────────────────────────────\n \"\"\"\n w = IRON_CONDOR_WEIGHTS\n\n # --- IV Rank ---\n ivr = iv_rank.iv_rank\n if ivr > 80:\n iv_score = 1.0\n elif ivr >= 70:\n iv_score = 0.85\n elif ivr >= 50:\n iv_score = 0.55\n elif ivr >= 30:\n iv_score = 0.25\n else:\n iv_score = 0.10\n iv_pts = round(iv_score * w[\"iv_rank\"], 2)\n\n # --- RSI Neutrality ---\n rsi_dist = neutrality.rsi_distance_from_50\n if rsi_dist \u003c= 5:\n rsi_score = 1.0\n elif rsi_dist \u003c= 10:\n rsi_score = 0.75\n elif rsi_dist \u003c= 15:\n rsi_score = 0.40\n elif rsi_dist \u003c= 20:\n rsi_score = 0.20\n else:\n rsi_score = 0.05\n rsi_pts = round(rsi_score * w[\"rsi_neutrality\"], 2)\n\n # --- ADX Range-Bound ---\n adx = neutrality.adx_value\n if adx \u003c 15:\n adx_score = 0.60\n elif adx \u003c= 20:\n adx_score = 1.0\n elif adx \u003c= 25:\n adx_score = 0.75\n elif adx \u003c= 30:\n adx_score = 0.35\n else:\n adx_score = 0.10\n adx_pts = round(adx_score * w[\"adx_range\"], 2)\n\n # --- Price Position ---\n pctb_dist = neutrality.percent_b_distance_from_50\n if pctb_dist \u003c= 0.10:\n pos_score = 1.0\n elif pctb_dist \u003c= 0.20:\n pos_score = 0.65\n elif pctb_dist \u003c= 0.30:\n pos_score = 0.30\n else:\n pos_score = 0.10\n pos_pts = round(pos_score * w[\"price_position\"], 2)\n\n # --- MACD Neutrality ---\n macd_pct = neutrality.macd_hist_pct_of_price\n if macd_pct \u003c 0.05:\n macd_score = 1.0\n elif macd_pct \u003c 0.10:\n macd_score = 0.70\n elif macd_pct \u003c 0.20:\n macd_score = 0.35\n else:\n macd_score = 0.10\n macd_pts = round(macd_score * w[\"macd_neutrality\"], 2)\n\n total = iv_pts + rsi_pts + adx_pts + pos_pts + macd_pts\n breakdown = {\n \"iv_rank\": iv_pts,\n \"rsi_neutrality\": rsi_pts,\n \"adx_range\": adx_pts,\n \"price_position\": pos_pts,\n \"macd_neutrality\": macd_pts,\n }\n\n return round(total, 2), breakdown\n\n\n# =============================================================================\n# Scoring Functions — Butterfly\n# =============================================================================\n\ndef score_butterfly(\n df: pd.DataFrame,\n squeeze: SqueezeSignal,\n neutrality: NeutralitySignal,\n) -> tuple[float, dict[str, float]]:\n \"\"\"\n Score a Long Butterfly setup.\n\n Returns:\n (total_score, component_breakdown)\n\n Scoring Table:\n ──────────────────────────────────────────────────────────────────\n BB Squeeze (30 pts): Volatility compression\n Percentile \u003c10 → 1.0 10-25 → 0.80 25-40 → 0.50\n 40-60 → 0.25 >60 → 0.05\n Bonus: squeeze_duration >10 → +0.10 (capped at 1.0)\n\n RSI Neutrality (25 pts): Dead center\n 45-55 → 1.0 42-45/55-58 → 0.75 40-42/58-60 → 0.50\n 35-40/60-65 → 0.20 \u003c35/>65 → 0.05\n\n ADX Weakness (20 pts): No trend\n \u003c15 → 1.0 15-18 → 0.80 18-22 → 0.50\n 22-28 → 0.20 >28 → 0.05\n\n Price Centering (15 pts): %B near 0.50\n ±0.05 → 1.0 ±0.10 → 0.75 ±0.15 → 0.45\n ±0.25 → 0.20 >±0.25 → 0.05\n\n MACD Flatness (10 pts): Histogram near zero\n \u003c0.03% → 1.0 0.03-0.07% → 0.70 0.07-0.15% → 0.30\n >0.15% → 0.05\n ──────────────────────────────────────────────────────────────────\n \"\"\"\n w = BUTTERFLY_WEIGHTS\n\n # --- BB Squeeze ---\n pctile = squeeze.bandwidth_percentile\n if pctile \u003c 10:\n sq_score = 1.0\n elif pctile \u003c 25:\n sq_score = 0.80\n elif pctile \u003c 40:\n sq_score = 0.50\n elif pctile \u003c 60:\n sq_score = 0.25\n else:\n sq_score = 0.05\n\n # Duration bonus\n if squeeze.squeeze_duration > 10:\n sq_score = min(1.0, sq_score + 0.10)\n elif squeeze.squeeze_duration > 5:\n sq_score = min(1.0, sq_score + 0.05)\n\n sq_pts = round(sq_score * w[\"squeeze\"], 2)\n\n # --- RSI Neutrality (tighter than iron condor) ---\n rsi_dist = neutrality.rsi_distance_from_50\n if rsi_dist \u003c= 5:\n rsi_score = 1.0\n elif rsi_dist \u003c= 8:\n rsi_score = 0.75\n elif rsi_dist \u003c= 10:\n rsi_score = 0.50\n elif rsi_dist \u003c= 15:\n rsi_score = 0.20\n else:\n rsi_score = 0.05\n rsi_pts = round(rsi_score * w[\"rsi_neutrality\"], 2)\n\n # --- ADX Weakness ---\n adx = neutrality.adx_value\n if adx \u003c 15:\n adx_score = 1.0\n elif adx \u003c 18:\n adx_score = 0.80\n elif adx \u003c 22:\n adx_score = 0.50\n elif adx \u003c 28:\n adx_score = 0.20\n else:\n adx_score = 0.05\n adx_pts = round(adx_score * w[\"adx_weakness\"], 2)\n\n # --- Price Centering (tighter than iron condor) ---\n pctb_dist = neutrality.percent_b_distance_from_50\n if pctb_dist \u003c= 0.05:\n ctr_score = 1.0\n elif pctb_dist \u003c= 0.10:\n ctr_score = 0.75\n elif pctb_dist \u003c= 0.15:\n ctr_score = 0.45\n elif pctb_dist \u003c= 0.25:\n ctr_score = 0.20\n else:\n ctr_score = 0.05\n ctr_pts = round(ctr_score * w[\"price_centering\"], 2)\n\n # --- MACD Flatness ---\n macd_pct = neutrality.macd_hist_pct_of_price\n if macd_pct \u003c 0.03:\n macd_score = 1.0\n elif macd_pct \u003c 0.07:\n macd_score = 0.70\n elif macd_pct \u003c 0.15:\n macd_score = 0.30\n else:\n macd_score = 0.05\n macd_pts = round(macd_score * w[\"macd_flatness\"], 2)\n\n total = sq_pts + rsi_pts + adx_pts + ctr_pts + macd_pts\n breakdown = {\n \"squeeze\": sq_pts,\n \"rsi_neutrality\": rsi_pts,\n \"adx_weakness\": adx_pts,\n \"price_centering\": ctr_pts,\n \"macd_flatness\": macd_pts,\n }\n\n return round(total, 2), breakdown\n\n\n# =============================================================================\n# Scoring Functions — Calendar Spread\n# =============================================================================\n\ndef score_calendar(\n df: pd.DataFrame,\n iv_ts: IVTermStructureSignal,\n neutrality: NeutralitySignal,\n) -> tuple[float, dict[str, float]]:\n \"\"\"\n Score a Calendar Spread setup.\n\n Returns:\n (total_score, component_breakdown)\n\n Scoring Table:\n ──────────────────────────────────────────────────────────────────\n IV Term Structure (30 pts): Front vs Back IV\n Inverted >15% → 1.0 Inverted 10-15% → 0.85\n Inverted 5-10% → 0.70 Flat (±5%) → 0.35\n Normal (back > front) → 0.10\n Unavailable → 0.35 (neutral default)\n\n Price Stability (20 pts): Low recent BBW\n BBW percentile \u003c20 → 1.0 20-35 → 0.80 35-50 → 0.55\n 50-70 → 0.30 >70 → 0.10\n\n RSI Neutrality (20 pts): Price staying put\n 45-55 → 1.0 40-45/55-60 → 0.75 35-40/60-65 → 0.40\n 30-35/65-70 → 0.20 \u003c30/>70 → 0.05\n\n ADX Moderate (15 pts): Some structure, not trending hard\n \u003c12 → 0.40 12-18 → 0.80 18-25 → 1.0\n 25-32 → 0.50 >32 → 0.15\n\n MACD Neutrality (15 pts): No directional acceleration\n \u003c0.05% → 1.0 0.05-0.10% → 0.70 0.10-0.20% → 0.35\n >0.20% → 0.10\n ──────────────────────────────────────────────────────────────────\n \"\"\"\n w = CALENDAR_WEIGHTS\n\n # --- IV Term Structure ---\n if iv_ts.iv_differential_pct is not None:\n diff = iv_ts.iv_differential_pct\n if diff > 15:\n ivts_score = 1.0\n elif diff > 10:\n ivts_score = 0.85\n elif diff > 5:\n ivts_score = 0.70\n elif diff > -5:\n ivts_score = 0.35 # flat\n else:\n ivts_score = 0.10 # normal (back > front)\n else:\n ivts_score = 0.35 # no data → neutral default\n ivts_pts = round(ivts_score * w[\"iv_term_structure\"], 2)\n\n # --- Price Stability (use BB Width percentile) ---\n bb_suffix = f\"{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\"\n bbw_col = f\"BBB_{bb_suffix}\"\n bbw = df[bbw_col].dropna()\n lookback = min(252, len(bbw))\n bbw_window = bbw.iloc[-lookback:]\n current_bbw = float(bbw.iloc[-1])\n stability_pctile = float((bbw_window \u003c current_bbw).sum() / len(bbw_window) * 100)\n\n if stability_pctile \u003c 20:\n stab_score = 1.0\n elif stability_pctile \u003c 35:\n stab_score = 0.80\n elif stability_pctile \u003c 50:\n stab_score = 0.55\n elif stability_pctile \u003c 70:\n stab_score = 0.30\n else:\n stab_score = 0.10\n stab_pts = round(stab_score * w[\"price_stability\"], 2)\n\n # --- RSI Neutrality ---\n rsi_dist = neutrality.rsi_distance_from_50\n if rsi_dist \u003c= 5:\n rsi_score = 1.0\n elif rsi_dist \u003c= 10:\n rsi_score = 0.75\n elif rsi_dist \u003c= 15:\n rsi_score = 0.40\n elif rsi_dist \u003c= 20:\n rsi_score = 0.20\n else:\n rsi_score = 0.05\n rsi_pts = round(rsi_score * w[\"rsi_neutrality\"], 2)\n\n # --- ADX Moderate (calendar likes some structure, not chaos or strong trend) ---\n adx = neutrality.adx_value\n if adx \u003c 12:\n adx_score = 0.40 # too choppy\n elif adx \u003c 18:\n adx_score = 0.80 # mild\n elif adx \u003c= 25:\n adx_score = 1.0 # ideal moderate range\n elif adx \u003c= 32:\n adx_score = 0.50 # getting trendy\n else:\n adx_score = 0.15 # strong trend — calendar at risk\n adx_pts = round(adx_score * w[\"adx_moderate\"], 2)\n\n # --- MACD Neutrality ---\n macd_pct = neutrality.macd_hist_pct_of_price\n if macd_pct \u003c 0.05:\n macd_score = 1.0\n elif macd_pct \u003c 0.10:\n macd_score = 0.70\n elif macd_pct \u003c 0.20:\n macd_score = 0.35\n else:\n macd_score = 0.10\n macd_pts = round(macd_score * w[\"macd_neutrality\"], 2)\n\n total = ivts_pts + stab_pts + rsi_pts + adx_pts + macd_pts\n breakdown = {\n \"iv_term_structure\": ivts_pts,\n \"price_stability\": stab_pts,\n \"rsi_neutrality\": rsi_pts,\n \"adx_moderate\": adx_pts,\n \"macd_neutrality\": macd_pts,\n }\n\n return round(total, 2), breakdown\n\n\n# =============================================================================\n# Strike Calculation\n# =============================================================================\n\ndef calculate_iron_condor_strikes(\n price: float,\n bb_upper: float,\n bb_middle: float,\n bb_lower: float,\n) -> IronCondorStrikes:\n \"\"\"\n Calculate Iron Condor strikes using Bollinger Band levels.\n\n Inner strikes (sell) at 1-sigma: midpoint between SMA and band edge.\n Outer strikes (buy) at 2-sigma: the actual BB band edges.\n\n The 1-sigma / 2-sigma structure gives a natural risk/reward framework:\n the sold strikes are within 1 standard deviation of the mean, and the\n bought wings provide protection at 2 standard deviations.\n \"\"\"\n # 1-sigma levels (midpoint between SMA and 2-sigma band)\n sigma_1_upper = (bb_middle + bb_upper) / 2.0\n sigma_1_lower = (bb_middle + bb_lower) / 2.0\n\n # Round to standard strikes\n put_short = _round_to_strike(sigma_1_lower, price)\n put_long = _round_to_strike(bb_lower, price)\n call_short = _round_to_strike(sigma_1_upper, price)\n call_long = _round_to_strike(bb_upper, price)\n\n # Ensure logical ordering\n if put_long >= put_short:\n put_long = put_short - _strike_interval(price)\n if call_long \u003c= call_short:\n call_long = call_short + _strike_interval(price)\n\n wing_width = put_short - put_long # same on both sides ideally\n\n return IronCondorStrikes(\n put_long=put_long,\n put_short=put_short,\n call_short=call_short,\n call_long=call_long,\n max_profit_low=put_short,\n max_profit_high=call_short,\n breakeven_lower=put_short, # approx (actual depends on net credit)\n breakeven_upper=call_short, # approx\n wing_width=wing_width,\n description=(\n f\"SELL {put_short}P / BUY {put_long}P + \"\n f\"SELL {call_short}C / BUY {call_long}C. \"\n f\"Max profit zone: ${put_short}-${call_short}.\"\n ),\n )\n\n\ndef calculate_butterfly_strikes(\n price: float,\n bb_upper: float,\n bb_middle: float,\n bb_lower: float,\n) -> ButterflyStrikes:\n \"\"\"\n Calculate Butterfly strikes: Buy 1 low, Sell 2 middle, Buy 1 high.\n\n Center (sell 2) at SMA. Wings at 1-sigma (midpoints of BB bands).\n Equal-width butterfly ensures balanced risk.\n \"\"\"\n middle = _round_to_strike(bb_middle, price)\n\n # 1-sigma width\n sigma_1 = (bb_upper - bb_lower) / 4.0\n wing_width = max(_strike_interval(price), _round_to_strike(sigma_1, price))\n # Ensure wing_width is at least one strike interval\n if wing_width \u003c _strike_interval(price):\n wing_width = _strike_interval(price)\n\n lower = middle - wing_width\n upper = middle + wing_width\n\n return ButterflyStrikes(\n lower_long=lower,\n middle_short=middle,\n upper_long=upper,\n max_profit_price=middle,\n breakeven_lower=lower, # approx (actual depends on net debit)\n breakeven_upper=upper, # approx\n width=wing_width,\n description=(\n f\"BUY 1x {lower}C + SELL 2x {middle}C + BUY 1x {upper}C. \"\n f\"Max profit at ${middle} expiration.\"\n ),\n )\n\n\ndef calculate_calendar_strikes(\n price: float,\n iv_ts: IVTermStructureSignal,\n) -> CalendarStrikes:\n \"\"\"\n Calculate Calendar Spread strikes: same strike, different expirations.\n\n Strike = ATM (current price rounded to nearest strike interval).\n Sell front-month, buy back-month.\n \"\"\"\n strike = _round_to_strike(price, price)\n\n front_expiry = iv_ts.front_expiry or \"nearest_monthly\"\n back_expiry = iv_ts.back_expiry or \"next_monthly\"\n\n # Theta advantage description\n if iv_ts.iv_differential_pct is not None and iv_ts.iv_differential_pct > 5:\n theta_adv = (\n f\"Front IV ({iv_ts.front_iv}%) > Back IV ({iv_ts.back_iv}%) \"\n f\"by {iv_ts.iv_differential_pct:.1f}%. Strong theta crush advantage.\"\n )\n elif iv_ts.iv_differential_pct is not None:\n theta_adv = (\n f\"IV differential: {iv_ts.iv_differential_pct:.1f}%. \"\n f\"Moderate theta advantage.\"\n )\n else:\n theta_adv = \"IV data unavailable. Using HV proxy or neutral assumption.\"\n\n return CalendarStrikes(\n strike=strike,\n front_expiry=front_expiry,\n back_expiry=back_expiry,\n front_iv=iv_ts.front_iv,\n back_iv=iv_ts.back_iv,\n iv_differential_pct=iv_ts.iv_differential_pct,\n theta_advantage=theta_adv,\n description=(\n f\"SELL {strike}C ({front_expiry}) / BUY {strike}C ({back_expiry}). \"\n f\"Harvest theta decay on front leg while back leg retains value.\"\n ),\n )\n\n\ndef _strike_interval(price: float) -> float:\n \"\"\"Return standard option strike interval for a given stock price.\"\"\"\n if price \u003c 25:\n return 1.0\n elif price \u003c 50:\n return 1.0\n elif price \u003c 200:\n return 5.0\n else:\n return 5.0\n\n\n# =============================================================================\n# Rationale Builder\n# =============================================================================\n\ndef build_multi_leg_rationale(\n strategy: MultiLegStrategyType,\n result: MultiLegResult,\n breakdown: dict[str, float],\n) -> list[str]:\n \"\"\"\n Generate a human-readable rationale for the multi-leg conviction score.\n \"\"\"\n lines: list[str] = []\n\n # Header\n lines.append(f\"Strategy: {strategy.label} ({strategy.philosophy})\")\n lines.append(f\"Ideal Setup: {strategy.ideal_setup}\")\n lines.append(f\"Legs: {strategy.legs}\")\n lines.append(\"\")\n lines.append(\n f\"Score: {result.conviction_score:.1f}/100 -> {result.tier}\"\n )\n lines.append(\"\")\n\n # Volume\n vol_adj_sign = \"+\" if result.volume_adjustment >= 0 else \"\"\n lines.append(\n f\"Volume: RV={result.relative_volume} \"\n f\"({vol_adj_sign}{result.volume_adjustment:.0f} adjustment)\"\n )\n if result.relative_volume > 1.5:\n lines.append(\n \" WARNING: High volume may signal directional move — \"\n \"non-directional strategies at risk\"\n )\n lines.append(\"\")\n\n # Strategy-specific components\n if strategy == MultiLegStrategyType.IRON_CONDOR:\n _rationale_iron_condor(lines, result, breakdown)\n elif strategy == MultiLegStrategyType.BUTTERFLY:\n _rationale_butterfly(lines, result, breakdown)\n elif strategy == MultiLegStrategyType.CALENDAR:\n _rationale_calendar(lines, result, breakdown)\n\n return lines\n\n\ndef _rationale_iron_condor(\n lines: list[str],\n result: MultiLegResult,\n breakdown: dict[str, float],\n) -> None:\n \"\"\"Append iron condor-specific rationale lines.\"\"\"\n w = IRON_CONDOR_WEIGHTS\n iv = result.iv_rank\n n = result.neutrality\n\n lines.append(f\"[IV Rank +{breakdown['iv_rank']:.1f}/{w['iv_rank']}]\")\n if iv:\n lines.append(\n f\" IV Rank (BBW proxy): {iv.iv_rank:.0f}% ({iv.regime})\"\n )\n lines.append(\n f\" BBW: {iv.current_bbw:.2f} \"\n f\"(1Y range: {iv.bbw_1y_low:.2f} - {iv.bbw_1y_high:.2f})\"\n )\n if iv.iv_rank >= 70:\n lines.append(\" Premiums are RICH — ideal for credit strategy\")\n elif iv.iv_rank \u003c 30:\n lines.append(\" Premiums are THIN — poor risk/reward for credit\")\n\n lines.append(f\"[RSI Neutrality +{breakdown['rsi_neutrality']:.1f}/{w['rsi_neutrality']}]\")\n if n:\n lines.append(\n f\" RSI({RSI_LENGTH}) = {n.rsi_value} \"\n f\"(distance from 50: {n.rsi_distance_from_50:.1f})\"\n )\n\n lines.append(f\"[ADX Range +{breakdown['adx_range']:.1f}/{w['adx_range']}]\")\n if n:\n lines.append(f\" ADX({ADX_LENGTH}) = {n.adx_value}\")\n if n.adx_value \u003c 20:\n lines.append(\" Range-bound environment confirmed\")\n elif n.adx_value > 30:\n lines.append(\" WARNING: Strong trend detected — condor at risk\")\n\n lines.append(\n f\"[Price Position +{breakdown['price_position']:.1f}/{w['price_position']}]\"\n )\n if n:\n lines.append(\n f\" %B = {n.percent_b:.4f} \"\n f\"(distance from center: {n.percent_b_distance_from_50:.4f})\"\n )\n\n lines.append(\n f\"[MACD Neutrality +{breakdown['macd_neutrality']:.1f}/{w['macd_neutrality']}]\"\n )\n if n:\n lines.append(\n f\" |Histogram|/Price = {n.macd_hist_pct_of_price:.4f}%\"\n )\n\n # Strikes\n if result.iron_condor_strikes:\n s = result.iron_condor_strikes\n lines.append(\"\")\n lines.append(\"Strikes:\")\n lines.append(f\" BUY {s.put_long}P | SELL {s.put_short}P\")\n lines.append(f\" SELL {s.call_short}C | BUY {s.call_long}C\")\n lines.append(\n f\" Max Profit Zone: ${s.max_profit_low} - ${s.max_profit_high}\"\n )\n lines.append(f\" Wing Width: ${s.wing_width:.2f}\")\n\n\ndef _rationale_butterfly(\n lines: list[str],\n result: MultiLegResult,\n breakdown: dict[str, float],\n) -> None:\n \"\"\"Append butterfly-specific rationale lines.\"\"\"\n w = BUTTERFLY_WEIGHTS\n sq = result.squeeze\n n = result.neutrality\n\n lines.append(f\"[BB Squeeze +{breakdown['squeeze']:.1f}/{w['squeeze']}]\")\n if sq:\n lines.append(\n f\" Bandwidth: {sq.bandwidth:.4f} \"\n f\"(percentile: {sq.bandwidth_percentile:.0f}%)\"\n )\n if sq.is_squeezing:\n lines.append(\n f\" SQUEEZE ACTIVE — {sq.squeeze_duration} consecutive bars\"\n )\n else:\n lines.append(\" No active squeeze\")\n\n lines.append(f\"[RSI Neutrality +{breakdown['rsi_neutrality']:.1f}/{w['rsi_neutrality']}]\")\n if n:\n lines.append(\n f\" RSI({RSI_LENGTH}) = {n.rsi_value} \"\n f\"(distance from 50: {n.rsi_distance_from_50:.1f})\"\n )\n\n lines.append(f\"[ADX Weakness +{breakdown['adx_weakness']:.1f}/{w['adx_weakness']}]\")\n if n:\n lines.append(f\" ADX({ADX_LENGTH}) = {n.adx_value}\")\n if n.adx_value \u003c 15:\n lines.append(\" Very weak trend — ideal for butterfly\")\n\n lines.append(\n f\"[Price Centering +{breakdown['price_centering']:.1f}/{w['price_centering']}]\"\n )\n if n:\n lines.append(\n f\" %B = {n.percent_b:.4f} \"\n f\"(distance from 0.50: {n.percent_b_distance_from_50:.4f})\"\n )\n\n lines.append(\n f\"[MACD Flatness +{breakdown['macd_flatness']:.1f}/{w['macd_flatness']}]\"\n )\n if n:\n lines.append(\n f\" |Histogram|/Price = {n.macd_hist_pct_of_price:.4f}%\"\n )\n\n # Strikes\n if result.butterfly_strikes:\n s = result.butterfly_strikes\n lines.append(\"\")\n lines.append(\"Strikes:\")\n lines.append(\n f\" BUY 1x {s.lower_long}C | \"\n f\"SELL 2x {s.middle_short}C | \"\n f\"BUY 1x {s.upper_long}C\"\n )\n lines.append(f\" Max Profit Price: ${s.max_profit_price}\")\n lines.append(\n f\" Profit Zone: ~${s.breakeven_lower} - ${s.breakeven_upper}\"\n )\n lines.append(f\" Wing Width: ${s.width:.2f}\")\n\n\ndef _rationale_calendar(\n lines: list[str],\n result: MultiLegResult,\n breakdown: dict[str, float],\n) -> None:\n \"\"\"Append calendar spread-specific rationale lines.\"\"\"\n w = CALENDAR_WEIGHTS\n ivts = result.iv_term_structure\n n = result.neutrality\n\n lines.append(\n f\"[IV Term Structure +{breakdown['iv_term_structure']:.1f}/{w['iv_term_structure']}]\"\n )\n if ivts:\n lines.append(f\" Data Source: {ivts.data_source}\")\n if ivts.front_iv is not None and ivts.back_iv is not None:\n lines.append(\n f\" Front IV: {ivts.front_iv}% | Back IV: {ivts.back_iv}%\"\n )\n lines.append(\n f\" Differential: {ivts.iv_differential_pct:+.1f}%\"\n )\n if ivts.is_inverted:\n lines.append(\n \" INVERTED TERM STRUCTURE — calendar opportunity confirmed\"\n )\n else:\n lines.append(\" Normal term structure — reduced edge\")\n else:\n lines.append(\" IV data unavailable — using neutral assumption\")\n\n lines.append(\n f\"[Price Stability +{breakdown['price_stability']:.1f}/{w['price_stability']}]\"\n )\n lines.append(f\" Low recent volatility favours calendar hold\")\n\n lines.append(\n f\"[RSI Neutrality +{breakdown['rsi_neutrality']:.1f}/{w['rsi_neutrality']}]\"\n )\n if n:\n lines.append(\n f\" RSI({RSI_LENGTH}) = {n.rsi_value} \"\n f\"(distance from 50: {n.rsi_distance_from_50:.1f})\"\n )\n\n lines.append(\n f\"[ADX Moderate +{breakdown['adx_moderate']:.1f}/{w['adx_moderate']}]\"\n )\n if n:\n lines.append(f\" ADX({ADX_LENGTH}) = {n.adx_value}\")\n\n lines.append(\n f\"[MACD Neutrality +{breakdown['macd_neutrality']:.1f}/{w['macd_neutrality']}]\"\n )\n if n:\n lines.append(\n f\" |Histogram|/Price = {n.macd_hist_pct_of_price:.4f}%\"\n )\n\n # Strikes\n if result.calendar_strikes:\n s = result.calendar_strikes\n lines.append(\"\")\n lines.append(\"Strikes:\")\n lines.append(f\" Strike: ${s.strike}\")\n lines.append(f\" SELL {s.front_expiry} | BUY {s.back_expiry}\")\n lines.append(f\" Theta Advantage: {s.theta_advantage}\")\n\n\n# =============================================================================\n# Directional Risk Gate\n# =============================================================================\n\ndef _compute_directional_penalty(neutrality: NeutralitySignal) -> float:\n \"\"\"\n Apply a penalty when conditions are too directional for neutral strategies.\n\n Strong directional signals (RSI extreme + high ADX) get a penalty\n that caps the conviction score, preventing false confidence.\n \"\"\"\n penalty = 0.0\n\n # RSI extreme penalty\n if neutrality.rsi_distance_from_50 > 25:\n penalty -= 15.0\n elif neutrality.rsi_distance_from_50 > 20:\n penalty -= 10.0\n\n # High ADX penalty (strong trend)\n if neutrality.adx_value > 35:\n penalty -= 10.0\n elif neutrality.adx_value > 30:\n penalty -= 5.0\n\n return penalty\n\n\n# =============================================================================\n# Main Analysis Pipeline\n# =============================================================================\n\ndef analyse_multi_leg(\n ticker: str,\n strategy: MultiLegStrategyType,\n period: str = \"2y\",\n interval: str = \"1d\",\n) -> MultiLegResult:\n \"\"\"\n Run the full multi-leg strategy analysis pipeline.\n\n Steps:\n 1. Fetch OHLCV data\n 2. Compute all technical indicators\n 3. Compute strategy-specific signals (IV rank, squeeze, term structure)\n 4. Score the setup\n 5. Apply volume adjustment and directional penalty\n 6. Calculate suggested strikes\n 7. Build rationale\n\n Parameters:\n ticker: Stock symbol (e.g. 'AAPL', 'SPY')\n strategy: Multi-leg strategy to evaluate\n period: Data lookback period (default '2y')\n interval: Candle interval (default '1d')\n\n Returns:\n MultiLegResult with full analysis\n \"\"\"\n # Step 1: Fetch data\n df = fetch_ohlcv(ticker, period=period, interval=interval)\n\n # Step 2: Compute indicators (reuse from main engine)\n df = compute_all_indicators(df)\n\n # Step 3: Get current price and BB values\n price = round(float(df.iloc[-1][\"Close\"]), 2)\n bb_suffix = f\"{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\"\n bb_upper = float(df.iloc[-1][f\"BBU_{bb_suffix}\"])\n bb_middle = float(df.iloc[-1][f\"BBM_{bb_suffix}\"])\n bb_lower = float(df.iloc[-1][f\"BBL_{bb_suffix}\"])\n\n # Step 4: Compute shared signals\n neutrality = compute_neutrality(df)\n\n # Step 5: Strategy-specific scoring\n iv_rank_sig = None\n squeeze_sig = None\n iv_ts_sig = None\n ic_strikes = None\n bf_strikes = None\n cal_strikes = None\n breakdown = {}\n\n if strategy == MultiLegStrategyType.IRON_CONDOR:\n iv_rank_sig = compute_iv_rank(df)\n iv_rank_sig.component_score = 0 # will be set by scorer\n base_score, breakdown = score_iron_condor(df, iv_rank_sig, neutrality)\n ic_strikes = calculate_iron_condor_strikes(\n price, bb_upper, bb_middle, bb_lower\n )\n\n elif strategy == MultiLegStrategyType.BUTTERFLY:\n squeeze_sig = compute_squeeze(df)\n base_score, breakdown = score_butterfly(df, squeeze_sig, neutrality)\n bf_strikes = calculate_butterfly_strikes(\n price, bb_upper, bb_middle, bb_lower\n )\n\n elif strategy == MultiLegStrategyType.CALENDAR:\n iv_ts_sig = fetch_iv_term_structure(ticker)\n iv_ts_sig = _enrich_iv_with_hv_proxy(iv_ts_sig, df)\n base_score, breakdown = score_calendar(df, iv_ts_sig, neutrality)\n cal_strikes = calculate_calendar_strikes(price, iv_ts_sig)\n\n else:\n raise ValueError(f\"Unknown multi-leg strategy: {strategy}\")\n\n # Step 6: Volume adjustment\n rel_vol, vol_adj = score_volume_neutral(df)\n\n # Step 7: Directional penalty\n dir_penalty = _compute_directional_penalty(neutrality)\n\n # Step 8: Final score\n conviction = base_score + vol_adj + dir_penalty\n conviction = round(max(0.0, min(100.0, conviction)), 2)\n tier = ConvictionTier.from_score(conviction)\n\n # Step 9: Build result\n result = MultiLegResult(\n ticker=ticker.upper(),\n strategy=strategy.value,\n strategy_label=strategy.label,\n strategy_type=\"multi_leg\",\n price=price,\n conviction_score=conviction,\n tier=tier.value,\n iv_rank=iv_rank_sig,\n neutrality=neutrality,\n squeeze=squeeze_sig,\n iv_term_structure=iv_ts_sig,\n relative_volume=rel_vol,\n volume_adjustment=vol_adj,\n iron_condor_strikes=ic_strikes,\n butterfly_strikes=bf_strikes,\n calendar_strikes=cal_strikes,\n data_quality=\"HIGH\",\n )\n\n # Step 10: Rationale\n result.rationale = build_multi_leg_rationale(strategy, result, breakdown)\n\n return result\n\n\n# =============================================================================\n# Report Printer\n# =============================================================================\n\ndef print_multi_leg_report(result: MultiLegResult) -> None:\n \"\"\"Pretty-print a multi-leg conviction report to stdout.\"\"\"\n\n print()\n print(\"=\" * 70)\n print(f\" CONVICTION REPORT: {result.ticker} (v{__version__})\")\n print(f\" Strategy: {result.strategy_label}\")\n print(\"=\" * 70)\n print(f\" Price: ${result.price}\")\n print(f\" Quality: {result.data_quality}\")\n print(f\" Conviction: {result.conviction_score:.1f} / 100\")\n print(f\" Action Tier: {result.tier}\")\n print(\"-\" * 70)\n for line in result.rationale:\n print(f\" {line}\")\n print(\"=\" * 70)\n print()","content_type":"text/x-python; charset=utf-8","language":"python","size":58944,"content_sha256":"a18a5901eccd47d1973dcf969a4e92be79cc7aaa80fb2291ce4f20d2c477e209"},{"filename":"scripts/numba.py","content":"# Mock numba module for Python 3.14+ compatibility\n# pandas_ta requires numba but numba doesn't support Python 3.14 yet\n# This provides a fallback that just executes the function normally\n\ndef njit(*args, **kwargs):\n \"\"\"No-Just-In-Time compiler decorator fallback.\n \n When numba is not available, this simply returns the original function\n without compilation, allowing pandas_ta to work in pure Python mode.\n \"\"\"\n def decorator(func):\n return func\n \n # Handle both @njit and @njit() syntax\n if args and callable(args[0]):\n return args[0]\n return decorator\n\n# Other numba exports that pandas_ta might use\nclass types:\n pass\n\nclass cuda:\n pass\n\ndef jit(*args, **kwargs):\n def decorator(func):\n return func\n if args and callable(args[0]):\n return args[0]\n return decorator\n\ndef vectorize(*args, **kwargs):\n def decorator(func):\n return func\n if args and callable(args[0]):\n return args[0]\n return decorator\n\ndef guvectorize(*args, **kwargs):\n def decorator(func):\n return func\n if args and callable(args[0]):\n return args[0]\n return decorator\n","content_type":"text/x-python; charset=utf-8","language":"python","size":1160,"content_sha256":"c4578e726aea85838b14044b9920803dc5c5109bf713c47f83bdccf704018aa3"},{"filename":"scripts/options_math.py","content":"\"\"\"\nCore Options Mathematics Module\n\nProvides Black-Scholes pricing, Greeks calculation, Probability of Profit (POP),\nand volatility analysis for options trading.\n\nBuilt from first principles for mathematical rigor.\n\"\"\"\n\nimport numpy as np\nfrom scipy.stats import norm\nfrom scipy.optimize import brentq\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, List\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Account Constraints (Defaults — override via CLI --account)\nDEFAULT_ACCOUNT_TOTAL = 500.0\nMAX_RISK_PER_TRADE = 100.0\nMIN_CASH_BUFFER = 150.0\n# Legacy aliases for backward compatibility\nACCOUNT_TOTAL = DEFAULT_ACCOUNT_TOTAL\nAVAILABLE_CAPITAL = DEFAULT_ACCOUNT_TOTAL - MIN_CASH_BUFFER\n\n\n@dataclass\nclass OptionData:\n \"\"\"Container for option contract data\"\"\"\n strike: float\n expiration: str\n dte: int\n bid: float\n ask: float\n last_price: float\n implied_vol: float\n volume: int\n open_interest: int = 0\n underlying_price: float = 0.0\n option_type: str = 'call' # 'call' or 'put'\n \n @property\n def mid_price(self) -> float:\n \"\"\"Midpoint of bid-ask\"\"\"\n if self.bid > 0 and self.ask > 0:\n return (self.bid + self.ask) / 2\n return self.last_price\n \n @property\n def spread(self) -> float:\n \"\"\"Bid-ask spread\"\"\"\n if self.bid > 0 and self.ask > 0:\n return self.ask - self.bid\n return 0.0\n \n @property\n def spread_pct(self) -> float:\n \"\"\"Bid-ask spread as percentage of mid price\"\"\"\n mid = self.mid_price\n if mid > 0:\n return self.spread / mid\n return 1.0\n\n\n@dataclass \nclass Greeks:\n \"\"\"Option Greeks\"\"\"\n delta: float\n gamma: float\n theta: float\n vega: float\n rho: float\n \n \nclass BlackScholes:\n \"\"\"\n Black-Scholes-Merton option pricing model.\n Implements from first principles without external dependencies.\n \"\"\"\n \n @staticmethod\n def d1(S: float, K: float, T: float, r: float, sigma: float) -> float:\n \"\"\"\n Calculate d1 for Black-Scholes\n \n S: Underlying price (must be positive)\n K: Strike price (must be positive)\n T: Time to expiration in years (must be non-negative)\n r: Risk-free rate\n sigma: Implied volatility (must be positive)\n \n Raises:\n ValueError: If S \u003c= 0, K \u003c= 0, T \u003c 0, or sigma \u003c= 0\n \"\"\"\n if S \u003c= 0:\n raise ValueError(f\"Spot price must be positive, got {S}\")\n if K \u003c= 0:\n raise ValueError(f\"Strike price must be positive, got {K}\")\n if T \u003c 0:\n raise ValueError(f\"Time to expiration must be non-negative, got {T}\")\n if sigma \u003c= 0:\n raise ValueError(f\"Volatility must be positive, got {sigma}\")\n return (np.log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))\n \n @staticmethod\n def d2(S: float, K: float, T: float, r: float, sigma: float) -> float:\n \"\"\"Calculate d2 for Black-Scholes\"\"\"\n return BlackScholes.d1(S, K, T, r, sigma) - sigma * np.sqrt(T)\n \n @staticmethod\n def call_price(S: float, K: float, T: float, r: float, sigma: float) -> float:\n \"\"\"Calculate call option price\"\"\"\n if T \u003c= 0:\n return max(0, S - K)\n if sigma \u003c= 0:\n return max(0, S - K)\n \n d1 = BlackScholes.d1(S, K, T, r, sigma)\n d2 = BlackScholes.d2(S, K, T, r, sigma)\n \n return S * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2)\n \n @staticmethod\n def put_price(S: float, K: float, T: float, r: float, sigma: float) -> float:\n \"\"\"Calculate put option price\"\"\"\n if T \u003c= 0:\n return max(0, K - S)\n if sigma \u003c= 0:\n return max(0, K - S)\n \n d1 = BlackScholes.d1(S, K, T, r, sigma)\n d2 = BlackScholes.d2(S, K, T, r, sigma)\n \n return K * np.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)\n \n @staticmethod\n def calculate_greeks(S: float, K: float, T: float, r: float, \n sigma: float, option_type: str = 'call') -> Greeks:\n \"\"\"\n Calculate option Greeks using closed-form solutions\n \n All Greeks are per-share (not per-contract)\n \"\"\"\n if T \u003c= 0 or sigma \u003c= 0:\n # At expiration - delta is 0 or 1, others are 0\n if option_type == 'call':\n delta = 1.0 if S > K else 0.0\n else:\n delta = -1.0 if S \u003c K else 0.0\n return Greeks(delta=delta, gamma=0.0, theta=0.0, vega=0.0, rho=0.0)\n \n d1 = BlackScholes.d1(S, K, T, r, sigma)\n d2 = BlackScholes.d2(S, K, T, r, sigma)\n \n # Standard normal PDF\n nd1 = norm.pdf(d1)\n \n # Delta\n if option_type == 'call':\n delta = norm.cdf(d1)\n else:\n delta = norm.cdf(d1) - 1\n \n # Gamma (same for calls and puts)\n gamma = nd1 / (S * sigma * np.sqrt(T))\n \n # Theta (daily, not annual)\n if option_type == 'call':\n theta = -(S * nd1 * sigma) / (2 * np.sqrt(T)) - r * K * np.exp(-r * T) * norm.cdf(d2)\n else:\n theta = -(S * nd1 * sigma) / (2 * np.sqrt(T)) + r * K * np.exp(-r * T) * norm.cdf(-d2)\n theta = theta / 365.0 # Convert to daily\n \n # Vega (per 1% change in vol)\n vega = S * nd1 * np.sqrt(T) / 100.0\n \n # Rho (per 1% change in rate)\n if option_type == 'call':\n rho = K * T * np.exp(-r * T) * norm.cdf(d2) / 100.0\n else:\n rho = -K * T * np.exp(-r * T) * norm.cdf(-d2) / 100.0\n \n return Greeks(delta=delta, gamma=gamma, theta=theta, vega=vega, rho=rho)\n \n @staticmethod\n def implied_vol(S: float, K: float, T: float, r: float, \n market_price: float, option_type: str = 'call',\n precision: float = 1e-6) -> Optional[float]:\n \"\"\"\n Calculate implied volatility using Brent's method\n \n Returns None if implied vol cannot be calculated\n \"\"\"\n if T \u003c= 0 or market_price \u003c= 0:\n return None\n \n # Intrinsic value bounds\n if option_type == 'call':\n intrinsic = max(0, S - K)\n else:\n intrinsic = max(0, K - S)\n \n if market_price \u003c intrinsic:\n return None\n \n try:\n def price_diff(sigma):\n if sigma \u003c= 0:\n return float('inf')\n if option_type == 'call':\n return BlackScholes.call_price(S, K, T, r, sigma) - market_price\n else:\n return BlackScholes.put_price(S, K, T, r, sigma) - market_price\n \n # Try to find implied vol between 0.001 and 5.0 (0.1% to 500%)\n iv = brentq(price_diff, 0.001, 5.0, xtol=precision)\n return iv\n except (ValueError, RuntimeError):\n return None\n\n\nclass ProbabilityCalculator:\n \"\"\"\n Calculate probabilities of profit for various option strategies\n \"\"\"\n \n def __init__(self, risk_free_rate: float = 0.045):\n self.r = risk_free_rate\n \n def pop_single_leg(self, S: float, K: float, T: float, sigma: float,\n premium: float, option_type: str = 'call',\n position: str = 'long') -> float:\n \"\"\"\n Probability of Profit for single leg option\n \n For long call: POP = P(S_T > K + premium_paid)\n For short call: POP = P(S_T \u003c K + premium_received)\n etc.\n \"\"\"\n if T \u003c= 0 or sigma \u003c= 0:\n return 0.5\n \n # Breakeven price\n if option_type == 'call':\n if position == 'long':\n breakeven = K + premium\n z = (np.log(breakeven / S) - (self.r - 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))\n return 1 - norm.cdf(z) # P(S_T > breakeven)\n else: # short\n breakeven = K + premium\n z = (np.log(breakeven / S) - (self.r - 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))\n return norm.cdf(z) # P(S_T \u003c breakeven)\n else: # put\n if position == 'long':\n breakeven = K - premium\n z = (np.log(breakeven / S) - (self.r - 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))\n return norm.cdf(z) # P(S_T \u003c breakeven)\n else: # short\n breakeven = K - premium\n z = (np.log(breakeven / S) - (self.r - 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))\n return 1 - norm.cdf(z) # P(S_T > breakeven)\n \n def pop_vertical_spread(self, S: float, K1: float, K2: float, T: float, \n sigma: float, net_premium: float, \n spread_type: str = 'call_credit') -> float:\n \"\"\"\n Probability of Profit for vertical spreads\n \n spread_type: 'call_credit', 'call_debit', 'put_credit', 'put_debit'\n \"\"\"\n if T \u003c= 0 or sigma \u003c= 0:\n return 0.5\n \n # Breakeven depends on spread type\n if spread_type in ['call_credit', 'call_debit']:\n # Call spreads: breakeven = lower_strike + net_premium\n lower_strike = min(K1, K2)\n breakeven = lower_strike + net_premium\n else:\n # Put spreads: breakeven = higher_strike - net_premium\n higher_strike = max(K1, K2)\n breakeven = higher_strike - net_premium\n \n # Calculate probability\n if breakeven \u003c= 0 or S \u003c= 0:\n return 0.5\n drift = (self.r - 0.5 * sigma**2) * T\n vol = sigma * np.sqrt(T)\n if vol \u003c= 0:\n return 0.5\n z = (np.log(breakeven / S) - drift) / vol\n \n if spread_type in ['call_credit', 'put_debit']:\n # Profit when price stays below breakeven (call credit) or below (put debit)\n return norm.cdf(z)\n else:\n # Profit when price stays above breakeven (call debit) or above (put credit)\n return 1 - norm.cdf(z)\n \n def monte_carlo_pop_iron_condor(self, S: float, lower_be: float, upper_be: float,\n T: float, sigma: float, n_sims: int = 100000,\n steps_per_day: int = 1) -> float:\n \"\"\"\n Monte Carlo simulation for Iron Condor Probability of Profit.\n\n Unlike Black-Scholes which calculates probability AT expiration only,\n this simulates price paths and checks if price stays within bounds\n at ANY point (\"touch\" probability). Early touch of breakeven = loss.\n\n Uses Geometric Brownian Motion: dS/S = r*dt + sigma*sqrt(dt)*Z\n\n This matches how Tastytrade and other professional platforms calculate\n POP for Iron Condors, accounting for early breach risk.\n\n Args:\n S: Current underlying price\n lower_be: Lower breakeven price (put side)\n upper_be: Upper breakeven price (call side)\n T: Time to expiration in years\n sigma: Implied volatility (annualized)\n n_sims: Number of Monte Carlo simulations (default 100,000)\n steps_per_day: Number of time steps per trading day (default 1)\n\n Returns:\n Probability of profit (0.0 to 1.0) - proportion of paths that\n never touched either breakeven during the entire period.\n \"\"\"\n if T \u003c= 0 or sigma \u003c= 0 or lower_be >= upper_be or S \u003c= 0:\n return 0.5\n\n # Ensure reasonable bounds\n lower_be = max(lower_be, S * 0.5) # Sanity check\n upper_be = min(upper_be, S * 2.0) # Sanity check\n\n # Trading days to expiration (252 trading days per year)\n trading_days = max(int(T * 252), 1)\n n_steps = trading_days * steps_per_day\n dt = T / n_steps\n\n # Set random seed for reproducibility\n np.random.seed(42)\n\n # Geometric Brownian Motion parameters\n # dS/S = r*dt + sigma*sqrt(dt)*Z\n drift = (self.r - 0.5 * sigma**2) * dt\n diffusion = sigma * np.sqrt(dt)\n\n # Initialize all paths at current price\n # Shape: (n_sims,) - current price for each simulation\n prices = np.full(n_sims, S, dtype=np.float64)\n\n # Track which paths have remained within bounds (initially all True)\n in_bounds = np.ones(n_sims, dtype=bool)\n\n # Simulate path evolution step by step\n for _ in range(n_steps):\n # Generate random shocks for this step (only for paths still in bounds)\n Z = np.random.standard_normal(n_sims)\n\n # Update prices using GBM: S_t+1 = S_t * exp((r - 0.5*sigma^2)*dt + sigma*sqrt(dt)*Z)\n prices = prices * np.exp(drift + diffusion * Z)\n\n # Check for breaches - paths that exit bounds are marked as out\n in_bounds = in_bounds & (prices > lower_be) & (prices \u003c upper_be)\n\n # Early termination optimization: if all paths have breached, stop early\n if not np.any(in_bounds):\n return 0.0\n\n # POP = proportion of paths that never touched either breakeven\n return np.mean(in_bounds)\n\n def monte_carlo_pop_vertical(self, S: float, breakeven: float, T: float,\n sigma: float, spread_type: str = 'put_credit',\n n_sims: int = 100000) -> float:\n \"\"\"\n Monte Carlo simulation for vertical spread Probability of Profit at expiration.\n \n Unlike Black-Scholes closed-form which can underestimate POP due to \n log-normal distribution assumptions, this simulates price at expiration\n using geometric Brownian motion for more accurate results.\n \n Args:\n S: Current underlying price\n breakeven: Breakeven price at expiration\n T: Time to expiration in years\n sigma: Annualized volatility\n spread_type: 'put_credit' (profit above breakeven) or \n 'call_credit' (profit below breakeven)\n n_sims: Number of Monte Carlo simulations (default 100k for accuracy)\n \n Returns:\n Probability of profit (0.0 to 1.0)\n \"\"\"\n if T \u003c= 0 or sigma \u003c= 0 or S \u003c= 0 or breakeven \u003c= 0:\n return 0.5 if S > breakeven else 0.0\n \n # Set random seed for reproducibility\n np.random.seed(42)\n \n # GBM parameters for terminal price distribution\n # ln(S_T/S_0) ~ N((r - 0.5*sigma^2)*T, sigma^2*T)\n drift = (self.r - 0.5 * sigma**2) * T\n vol = sigma * np.sqrt(T)\n \n # Generate terminal prices directly (more efficient than path simulation)\n # S_T = S_0 * exp(drift + vol * Z)\n Z = np.random.standard_normal(n_sims)\n S_T = S * np.exp(drift + vol * Z)\n \n # Calculate POP based on spread type\n if spread_type == 'put_credit':\n # Profit when price stays above breakeven\n profitable = S_T >= breakeven\n elif spread_type == 'call_credit':\n # Profit when price stays below breakeven\n profitable = S_T \u003c= breakeven\n elif spread_type == 'put_debit':\n # Profit when price below breakeven\n profitable = S_T \u003c= breakeven\n elif spread_type == 'call_debit':\n # Profit when price above breakeven\n profitable = S_T >= breakeven\n else:\n return 0.5\n \n return np.mean(profitable)\n\n def expected_value(self, pop: float, max_profit: float, max_loss: float) -> float:\n \"\"\"\n Calculate Expected Value of a trade\n\n EV = (POP × max_profit) - ((1-POP) × max_loss)\n\n Returns 0.0 if inputs are non-finite or POP is out of range.\n \"\"\"\n if not (np.isfinite(pop) and np.isfinite(max_profit) and np.isfinite(max_loss)):\n return 0.0\n pop = max(0.0, min(1.0, pop))\n return (pop * max_profit) - ((1 - pop) * max_loss)\n \n def risk_adjusted_return(self, ev: float, max_loss: float, \n annualize_factor: float = 1.0) -> float:\n \"\"\"\n Calculate risk-adjusted return (Sharpe-like metric)\n \n EV per dollar of risk, optionally annualized\n \"\"\"\n if max_loss \u003c= 0:\n return 0.0\n return (ev / max_loss) * annualize_factor\n \n def monte_carlo_pop(self, S: float, T: float, sigma: float, \n payoff_func, n_sims: int = 100000) -> float:\n \"\"\"\n Monte Carlo simulation for POP\n \n payoff_func: function that takes array of terminal prices and returns \n array of payoffs (positive = profit)\n \"\"\"\n np.random.seed(42) # Reproducible results\n \n dt = T / 252 # Daily steps (trading days)\n n_steps = int(T)\n \n # Geometric Brownian Motion\n Z = np.random.standard_normal((n_steps, n_sims))\n \n drift = (self.r - 0.5 * sigma**2) * dt\n diffusion = sigma * np.sqrt(dt)\n \n prices = S * np.exp(np.cumsum(drift + diffusion * Z, axis=0))\n terminal_prices = prices[-1]\n \n payoffs = payoff_func(terminal_prices)\n profitable = payoffs > 0\n \n return np.mean(profitable)\n\n\nclass VolatilityAnalyzer:\n \"\"\"\n Analyze volatility surfaces, skew, and term structure\n \"\"\"\n \n def __init__(self):\n self.historical_vols = {} # Cache for historical vol calculations\n \n def calculate_iv_rank(self, current_iv: float, iv_history: List[float]) -> float:\n \"\"\"\n Calculate IV Rank: where current IV falls in historical range\n \n IVR = (current - min) / (max - min) * 100\n \"\"\"\n if not iv_history or len(iv_history) \u003c 2:\n return 50.0 # Neutral if no history\n \n min_iv = min(iv_history)\n max_iv = max(iv_history)\n \n if max_iv - min_iv \u003c 0.01:\n return 50.0\n \n ivr = (current_iv - min_iv) / (max_iv - min_iv) * 100\n return max(0, min(100, ivr))\n \n def calculate_iv_percentile(self, current_iv: float, iv_history: List[float]) -> float:\n \"\"\"\n Calculate IV Percentile: percentage of days IV was below current\n \"\"\"\n if not iv_history:\n return 50.0\n \n below = sum(1 for iv in iv_history if iv \u003c current_iv)\n return (below / len(iv_history)) * 100\n \n def skew_score(self, atm_iv: float, otm_put_iv: float, otm_call_iv: float) -> float:\n \"\"\"\n Calculate volatility skew score\n \n Positive = puts more expensive (fear of downside)\n Negative = calls more expensive (speculation/bullish)\n Near 0 = balanced\n \"\"\"\n if atm_iv \u003c= 0:\n return 0.0\n \n put_skew = (otm_put_iv - atm_iv) / atm_iv\n call_skew = (otm_call_iv - atm_iv) / atm_iv\n \n return put_skew - call_skew\n \n def term_structure_slope(self, ivs_by_dte: dict) -> float:\n \"\"\"\n Calculate term structure slope\n \n Positive = backwardation (near > far, fear/uncertainty)\n Negative = contango (far > near, normal)\n \"\"\"\n if len(ivs_by_dte) \u003c 2:\n return 0.0\n \n sorted_dtes = sorted(ivs_by_dte.keys())\n short_dte = sorted_dtes[0]\n long_dte = sorted_dtes[-1]\n \n short_iv = ivs_by_dte[short_dte]\n long_iv = ivs_by_dte[long_dte]\n \n if long_iv \u003c= 0:\n return 0.0\n \n # Annualized slope\n return (short_iv - long_iv) / long_iv * 100\n\n\n##############################################################################\n# Kelly Criterion for Options Trading\n##############################################################################\n\n@dataclass\nclass KellyResult:\n \"\"\"Result of Kelly criterion calculation.\"\"\"\n raw_fraction: float\n adjusted_fraction: float\n edge: float\n expected_value: float\n\n\ndef kelly_criterion(pop: float, win_amount: float, loss_amount: float) -> KellyResult:\n \"\"\"\n Calculate Kelly Criterion with validation.\n \n Args:\n pop: Probability of profit (0-1)\n win_amount: Average win amount\n loss_amount: Average loss amount (positive number)\n \n Returns:\n KellyResult with raw/adjusted fractions and edge\n \n Raises:\n ValueError: If inputs are invalid\n \"\"\"\n if not 0 \u003c= pop \u003c= 1:\n raise ValueError(f\"POP must be in [0,1], got {pop}\")\n if win_amount \u003c= 0:\n raise ValueError(f\"Win amount must be positive, got {win_amount}\")\n if loss_amount \u003c= 0:\n raise ValueError(f\"Loss amount must be positive, got {loss_amount}\")\n \n loss_prob = 1 - pop\n odds = win_amount / loss_amount\n \n # Raw Kelly\n f_star = (pop * odds - loss_prob) / odds if odds > 0 else 0.0\n \n # Edge calculation\n ev = pop * win_amount - loss_prob * loss_amount\n edge = ev / loss_amount if loss_amount > 0 else 0.0\n \n # Apply half Kelly and cap at 25%\n adjusted = f_star * 0.5\n adjusted = max(0.0, min(adjusted, 0.25))\n \n return KellyResult(\n raw_fraction=f_star,\n adjusted_fraction=adjusted,\n edge=edge,\n expected_value=ev\n )\n\n\ndef fits_account_constraints(max_loss: float, margin_required: float = 0,\n account_total: float = DEFAULT_ACCOUNT_TOTAL,\n max_risk_per_trade: float = MAX_RISK_PER_TRADE,\n min_cash_buffer: float = MIN_CASH_BUFFER) -> bool:\n \"\"\"\n Check if trade fits within hard account constraints\n \n account_total: Total account balance (passed via --account CLI flag)\n max_risk_per_trade: Max dollar risk per trade (passed via --max-risk CLI flag)\n min_cash_buffer: Min cash to keep in reserve (passed via --min-cash CLI flag)\n \"\"\"\n if max_loss > max_risk_per_trade:\n return False\n \n available = account_total - min_cash_buffer\n if margin_required > available:\n return False\n \n return True\n\n\ndef optimal_spread_width(underlying_price: float, max_loss: float = MAX_RISK_PER_TRADE) -> float:\n \"\"\"\n Calculate optimal spread width given account constraints\n \n For small accounts ($100 max risk):\n - Need $2-5 wide spreads to fit account\n \"\"\"\n # Assuming we sell at ~20% of width, max width for $100 risk\n # is about $5 wide ($100 / 0.20 = $500, but we need cushion)\n \n if underlying_price \u003c 50:\n return 2.0 # Small cap stocks\n elif underlying_price \u003c 200:\n return 3.0 # Mid cap\n else:\n return 5.0 # Large cap/ETFs\n\n\ndef calculate_realized_volatility(prices: List[float], periods: int = 20) -> float:\n \"\"\"\n Calculate annualized realized volatility from price series\n \n Uses standard deviation of log returns\n \"\"\"\n if len(prices) \u003c periods + 1:\n return 0.0\n \n # Calculate log returns\n log_returns = np.diff(np.log(prices[-periods:]))\n \n # Annualized volatility (252 trading days)\n daily_vol = np.std(log_returns)\n annual_vol = daily_vol * np.sqrt(252)\n \n return annual_vol\n","content_type":"text/x-python; charset=utf-8","language":"python","size":23350,"content_sha256":"77e3846ba69c0473e6c042dffe49ab94cff93c0676a8007a0ec35bbc86f9fa67"},{"filename":"scripts/position_sizer.py","content":"\"\"\"\nPosition Sizer — Kelly Criterion Adapted for Small Options Accounts\n\nCalculates optimal position size for vertical spread trades using the\nKelly criterion, with aggressive guardrails for capital preservation\non small accounts (default: $390).\n\nDesigned to integrate with:\n - Conviction Engine (80+ score = EXECUTE signal)\n - Options Profit Calculator (POP, max loss, max profit)\n\nKelly Formula Used:\n Kelly % = (POP * Win - (1 - POP) * Loss) / Win\n\n Where:\n POP = Probability of Profit (0.0 to 1.0)\n Win = Max profit per spread (dollars)\n Loss = Max loss per spread (dollars, positive number)\n\n We apply a fractional Kelly (default quarter-Kelly = 0.25) to\n reduce volatility of returns and account drawdown risk.\n\nExample Usage:\n >>> from position_sizer import calculate_position, format_recommendation\n\n # Spread with $200 max loss, $100 max profit, 65% POP\n >>> result = calculate_position(\n ... account_value=390,\n ... max_loss_per_spread=200,\n ... win_amount=100,\n ... pop=0.65,\n ... )\n >>> result['recommendation']\n 'SKIP'\n >>> result['contracts']\n 0\n\n # Tighter spread: $80 max loss, $40 max profit, 55% POP\n >>> result = calculate_position(\n ... account_value=390,\n ... max_loss_per_spread=80,\n ... win_amount=40,\n ... pop=0.55,\n ... )\n >>> result['recommendation']\n 'EXECUTE'\n >>> result['contracts']\n 1\n\"\"\"\n\nfrom __future__ import annotations\n\nimport math\nfrom typing import Optional, Dict, Any\n\n# Import enhanced Kelly for unified implementation\nfrom enhanced_kelly import EnhancedKellySizer, KellyFraction, PositionResult\n\n\n# ---------------------------------------------------------------------------\n# Constants — Account Guardrails\n# ---------------------------------------------------------------------------\n\nDEFAULT_ACCOUNT_VALUE: float = 390.0\nDEFAULT_KELLY_FRACTION: float = 0.25 # Quarter-Kelly\nDEFAULT_MAX_POSITION_PCT: float = 0.25 # 25% of account per trade\nMAX_SINGLE_TRADE_RISK: float = 100.0 # Hard dollar cap\nCASH_BUFFER: float = 150.0 # Always keep this much cash\nMAX_DEPLOYABLE_CAPITAL: float = DEFAULT_ACCOUNT_VALUE - CASH_BUFFER # $240\nWIDE_SPREAD_THRESHOLD: float = 150.0 # Flag spreads wider than this\n\n\n# ---------------------------------------------------------------------------\n# Core: Kelly Criterion (delegated to enhanced_kelly module)\n# ---------------------------------------------------------------------------\n\ndef kelly_criterion(pop: float, win_amount: float, loss_amount: float) -> float:\n \"\"\"\n Compute the full Kelly criterion percentage.\n \n This is a convenience wrapper around EnhancedKellySizer.kelly_criterion()\n for backwards compatibility.\n \n Args:\n pop: Probability of profit, between 0.0 and 1.0.\n win_amount: Dollar profit if the trade wins (positive).\n loss_amount: Dollar loss if the trade loses (positive).\n\n Returns:\n Kelly percentage as a decimal (e.g. 0.10 = 10% of bankroll).\n Returns 0.0 if the edge is zero or negative.\n \"\"\"\n if not 0.0 \u003c= pop \u003c= 1.0:\n raise ValueError(f\"POP must be between 0 and 1, got {pop}\")\n if win_amount \u003c= 0:\n raise ValueError(f\"win_amount must be positive, got {win_amount}\")\n if loss_amount \u003c= 0:\n raise ValueError(f\"loss_amount must be positive, got {loss_amount}\")\n \n sizer = EnhancedKellySizer()\n kelly, _ = sizer.kelly_criterion(pop, win_amount, loss_amount)\n return kelly\n\n\n# ---------------------------------------------------------------------------\n# Core: Position Sizing\n# ---------------------------------------------------------------------------\n\ndef calculate_position(\n account_value: float = DEFAULT_ACCOUNT_VALUE,\n max_loss_per_spread: float = 0.0,\n win_amount: float = 0.0,\n pop: float = 0.0,\n kelly_fraction: float = DEFAULT_KELLY_FRACTION,\n max_position_pct: float = DEFAULT_MAX_POSITION_PCT,\n) -> dict:\n \"\"\"Calculate position size with Kelly criterion and account guardrails.\n\n This is the main entry point. Feed it the outputs from the options\n profit calculator (POP, max loss, max profit) and it returns a\n sized recommendation.\n\n Args:\n account_value: Total account value in dollars.\n max_loss_per_spread: Maximum loss per single spread contract\n (positive number, in dollars).\n win_amount: Maximum profit per single spread contract\n (positive number, in dollars).\n pop: Probability of profit from the calculator (0.0 to 1.0).\n kelly_fraction: Fraction of Kelly to use (0.25 = quarter-Kelly).\n max_position_pct: Maximum percentage of account to risk on\n any single trade (0.0 to 1.0).\n\n Returns:\n Dictionary with keys:\n contracts (int): Number of spread contracts to trade.\n total_risk (float): Total dollar risk (contracts * max_loss).\n risk_pct (float): Risk as percentage of account (0.0 to 1.0).\n kelly_full_pct (float): What full Kelly recommends.\n kelly_adj_pct (float): Fractional Kelly (after applying fraction).\n recommendation (str): 'EXECUTE', 'REDUCE', or 'SKIP'.\n reason (str): Human-readable explanation of the decision.\n\n Examples:\n >>> # Wide spread on a $390 account — too risky\n >>> r = calculate_position(390, 200, 100, 0.65)\n >>> r['recommendation']\n 'SKIP'\n >>> r['contracts']\n 0\n\n >>> # Tight spread, modest edge\n >>> r = calculate_position(390, 80, 40, 0.55)\n >>> r['recommendation']\n 'EXECUTE'\n >>> r['contracts']\n 1\n\n >>> # Strong edge, high POP\n >>> r = calculate_position(390, 50, 150, 0.70)\n >>> r['recommendation']\n 'EXECUTE'\n >>> r['contracts']\n 1\n \"\"\"\n # Input validation\n if account_value \u003c= 0:\n raise ValueError(f\"account_value must be positive, got {account_value}\")\n if max_loss_per_spread \u003c= 0:\n raise ValueError(f\"max_loss_per_spread must be positive, got {max_loss_per_spread}\")\n if win_amount \u003c= 0:\n raise ValueError(f\"win_amount must be positive, got {win_amount}\")\n if not 0.0 \u003c= pop \u003c= 1.0:\n raise ValueError(f\"pop must be between 0 and 1, got {pop}\")\n\n # Compute Kelly\n full_kelly = kelly_criterion(pop, win_amount, max_loss_per_spread)\n adj_kelly = full_kelly * kelly_fraction\n\n # Base result template\n result = {\n \"contracts\": 0,\n \"total_risk\": 0.0,\n \"risk_pct\": 0.0,\n \"kelly_full_pct\": round(full_kelly, 4),\n \"kelly_adj_pct\": round(adj_kelly, 4),\n \"recommendation\": \"SKIP\",\n \"reason\": \"\",\n }\n\n # --- Decision Gate 1: Kelly says no edge ---\n if full_kelly \u003c= 0:\n result[\"reason\"] = (\n f\"Negative edge. Kelly = {full_kelly:.2%}. \"\n f\"POP of {pop:.0%} does not compensate for \"\n f\"{max_loss_per_spread / win_amount:.1f}:1 risk/reward ratio.\"\n )\n return result\n\n # --- Decision Gate 2: Single spread exceeds wide-spread threshold ---\n if max_loss_per_spread > WIDE_SPREAD_THRESHOLD:\n result[\"reason\"] = (\n f\"Max loss per spread (${max_loss_per_spread:.0f}) exceeds \"\n f\"${WIDE_SPREAD_THRESHOLD:.0f} threshold. \"\n f\"Find tighter strikes to reduce per-contract risk.\"\n )\n return result\n\n # --- Compute deployable capital ---\n deployable = min(\n account_value - CASH_BUFFER, # respect cash buffer\n MAX_DEPLOYABLE_CAPITAL, # hard cap\n )\n deployable = max(deployable, 0.0) # can't deploy negative\n\n if deployable \u003c= 0:\n result[\"reason\"] = (\n f\"Insufficient deployable capital. Account ${account_value:.0f} \"\n f\"minus ${CASH_BUFFER:.0f} buffer = ${deployable:.0f} available.\"\n )\n return result\n\n # --- Compute Kelly-optimal dollar risk ---\n max_risk_by_pct = account_value * max_position_pct\n hard_dollar_cap = min(MAX_SINGLE_TRADE_RISK, max_risk_by_pct, deployable)\n kelly_dollar_risk = adj_kelly * account_value\n\n # --- Determine contracts ---\n effective_risk = min(kelly_dollar_risk, hard_dollar_cap)\n contracts = max(int(math.floor(effective_risk / max_loss_per_spread)), 0)\n\n # At least 1 contract if Kelly is positive and we can afford it\n if contracts == 0 and full_kelly > 0 and max_loss_per_spread \u003c= hard_dollar_cap:\n contracts = 1\n\n # Final affordability check\n if contracts * max_loss_per_spread > hard_dollar_cap:\n contracts = max(int(math.floor(hard_dollar_cap / max_loss_per_spread)), 0)\n\n if contracts == 0:\n result[\"reason\"] = (\n f\"Kelly is positive ({full_kelly:.2%}) but min spread cost \"\n f\"(${max_loss_per_spread:.0f}) exceeds risk cap \"\n f\"(${hard_dollar_cap:.0f}).\"\n )\n return result\n\n total_risk = contracts * max_loss_per_spread\n risk_pct = total_risk / account_value\n\n # --- Determine recommendation ---\n was_capped = kelly_dollar_risk > hard_dollar_cap\n recommendation = \"REDUCE\" if was_capped else \"EXECUTE\"\n\n if was_capped:\n reason = (\n f\"Kelly suggests risking ${kelly_dollar_risk:.0f} \"\n f\"({adj_kelly:.2%} of account) but guardrails cap at \"\n f\"${hard_dollar_cap:.0f}. Sized down to {contracts} contract(s).\"\n )\n else:\n reason = (\n f\"Edge confirmed. {contracts} contract(s) at \"\n f\"${max_loss_per_spread:.0f} risk each = ${total_risk:.0f} total \"\n f\"({risk_pct:.1%} of account).\"\n )\n\n result.update({\n \"contracts\": contracts,\n \"total_risk\": round(total_risk, 2),\n \"risk_pct\": round(risk_pct, 4),\n \"recommendation\": recommendation,\n \"reason\": reason,\n })\n\n return result\n\n\n# ---------------------------------------------------------------------------\n# Helper: Pretty-Print Recommendation\n# ---------------------------------------------------------------------------\n\ndef format_recommendation(result: dict) -> str:\n \"\"\"Format a position sizing result for CLI display.\n\n Args:\n result: Dictionary returned by calculate_position().\n\n Returns:\n Multi-line formatted string.\n\n Example:\n >>> r = calculate_position(390, 80, 40, 0.55)\n >>> print(format_recommendation(r)) # doctest: +SKIP\n ╔══════════════════════════════════════╗\n ║ POSITION SIZER — EXECUTE ║\n ...\n \"\"\"\n rec = result[\"recommendation\"]\n\n # Status bar icons\n status_icons = {\n \"EXECUTE\": \"[GO]\",\n \"REDUCE\": \"[!!]\",\n \"SKIP\": \"[XX]\",\n }\n icon = status_icons.get(rec, \"\")\n\n border = \"=\" * 50\n lines = [\n f\"\\n{'=' * 50}\",\n f\" POSITION SIZER {icon} {rec}\",\n f\"{'=' * 50}\",\n \"\",\n f\" Kelly (full): {result['kelly_full_pct']:>8.2%}\",\n f\" Kelly (adjusted): {result['kelly_adj_pct']:>8.2%}\",\n \"\",\n f\" Contracts: {result['contracts']:>8d}\",\n f\" Total Risk: ${result['total_risk']:>7.2f}\",\n f\" Risk % of Account: {result['risk_pct']:>8.2%}\",\n \"\",\n f\" {result['reason']}\",\n \"\",\n f\"{'=' * 50}\",\n ]\n\n return \"\\n\".join(lines)\n\n\n# ---------------------------------------------------------------------------\n# Helper: Strike Adjustment Suggestions\n# ---------------------------------------------------------------------------\n\ndef suggest_strike_adjustment(\n max_loss: float,\n account_value: float = DEFAULT_ACCOUNT_VALUE,\n) -> str:\n \"\"\"Provide guidance on tightening strikes to fit account size.\n\n When a spread's max loss is too large for the account, this function\n suggests target strike widths and max-loss ranges that would be\n tradeable.\n\n Args:\n max_loss: Current max loss per spread (dollars).\n account_value: Account value (dollars).\n\n Returns:\n Human-readable suggestion string.\n\n Examples:\n >>> print(suggest_strike_adjustment(200, 390)) # doctest: +SKIP\n STRIKE ADJUSTMENT NEEDED\n ...\n \"\"\"\n deployable = max(account_value - CASH_BUFFER, 0)\n hard_cap = min(MAX_SINGLE_TRADE_RISK, deployable)\n\n lines = []\n\n if max_loss \u003c= hard_cap:\n lines.append(\"Current spread width is within account limits.\")\n lines.append(f\"Max loss ${max_loss:.0f} fits under ${hard_cap:.0f} cap.\")\n return \"\\n\".join(lines)\n\n lines.append(\"STRIKE ADJUSTMENT NEEDED\")\n lines.append(f\" Current max loss: ${max_loss:.0f}\")\n lines.append(f\" Account risk cap: ${hard_cap:.0f}\")\n lines.append(f\" Overshoot: ${max_loss - hard_cap:.0f}\")\n lines.append(\"\")\n lines.append(\"Suggestions:\")\n\n # For vertical spreads, max loss = (strike width - net credit) * 100\n # or (strike width * 100) - net credit for credit spreads.\n # We work backward from the hard cap.\n lines.append(f\" 1. Target max loss under ${hard_cap:.0f} per spread.\")\n lines.append(f\" 2. For a $1-wide spread: max loss ~ $50-$80 (good fit).\")\n lines.append(f\" 3. For a $2-wide spread: max loss ~ $100-$150 (borderline).\")\n lines.append(f\" 4. For a $5-wide spread: max loss ~ $300-$400 (too wide).\")\n lines.append(\"\")\n lines.append(\"Rules of thumb for a ${:.0f} account:\".format(account_value))\n lines.append(f\" - Prefer $1-$2 wide strikes\")\n lines.append(f\" - Collect at least 30% of spread width as credit\")\n lines.append(f\" - Keep max loss under ${hard_cap:.0f} per contract\")\n\n return \"\\n\".join(lines)\n\n\n# ---------------------------------------------------------------------------\n# Batch Analysis Helper\n# ---------------------------------------------------------------------------\n\ndef screen_trades(\n trades: list[dict],\n account_value: float = DEFAULT_ACCOUNT_VALUE,\n kelly_fraction: float = DEFAULT_KELLY_FRACTION,\n) -> list[dict]:\n \"\"\"Screen multiple trade candidates and rank by Kelly edge.\n\n Args:\n trades: List of dicts, each with keys:\n 'ticker' (str), 'max_loss' (float), 'win_amount' (float),\n 'pop' (float), and optionally 'conviction' (int).\n account_value: Account value in dollars.\n kelly_fraction: Fractional Kelly to apply.\n\n Returns:\n List of dicts sorted by Kelly edge (best first), each augmented\n with position sizing results.\n\n Example:\n >>> candidates = [\n ... {'ticker': 'AAPL', 'max_loss': 80, 'win_amount': 40, 'pop': 0.60},\n ... {'ticker': 'SPY', 'max_loss': 50, 'win_amount': 150, 'pop': 0.70},\n ... ]\n >>> ranked = screen_trades(candidates, account_value=390)\n >>> ranked[0]['ticker'] # Best edge first\n 'SPY'\n \"\"\"\n results = []\n for trade in trades:\n sizing = calculate_position(\n account_value=account_value,\n max_loss_per_spread=trade[\"max_loss\"],\n win_amount=trade[\"win_amount\"],\n pop=trade[\"pop\"],\n kelly_fraction=kelly_fraction,\n )\n entry = {**trade, **sizing}\n results.append(entry)\n\n # Sort: EXECUTE/REDUCE first, then by Kelly edge descending\n priority = {\"EXECUTE\": 0, \"REDUCE\": 1, \"SKIP\": 2}\n results.sort(key=lambda x: (priority.get(x[\"recommendation\"], 3), -x[\"kelly_full_pct\"]))\n\n return results\n\n\n# ---------------------------------------------------------------------------\n# Enhanced Kelly Integration\n# ---------------------------------------------------------------------------\n\ndef calculate_position_enhanced(\n spread_cost: float,\n max_loss: float,\n win_amount: float,\n conviction: float,\n pop: float,\n account_value: float = DEFAULT_ACCOUNT_VALUE,\n max_drawdown: float = 0.20,\n existing_correlation: float = 0.0,\n) -> Dict[str, Any]:\n \"\"\"\n Calculate position size using enhanced Kelly criterion.\n \n This function uses the EnhancedKellySizer for\n drawdown-constrained, conviction-based position sizing.\n \n Args:\n spread_cost: Cost to enter one spread.\n max_loss: Maximum loss per contract.\n win_amount: Maximum win per contract.\n conviction: Conviction score from engine (0-100).\n pop: Probability of profit.\n account_value: Account value in dollars.\n max_drawdown: Maximum acceptable drawdown.\n existing_correlation: Correlation with existing positions.\n \n Returns:\n Position sizing dictionary with enhanced metrics.\n \n Example:\n >>> result = calculate_position_enhanced(\n ... spread_cost=80, max_loss=80, win_amount=40,\n ... conviction=85, pop=0.65\n ... )\n >>> print(f\"Contracts: {result['contracts']}\")\n \"\"\"\n sizer = EnhancedKellySizer(account_value, max_drawdown)\n return sizer.calculate_position(\n spread_cost=spread_cost,\n max_loss=max_loss,\n win_amount=win_amount,\n conviction=conviction,\n pop=pop,\n existing_correlation=existing_correlation,\n )\n\n\n# ---------------------------------------------------------------------------\n# CLI Entry Point\n# ---------------------------------------------------------------------------\n\ndef _cli() -> None:\n \"\"\"Simple CLI for quick position sizing checks.\"\"\"\n import argparse\n\n parser = argparse.ArgumentParser(\n description=\"Position Sizer — Kelly Criterion for Small Options Accounts\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=\"\"\"\nExamples:\n python position_sizer.py --loss 80 --win 40 --pop 0.55\n python position_sizer.py --loss 200 --win 100 --pop 0.65 --account 390\n python position_sizer.py --loss 50 --win 150 --pop 0.70 --kelly-frac 0.25\n \"\"\",\n )\n parser.add_argument(\"--account\", type=float, default=DEFAULT_ACCOUNT_VALUE,\n help=f\"Account value (default: ${DEFAULT_ACCOUNT_VALUE:.0f})\")\n parser.add_argument(\"--loss\", type=float, required=True,\n help=\"Max loss per spread (dollars)\")\n parser.add_argument(\"--win\", type=float, required=True,\n help=\"Max profit per spread (dollars)\")\n parser.add_argument(\"--pop\", type=float, required=True,\n help=\"Probability of profit (0.0 to 1.0)\")\n parser.add_argument(\"--kelly-frac\", type=float, default=DEFAULT_KELLY_FRACTION,\n help=f\"Kelly fraction (default: {DEFAULT_KELLY_FRACTION})\")\n parser.add_argument(\"--max-pct\", type=float, default=DEFAULT_MAX_POSITION_PCT,\n help=f\"Max position %% (default: {DEFAULT_MAX_POSITION_PCT})\")\n\n args = parser.parse_args()\n\n result = calculate_position(\n account_value=args.account,\n max_loss_per_spread=args.loss,\n win_amount=args.win,\n pop=args.pop,\n kelly_fraction=args.kelly_frac,\n max_position_pct=args.max_pct,\n )\n\n print(format_recommendation(result))\n\n # If SKIP due to wide strikes, show adjustment guidance\n if result[\"recommendation\"] == \"SKIP\" and args.loss > WIDE_SPREAD_THRESHOLD:\n print()\n print(suggest_strike_adjustment(args.loss, args.account))\n\n\nif __name__ == \"__main__\":\n _cli()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":19444,"content_sha256":"bd7e123503ba0a7f4e9458f53460462fb82220b4157e9ee606e92be96a6ed48d"},{"filename":"scripts/quant_scanner.py","content":"#!/usr/bin/env python3\n\"\"\"\nQuantitative Options Scanner with Unified Conviction Engine\n\nA mathematically-rigorous options scanner built from first principles.\nCombines options chain analysis with technical indicators, regime detection,\nvolatility forecasting, and Kelly-optimal position sizing.\n\nFeatures:\n- Options chain analysis (IV surfaces, skew, term structure)\n- Multi-leg strategy optimization (spreads, iron condors, butterflies, calendars)\n- Black-Scholes/Monte Carlo POP calculations\n- Expected Value and risk-adjusted return scoring\n- Market regime detection (VIX-based)\n- GARCH volatility forecasting with VRP analysis\n- Kelly-optimal, drawdown-constrained position sizing\n- Walk-forward backtesting validation\n- Account-aware filtering for small accounts\n\nUsage:\n quant_scanner.py SPY --mode pop\n quant_scanner.py AAPL TSLA NVDA --mode income --max-loss 100\n quant_scanner.py SPY --mode ev --dte 30\n quant_scanner.py SPY --unified --strategy bull_put # Use unified engine\n\nAccount Constraints:\n- Total Account: $500 (default, override with --account)\n- Max Risk Per Trade: $100\n- Min Cash Buffer: $150\n- Available Capital: Account - Buffer\n\"\"\"\n\nimport argparse\nimport re\nimport sys\nimport json\nfrom typing import List, Optional, Protocol, runtime_checkable, Dict, Tuple, Any\nfrom pathlib import Path\n\n# Add parent directory to path for imports\nsys.path.insert(0, str(Path(__file__).parent))\n\nfrom options_math import (\n BlackScholes, ProbabilityCalculator, VolatilityAnalyzer,\n fits_account_constraints, MAX_RISK_PER_TRADE, DEFAULT_ACCOUNT_TOTAL,\n ACCOUNT_TOTAL, AVAILABLE_CAPITAL, MIN_CASH_BUFFER\n)\nfrom chain_analyzer import ChainFetcher, ChainAnalyzer, OptionChain\nfrom leg_optimizer import LegOptimizer, MultiLegStrategy, validate_strategy_risk\n\n# Import quantitative modules\nfrom regime_detector import RegimeDetector, RegimeResult, get_current_regime\nfrom vol_forecaster import VolatilityForecaster, VRPSignal\nfrom enhanced_kelly import EnhancedKellySizer, PositionResult\nfrom backtest_validator import BacktestValidator\n\nVALID_SPREAD_TYPES = {'put_credit', 'call_credit', 'put_debit', 'call_debit'}\n\n# Ticker validation: standard equity/options format only\n_TICKER_PATTERN = re.compile(r'^[A-Z0-9.\\-=]+

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

)\n\n\n@runtime_checkable\nclass ChainFetcherProtocol(Protocol):\n \"\"\"Protocol for options chain data fetchers.\"\"\"\n\n def fetch_quote(self, ticker: str) -> Optional[dict]: ...\n\n def fetch_multiple_expirations(\n self, ticker: str, num_expirations: int = 4,\n min_dte: int = 7, max_dte: int = 45\n ) -> list: ...\n\n\ndef validate_tickers(tickers: List[str]) -> List[str]:\n \"\"\"Validate ticker symbols against safe pattern.\"\"\"\n validated = []\n for raw in tickers:\n t = raw.strip().upper()\n if not t:\n raise ValueError(f\"Empty ticker symbol: {raw!r}\")\n if not _TICKER_PATTERN.match(t):\n raise ValueError(\n f\"Invalid ticker symbol: {raw!r}. \"\n \"Tickers must match ^[A-Z0-9.=-]+$ (no spaces, slashes, or shell characters).\"\n )\n if len(t) > 20:\n raise ValueError(f\"Ticker symbol too long: {raw!r} ({len(t)} chars, max 20)\")\n validated.append(t)\n return validated\n\n\nclass QuantConvictionEngine:\n \"\"\"\n Unified quantitative conviction engine combining:\n - Technical indicator analysis (Ichimoku, RSI, MACD, BB)\n - Market regime detection (VIX-based)\n - Volatility forecasting (GARCH + VRP)\n - Kelly-optimal position sizing\n - Walk-forward validation\n \n This engine provides a comprehensive, quantitatively-rigorous assessment\n of options trade opportunities by combining multiple orthogonal signals.\n \"\"\"\n \n def __init__(self, account_value: float = 390):\n self.account = account_value\n self.regime_detector = RegimeDetector()\n self.kelly_sizer = EnhancedKellySizer(account_value=account_value)\n self.current_regime: Optional[str] = None\n self.regime_metadata: Optional[Dict] = None\n self._cached_vrp: Optional[VRPSignal] = None\n \n def analyze(self, ticker: str, strategy: str = 'auto') -> Dict[str, Any]:\n \"\"\"\n Full quantitative analysis pipeline:\n \n 1. Detect market regime\n 2. Get volatility forecast and VRP\n 3. Apply regime-based adjustments\n 4. Calculate Kelly-optimal position size\n 5. Return unified recommendation\n \n Args:\n ticker: Stock ticker symbol\n strategy: Options strategy ('bull_put', 'bear_call', 'bull_call', \n 'bear_put', 'iron_condor', or 'auto')\n \n Returns:\n Dictionary with unified conviction analysis including:\n - regime: Current market regime detection\n - vrp_analysis: Volatility risk premium assessment\n - kelly_sizing: Optimal position sizing recommendation\n - final_score: Unified conviction score (0-100)\n - recommendation: EXECUTE, PREPARE, WATCH, or WAIT\n \"\"\"\n # Step 1: Market regime detection\n regime_result = self.regime_detector.detect_regime()\n self.current_regime = regime_result.regime\n self.regime_metadata = regime_result.regime_metadata\n \n # Step 2: Volatility forecasting and VRP analysis\n vol_forecaster = VolatilityForecaster(ticker)\n try:\n garch = vol_forecaster.fit_garch()\n forecast = vol_forecaster.forecast_vol(horizon=21)\n \n # Get current implied volatility (placeholder - would come from chain)\n # In practice, this would be fetched from options chain\n current_iv = self._estimate_current_iv(ticker, vol_forecaster)\n vrp_signal = vol_forecaster.vol_risk_premium(current_iv)\n self._cached_vrp = vrp_signal\n except Exception as e:\n # If GARCH fails, continue without VRP adjustment\n vrp_signal = None\n garch = None\n forecast = None\n \n # Step 3: Calculate base conviction score\n # This integrates with spread_conviction_engine if available\n base_score, technical_signals = self._get_technical_conviction(ticker, strategy)\n \n # Step 4: Apply regime-based adjustment\n regime_adjusted_score, regime_reason = self.apply_regime_adjustment(\n base_score, strategy\n )\n \n # Step 5: Apply VRP-based adjustment\n if vrp_signal:\n final_score, vrp_reason = self.apply_vrp_adjustment(\n regime_adjusted_score, current_iv, ticker, strategy\n )\n else:\n final_score = regime_adjusted_score\n vrp_reason = \"VRP analysis unavailable\"\n \n # Step 6: Determine tier\n tier = self._score_to_tier(final_score)\n \n # Step 7: Calculate Kelly-optimal position sizing\n position_result = self._calculate_position_sizing(\n ticker, strategy, final_score, tier\n )\n \n # Build unified result\n result = {\n 'ticker': ticker.upper(),\n 'strategy': strategy,\n 'timestamp': self._get_timestamp(),\n \n # Market regime\n 'regime': {\n 'current_regime': self.current_regime,\n 'confidence': regime_result.confidence,\n 'vix_level': regime_result.vix_level,\n 'vix_percentile': regime_result.percentile,\n 'regime_duration_estimate': regime_result.regime_metadata.get(\n 'regime_duration_estimate', 0\n ),\n 'is_favorable': self._is_regime_favorable(strategy)\n },\n \n # Volatility analysis\n 'volatility': {\n 'garch_fitted': garch is not None,\n 'current_iv': current_iv if vrp_signal else None,\n 'forecast_rv': vrp_signal.forecast_rv if vrp_signal else None,\n 'vrp': vrp_signal.vrp if vrp_signal else None,\n 'vrp_zscore': vrp_signal.vrp_zscore if vrp_signal else None,\n 'vrp_recommendation': vrp_signal.recommendation if vrp_signal else None,\n },\n \n # Conviction scoring\n 'conviction': {\n 'base_score': round(base_score, 1),\n 'regime_adjusted': round(regime_adjusted_score, 1),\n 'final_score': round(final_score, 1),\n 'tier': tier,\n 'regime_adjustment_reason': regime_reason,\n 'vrp_adjustment_reason': vrp_reason,\n },\n \n # Position sizing\n 'position': {\n 'contracts': position_result.contracts if position_result else 0,\n 'total_risk': position_result.total_risk if position_result else 0,\n 'kelly_fraction': position_result.kelly_fraction if position_result else 0,\n 'recommendation': position_result.recommendation if position_result else 'NO_TRADE',\n 'reasoning': position_result.reasoning if position_result else 'Analysis incomplete',\n },\n \n # Technical signals (if available)\n 'technical_signals': technical_signals,\n \n # Final recommendation\n 'recommendation': self._generate_final_recommendation(\n tier, position_result, vrp_signal\n )\n }\n \n return result\n \n def apply_regime_adjustment(self, base_score: float, strategy: str) -> Tuple[float, str]:\n \"\"\"\n Apply regime-based weight adjustments to conviction score.\n \n Args:\n base_score: Original conviction score (0-100)\n strategy: Options strategy being evaluated\n \n Returns:\n Tuple of (adjusted_score, reasoning)\n \"\"\"\n # Ensure we have current regime data\n if not self.current_regime:\n regime_result = self.regime_detector.detect_regime()\n self.current_regime = regime_result.regime\n self.regime_metadata = regime_result.regime_metadata\n \n if not self.current_regime:\n return base_score, \"No regime data available\"\n \n return self.regime_detector.regime_aware_score(\n base_score, self.current_regime, strategy\n )\n \n def apply_vrp_adjustment(self, base_score: float, current_iv: float, \n ticker: str, strategy: str) -> Tuple[float, str]:\n \"\"\"\n Adjust conviction based on volatility risk premium.\n \n Args:\n base_score: Current conviction score (0-100)\n current_iv: Current implied volatility (annualized %)\n ticker: Stock ticker\n strategy: Options strategy\n \n Returns:\n Tuple of (adjusted_score, reasoning)\n \"\"\"\n forecaster = VolatilityForecaster(ticker)\n try:\n forecaster.fit_garch()\n vrp_signal = forecaster.vol_risk_premium(current_iv)\n return forecaster.add_to_conviction(base_score, vrp_signal, strategy)\n except Exception as e:\n return base_score, f\"VRP adjustment failed: {str(e)}\"\n \n def _get_technical_conviction(self, ticker: str, strategy: str) -> Tuple[float, Dict]:\n \"\"\"\n Get technical conviction score from spread conviction engine.\n \n Returns base score and technical signal details.\n \"\"\"\n try:\n # Try to import and use spread_conviction_engine\n from spread_conviction_engine import analyse, StrategyType\n \n # Map strategy string to StrategyType\n strategy_map = {\n 'bull_put': StrategyType.BULL_PUT,\n 'bear_call': StrategyType.BEAR_CALL,\n 'bull_call': StrategyType.BULL_CALL,\n 'bear_put': StrategyType.BEAR_PUT,\n }\n \n if strategy in strategy_map:\n stype = strategy_map[strategy]\n else:\n # Default to bull_put if auto or unknown\n stype = StrategyType.BULL_PUT\n \n result = analyse(ticker, strategy=stype)\n \n signals = {\n 'ichimoku': {\n 'price_vs_cloud': result.ichimoku.price_vs_cloud,\n 'tk_cross': result.ichimoku.tk_cross,\n 'cloud_color': result.ichimoku.cloud_color,\n 'score': result.ichimoku.component_score,\n },\n 'rsi': {\n 'value': result.rsi.value,\n 'zone': result.rsi.zone,\n 'score': result.rsi.component_score,\n },\n 'macd': {\n 'histogram_direction': result.macd.hist_direction,\n 'crossover': result.macd.crossover,\n 'score': result.macd.component_score,\n },\n 'bollinger': {\n 'percent_b': result.bollinger.percent_b,\n 'bandwidth': result.bollinger.bandwidth,\n 'score': result.bollinger.component_score,\n },\n 'trend_bias': result.trend_bias,\n }\n \n return result.conviction_score, signals\n \n except Exception as e:\n # If spread_conviction_engine not available, return neutral\n return 50.0, {'error': f'Technical analysis unavailable: {str(e)}'}\n \n def _estimate_current_iv(self, ticker: str, forecaster: VolatilityForecaster) -> float:\n \"\"\"\n Estimate current implied volatility.\n \n In practice, this would come from the options chain.\n For now, use a heuristic based on realized vol plus typical premium.\n \"\"\"\n try:\n # Try to fetch from options chain\n fetcher = ChainFetcher(rate_limit_delay=0.1)\n quote = fetcher.fetch_quote(ticker)\n if quote and 'impliedVolatility' in quote:\n return quote['impliedVolatility'] * 100 # Convert to percentage\n except (ValueError, KeyError, ConnectionError) as e:\n logger.warning(f\"Failed to fetch IV from options chain: {e}\")\n \n # Fallback: use forecast RV plus typical VRP (2-3%)\n if forecaster._garch_result:\n rv = forecaster._garch_result.fitted_vol.iloc[-1]\n return rv + 2.5 # Add typical VRP\n \n # Last resort: assume 20% IV\n return 20.0\n \n def _score_to_tier(self, score: float) -> str:\n \"\"\"Convert numerical score to action tier.\"\"\"\n if score >= 80:\n return 'EXECUTE'\n elif score >= 60:\n return 'PREPARE'\n elif score >= 40:\n return 'WATCH'\n else:\n return 'WAIT'\n \n def _calculate_position_sizing(self, ticker: str, strategy: str, \n conviction: float, tier: str) -> Optional[PositionResult]:\n \"\"\"Calculate Kelly-optimal position size.\"\"\"\n # Only calculate for viable trades\n if tier not in ['EXECUTE', 'PREPARE']:\n return None\n \n # Estimate trade parameters\n # In practice, these would come from actual strategy analysis\n pop = self._estimate_pop(tier)\n max_loss = 85 # Typical credit spread max loss\n win_amount = 35 # Typical credit spread profit\n \n return self.kelly_sizer.calculate_position(\n spread_cost=max_loss,\n max_loss_per_spread=max_loss,\n win_amount=win_amount,\n conviction=conviction,\n pop=pop,\n ticker=ticker\n )\n \n def _estimate_pop(self, tier: str) -> float:\n \"\"\"Estimate probability of profit based on tier.\"\"\"\n pop_map = {\n 'EXECUTE': 0.70,\n 'PREPARE': 0.60,\n 'WATCH': 0.55,\n 'WAIT': 0.45\n }\n return pop_map.get(tier, 0.50)\n \n def _is_regime_favorable(self, strategy: str) -> bool:\n \"\"\"Check if current regime is favorable for strategy.\"\"\"\n if not self.current_regime:\n return True\n is_fav, _ = self.regime_detector.is_favorable_for_strategy(\n self.current_regime, strategy\n )\n return is_fav\n \n def _get_timestamp(self) -> str:\n \"\"\"Get current timestamp.\"\"\"\n from datetime import datetime\n return datetime.now().isoformat()\n \n def _generate_final_recommendation(self, tier: str, \n position: Optional[PositionResult],\n vrp: Optional[VRPSignal]) -> Dict:\n \"\"\"Generate final trade recommendation.\"\"\"\n rec = {\n 'action': tier,\n 'rationale': [],\n }\n \n if tier == 'EXECUTE':\n rec['rationale'].append('High conviction score (80+)')\n elif tier == 'PREPARE':\n rec['rationale'].append('Moderate conviction (60-79) - monitor for entry')\n elif tier == 'WATCH':\n rec['rationale'].append('Low conviction (40-59) - on watchlist')\n else:\n rec['rationale'].append('Insufficient conviction (\u003c40) - avoid')\n \n if position and position.contracts > 0:\n rec['rationale'].append(\n f\"Position size: {position.contracts} contract(s) \"\n f\"(${position.total_risk} risk)\"\n )\n \n if vrp:\n if vrp.recommendation in ['STRONG_SELL', 'SELL']:\n rec['rationale'].append(f\"VRP favors selling: {vrp.reasoning}\")\n elif vrp.recommendation in ['STRONG_BUY', 'BUY']:\n rec['rationale'].append(f\"VRP favors buying: {vrp.reasoning}\")\n \n return rec\n \n def run_backtest_validation(self, tickers: List[str], \n start_date: str = \"2022-01-01\",\n end_date: str = \"2024-01-01\",\n strategy: str = \"bull_put\") -> Dict:\n \"\"\"\n Run walk-forward backtest validation of the engine.\n \n Args:\n tickers: List of tickers to backtest\n start_date: Start date (YYYY-MM-DD)\n end_date: End date (YYYY-MM-DD)\n strategy: Strategy to test\n \n Returns:\n Validation report dictionary\n \"\"\"\n validator = BacktestValidator(\n self,\n start_date=start_date,\n end_date=end_date,\n strategy=strategy\n )\n \n results = validator.run_walk_forward(tickers)\n report = validator.validate_tiers(results)\n \n return report.to_dict()\n\n\nclass QuantScanner:\n \"\"\"\n Main quantitative options scanner with optional unified conviction engine.\n \"\"\"\n \n def __init__(self, account_total: float = DEFAULT_ACCOUNT_TOTAL,\n max_risk_per_trade: float = MAX_RISK_PER_TRADE,\n min_cash_buffer: float = MIN_CASH_BUFFER,\n fetcher: Optional[ChainFetcherProtocol] = None,\n analyzer: Optional[ChainAnalyzer] = None,\n optimizer: Optional[LegOptimizer] = None):\n self.account_total = account_total\n self.max_risk_per_trade = max_risk_per_trade\n self.min_cash_buffer = min_cash_buffer\n self.fetcher = fetcher or ChainFetcher(rate_limit_delay=0.3)\n self.analyzer = analyzer or ChainAnalyzer(self.fetcher)\n self.optimizer = optimizer or LegOptimizer(\n account_total=account_total,\n max_risk_per_trade=max_risk_per_trade,\n min_cash_buffer=min_cash_buffer)\n self.vol_analyzer = VolatilityAnalyzer()\n self._unified_engine: Optional[QuantConvictionEngine] = None\n \n def scan_ticker(self, ticker: str, mode: str = 'pop',\n min_dte: int = 7, max_dte: int = 45,\n max_loss_limit: float = MAX_RISK_PER_TRADE,\n min_pop: float = 0.0,\n min_width: float = 1.0,\n max_width: float = None, # None = auto-calculate based on price\n verbose: bool = False,\n use_unified_engine: bool = False,\n unified_strategy: str = 'auto') -> Optional[dict]:\n \"\"\"\n Scan a single ticker and return best strategies.\n \n Args:\n ticker: Stock ticker symbol\n mode: Scanning mode ('pop', 'ev', 'income', 'earnings')\n use_unified_engine: Whether to use the unified QuantConvictionEngine\n unified_strategy: Strategy for unified engine ('bull_put', 'bear_call', etc.)\n \n Returns:\n Dictionary with scan results or unified engine analysis\n \"\"\"\n # Use unified engine if requested\n if use_unified_engine:\n if not self._unified_engine:\n self._unified_engine = QuantConvictionEngine(self.account_total)\n \n result = self._unified_engine.analyze(ticker, unified_strategy)\n \n if verbose:\n self._print_unified_result(result)\n \n return result\n \n # Standard scanning logic\n if verbose:\n print(f\"\\n{'='*60}\")\n print(f\"Scanning {ticker} | Mode: {mode.upper()}\")\n print(f\"{'='*60}\")\n \n # Fetch quote\n quote = self.fetcher.fetch_quote(ticker)\n if not quote:\n if verbose:\n print(f\"ERROR: Could not fetch quote for {ticker}\")\n return None\n \n price = quote.get('regularMarketPrice', 0)\n if price == 0:\n if verbose:\n print(f\"ERROR: No price data for {ticker}\")\n return None\n \n market_state = quote.get('marketState', 'UNKNOWN')\n \n if verbose:\n print(f\"\\nCurrent Price: ${price:.2f}\")\n change = quote.get('regularMarketChange', 0)\n change_pct = quote.get('regularMarketChangePercent', 0)\n print(f\"Change: ${change:+.2f} ({change_pct:+.2f}%)\")\n if market_state not in ('REGULAR',):\n print(f\" ⚠ Market: {market_state} — bid/ask may be stale/wider than during market hours\")\n \n # Fetch multiple expiration chains\n chains = self.fetcher.fetch_multiple_expirations(\n ticker, num_expirations=4, min_dte=min_dte, max_dte=max_dte\n )\n \n if not chains:\n if verbose:\n print(f\"ERROR: No options chains available for {ticker}\")\n return None\n \n if verbose:\n print(f\"\\nFound {len(chains)} expiration dates:\")\n for chain in chains:\n print(f\" - {chain.expiration_date[:10]} ({chain.dte} DTE)\")\n \n # Analyze chains and find strategies\n all_strategies = []\n \n for chain in chains:\n # Skip if too illiquid\n liquidity = self.analyzer.analyze_liquidity(chain)\n if liquidity['score'] \u003c 30:\n if verbose:\n print(f\" Skipping {chain.dte} DTE - poor liquidity\")\n continue\n \n # Find vertical spreads\n put_spreads = self.optimizer.optimize_vertical_spreads(\n chain, spread_type='put_credit', max_width=max_width\n )\n call_spreads = self.optimizer.optimize_vertical_spreads(\n chain, spread_type='call_credit', max_width=max_width\n )\n \n # Find iron condors\n condors = self.optimizer.optimize_iron_condors(chain)\n \n all_strategies.extend(put_spreads)\n all_strategies.extend(call_spreads)\n all_strategies.extend(condors)\n \n if not all_strategies:\n if verbose:\n print(f\"\\nNo viable strategies found for {ticker}\")\n return None\n \n # Validate: reject infinite/undefined risk strategies\n validated = []\n for s in all_strategies:\n is_valid, reason = validate_strategy_risk(s)\n if is_valid:\n validated.append(s)\n elif verbose:\n print(f\" REJECTED: {s.strategy_type} — {reason}\")\n all_strategies = validated\n \n if not all_strategies:\n if verbose:\n print(f\"\\nNo valid strategies after risk validation for {ticker}\")\n return None\n \n # Filter by spread width (min and max)\n def _calc_width(strategy, underlying_price: float) -> float:\n \"\"\"Calculate strategy width.\"\"\"\n strikes = [leg.strike for leg in strategy.legs]\n if len(strikes) >= 2:\n return max(strikes) - min(strikes)\n elif len(strikes) == 1:\n return abs(strikes[0] - underlying_price)\n return 0.0\n\n width_filtered = []\n for s in all_strategies:\n width = _calc_width(s, price)\n if width >= min_width and (max_width is None or width \u003c= max_width):\n width_filtered.append(s)\n if verbose and len(width_filtered) != len(all_strategies):\n max_width_str = f\"${max_width:.0f}\" if max_width else \"auto\"\n print(f\" Width filter (${min_width:.0f}-{max_width_str}): {len(all_strategies)} → {len(width_filtered)}\")\n all_strategies = width_filtered\n \n if not all_strategies:\n if verbose:\n print(f\"\\nNo strategies after width filter for {ticker}\")\n return None\n \n if verbose:\n print(f\"\\nFound {len(all_strategies)} validated strategies\")\n \n # Score strategies based on mode\n scored = self.optimizer.score_strategies(all_strategies, mode=mode)\n \n # Filter by max loss and min POP\n fitting_strategies = [\n s for s in scored \n if s.max_loss \u003c= max_loss_limit and s.pop >= min_pop\n ]\n \n if not fitting_strategies:\n if min_pop > 0:\n if verbose:\n print(f\"\\nNo strategies meet min POP {min_pop*100:.0f}% and max loss ${max_loss_limit:.0f}\")\n return None\n fitting_strategies = sorted(scored, key=lambda x: x.max_loss)[:3]\n \n # Take top 3\n top_strategies = fitting_strategies[:3]\n \n if verbose:\n self._print_strategies(ticker, price, top_strategies, mode)\n \n return {\n 'ticker': ticker,\n 'price': price,\n 'mode': mode,\n 'strategies': [s.to_dict() for s in top_strategies],\n 'total_found': len(all_strategies),\n 'fitting_count': len(fitting_strategies)\n }\n \n def _print_unified_result(self, result: Dict):\n \"\"\"Pretty print unified engine result.\"\"\"\n print(f\"\\n{'='*70}\")\n print(f\"UNIFIED QUANTITATIVE CONVICTION ANALYSIS\")\n print(f\"{'='*70}\")\n print(f\"Ticker: {result['ticker']} | Strategy: {result['strategy']}\")\n print(f\"Timestamp: {result['timestamp']}\")\n \n # Market Regime\n print(f\"\\n📊 MARKET REGIME\")\n print(f\" Current: {result['regime']['current_regime']}\")\n print(f\" Confidence: {result['regime']['confidence']:.1%}\")\n print(f\" VIX Level: {result['regime']['vix_level']}\")\n print(f\" VIX Percentile: {result['regime']['vix_percentile']}%\")\n print(f\" Favorable for strategy: {'✅ Yes' if result['regime']['is_favorable'] else '⚠️ No'}\")\n \n # Volatility Analysis\n vol = result['volatility']\n print(f\"\\n📈 VOLATILITY ANALYSIS\")\n if vol['garch_fitted']:\n print(f\" Current IV: {vol['current_iv']:.1f}%\")\n print(f\" Forecast RV: {vol['forecast_rv']:.1f}%\")\n print(f\" VRP: {vol['vrp']:+.1f}% (z={vol['vrp_zscore']:+.1f})\")\n print(f\" Recommendation: {vol['vrp_recommendation']}\")\n else:\n print(f\" GARCH modeling failed\")\n \n # Conviction Scoring\n conv = result['conviction']\n print(f\"\\n🎯 CONVICTION SCORING\")\n print(f\" Base Score: {conv['base_score']:.1f}/100\")\n print(f\" Regime Adjusted: {conv['regime_adjusted']:.1f}/100\")\n print(f\" Final Score: {conv['final_score']:.1f}/100\")\n print(f\" Tier: {conv['tier']}\")\n print(f\" Regime: {conv['regime_adjustment_reason']}\")\n print(f\" VRP: {conv['vrp_adjustment_reason']}\")\n \n # Position Sizing\n pos = result['position']\n print(f\"\\n💰 POSITION SIZING\")\n print(f\" Contracts: {pos['contracts']}\")\n print(f\" Total Risk: ${pos['total_risk']:.2f}\")\n print(f\" Kelly Fraction: {pos['kelly_fraction']:.2%}\")\n print(f\" Recommendation: {pos['recommendation']}\")\n print(f\" Reasoning: {pos['reasoning']}\")\n \n # Final Recommendation\n rec = result['recommendation']\n print(f\"\\n🚦 FINAL RECOMMENDATION: {rec['action']}\")\n for rationale in rec['rationale']:\n print(f\" • {rationale}\")\n \n print(f\"\\n{'='*70}\")\n \n def _print_strategies(self, ticker: str, price: float, \n strategies: List[MultiLegStrategy], mode: str):\n \"\"\"Pretty print strategy results\"\"\"\n \n print(f\"\\n{'─'*60}\")\n print(f\"TOP STRATEGIES FOR {ticker} (Mode: {mode.upper()})\")\n print(f\"{'─'*60}\")\n \n for i, s in enumerate(strategies, 1):\n print(f\"\\n{'▓'*60}\")\n print(f\"STRATEGY #{i}: {s.strategy_type.upper()}\")\n print(f\"{'▓'*60}\")\n \n print(f\" Expiration: {s.legs[0].expiration[:10]} ({s.legs[0].dte} DTE)\")\n \n print(f\"\\n LEGS:\")\n for leg in s.legs:\n action = \"SELL\" if leg.action == 'sell' else \"BUY\"\n print(f\" {action:4} {leg.option_type.upper():4} @ ${leg.strike:7.2f} \"\n f\"| Premium: ${leg.premium:.2f} (mid) | DTE: {leg.dte}\")\n \n print(f\"\\n P&L PROFILE:\")\n print(f\" Max Profit: ${s.max_profit:7.2f}\")\n print(f\" Max Loss: ${s.max_loss:7.2f} {'✓ FITS' if s.fits_account else '✗ TOO RISKY'}\")\n print(f\" Breakeven(s): {', '.join(f'${b:.2f}' for b in s.breakevens)}\")\n \n print(f\"\\n PROBABILITY & VALUE:\")\n print(f\" Probability of Profit (POP): {s.pop*100:5.1f}%\")\n print(f\" Expected Value (EV): ${s.expected_value:+.2f}\")\n print(f\" Risk-Adjusted Return: {s.risk_adjusted_return:+.2f}\")\n \n if s.total_greeks:\n print(f\"\\n GREEKS (Per Contract):\")\n print(f\" Delta: {s.total_greeks.delta:+.3f}\")\n print(f\" Theta: ${s.total_greeks.theta:+.2f}/day\")\n \n # Recommendation\n if s.fits_account and s.pop > 0.6 and s.expected_value > 0:\n print(f\"\\n ★ RECOMMENDATION: EXECUTE\")\n elif s.fits_account and s.pop > 0.5:\n print(f\"\\n ○ RECOMMENDATION: CONSIDER\")\n else:\n print(f\"\\n ✗ RECOMMENDATION: PASS\")\n \n print(f\"\\n{'='*60}\")\n \n def scan_multiple(self, tickers: List[str], mode: str = 'pop',\n min_dte: int = 7, max_dte: int = 45,\n max_loss_limit: float = MAX_RISK_PER_TRADE,\n min_pop: float = 0.0,\n min_width: float = 1.0,\n max_width: float = None, # None = auto-calculate based on price\n json_output: bool = False,\n use_unified_engine: bool = False,\n unified_strategy: str = 'auto') -> List[dict]:\n \"\"\"\n Scan multiple tickers and return aggregated results.\n \n Args:\n use_unified_engine: Whether to use unified QuantConvictionEngine\n unified_strategy: Strategy for unified engine analysis\n \"\"\"\n tickers = validate_tickers(tickers)\n results = []\n \n if not json_output:\n print(f\"\\n{'#'*70}\")\n print(f\"# QUANTITATIVE OPTIONS SCANNER\")\n if use_unified_engine:\n print(f\"# Mode: UNIFIED CONVICTION ENGINE\")\n print(f\"# Strategy: {unified_strategy}\")\n else:\n print(f\"# Mode: {mode.upper()}\")\n print(f\"# Tickers: {', '.join(tickers)}\")\n print(f\"# Account: ${self.account_total} | Max Risk: ${max_loss_limit}\")\n print(f\"# DTE Range: {min_dte}-{max_dte}\")\n if not use_unified_engine:\n if min_pop > 0:\n print(f\"# Min POP: {min_pop*100:.0f}%\")\n if min_width > 1:\n print(f\"# Min Width: ${min_width:.0f}\")\n if max_width is not None and max_width \u003c 5:\n print(f\"# Max Width: ${max_width:.0f}\")\n print(f\"{'#'*70}\\n\")\n \n for ticker in tickers:\n result = self.scan_ticker(\n ticker, mode, min_dte, max_dte, max_loss_limit, min_pop,\n min_width=min_width, max_width=max_width, \n verbose=not json_output,\n use_unified_engine=use_unified_engine,\n unified_strategy=unified_strategy\n )\n if result:\n results.append(result)\n \n # Summary\n if not json_output:\n print(f\"\\n{'#'*70}\")\n print(f\"# SCAN SUMMARY\")\n print(f\"{'#'*70}\")\n print(f\"\\nTickers Scanned: {len(tickers)}\")\n print(f\"Tickers with Opportunities: {len(results)}\")\n \n if use_unified_engine:\n execute_count = sum(1 for r in results if r.get('conviction', {}).get('tier') == 'EXECUTE')\n prepare_count = sum(1 for r in results if r.get('conviction', {}).get('tier') == 'PREPARE')\n print(f\"\\nEXECUTE Recommendations: {execute_count}\")\n print(f\"PREPARE Recommendations: {prepare_count}\")\n \n print(f\"\\nAccount Constraints:\")\n available = self.account_total - self.min_cash_buffer\n print(f\" Total Account: ${self.account_total}\")\n print(f\" Max Risk/Trade: ${max_loss_limit}\")\n print(f\" Min Cash Buffer: ${self.min_cash_buffer}\")\n print(f\" Available Capital: ${available}\")\n \n return results\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description='Quantitative Options Scanner - Mathematical options analysis',\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=\"\"\"\nExamples:\n %(prog)s SPY --mode pop # Maximize POP for SPY\n %(prog)s AAPL TSLA --mode ev # Max expected value\n %(prog)s SPY QQQ --mode income # Income/theta plays\n %(prog)s NVDA --mode earnings # Earnings/vol crush plays\n %(prog)s SPY --json # Machine-readable output\n %(prog)s SPY --unified --strategy bull_put # Use unified conviction engine\n %(prog)s AAPL --unified --backtest # Validate with backtesting\n %(prog)s SPY --min-pop 75 # Only trades with 75%%+ POP\n %(prog)s SPY --min-width 2 --min-pop 75 # $2+ wide spreads, 75%%+ POP\n %(prog)s SPY --min-width 2 --max-width 3 # Only $2-3 wide spreads\n %(prog)s SPY --dte 14 --max-loss 50 # Custom DTE and risk\n \"\"\"\n )\n \n parser.add_argument('tickers', nargs='+', \n help='Stock ticker(s) to scan')\n \n parser.add_argument('--mode', '-m', \n choices=['pop', 'ev', 'income', 'earnings'],\n default='pop',\n help='Scanning mode (default: pop)')\n \n parser.add_argument('--min-dte', type=int, default=7,\n help='Minimum days to expiration (default: 7)')\n \n parser.add_argument('--max-dte', type=int, default=45,\n help='Maximum days to expiration (default: 45)')\n \n parser.add_argument('--max-loss', type=float, default=MAX_RISK_PER_TRADE,\n help=f'Maximum loss per trade (default: ${MAX_RISK_PER_TRADE})')\n \n parser.add_argument('--min-pop', type=float, default=0.0,\n help='Minimum Probability of Profit %% (default: 0)')\n \n parser.add_argument('--min-width', type=float, default=1.0,\n help='Minimum spread width in dollars (default: 1)')\n \n parser.add_argument('--max-width', type=float, default=None,\n help='Maximum spread width in dollars (default: auto-calculate based on price)'),\n \n parser.add_argument('--json', '-j', action='store_true',\n help='Output JSON format')\n \n parser.add_argument('--account', type=float, default=DEFAULT_ACCOUNT_TOTAL,\n help=f'Total account balance (default: ${DEFAULT_ACCOUNT_TOTAL:.0f})')\n \n parser.add_argument('--max-risk', type=float, default=MAX_RISK_PER_TRADE,\n help=f'Maximum risk per trade in dollars (default: ${MAX_RISK_PER_TRADE:.0f})')\n \n parser.add_argument('--min-cash', type=float, default=MIN_CASH_BUFFER,\n help=f'Minimum cash buffer to keep in reserve (default: ${MIN_CASH_BUFFER:.0f})')\n \n parser.add_argument('--verbose', '-v', action='store_true',\n help='Verbose output')\n \n # Unified engine options\n parser.add_argument('--unified', action='store_true',\n help='Use unified QuantConvictionEngine for comprehensive analysis')\n \n parser.add_argument('--strategy', default='auto',\n choices=['auto', 'bull_put', 'bear_call', 'bull_call', 'bear_put', 'iron_condor'],\n help='Strategy for unified engine (default: auto)')\n \n parser.add_argument('--backtest', action='store_true',\n help='Run backtest validation with unified engine')\n \n parser.add_argument('--backtest-start', default='2022-01-01',\n help='Backtest start date (default: 2022-01-01)')\n \n parser.add_argument('--backtest-end', default='2024-01-01',\n help='Backtest end date (default: 2024-01-01)')\n \n args = parser.parse_args()\n \n # Validate\n if args.min_dte \u003c 0 or args.max_dte > 365:\n print(\"ERROR: DTE must be between 0 and 365\")\n sys.exit(1)\n \n if args.min_dte > args.max_dte:\n print(f\"ERROR: min-dte ({args.min_dte}) cannot exceed max-dte ({args.max_dte})\")\n sys.exit(1)\n \n if not (0 \u003c= args.min_pop \u003c= 100):\n print(\"ERROR: Min POP must be between 0 and 100 (inclusive)\")\n sys.exit(1)\n \n if args.account \u003c= 0:\n print(\"ERROR: Account balance must be positive\")\n sys.exit(1)\n \n if args.max_loss \u003c= 0:\n print(\"ERROR: Max loss must be positive\")\n sys.exit(1)\n \n if args.max_loss > args.account:\n print(f\"ERROR: Max loss cannot exceed account total (${args.account:.0f})\")\n sys.exit(1)\n \n if args.max_width is not None and args.min_width > args.max_width:\n print(f\"ERROR: min-width ({args.min_width}) cannot exceed max-width ({args.max_width})\")\n sys.exit(1)\n \n # Run scan\n scanner = QuantScanner(account_total=args.account,\n max_risk_per_trade=args.max_risk,\n min_cash_buffer=args.min_cash)\n \n if args.backtest and args.unified:\n # Run backtest validation\n print(f\"\\n{'='*70}\")\n print(f\"BACKTEST VALIDATION MODE\")\n print(f\"{'='*70}\")\n print(f\"Period: {args.backtest_start} to {args.backtest_end}\")\n print(f\"Tickers: {', '.join(args.tickers)}\")\n print(f\"Strategy: {args.strategy}\")\n print(f\"\\nRunning walk-forward backtest...\")\n \n engine = QuantConvictionEngine(args.account)\n report = engine.run_backtest_validation(\n args.tickers,\n start_date=args.backtest_start,\n end_date=args.backtest_end,\n strategy=args.strategy\n )\n \n if args.json:\n print(json.dumps(report, indent=2))\n else:\n print(\"\\n\" + \"=\"*70)\n print(\"BACKTEST VALIDATION REPORT\")\n print(\"=\"*70)\n \n if 'error' in report:\n print(f\"Error: {report['error']}\")\n else:\n print(f\"\\nTier Statistics:\")\n for tier, stats in report.get('tier_stats', {}).items():\n if stats.get('count', 0) > 0:\n print(f\" {tier:8s}: n={stats['count']:3d}, \"\n f\"win_rate={stats['win_rate']:.1%}, \"\n f\"exp=${stats['expectancy']:.0f}\")\n \n print(f\"\\nStatistical Tests:\")\n for test, p_val in report.get('p_values', {}).items():\n sig = \"***\" if p_val \u003c 0.01 else \"**\" if p_val \u003c 0.05 else \"*\" if p_val \u003c 0.1 else \"\"\n print(f\" {test}: p={p_val:.4f} {sig}\")\n \n print(f\"\\nTier Separation Score: {report.get('tier_separation_score', 0):.2f}\")\n print(f\"Overall Expectancy: ${report.get('overall_expectancy', 0):.0f}\")\n print(f\"Recommendation: {report.get('recommendation', 'UNKNOWN')}\")\n \n print(\"=\"*70)\n \n sys.exit(0)\n \n # Normal scan mode\n results = scanner.scan_multiple(\n tickers=args.tickers,\n mode=args.mode,\n min_dte=args.min_dte,\n max_dte=args.max_dte,\n max_loss_limit=args.max_loss,\n min_pop=args.min_pop / 100.0, # Convert percentage to decimal\n min_width=args.min_width,\n max_width=args.max_width,\n json_output=args.json,\n use_unified_engine=args.unified,\n unified_strategy=args.strategy\n )\n \n if args.json:\n print(json.dumps(results, indent=2))\n \n # Exit 0 if any results found, 1 if no viable strategies at all\n sys.exit(0 if results else 1)\n\n\nif __name__ == '__main__':\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":42685,"content_sha256":"da033a1b2d6274669c6dfd7d83e556a2c43740f062aea13bd1f8d87c4e0cdb6a"},{"filename":"scripts/quantitative_integration.py","content":"#!/usr/bin/env python3\n\"\"\"\n===============================================================================\nQuantitative Integration — Regime & Vol-Aware Conviction Engine\n===============================================================================\n\nIntegration module for the Options Spread Conviction Engine that provides:\n- Regime-aware scoring\n- Volatility forecasting with GARCH\n- Enhanced Kelly position sizing\n- Walk-forward backtesting\n\nThis module re-exports the canonical QuantConvictionEngine from quant_scanner.py\nfor backwards compatibility.\n\nUsage:\n from quantitative_integration import QuantConvictionEngine\n \n engine = QuantConvictionEngine()\n result = engine.analyze(\"AAPL\", \"bull_put\", regime_aware=True, vol_aware=True)\n \n # Get position sizing\n sizing = engine.calculate_position(result, pop=0.65, max_loss=80)\n\n===============================================================================\n\"\"\"\n\nfrom __future__ import annotations\n\nimport warnings\nfrom typing import Optional, Dict, Any, Tuple, List\n\n# Import and extend the canonical QuantConvictionEngine from quant_scanner\n# This eliminates duplicate implementations while allowing for extension\nfrom quant_scanner import QuantConvictionEngine as BaseEngine\n\nclass QuantConvictionEngine(BaseEngine):\n \"\"\"\n Extended quantitative conviction engine.\n \n Inherits from quant_scanner.QuantConvictionEngine for unified implementation.\n Additional functionality can be added here as needed.\n \"\"\"\n pass\n\n# Keep these imports for backwards compatibility\ntry:\n from regime_detector import RegimeDetector, get_current_regime\n HAS_REGIME = True\nexcept ImportError as e:\n HAS_REGIME = False\n warnings.warn(f\"RegimeDetector not available: {e}\", UserWarning)\n\ntry:\n from vol_forecaster import VolatilityForecaster, VRPSignal\n HAS_VOL = True\nexcept ImportError as e:\n HAS_VOL = False\n warnings.warn(f\"VolatilityForecaster not available: {e}\", UserWarning)\n\ntry:\n from enhanced_kelly import EnhancedKellySizer, PositionResult\n HAS_KELLY = True\nexcept ImportError as e:\n HAS_KELLY = False\n warnings.warn(f\"EnhancedKellySizer not available: {e}\", UserWarning)\n\ntry:\n from backtest_validator import BacktestValidator, ValidationReport\n HAS_BACKTEST = True\nexcept ImportError as e:\n HAS_BACKTEST = False\n warnings.warn(f\"BacktestValidator not available: {e}\", UserWarning)\n\n\n# Suppress warnings\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n\n\n# Keep QuantResult for backwards compatibility\nfrom dataclasses import dataclass\nfrom spread_conviction_engine import ConvictionResult\n\n@dataclass\nclass QuantResult:\n \"\"\"Extended result with quantitative analysis.\"\"\"\n base_result: ConvictionResult\n regime: Optional[str] = None\n regime_adjustment: float = 0.0\n regime_reasoning: str = \"\"\n vrp_signal: Optional[VRPSignal] = None\n vrp_adjustment: float = 0.0\n vrp_reasoning: str = \"\"\n final_score: float = 0.0\n kelly_sizing: Optional[Dict[str, Any]] = None\n\n\ndef format_quant_report(quant_result: QuantResult, include_kelly: bool = True) -> str:\n \"\"\"\n Format quantitative analysis report.\n \n Args:\n quant_result: Result from QuantConvictionEngine.analyze().\n include_kelly: Whether to include Kelly sizing in report.\n \n Returns:\n Formatted string report.\n \"\"\"\n base = quant_result.base_result\n \n lines = [\n \"\",\n \"=\" * 70,\n f\" QUANTITATIVE CONVICTION REPORT: {base.ticker}\",\n \"=\" * 70,\n f\" Base Score: {base.conviction_score:.1f}/100 ({base.tier})\",\n ]\n \n if quant_result.regime:\n lines.append(f\" Regime: {quant_result.regime}\")\n lines.append(f\" Regime Adj: {quant_result.regime_adjustment:+.1f}\")\n lines.append(f\" → {quant_result.regime_reasoning}\")\n \n if quant_result.vrp_signal:\n lines.append(f\" VRP: {quant_result.vrp_signal.vrp:+.1%}\")\n lines.append(f\" VRP Adj: {quant_result.vrp_adjustment:+.1f}\")\n lines.append(f\" → {quant_result.vrp_reasoning}\")\n \n lines.extend([\n \"-\" * 70,\n f\" FINAL SCORE: {quant_result.final_score:.1f}/100\",\n \"=\" * 70,\n ])\n \n return \"\\n\".join(lines)\n\n\n# =============================================================================\n# CLI Integration\n# =============================================================================\n\ndef main():\n \"\"\"CLI entry point for quantitative analysis.\"\"\"\n import argparse\n \n parser = argparse.ArgumentParser(\n description=\"Quantitative Conviction Engine — Extended analysis\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=\"\"\"\nExamples:\n python quantitative_integration.py AAPL --regime-aware\n python quantitative_integration.py SPY --vol-aware\n python quantitative_integration.py TSLA --regime-aware --vol-aware --json\n python quantitative_integration.py --backtest SPY QQQ --start 2022-01-01 --end 2024-01-01\n \"\"\",\n )\n parser.add_argument(\"tickers\", nargs=\"*\", help=\"Ticker symbols\")\n parser.add_argument(\"--strategy\", default=\"bull_put\", help=\"Strategy type\")\n parser.add_argument(\"--regime-aware\", action=\"store_true\", help=\"Apply regime detection\")\n parser.add_argument(\"--vol-aware\", action=\"store_true\", help=\"Apply VRP analysis\")\n parser.add_argument(\"--iv\", type=float, help=\"Implied volatility override\")\n parser.add_argument(\"--pop\", type=float, help=\"Probability of profit for Kelly sizing\")\n parser.add_argument(\"--max-loss\", type=float, help=\"Max loss per contract\")\n parser.add_argument(\"--win-amount\", type=float, help=\"Max win per contract\")\n parser.add_argument(\"--backtest\", action=\"store_true\", help=\"Run backtest\")\n parser.add_argument(\"--start\", default=\"2022-01-01\", help=\"Backtest start date\")\n parser.add_argument(\"--end\", default=\"2024-01-01\", help=\"Backtest end date\")\n parser.add_argument(\"--hold-days\", type=int, default=5, help=\"Hold days for backtest\")\n parser.add_argument(\"--json\", action=\"store_true\", help=\"Output as JSON\")\n \n args = parser.parse_args()\n \n engine = QuantConvictionEngine()\n \n if args.backtest:\n if not args.tickers:\n print(\"Error: --backtest requires tickers\")\n return\n \n print(f\"Running backtest for {args.tickers}...\")\n report = engine.run_backtest(\n args.tickers,\n args.start,\n args.end,\n args.strategy,\n args.hold_days\n )\n \n if report:\n if args.json:\n import json\n print(json.dumps(report.to_dict(), indent=2))\n else:\n print(\"\\nBacktest Report:\")\n print(f\" Overall Expectancy: ${report.overall_expectancy:.0f}\")\n print(f\" Tier Separation: {report.tier_separation_score:.2f}\")\n print(f\" Recommendation: {report.recommendation}\")\n print(\"\\n Tier Statistics:\")\n for tier, stats in report.tier_stats.items():\n if stats.count > 0:\n print(f\" {tier}: n={stats.count}, win_rate={stats.win_rate:.1%}, \"\n f\"exp=${stats.expectancy:.0f}\")\n else:\n print(\"Backtest not available\")\n \n else:\n # Run analysis for each ticker\n results = []\n for ticker in args.tickers:\n try:\n result = engine.analyze(\n ticker,\n args.strategy,\n regime_aware=args.regime_aware,\n vol_aware=args.vol_aware,\n iv_override=args.iv\n )\n results.append(result)\n \n if not args.json:\n print(format_quant_report(result))\n \n # Show Kelly sizing if POP provided\n if args.pop and args.max_loss:\n win = args.win_amount or args.max_loss * 0.5\n sizing = engine.calculate_position(\n result, args.pop, args.max_loss, win, ticker=ticker\n )\n if sizing:\n print(f\"\\n Position Sizing:\")\n print(f\" Contracts: {sizing.contracts}\")\n print(f\" Total Risk: ${sizing.total_risk:.0f}\")\n print(f\" Kelly Frac: {sizing.kelly_fraction:.2%}\")\n print(f\" Recommendation: {sizing.recommendation}\")\n print(\"=\" * 70)\n \n except Exception as e:\n print(f\"Error analyzing {ticker}: {e}\")\n \n if args.json and results:\n import json\n output = []\n for r in results:\n out = {\n \"ticker\": r.base_result.ticker,\n \"base_score\": r.base_result.conviction_score,\n \"base_tier\": r.base_result.tier,\n \"final_score\": r.final_score,\n \"regime\": r.regime,\n \"regime_adjustment\": r.regime_adjustment,\n \"vrp\": r.vrp_signal.vrp if r.vrp_signal else None,\n \"vrp_adjustment\": r.vrp_adjustment,\n }\n if r.kelly_sizing:\n out[\"kelly_sizing\"] = r.kelly_sizing\n output.append(out)\n print(json.dumps(output, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":9765,"content_sha256":"915588db9cece5f3e72cc48d631cc9e2c569a3c5fe209b99b836d7cf1f09305c"},{"filename":"scripts/regime_detector.py","content":"\"\"\"\nMarket Regime Detection for Options Conviction Engine\nClassifies market state using VIX percentiles\n\nAuthor: Leonardo Da Pinchy\nVersion: 1.0.0\n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport yfinance as yf\nfrom datetime import datetime, timedelta\nfrom typing import Tuple, Dict, Optional, Literal\nfrom dataclasses import dataclass\nimport warnings\nimport threading\n\n\n@dataclass\nclass RegimeResult:\n \"\"\"Container for regime detection results.\"\"\"\n regime: str\n confidence: float\n vix_level: float\n percentile: float\n vix_ma20: float\n threshold_low: float\n threshold_high: float\n lookback_days: int\n regime_metadata: Dict\n\n\nclass RegimeDetector:\n \"\"\"\n Detects market regime based on VIX levels and percentiles.\n \n Regimes:\n - CRISIS: VIX > 80th percentile (extreme fear)\n - HIGH_VOL: VIX 60-80th percentile (elevated volatility)\n - NORMAL: VIX 40-60th percentile (normal conditions)\n - LOW_VOL: VIX 20-40th percentile (compressed volatility)\n - EUPHORIA: VIX \u003c 20th percentile (complacency)\n \n The VIX (CBOE Volatility Index) measures implied volatility of S&P 500\n options and serves as a \"fear gauge\" for the broader market.\n \n References:\n - Whaley, R. (2000). \"The Investor Fear Gauge.\" Journal of Portfolio Management\n - Sinclair, E. (2013). \"Volatility Trading.\" Wiley\n \"\"\"\n \n REGIMES = ['CRISIS', 'HIGH_VOL', 'NORMAL', 'LOW_VOL', 'EUPHORIA']\n \n # Default lookback for percentile calculation (252 trading days = 1 year)\n DEFAULT_LOOKBACK = 252\n \n # Regime thresholds (percentiles)\n THRESHOLDS = {\n 'CRISIS': (80.0, 100.0),\n 'HIGH_VOL': (60.0, 80.0),\n 'NORMAL': (40.0, 60.0),\n 'LOW_VOL': (20.0, 40.0),\n 'EUPHORIA': (0.0, 20.0)\n }\n \n def __init__(self, lookback_days: int = DEFAULT_LOOKBACK):\n \"\"\"\n Initialize RegimeDetector.\n \n Args:\n lookback_days: Number of trading days for percentile calculation (default: 252)\n \"\"\"\n self.lookback = lookback_days\n self.vix_ticker = \"^VIX\"\n self._vix_cache: Optional[pd.Series] = None\n self._cache_date: Optional[datetime] = None\n self._cache_lock = threading.Lock()\n self.cache_ttl = 3600 # Cache TTL in seconds (configurable)\n \n def fetch_vix_history(self, end_date: Optional[datetime] = None) -> pd.Series:\n \"\"\"\n Fetch VIX historical data for percentile calculation.\n \n Args:\n end_date: End date for historical data (default: today)\n \n Returns:\n Series of VIX closing prices indexed by date\n \n Raises:\n ValueError: If unable to fetch VIX data\n \"\"\"\n if end_date is None:\n end_date = datetime.now()\n \n # Need extra days for lookback + percentile calculation buffer\n start_date = end_date - timedelta(days=int(self.lookback * 1.5) + 30)\n \n try:\n vix = yf.Ticker(self.vix_ticker)\n hist = vix.history(start=start_date, end=end_date)\n \n if hist.empty:\n raise ValueError(\"No VIX data returned from yfinance\")\n \n # Use closing prices\n closes = hist['Close'].dropna()\n \n if len(closes) \u003c self.lookback // 2:\n warnings.warn(f\"Limited VIX data: only {len(closes)} days available\")\n \n return closes\n \n except Exception as e:\n raise ValueError(f\"Failed to fetch VIX data: {e}\")\n \n def calculate_percentile(self, current_vix: float, \n vix_history: pd.Series,\n date: Optional[datetime] = None) -> float:\n \"\"\"\n Calculate VIX percentile rank over lookback period.\n \n Args:\n current_vix: Current VIX level\n vix_history: Historical VIX series\n date: Optional date being evaluated (for excluding current observation)\n \n Returns:\n Percentile rank (0-100)\n \"\"\"\n if len(vix_history) \u003c 60: # Require at least 60 days\n raise ValueError(f\"Insufficient VIX history: {len(vix_history)} days (min 60)\")\n \n # Use last lookback_days for percentile calculation\n recent = vix_history.tail(self.lookback)\n \n # Exclude current observation from historical comparison\n if date is None or date >= vix_history.index[-1]:\n comparison_data = recent.iloc[:-1] # Exclude most recent\n else:\n comparison_data = recent\n \n percentile = (comparison_data \u003c= current_vix).mean() * 100\n \n return float(percentile)\n \n def detect_regime(self, date: Optional[datetime] = None,\n use_cache: bool = True) -> RegimeResult:\n \"\"\"\n Detect current market regime based on VIX percentiles.\n \n Args:\n date: Date to evaluate (default: latest available)\n use_cache: Whether to use cached VIX data if available\n \n Returns:\n RegimeResult with regime classification and metadata\n \"\"\"\n # Fetch VIX history with thread-safe cache\n with self._cache_lock:\n cache_valid = (\n use_cache and \n self._vix_cache is not None and \n self._cache_date is not None and\n (datetime.now() - self._cache_date).seconds \u003c self.cache_ttl\n )\n \n if cache_valid:\n vix_history = self._vix_cache\n else:\n vix_history = self.fetch_vix_history(date)\n if use_cache:\n self._vix_cache = vix_history\n self._cache_date = datetime.now()\n \n # Get current VIX level\n if date is not None:\n # Find nearest available date\n mask = vix_history.index \u003c= pd.Timestamp(date)\n if not mask.any():\n raise ValueError(f\"No VIX data available for date {date}\")\n current_vix = vix_history[mask].iloc[-1]\n available_date = vix_history[mask].index[-1]\n else:\n current_vix = vix_history.iloc[-1]\n available_date = vix_history.index[-1]\n \n # Calculate percentile\n percentile = self.calculate_percentile(current_vix, vix_history, date)\n \n # Calculate 20-day moving average for trend context\n vix_ma20 = vix_history.tail(20).mean()\n \n # Determine regime\n regime = self._percentile_to_regime(percentile)\n \n # Calculate confidence (distance from nearest threshold, normalized)\n confidence = self._calculate_confidence(percentile, regime)\n \n # Get threshold boundaries\n threshold_low, threshold_high = self.THRESHOLDS[regime]\n \n # Build metadata\n metadata = {\n 'available_date': available_date.strftime('%Y-%m-%d') if hasattr(available_date, 'strftime') else str(available_date),\n 'data_points': len(vix_history),\n 'vix_vs_ma20': 'above' if current_vix > vix_ma20 else 'below',\n 'percentile_distance_from_center': abs(percentile - 50),\n 'regime_duration_estimate': self._estimate_regime_duration(vix_history, regime)\n }\n \n return RegimeResult(\n regime=regime,\n confidence=confidence,\n vix_level=round(current_vix, 2),\n percentile=round(percentile, 1),\n vix_ma20=round(vix_ma20, 2),\n threshold_low=threshold_low,\n threshold_high=threshold_high,\n lookback_days=self.lookback,\n regime_metadata=metadata\n )\n \n def _percentile_to_regime(self, percentile: float) -> str:\n \"\"\"Convert percentile to regime classification.\"\"\"\n if percentile >= 80:\n return 'CRISIS'\n elif percentile >= 60:\n return 'HIGH_VOL'\n elif percentile >= 40:\n return 'NORMAL'\n elif percentile >= 20:\n return 'LOW_VOL'\n else:\n return 'EUPHORIA'\n \n def _calculate_confidence(self, percentile: float, regime: str) -> float:\n \"\"\"\n Calculate confidence score based on distance from threshold.\n \n Returns 0.5 to 1.0 where:\n - 1.0 = Deep in regime (far from threshold)\n - 0.5 = At threshold boundary\n \"\"\"\n low, high = self.THRESHOLDS[regime]\n \n if regime == 'CRISIS':\n distance = (percentile - low) / 20 # Always 80-100 range\n elif regime == 'EUPHORIA':\n distance = (high - percentile) / 20 # Always 0-20 range\n else:\n dist_to_low = percentile - low\n dist_to_high = high - percentile\n distance = max(dist_to_low, dist_to_high) / 20\n \n return round(0.5 + (distance * 0.5), 3)\n \n def _estimate_regime_duration(self, vix_history: pd.Series, \n current_regime: str) -> int:\n \"\"\"Count consecutive days in current regime.\"\"\"\n if len(vix_history) \u003c 20:\n return 0\n \n # Calculate regime for each historical day\n recent = vix_history.tail(60)\n regimes = []\n \n for i in range(len(recent)):\n # Calculate percentile using data up to that point\n window = recent.iloc[:i+1]\n if len(window) \u003c 20:\n regimes.append(None)\n continue\n current_vix = window.iloc[-1]\n comparison = window.iloc[:-1] if len(window) > 1 else window\n pct = (comparison \u003c= current_vix).mean() * 100 if len(comparison) > 0 else 50\n regimes.append(self._percentile_to_regime(pct))\n \n # Count consecutive current regime at end\n count = 0\n for regime in reversed(regimes):\n if regime == current_regime:\n count += 1\n else:\n break\n return count\n \n def get_regime_weights(self, regime: str) -> Dict[str, Dict[str, float]]:\n \"\"\"\n Return weight adjustments per regime for each strategy.\n \n These adjustments modify the conviction score based on how well\n a strategy fits the current market regime.\n \n Philosophy:\n - CRISIS/HIGH_VOL: Credit spreads benefit from elevated IV (selling premium)\n - LOW_VOL/EUPHORIA: Debit spreads benefit from compressed IV (buying options)\n - NORMAL: No systematic edge from regime\n \n Returns:\n Dict mapping strategy names to weight adjustment dicts.\n Adjustments are additive to base conviction scores.\n \"\"\"\n adjustments = {\n 'CRISIS': {\n 'bull_put': {'credit_boost': 0.15, 'mean_reversion_boost': 0.10},\n 'bear_call': {'credit_boost': 0.15, 'mean_reversion_boost': 0.10},\n 'iron_condor': {'premium_boost': 0.25, 'iv_edge': 0.15},\n 'butterfly': {'range_bound_boost': 0.10},\n 'calendar': {'iv_term_structure_boost': 0.20},\n 'bull_call': {'debit_penalty': -0.15, 'breakout_penalty': -0.10},\n 'bear_put': {'debit_penalty': -0.15, 'breakout_penalty': -0.10}\n },\n 'HIGH_VOL': {\n 'bull_put': {'credit_boost': 0.12, 'mean_reversion_boost': 0.08},\n 'bear_call': {'credit_boost': 0.12, 'mean_reversion_boost': 0.08},\n 'iron_condor': {'premium_boost': 0.18, 'iv_edge': 0.10},\n 'butterfly': {'range_bound_boost': 0.08},\n 'calendar': {'iv_term_structure_boost': 0.15},\n 'bull_call': {'debit_penalty': -0.10, 'breakout_penalty': -0.05},\n 'bear_put': {'debit_penalty': -0.10, 'breakout_penalty': -0.05}\n },\n 'NORMAL': {\n 'bull_put': {},\n 'bear_call': {},\n 'iron_condor': {},\n 'butterfly': {},\n 'calendar': {},\n 'bull_call': {},\n 'bear_put': {}\n },\n 'LOW_VOL': {\n 'bull_put': {'credit_penalty': -0.08, 'mean_reversion_penalty': -0.05},\n 'bear_call': {'credit_penalty': -0.08, 'mean_reversion_penalty': -0.05},\n 'iron_condor': {'premium_penalty': -0.12, 'iv_edge': -0.08},\n 'butterfly': {'range_bound_penalty': -0.05},\n 'calendar': {'iv_term_structure_penalty': -0.10},\n 'bull_call': {'debit_boost': 0.12, 'breakout_boost': 0.08},\n 'bear_put': {'debit_boost': 0.12, 'breakout_boost': 0.08}\n },\n 'EUPHORIA': {\n 'bull_put': {'credit_penalty': -0.15, 'mean_reversion_penalty': -0.12},\n 'bear_call': {'credit_penalty': -0.15, 'mean_reversion_penalty': -0.12},\n 'iron_condor': {'premium_penalty': -0.20, 'iv_edge': -0.15},\n 'butterfly': {'range_bound_penalty': -0.10},\n 'calendar': {'iv_term_structure_penalty': -0.15},\n 'bull_call': {'debit_boost': 0.18, 'breakout_boost': 0.15, 'momentum_boost': 0.10},\n 'bear_put': {'debit_boost': 0.18, 'breakout_boost': 0.15, 'contrarian_boost': 0.08}\n }\n }\n \n return adjustments.get(regime, adjustments['NORMAL'])\n \n def regime_aware_score(self, base_score: float, regime: str,\n strategy: str) -> Tuple[float, str]:\n \"\"\"\n Adjust conviction score based on regime fit.\n \n Args:\n base_score: Original conviction score (0-100)\n regime: Current market regime\n strategy: Options strategy being evaluated\n \n Returns:\n Tuple of (adjusted_score, reasoning)\n \"\"\"\n weights = self.get_regime_weights(regime)\n strategy_weights = weights.get(strategy, {})\n \n if not strategy_weights:\n return base_score, f\"No regime adjustment for {strategy} in {regime} regime\"\n \n # Calculate total adjustment\n total_adjustment = sum(strategy_weights.values())\n \n # Apply adjustment (convert from fraction to points, max +/- 15 points)\n adjustment_points = total_adjustment * 100\n adjustment_points = max(-15, min(15, adjustment_points)) # Cap at +/- 15 points\n \n adjusted_score = base_score + adjustment_points\n adjusted_score = max(0, min(100, adjusted_score)) # Keep in valid range\n \n # Build reasoning\n reasons = []\n for key, value in strategy_weights.items():\n if value > 0:\n reasons.append(f\"+{value:.0%} {key.replace('_', ' ')}\")\n elif value \u003c 0:\n reasons.append(f\"{value:.0%} {key.replace('_', ' ')}\")\n \n reasoning = f\"{regime} regime: {', '.join(reasons)}\" if reasons else f\"Neutral in {regime} regime\"\n \n return round(adjusted_score, 1), reasoning\n \n def is_favorable_for_strategy(self, regime: str, strategy: str) -> Tuple[bool, str]:\n \"\"\"\n Quick check if regime is favorable for a given strategy.\n \n Returns:\n Tuple of (is_favorable, explanation)\n \"\"\"\n favorable_combos = {\n 'CRISIS': ['iron_condor', 'bull_put', 'bear_call', 'calendar'],\n 'HIGH_VOL': ['iron_condor', 'bull_put', 'bear_call'],\n 'NORMAL': ['all'], # No systematic bias\n 'LOW_VOL': ['bull_call', 'bear_put', 'butterfly'],\n 'EUPHORIA': ['bull_call', 'bear_put'] # Momentum plays\n }\n \n favorable = favorable_combos.get(regime, [])\n \n if 'all' in favorable or strategy in favorable:\n return True, f\"{regime} regime is favorable for {strategy}\"\n else:\n return False, f\"{regime} regime creates headwinds for {strategy}\"\n\n\n# Convenience function for quick regime check\ndef get_current_regime() -> RegimeResult:\n \"\"\"Get current market regime with default settings.\"\"\"\n detector = RegimeDetector()\n return detector.detect_regime()\n\n\nif __name__ == \"__main__\":\n \"\"\"Demo: Print current market regime and strategy recommendations.\"\"\"\n print(\"=\" * 70)\n print(\"MARKET REGIME DETECTION - OPTIONS CONVICTION ENGINE\")\n print(\"=\" * 70)\n \n try:\n detector = RegimeDetector()\n result = detector.detect_regime()\n \n print(f\"\\nCurrent Regime: {result.regime}\")\n print(f\"Confidence: {result.confidence:.1%}\")\n print(f\"\\nVIX Level: {result.vix_level}\")\n print(f\"VIX Percentile: {result.percentile}%\")\n print(f\"VIX 20-Day MA: {result.vix_ma20}\")\n print(f\"Regime Threshold: {result.threshold_low}% - {result.threshold_high}%\")\n print(f\"\\nEstimated Regime Duration: {result.regime_metadata['regime_duration_estimate']} days\")\n print(f\"VIX vs 20-MA: {result.regime_metadata['vix_vs_ma20']}\")\n \n print(\"\\n\" + \"-\" * 70)\n print(\"STRATEGY RECOMMENDATIONS\")\n print(\"-\" * 70)\n \n strategies = ['bull_put', 'bear_call', 'bull_call', 'bear_put', \n 'iron_condor', 'butterfly', 'calendar']\n \n for strategy in strategies:\n is_fav, explanation = detector.is_favorable_for_strategy(\n result.regime, strategy\n )\n weights = detector.get_regime_weights(result.regime).get(strategy, {})\n \n if weights:\n net_adjustment = sum(weights.values())\n adj_str = f\" (adjustment: {net_adjustment:+.0%})\"\n else:\n adj_str = \"\"\n \n status = \"✓ FAVORABLE\" if is_fav else \"⚠ CHALLENGING\"\n print(f\"\\n{strategy.upper()}: {status}{adj_str}\")\n print(f\" → {explanation}\")\n \n print(\"\\n\" + \"=\" * 70)\n \n except Exception as e:\n print(f\"Error detecting regime: {e}\")\n import traceback\n traceback.print_exc()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":18260,"content_sha256":"5a1a7442e3d2acae10eda34e82b279f18c4006ca082b644a7f85b8256f932fa2"},{"filename":"scripts/setup-venv.sh","content":"#!/usr/bin/env bash\n# Setup virtual environment for Options Spread Conviction Engine\n# This isolates dependencies to avoid system conflicts (numba/numpy)\n\nset -e\n\nSCRIPT_DIR=\"$(cd \"$(dirname \"${BASH_SOURCE[0]}\")\" && pwd)\"\nVENV_DIR=\"$HOME/.openclaw/venvs/options-spread-conviction-engine\"\n\necho \"📊 Setting up Options Spread Conviction Engine...\"\n\n# Check if Python 3 is available\nif ! command -v python3 &> /dev/null; then\n echo \"❌ Error: python3 is required but not installed\"\n exit 1\nfi\n\n# Create virtual environment if it doesn't exist\nif [ ! -d \"$VENV_DIR\" ]; then\n echo \"Creating virtual environment at $VENV_DIR...\"\n python3 -m venv \"$VENV_DIR\"\nfi\n\n# Activate and install dependencies\necho \"Installing dependencies (this may take a minute)...\"\nsource \"$VENV_DIR/bin/activate\"\n\n# Upgrade pip first\npip install --upgrade pip setuptools wheel\n\n# Install numpy first (required by pandas_ta)\necho \"Installing numpy...\"\npip install numpy\n\n# Install pandas and yfinance\necho \"Installing pandas and yfinance...\"\npip install pandas yfinance\n\n# Install pandas_ta without numba (Python 3.14+ compatibility)\necho \"Installing pandas_ta (pure Python mode, numba not available for Python 3.14)...\"\nNUMBA_DISABLE_JIT=1 pip install pandas_ta --no-deps\npip install scipy tqdm # Required dependencies\n\necho \"✅ Setup complete!\"\necho \"\"\necho \"Virtual environment: $VENV_DIR\"\necho \"Python: $(which python3)\"\necho \"\"\necho \"Test with: conviction-engine AAPL\"\n","content_type":"application/x-sh; charset=utf-8","language":"bash","size":1460,"content_sha256":"ce9dd309025d6478e81292363060e9dff05a9334987f552002d43b472c713df6"},{"filename":"scripts/spread_conviction_engine.py","content":"#!/usr/bin/env python3\n\"\"\"\n===============================================================================\nSpread Conviction Engine — Unified Multi-Strategy Vertical Spread Scoring\n===============================================================================\n\nAuthor: Financial Toolkit (OpenClaw)\nCreated: 2026-02-09\nVersion: 2.0.0\nLicense: MIT\n\n v2.0.0 Additions:\n - Multi-leg strategies: Iron Condors, Butterflies, Calendar Spreads\n - IV Rank approximation via Bollinger Bandwidth percentile\n - IV Term Structure analysis from live options chains\n - Squeeze detection for butterfly setups\n - See multi_leg_strategies.py for full implementation\n\nDescription:\n A unified conviction engine that scores four vertical spread strategies:\n\n ┌────────────┬────────┬──────────────────┬────────────────────────────────┐\n │ Strategy │ Type │ Philosophy │ Ideal Setup │\n ├────────────┼────────┼──────────────────┼────────────────────────────────┤\n │ bull_put │ Credit │ Mean Reversion │ Bullish trend + oversold dip │\n │ bear_call │ Credit │ Mean Reversion │ Bearish trend + overbought rip │\n │ bull_call │ Debit │ Breakout │ Strong bullish momentum │\n │ bear_put │ Debit │ Breakout │ Strong bearish momentum │\n └────────────┴────────┴──────────────────┴────────────────────────────────┘\n\n v1.1.0 Additions:\n - Volume Multiplier: Cross-references relative volume (RV) to momentum.\n - Dynamic Strike Suggestions: Recommends strikes based on Bollinger Band 1-sigma levels.\n\n This extends the original ``advanced_signals.py`` (Bull Put Spread only)\n to a general-purpose spread selection tool. All four indicator families\n (Ichimoku, RSI, MACD, Bollinger Bands) are computed identically; only\n the *interpretation and scoring weights* change per strategy.\n\n Credit spreads prioritise **mean-reversion** setups: buying dips\n (bull_put) or selling rips (bear_call) within a prevailing trend.\n\n Debit spreads prioritise **breakout** setups: strong directional\n momentum confirmed by expanding volatility (Bollinger Bandwidth).\n\nAcademic Notes:\n • Ichimoku → Trend structure & equilibrium (Hosoda, 1968)\n • RSI → Momentum & mean-reversion potential (Wilder, 1978)\n • MACD → Trend momentum & acceleration (Appel, 1979)\n • Bollinger → Volatility regime & price envelopes (Bollinger, 2001)\n • Combining orthogonal signals reduces false-positive rate compared to\n any single-indicator strategy (Pring, 2002; Murphy, 1999).\n\nDependencies:\n pandas >= 2.0, pandas_ta >= 0.4.0, yfinance >= 1.0\n\nUsage:\n $ python3 spread_conviction_engine.py AAPL\n $ python3 spread_conviction_engine.py SPY --strategy bear_call\n $ python3 spread_conviction_engine.py QQQ --strategy bull_call --period 2y\n $ python3 spread_conviction_engine.py AAPL MSFT --strategy bear_put --json\n\n===============================================================================\n\"\"\"\n\n# =============================================================================\n# Imports\n# =============================================================================\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport re\nimport sys\nimport warnings\nfrom dataclasses import dataclass, field, asdict\nfrom enum import Enum\nfrom typing import Any, Optional\n\n# Module version — single source of truth\n__version__ = \"2.0.0\"\n\nimport pandas as pd\nimport pandas_ta as ta\nimport yfinance as yf\n\n# Suppress noisy FutureWarnings and deprecation warnings from yfinance/pandas\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nwarnings.filterwarnings(\"ignore\", message=\".*deprecated.*\")\n\n\n# =============================================================================\n# Constants & Configuration\n# =============================================================================\n\n# Indicator parameters — identical to advanced_signals.py.\n# Ichimoku uses extended (~3×) periods to filter short-term noise and align\n# the cloud with intermediate-term trend structure suitable for multi-week\n# options positions. With senkou=120 and kijun=60, approximately 180 trading\n# days of history are required before the cloud is fully populated.\nICHIMOKU_TENKAN: int = 20 # Conversion Line (Tenkan-sen); standard = 9\nICHIMOKU_KIJUN: int = 60 # Base Line (Kijun-sen); standard = 26\nICHIMOKU_SENKOU: int = 120 # Leading Span B (Senkou Span B); standard = 52\nICHIMOKU_CHIKOU: int = 30 # Lagging Span displacement; standard = 26\nRSI_LENGTH: int = 14\nADX_LENGTH: int = 14\nMACD_FAST: int = 12\nMACD_SLOW: int = 26\nMACD_SIGNAL: int = 9\nBBANDS_LENGTH: int = 20\nBBANDS_STD: float = 2.0\nVOLUME_WINDOW: int = 20\n\n\n# =============================================================================\n# Strategy Framework\n# =============================================================================\n\nclass StrategyType(str, Enum):\n \"\"\"\n Supported vertical spread strategies.\n\n Properties expose directional and credit/debit classification so that\n scoring functions can branch cleanly without string comparisons.\n \"\"\"\n BULL_PUT = \"bull_put\"\n BEAR_CALL = \"bear_call\"\n BULL_CALL = \"bull_call\"\n BEAR_PUT = \"bear_put\"\n\n @property\n def is_bullish(self) -> bool:\n \"\"\"True for strategies that profit from upward price movement.\"\"\"\n return self in (StrategyType.BULL_PUT, StrategyType.BULL_CALL)\n\n @property\n def is_bearish(self) -> bool:\n \"\"\"True for strategies that profit from downward price movement.\"\"\"\n return self in (StrategyType.BEAR_CALL, StrategyType.BEAR_PUT)\n\n @property\n def is_credit(self) -> bool:\n \"\"\"True for net-credit strategies (mean-reversion philosophy).\"\"\"\n return self in (StrategyType.BULL_PUT, StrategyType.BEAR_CALL)\n\n @property\n def is_debit(self) -> bool:\n \"\"\"True for net-debit strategies (breakout philosophy).\"\"\"\n return self in (StrategyType.BULL_CALL, StrategyType.BEAR_PUT)\n\n @property\n def label(self) -> str:\n \"\"\"Human-friendly strategy label for reports.\"\"\"\n labels = {\n StrategyType.BULL_PUT: \"Bull Put Spread (Credit)\",\n StrategyType.BEAR_CALL: \"Bear Call Spread (Credit)\",\n StrategyType.BULL_CALL: \"Bull Call Spread (Debit)\",\n StrategyType.BEAR_PUT: \"Bear Put Spread (Debit)\",\n }\n return labels[self]\n\n @property\n def philosophy(self) -> str:\n \"\"\"Trading philosophy label.\"\"\"\n return \"Mean Reversion\" if self.is_credit else \"Breakout / Momentum\"\n\n @property\n def ideal_setup(self) -> str:\n \"\"\"One-line description of the ideal market conditions.\"\"\"\n setups = {\n StrategyType.BULL_PUT: \"Bullish trend + oversold pullback → bounce expected\",\n StrategyType.BEAR_CALL: \"Bearish trend + overbought rally → rejection expected\",\n StrategyType.BULL_CALL: \"Strong bullish momentum + expanding volatility → breakout\",\n StrategyType.BEAR_PUT: \"Strong bearish momentum + expanding volatility → breakdown\",\n }\n return setups[self]\n\n\n@dataclass(frozen=True)\nclass StrategyWeights:\n \"\"\"\n Per-strategy component weights (must sum to 100).\n\n Credit spreads give more weight to RSI (mean-reversion entry timing)\n and Ichimoku (trend structure to revert within).\n\n Debit spreads give more weight to MACD (momentum confirmation) and\n maintain Bollinger weight (bandwidth expansion validates breakout).\n \"\"\"\n ichimoku: int\n rsi: int\n macd: int\n bollinger: int\n adx: int\n\n def __post_init__(self) -> None:\n total = self.ichimoku + self.rsi + self.macd + self.bollinger + self.adx\n assert total == 100, f\"Weights must sum to 100, got {total}\"\n\n\nSTRATEGY_WEIGHTS: dict[StrategyType, StrategyWeights] = {\n # Credit: Trend structure (25) + entry timing (20) + momentum (15) + vol (25) + strength (15)\n StrategyType.BULL_PUT: StrategyWeights(ichimoku=25, rsi=20, macd=15, bollinger=25, adx=15),\n StrategyType.BEAR_CALL: StrategyWeights(ichimoku=25, rsi=20, macd=15, bollinger=25, adx=15),\n # Debit: Trend confirm (20) + direction (10) + momentum (30) + vol (25) + strength (15)\n StrategyType.BULL_CALL: StrategyWeights(ichimoku=20, rsi=10, macd=30, bollinger=25, adx=15),\n StrategyType.BEAR_PUT: StrategyWeights(ichimoku=20, rsi=10, macd=30, bollinger=25, adx=15),\n}\n\n\n# =============================================================================\n# Enumerations — Readable Signal Labels\n# =============================================================================\n\nclass TrendBias(str, Enum):\n \"\"\"Qualitative trend classification (objective, strategy-independent).\"\"\"\n STRONG_BULL = \"STRONG_BULL\"\n BULL = \"BULL\"\n NEUTRAL = \"NEUTRAL\"\n BEAR = \"BEAR\"\n STRONG_BEAR = \"STRONG_BEAR\"\n\n\nclass ConvictionTier(str, Enum):\n \"\"\"\n Maps the raw 0–100 score to an actionable tier.\n\n The tiers encode a patience framework:\n - WAIT: Conditions are poor for this strategy. Do nothing.\n - WATCH: Getting interesting. Add to watchlist.\n - PREPARE: Conditions are favourable. Size the trade.\n - EXECUTE: High conviction. Enter the spread.\n \"\"\"\n WAIT = \"WAIT\" # 0–39\n WATCH = \"WATCH\" # 40–59\n PREPARE = \"PREPARE\" # 60–79\n EXECUTE = \"EXECUTE\" # 80–100\n\n @classmethod\n def from_score(cls, score: float) -> \"ConvictionTier\":\n \"\"\"Classify a numeric conviction score into an action tier.\"\"\"\n if score >= 80:\n return cls.EXECUTE\n elif score >= 60:\n return cls.PREPARE\n elif score >= 40:\n return cls.WATCH\n else:\n return cls.WAIT\n\n\n# =============================================================================\n# Data Classes — Structured Signal Output\n# =============================================================================\n\n@dataclass\nclass IchimokuSignal:\n \"\"\"\n Ichimoku Kinko Hyo signal decomposition.\n\n Attributes:\n price_vs_cloud: 'ABOVE', 'BELOW', or 'INSIDE'\n tk_cross: 'BULLISH' or 'BEARISH' (Tenkan vs Kijun)\n cloud_color: 'GREEN' if Senkou A > Senkou B, else 'RED'\n cloud_thickness: Absolute distance between Senkou A and B\n tenkan: Current Tenkan-sen value\n kijun: Current Kijun-sen value\n senkou_a: Current Senkou Span A value\n senkou_b: Current Senkou Span B value\n component_score: Sub-score contribution (0 to weight)\n \"\"\"\n price_vs_cloud: str\n tk_cross: str\n cloud_color: str\n cloud_thickness: float\n tenkan: float\n kijun: float\n senkou_a: float\n senkou_b: float\n component_score: float = 0.0\n\n\n@dataclass\nclass RSISignal:\n \"\"\"\n Relative Strength Index signal.\n\n Attributes:\n value: Current RSI reading\n zone: Human-readable zone label (strategy-specific)\n component_score: Sub-score contribution (0 to weight)\n \"\"\"\n value: float\n zone: str\n component_score: float = 0.0\n\n\n@dataclass\nclass MACDSignal:\n \"\"\"\n Moving Average Convergence Divergence signal.\n\n Attributes:\n macd_value: MACD line value\n signal_value: Signal line value\n histogram: Current histogram bar\n hist_direction: 'RISING', 'FALLING', or 'FLAT'\n crossover: 'BULLISH_CROSS', 'BEARISH_CROSS', or 'NONE'\n macd_above_signal: True if MACD line > Signal line\n component_score: Sub-score contribution (0 to weight)\n \"\"\"\n macd_value: float\n signal_value: float\n histogram: float\n hist_direction: str\n crossover: str\n macd_above_signal: bool\n component_score: float = 0.0\n\n\n@dataclass\nclass BollingerSignal:\n \"\"\"\n Bollinger Bands signal.\n\n Key metrics:\n %B = (Price − Lower) / (Upper − Lower)\n 0 → at lower band, 0.5 → at SMA, 1.0 → at upper band\n Bandwidth = (Upper − Lower) / Middle × 100\n\n Attributes:\n upper: Upper Bollinger Band\n middle: Middle Band (SMA)\n lower: Lower Bollinger Band\n percent_b: %B value\n bandwidth: Normalised bandwidth\n component_score: Sub-score contribution (0 to weight)\n \"\"\"\n upper: float\n middle: float\n lower: float\n percent_b: float\n bandwidth: float\n component_score: float = 0.0\n\n\n@dataclass\nclass ADXSignal:\n \"\"\"\n Average Directional Index (ADX) signal.\n\n Attributes:\n value: Current ADX value\n trend_strength: Label (e.g., 'WEAK', 'MODERATE', 'STRONG')\n component_score: Sub-score contribution (0 to weight)\n \"\"\"\n value: float\n trend_strength: str\n component_score: float = 0.0\n\n\n@dataclass\nclass VolumeSignal:\n \"\"\"\n Volume analysis signal.\n\n Attributes:\n relative_volume: Current volume / 20-day SMA volume\n is_elevated: True if relative_volume > 1.25\n adjustment: Score adjustment (+/- points) based on volume strength\n \"\"\"\n relative_volume: float\n is_elevated: bool\n adjustment: float = 0.0\n\n\n@dataclass\nclass SuggestedStrikes:\n \"\"\"\n Dynamic strike recommendations based on volatility bands.\n \"\"\"\n short_strike: float\n long_strike: float\n description: str\n\n\n@dataclass\nclass ConvictionResult:\n \"\"\"\n Final output of the Spread Conviction Engine.\n\n Attributes:\n ticker: Symbol analysed\n strategy: Strategy type string\n strategy_label: Human-friendly strategy name\n price: Latest closing price\n conviction_score: Aggregate score (0–100)\n tier: Action tier (WAIT / WATCH / PREPARE / EXECUTE)\n trend_bias: Overall qualitative trend assessment\n volume: Volume strength signal\n strikes: Recommended short/long strikes\n data_quality: Assessment of input data (HIGH, MEDIUM, LOW)\n rationale: Human-readable explanation of the score\n \"\"\"\n ticker: str\n strategy: str\n strategy_label: str\n price: float\n conviction_score: float\n tier: str\n trend_bias: str\n ichimoku: IchimokuSignal\n rsi: RSISignal\n macd: MACDSignal\n bollinger: BollingerSignal\n adx: ADXSignal\n volume: VolumeSignal\n strikes: SuggestedStrikes\n data_quality: str = \"HIGH\"\n rationale: list = field(default_factory=list)\n\n def to_dict(self) -> dict[str, Any]:\n \"\"\"Serialise to a plain dictionary (JSON-safe).\"\"\"\n return asdict(self)\n\n\n# =============================================================================\n# Data Fetching\n# =============================================================================\n\ndef fetch_ohlcv(ticker: str, period: str = \"2y\", interval: str = \"1d\") -> pd.DataFrame:\n \"\"\"\n Download OHLCV data from Yahoo Finance.\n\n Parameters:\n ticker: Stock symbol (e.g. 'AAPL', 'SPY')\n period: Lookback period ('6mo', '1y', '2y', '5y', 'max').\n Default '2y' ensures sufficient data for extended Ichimoku.\n interval: Candle interval ('1h', '1d', '1wk')\n\n Returns:\n pd.DataFrame with columns: Open, High, Low, Close, Volume\n\n Raises:\n ValueError: If no data is returned for the given ticker or invalid ticker format,\n or if period/interval are not valid Yahoo Finance values.\n \"\"\"\n # Validate ticker format (1-5 uppercase letters)\n if not re.match(r'^[A-Z]{1,5}

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

, ticker.upper()):\n raise ValueError(f\"Invalid ticker format: '{ticker}'. Expected 1-5 uppercase letters (e.g., AAPL, SPY)\")\n \n # Validate period and interval\n valid_periods = {\"1d\", \"5d\", \"1mo\", \"3mo\", \"6mo\", \"1y\", \"2y\", \"5y\", \"10y\", \"ytd\", \"max\"}\n valid_intervals = {\"1m\", \"2m\", \"5m\", \"15m\", \"30m\", \"60m\", \"90m\", \"1h\", \"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"}\n \n if period not in valid_periods:\n raise ValueError(f\"Invalid period: {period}. Must be one of {valid_periods}\")\n if interval not in valid_intervals:\n raise ValueError(f\"Invalid interval: {interval}. Must be one of {valid_intervals}\")\n\n df = yf.download(ticker, period=period, interval=interval, progress=False)\n\n if df.empty:\n raise ValueError(f\"No data returned for ticker '{ticker}'. \"\n f\"Check symbol validity and market hours.\")\n\n # Flatten MultiIndex columns that yfinance sometimes creates\n if isinstance(df.columns, pd.MultiIndex):\n df.columns = df.columns.get_level_values(0)\n\n return df\n\n\n# =============================================================================\n# Indicator Computation\n# =============================================================================\n\ndef compute_ichimoku(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Compute Ichimoku Kinko Hyo indicators and merge into the DataFrame.\n\n Column names are auto-generated by pandas_ta based on parameter values:\n ITS_20, IKS_60, ISA_20, ISB_60, ICS_60\n \"\"\"\n ichimoku_df, _ = ta.ichimoku(\n df[\"High\"], df[\"Low\"], df[\"Close\"],\n tenkan=ICHIMOKU_TENKAN,\n kijun=ICHIMOKU_KIJUN,\n senkou=ICHIMOKU_SENKOU,\n )\n return pd.concat([df, ichimoku_df], axis=1)\n\n\ndef compute_rsi(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Compute RSI and add as a column.\"\"\"\n df[\"RSI\"] = ta.rsi(df[\"Close\"], length=RSI_LENGTH)\n return df\n\n\ndef compute_macd(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Compute MACD (line, signal, histogram) and merge into the DataFrame.\n\n Adds columns: MACD_12_26_9, MACDs_12_26_9, MACDh_12_26_9\n \"\"\"\n macd_df = ta.macd(df[\"Close\"], fast=MACD_FAST, slow=MACD_SLOW, signal=MACD_SIGNAL)\n return pd.concat([df, macd_df], axis=1)\n\n\ndef compute_bbands(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Compute Bollinger Bands and merge into the DataFrame.\n\n Adds columns: BBL_{l}_{s}_{s}, BBM_{l}_{s}_{s}, BBU_{l}_{s}_{s},\n BBB_{l}_{s}_{s} (bandwidth), BBP_{l}_{s}_{s} (%B)\n \"\"\"\n bbands_df = ta.bbands(df[\"Close\"], length=BBANDS_LENGTH, std=BBANDS_STD)\n return pd.concat([df, bbands_df], axis=1)\n\n\ndef compute_adx(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Compute ADX and merge into the DataFrame.\n Adds columns: ADX_14, DMP_14, DMN_14\n \"\"\"\n adx_df = ta.adx(df[\"High\"], df[\"Low\"], df[\"Close\"], length=ADX_LENGTH)\n return pd.concat([df, adx_df], axis=1)\n\n\ndef compute_volume_stats(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Compute relative volume stats.\n \"\"\"\n df[\"VOL_SMA\"] = ta.sma(df[\"Volume\"], length=VOLUME_WINDOW)\n df[\"REL_VOL\"] = df[\"Volume\"] / df[\"VOL_SMA\"]\n return df\n\n\ndef validate_indicator_columns(df: pd.DataFrame) -> None:\n \"\"\"\n Validate that all expected indicator columns exist after computation.\n Raises ValueError with descriptive message if columns are missing.\n \"\"\"\n required_cols = {\n \"ichimoku\": [\n f\"ITS_{ICHIMOKU_TENKAN}\",\n f\"IKS_{ICHIMOKU_KIJUN}\",\n f\"ISA_{ICHIMOKU_TENKAN}\",\n f\"ISB_{ICHIMOKU_KIJUN}\",\n ],\n \"rsi\": [\"RSI\"],\n \"macd\": [\n f\"MACD_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\",\n f\"MACDs_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\",\n f\"MACDh_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\",\n ],\n \"bollinger\": [\n f\"BBL_{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\",\n f\"BBM_{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\",\n f\"BBU_{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\",\n f\"BBB_{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\",\n f\"BBP_{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\",\n ],\n \"adx\": [f\"ADX_{ADX_LENGTH}\"],\n \"volume\": [\"VOL_SMA\", \"REL_VOL\"],\n }\n\n missing = []\n for indicator, cols in required_cols.items():\n for col in cols:\n if col not in df.columns:\n missing.append(f\"{indicator}:{col}\")\n\n if missing:\n raise ValueError(\n f\"Missing expected indicator columns: {', '.join(missing)}. \"\n f\"This may indicate a pandas_ta version change or insufficient data. \"\n f\"Expected columns are based on pandas_ta naming conventions.\"\n )\n\n\ndef compute_all_indicators(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Pipeline: compute every indicator in sequence.\n\n Single entry point for indicator computation.\n \"\"\"\n df = compute_ichimoku(df)\n df = compute_rsi(df)\n df = compute_macd(df)\n df = compute_bbands(df)\n df = compute_adx(df)\n df = compute_volume_stats(df)\n\n # Validate all expected columns exist\n validate_indicator_columns(df)\n\n return df\n\n\n# =============================================================================\n# Signal Scoring Functions — Strategy-Aware\n# =============================================================================\n# Each function extracts relevant indicator values, computes a normalised\n# sub-score (0.0–1.0), and scales it by the strategy-specific component\n# weight. The sub-score represents \"how favourable are conditions for the\n# SELECTED STRATEGY from this indicator's perspective?\"\n# =============================================================================\n\ndef score_ichimoku(\n df: pd.DataFrame,\n price: float,\n strategy: StrategyType,\n weights: StrategyWeights,\n) -> IchimokuSignal:\n \"\"\"\n Score Ichimoku signal for the given strategy.\n\n Bullish strategies (bull_put, bull_call):\n Price above cloud, bullish TK cross, green cloud = high score.\n\n Bearish strategies (bear_call, bear_put):\n Price below cloud, bearish TK cross, red cloud = high score.\n\n Sub-Signal Weights (internal):\n ┌──────────────────────────────┬────────┐\n │ Price vs Cloud │ 0.40 │\n │ TK Cross direction │ 0.25 │\n │ Cloud colour │ 0.20 │\n │ Cloud thickness (normalised) │ 0.15 │\n └──────────────────────────────┴────────┘\n\n Parameters:\n df: DataFrame with Ichimoku columns\n price: Current closing price\n strategy: Active strategy\n weights: Component weights for this strategy\n\n Returns:\n IchimokuSignal with populated component_score\n \"\"\"\n latest = df.iloc[-1]\n\n # Dynamic column names from constants (pandas_ta naming convention)\n tenkan = float(latest[f\"ITS_{ICHIMOKU_TENKAN}\"])\n kijun = float(latest[f\"IKS_{ICHIMOKU_KIJUN}\"])\n senkou_a = float(latest[f\"ISA_{ICHIMOKU_TENKAN}\"])\n senkou_b = float(latest[f\"ISB_{ICHIMOKU_KIJUN}\"])\n\n cloud_top = max(senkou_a, senkou_b)\n cloud_bottom = min(senkou_a, senkou_b)\n cloud_green = senkou_a > senkou_b\n\n # --- Sub-signal: Price vs Cloud ---\n if price > cloud_top:\n cloud_status = \"ABOVE\"\n elif price \u003c cloud_bottom:\n cloud_status = \"BELOW\"\n else:\n cloud_status = \"INSIDE\"\n\n if strategy.is_bullish:\n # Bullish: above cloud = good\n price_cloud_score = {\"ABOVE\": 1.0, \"INSIDE\": 0.3, \"BELOW\": 0.0}[cloud_status]\n else:\n # Bearish: below cloud = good\n price_cloud_score = {\"BELOW\": 1.0, \"INSIDE\": 0.3, \"ABOVE\": 0.0}[cloud_status]\n\n # --- Sub-signal: TK Cross ---\n tk_bullish = tenkan > kijun\n tk_label = \"BULLISH\" if tk_bullish else \"BEARISH\"\n\n if strategy.is_bullish:\n tk_score = 1.0 if tk_bullish else 0.0\n else:\n tk_score = 0.0 if tk_bullish else 1.0\n\n # --- Sub-signal: Cloud Colour ---\n cloud_color_label = \"GREEN\" if cloud_green else \"RED\"\n\n if strategy.is_bullish:\n cloud_score = 1.0 if cloud_green else 0.0\n else:\n cloud_score = 0.0 if cloud_green else 1.0\n\n # --- Sub-signal: Cloud Thickness ---\n # Normalise thickness relative to price; 5% of price = max score.\n thickness = abs(senkou_a - senkou_b)\n thickness_pct = (thickness / price) if price > 0 else 0.0\n thickness_score = min(thickness_pct / 0.05, 1.0)\n\n # Penalise thick cloud that works AGAINST the strategy direction.\n # For bullish strategies: thick red cloud is resistance (bad).\n # For bearish strategies: thick green cloud is support (bad).\n if strategy.is_bullish and not cloud_green:\n thickness_score *= 0.3\n elif strategy.is_bearish and cloud_green:\n thickness_score *= 0.3\n\n # --- Weighted combination ---\n raw = (\n 0.40 * price_cloud_score\n + 0.25 * tk_score\n + 0.20 * cloud_score\n + 0.15 * thickness_score\n )\n component_score = round(raw * weights.ichimoku, 2)\n\n return IchimokuSignal(\n price_vs_cloud=cloud_status,\n tk_cross=tk_label,\n cloud_color=cloud_color_label,\n cloud_thickness=round(thickness, 4),\n tenkan=round(tenkan, 2),\n kijun=round(kijun, 2),\n senkou_a=round(senkou_a, 2),\n senkou_b=round(senkou_b, 2),\n component_score=component_score,\n )\n\n\ndef score_rsi(\n df: pd.DataFrame,\n strategy: StrategyType,\n weights: StrategyWeights,\n) -> RSISignal:\n \"\"\"\n Score RSI for the given strategy using a lookup table for contiguous ranges.\n \"\"\"\n rsi_val = float(df.iloc[-1][\"RSI\"])\n\n # Lookup tables: (min_inclusive, max_exclusive, score, label)\n # The last entry in each list uses a high max (101) to cover up to 100.\n # We sort them by min_inclusive to ensure predictable lookup.\n tables = {\n StrategyType.BULL_PUT: [\n (0, 25, 0.10, \"EXTREME_OVERSOLD (\u003c25)\"),\n (25, 30, 0.40, \"DEEP_OVERSOLD (25-30)\"),\n (30, 45, 1.00, \"OVERSOLD_BOUNCE (30-45)\"),\n (45, 55, 0.80, \"NEUTRAL_BULLISH (45-55)\"),\n (55, 65, 0.60, \"BULLISH (55-65)\"),\n (65, 75, 0.30, \"OVERBOUGHT_CAUTION (65-75)\"),\n (75, 101, 0.10, \"EXTREME_OVERBOUGHT (>75)\"),\n ],\n StrategyType.BEAR_CALL: [\n (0, 25, 0.10, \"EXTREME_OVERSOLD (\u003c25)\"),\n (25, 35, 0.30, \"OVERSOLD_CAUTION (25-35)\"),\n (35, 45, 0.60, \"BEARISH (35-45)\"),\n (45, 55, 0.80, \"NEUTRAL_BEARISH (45-55)\"),\n (55, 70, 1.00, \"OVERBOUGHT_REJECTION (55-70)\"),\n (70, 75, 0.40, \"DEEP_OVERBOUGHT (70-75)\"),\n (75, 101, 0.10, \"EXTREME_OVERBOUGHT (>75)\"),\n ],\n StrategyType.BULL_CALL: [\n (0, 35, 0.05, \"WRONG_DIRECTION (\u003c35)\"),\n (35, 45, 0.25, \"WEAK_MOMENTUM (35-45)\"),\n (45, 55, 0.70, \"BUILDING_MOMENTUM (45-55)\"),\n (55, 70, 1.00, \"STRONG_BULLISH_MOMENTUM (55-70)\"),\n (70, 80, 0.50, \"VERY_STRONG (70-80)\"),\n (80, 101, 0.15, \"PARABOLIC_RISK (>80)\"),\n ],\n StrategyType.BEAR_PUT: [\n (0, 20, 0.15, \"CAPITULATION_RISK (\u003c20)\"),\n (20, 30, 0.50, \"VERY_STRONG_BEARISH (20-30)\"),\n (30, 45, 1.00, \"STRONG_BEARISH_MOMENTUM (30-45)\"),\n (45, 55, 0.70, \"BUILDING_BEARISH (45-55)\"),\n (55, 65, 0.25, \"WEAK_BEARISH (55-65)\"),\n (65, 101, 0.05, \"WRONG_DIRECTION (>65)\"),\n ]\n }\n\n raw, zone = 0.0, \"UNKNOWN\"\n for (low, high, score, label) in tables[strategy]:\n if low \u003c= rsi_val \u003c high:\n raw, zone = score, label\n break\n\n component_score = round(raw * weights.rsi, 2)\n\n return RSISignal(\n value=round(rsi_val, 2),\n zone=zone,\n component_score=component_score,\n )\n\n\ndef _detect_crossover(df: pd.DataFrame, macd_col: str, signal_col: str, lookback: int = 3) -> tuple[str, bool, bool]:\n \"\"\"\n Detect MACD/Signal crossover within the last ``lookback`` bars.\n\n Returns:\n Tuple of (crossover_label, is_bullish_cross, is_bearish_cross)\n \"\"\"\n n = min(lookback + 1, len(df))\n for i in range(2, n + 1):\n row_curr = df.iloc[-i + 1]\n row_prev = df.iloc[-i]\n prev_macd = float(row_prev[macd_col])\n prev_sig = float(row_prev[signal_col])\n curr_macd = float(row_curr[macd_col])\n curr_sig = float(row_curr[signal_col])\n\n if prev_macd \u003c= prev_sig and curr_macd > curr_sig:\n return \"BULLISH_CROSS\", True, False\n elif prev_macd >= prev_sig and curr_macd \u003c curr_sig:\n return \"BEARISH_CROSS\", False, True\n\n return \"NONE\", False, False\n\n\ndef score_macd(\n df: pd.DataFrame,\n price: float,\n strategy: StrategyType,\n weights: StrategyWeights,\n) -> MACDSignal:\n \"\"\"\n Score MACD for the given strategy.\n\n Credit strategies (mean reversion) focus on *decelerating* adverse\n momentum — a rising histogram in a dip (bull_put) or a falling\n histogram in a rally (bear_call).\n\n Debit strategies (breakout) focus on *accelerating* favourable\n momentum — both the MACD line position and histogram strength matter.\n\n ── Credit Sub-Signal Weights ──────────────────────────────────────\n │ MACD vs Signal (favourable side) │ 0.40 │\n │ Histogram direction (decelerating) │ 0.35 │\n │ Recent favourable crossover │ 0.25 │\n ──────────────────────────────────────────────────────────────────\n\n ── Debit Sub-Signal Weights ───────────────────────────────────────\n │ MACD vs Signal + zero-line │ 0.30 │\n │ Histogram strength (dir + sign) │ 0.45 │\n │ Recent favourable crossover │ 0.25 │\n ──────────────────────────────────────────────────────────────────\n\n Parameters:\n df: DataFrame with MACD columns\n strategy: Active strategy\n weights: Component weights for this strategy\n\n Returns:\n MACDSignal with populated component_score\n \"\"\"\n macd_col = f\"MACD_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\"\n signal_col = f\"MACDs_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\"\n hist_col = f\"MACDh_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\"\n\n latest = df.iloc[-1]\n prev = df.iloc[-2]\n\n macd_val = float(latest[macd_col])\n signal_val = float(latest[signal_col])\n hist_val = float(latest[hist_col])\n prev_hist = float(prev[hist_col])\n\n macd_above = macd_val > signal_val\n\n # --- Histogram direction ---\n # Threshold for FLAT is relative to price (0.01%)\n hist_diff = hist_val - prev_hist\n threshold = price * 0.0001\n if abs(hist_diff) \u003c threshold:\n hist_direction = \"FLAT\"\n elif hist_diff > 0:\n hist_direction = \"RISING\"\n else:\n hist_direction = \"FALLING\"\n\n # --- Crossover detection ---\n crossover_label, is_bull_cross, is_bear_cross = _detect_crossover(\n df, macd_col, signal_col, lookback=3\n )\n\n # --- Strategy-specific scoring ---\n if strategy.is_credit:\n # ── Credit: Mean Reversion ───────────────────────────────────\n # bull_put wants: MACD above signal, rising histogram, bullish cross\n # bear_call wants: MACD below signal, falling histogram, bearish cross\n if strategy.is_bullish:\n # bull_put\n above_score = 1.0 if macd_above else 0.0\n hist_dir_score = (\n 1.0 if hist_direction == \"RISING\"\n else 0.4 if hist_direction == \"FLAT\"\n else 0.0\n )\n cross_score = (\n 1.0 if is_bull_cross\n else 0.0 if is_bear_cross\n else 0.2\n )\n else:\n # bear_call (inverted)\n above_score = 0.0 if macd_above else 1.0\n hist_dir_score = (\n 1.0 if hist_direction == \"FALLING\"\n else 0.4 if hist_direction == \"FLAT\"\n else 0.0\n )\n cross_score = (\n 1.0 if is_bear_cross\n else 0.0 if is_bull_cross\n else 0.2\n )\n\n raw = (\n 0.40 * above_score\n + 0.35 * hist_dir_score\n + 0.25 * cross_score\n )\n\n else:\n # ── Debit: Breakout / Momentum ───────────────────────────────\n # Needs strong, *accelerating* directional momentum.\n if strategy.is_bullish:\n # bull_call\n # Sub-signal 1: MACD above signal + positive territory\n if macd_above and macd_val > 0:\n position_score = 1.0\n elif macd_above and macd_val \u003c= 0:\n position_score = 0.5\n else:\n position_score = 0.0\n\n # Sub-signal 2: Histogram positive AND rising\n if hist_val > 0 and hist_direction == \"RISING\":\n hist_strength_score = 1.0\n elif hist_val > 0 and hist_direction == \"FALLING\":\n hist_strength_score = 0.35\n elif hist_val \u003c= 0 and hist_direction == \"RISING\":\n hist_strength_score = 0.25\n else: # negative and falling\n hist_strength_score = 0.0\n\n # Sub-signal 3: Crossover\n cross_score = (\n 1.0 if is_bull_cross\n else 0.0 if is_bear_cross\n else 0.15\n )\n else:\n # bear_put (mirror of bull_call)\n # Sub-signal 1: MACD below signal + negative territory\n if not macd_above and macd_val \u003c 0:\n position_score = 1.0\n elif not macd_above and macd_val >= 0:\n position_score = 0.5\n else:\n position_score = 0.0\n\n # Sub-signal 2: Histogram negative AND falling\n if hist_val \u003c 0 and hist_direction == \"FALLING\":\n hist_strength_score = 1.0\n elif hist_val \u003c 0 and hist_direction == \"RISING\":\n hist_strength_score = 0.35\n elif hist_val >= 0 and hist_direction == \"FALLING\":\n hist_strength_score = 0.25\n else: # positive and rising\n hist_strength_score = 0.0\n\n # Sub-signal 3: Crossover\n cross_score = (\n 1.0 if is_bear_cross\n else 0.0 if is_bull_cross\n else 0.15\n )\n\n raw = (\n 0.30 * position_score\n + 0.45 * hist_strength_score\n + 0.25 * cross_score\n )\n\n component_score = round(raw * weights.macd, 2)\n\n return MACDSignal(\n macd_value=round(macd_val, 4),\n signal_value=round(signal_val, 4),\n histogram=round(hist_val, 4),\n hist_direction=hist_direction,\n crossover=crossover_label,\n macd_above_signal=macd_above,\n component_score=component_score,\n )\n\n\ndef score_bollinger(\n df: pd.DataFrame,\n strategy: StrategyType,\n weights: StrategyWeights,\n) -> BollingerSignal:\n \"\"\"\n Score Bollinger Bands for the given strategy.\n\n Credit strategies want price near a band (support/resistance) with\n *moderate* bandwidth — the mean-reversion sweet spot.\n\n Debit strategies want price pushing through a band with *expanding*\n bandwidth — confirming a genuine breakout, not a false signal.\n\n ── bull_put %B (Credit) ───────────────────────────────────────────\n │ 0.20–0.45 → 1.00 Near lower band, holding. Ideal support. │\n │ 0.45–0.60 → 0.80 Near middle band. Balanced. │\n │ 0.10–0.20 → 0.55 Testing lower band. Could bounce or break. │\n │ 0.60–0.80 → 0.50 Upper half. Less margin of safety. │\n │ \u003c 0.10 → 0.15 Breaking below bands. Breakdown risk. │\n │ > 0.80 → 0.25 Near upper band. Overextended. │\n ────────────────────────────────────────────────────────────────────\n\n ── bear_call %B (Credit) ──────────────────────────────────────────\n │ 0.55–0.80 → 1.00 Near upper band, rejecting. Ideal resistance. │\n │ 0.40–0.55 → 0.80 Near middle band. Balanced. │\n │ 0.80–0.90 → 0.55 Testing upper band. Could reject or break. │\n │ 0.20–0.40 → 0.50 Lower half. Less margin of safety. │\n │ > 0.90 → 0.15 Breaking above bands. Breakout risk. │\n │ \u003c 0.20 → 0.25 Near lower band. Move already made. │\n ────────────────────────────────────────────────────────────────────\n\n ── bull_call %B (Debit) ───────────────────────────────────────────\n │ > 0.80 → 1.00 Breaking above upper band. Strong breakout. │\n │ 0.60–0.80 → 0.85 Upper half, pushing higher. Good. │\n │ 0.45–0.60 → 0.50 Middle zone. Not yet breaking out. │\n │ 0.20–0.45 → 0.20 Lower half. Wrong direction. │\n │ \u003c 0.20 → 0.05 Near lower band. Completely wrong. │\n ────────────────────────────────────────────────────────────────────\n\n ── bear_put %B (Debit) ────────────────────────────────────────────\n │ \u003c 0.20 → 1.00 Breaking below lower band. Strong breakdown. │\n │ 0.20–0.40 → 0.85 Lower half, pushing lower. Good. │\n │ 0.40–0.55 → 0.50 Middle zone. Not yet breaking down. │\n │ 0.55–0.80 → 0.20 Upper half. Wrong direction. │\n │ > 0.80 → 0.05 Near upper band. Completely wrong. │\n ────────────────────────────────────────────────────────────────────\n\n Bandwidth Scoring:\n Credit: Moderate (3–10) is ideal; extremes are penalised.\n Debit: Expanding (>7) is ideal; tight squeezes are uncertain.\n\n Parameters:\n df: DataFrame with Bollinger Band columns\n strategy: Active strategy\n weights: Component weights for this strategy\n\n Returns:\n BollingerSignal with populated component_score\n \"\"\"\n bb_suffix = f\"{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\"\n latest = df.iloc[-1]\n\n upper = float(latest[f\"BBU_{bb_suffix}\"])\n middle = float(latest[f\"BBM_{bb_suffix}\"])\n lower = float(latest[f\"BBL_{bb_suffix}\"])\n percent_b = float(latest[f\"BBP_{bb_suffix}\"])\n bandwidth = float(latest[f\"BBB_{bb_suffix}\"])\n\n # --- Sub-signal 1: %B positioning (strategy-specific) ---\n\n if strategy == StrategyType.BULL_PUT:\n if 0.20 \u003c= percent_b \u003c= 0.45:\n pctb_score = 1.00\n elif 0.45 \u003c percent_b \u003c= 0.60:\n pctb_score = 0.80\n elif 0.10 \u003c= percent_b \u003c 0.20:\n pctb_score = 0.55\n elif 0.60 \u003c percent_b \u003c= 0.80:\n pctb_score = 0.50\n elif percent_b \u003c 0.10:\n pctb_score = 0.15\n else: # > 0.80\n pctb_score = 0.25\n\n elif strategy == StrategyType.BEAR_CALL:\n if 0.55 \u003c= percent_b \u003c= 0.80:\n pctb_score = 1.00\n elif 0.40 \u003c= percent_b \u003c 0.55:\n pctb_score = 0.80\n elif 0.80 \u003c percent_b \u003c= 0.90:\n pctb_score = 0.55\n elif 0.20 \u003c= percent_b \u003c 0.40:\n pctb_score = 0.50\n elif percent_b > 0.90:\n pctb_score = 0.15\n else: # \u003c 0.20\n pctb_score = 0.25\n\n elif strategy == StrategyType.BULL_CALL:\n if percent_b > 0.80:\n pctb_score = 1.00\n elif 0.60 \u003c= percent_b \u003c= 0.80:\n pctb_score = 0.85\n elif 0.45 \u003c= percent_b \u003c 0.60:\n pctb_score = 0.50\n elif 0.20 \u003c= percent_b \u003c 0.45:\n pctb_score = 0.20\n else: # \u003c 0.20\n pctb_score = 0.05\n\n else: # BEAR_PUT\n if percent_b \u003c 0.20:\n pctb_score = 1.00\n elif 0.20 \u003c= percent_b \u003c= 0.40:\n pctb_score = 0.85\n elif 0.40 \u003c percent_b \u003c= 0.55:\n pctb_score = 0.50\n elif 0.55 \u003c percent_b \u003c= 0.80:\n pctb_score = 0.20\n else: # > 0.80\n pctb_score = 0.05\n\n # --- Sub-signal 2: Bandwidth regime (credit vs debit) ---\n\n if strategy.is_credit:\n # Credit: moderate bandwidth is ideal (3–10%)\n if 3.0 \u003c= bandwidth \u003c= 10.0:\n bw_score = 1.0\n elif 10.0 \u003c bandwidth \u003c= 15.0:\n bw_score = 0.6 # Expanding — move already happening\n elif 2.0 \u003c= bandwidth \u003c 3.0:\n bw_score = 0.5 # Squeeze — direction uncertain\n elif bandwidth > 15.0:\n bw_score = 0.3 # Extreme expansion\n else: # \u003c 2.0\n bw_score = 0.3 # Very tight squeeze\n else:\n # Debit: expanding bandwidth confirms breakout\n if bandwidth > 10.0:\n bw_score = 1.0 # Strong expansion — breakout confirmed\n elif 7.0 \u003c= bandwidth \u003c= 10.0:\n bw_score = 0.80 # Starting to expand\n elif 3.0 \u003c= bandwidth \u003c 7.0:\n bw_score = 0.50 # Moderate — could go either way\n elif 2.0 \u003c= bandwidth \u003c 3.0:\n bw_score = 0.30 # Squeeze — breakout direction unknown\n else: # \u003c 2.0\n bw_score = 0.20 # Extremely tight — no momentum confirmation\n\n # --- Weighted combination ---\n # Credit: %B matters more (positioning for mean reversion)\n # Debit: bandwidth matters more (confirming breakout strength)\n if strategy.is_credit:\n raw = 0.65 * pctb_score + 0.35 * bw_score\n else:\n raw = 0.55 * pctb_score + 0.45 * bw_score\n\n component_score = round(raw * weights.bollinger, 2)\n\n return BollingerSignal(\n upper=round(upper, 2),\n middle=round(middle, 2),\n lower=round(lower, 2),\n percent_b=round(percent_b, 4),\n bandwidth=round(bandwidth, 4),\n component_score=component_score,\n )\n\n\ndef score_adx(\n df: pd.DataFrame,\n strategy: StrategyType,\n weights: StrategyWeights,\n) -> ADXSignal:\n \"\"\"\n Score ADX for the given strategy.\n\n ADX measures trend strength, regardless of direction.\n - ADX > 25: Strong trend\n - ADX \u003c 20: Weak trend / range-bound\n\n Scoring logic:\n - If strategy is CREDIT (mean reversion):\n Likes moderate ADX (15-25). Too high (>35) means trend is too strong\n to fade. Too low (\u003c15) means no clear structure.\n - If strategy is DEBIT (breakout):\n Likes rising and strong ADX (>25).\n\n Returns:\n ADXSignal with populated component_score\n \"\"\"\n latest = df.iloc[-1]\n adx_val = float(latest[f\"ADX_{ADX_LENGTH}\"])\n\n if adx_val >= 40:\n strength = \"VERY_STRONG\"\n elif adx_val >= 25:\n strength = \"STRONG\"\n elif adx_val >= 20:\n strength = \"MODERATE\"\n else:\n strength = \"WEAK\"\n\n if strategy.is_credit:\n # Credit likes moderate trend strength\n if 15 \u003c= adx_val \u003c= 30:\n raw = 1.0\n elif 30 \u003c adx_val \u003c= 40:\n raw = 0.6\n elif 10 \u003c= adx_val \u003c 15:\n raw = 0.5\n elif adx_val > 40:\n raw = 0.2 # Trend too strong to fade\n else:\n raw = 0.1\n else:\n # Debit likes strong trend strength\n if adx_val >= 25:\n raw = 1.0\n elif 20 \u003c= adx_val \u003c 25:\n raw = 0.7\n elif 15 \u003c= adx_val \u003c 20:\n raw = 0.4\n else:\n raw = 0.1\n\n component_score = round(raw * weights.adx, 2)\n\n return ADXSignal(\n value=round(adx_val, 2),\n trend_strength=strength,\n component_score=component_score,\n )\n\n\ndef score_volume(df: pd.DataFrame, strategy: StrategyType) -> VolumeSignal:\n \"\"\"\n Determine volume adjustment for conviction.\n Breakouts (debit) require high volume confirmation.\n Mean-reversion (credit) benefits from low-volume exhaustion at levels.\n\n Returns an adjustment factor (±10 points max) rather than a multiplier\n to prevent volume from masking weak indicator signals.\n \"\"\"\n rv = float(df.iloc[-1][\"REL_VOL\"])\n elevated = rv > 1.25\n\n # Base adjustment is 0 (no change)\n adjustment = 0.0\n\n if strategy.is_debit:\n # Debit: breakout needs volume confirmation\n if rv > 1.5:\n adjustment = 10.0 # Strong confirmation\n elif rv > 1.25:\n adjustment = 5.0 # Moderate confirmation\n elif rv \u003c 0.75:\n adjustment = -10.0 # Weak volume, questionable breakout\n elif rv \u003c 1.0:\n adjustment = -5.0 # Below average volume\n else:\n # Credit: mean-reversion likes volume exhaustion (low volume)\n if rv \u003c 0.75:\n adjustment = 10.0 # Exhaustion at support/resistance\n elif rv \u003c 1.0:\n adjustment = 5.0 # Below average\n elif rv > 1.5:\n adjustment = -10.0 # High volume move may continue\n elif rv > 1.25:\n adjustment = -5.0 # Elevated volume caution\n\n return VolumeSignal(\n relative_volume=round(rv, 2),\n is_elevated=elevated,\n adjustment=adjustment\n )\n\n\ndef calculate_strikes(\n price: float,\n strategy: StrategyType,\n bollinger: BollingerSignal\n) -> SuggestedStrikes:\n \"\"\"\n Calculate dynamic strikes based on 1-sigma Bollinger levels.\n Uses BBM (SMA) and bands to find logical targets.\n\n For credit spreads (bull_put, bear_call):\n short_strike = strike you SELL (collect premium)\n long_strike = strike you BUY (pay premium) for protection\n\n For debit spreads (bull_call, bear_put):\n long_strike = strike you BUY (pay premium, directional bet)\n short_strike = strike you SELL (collect premium) to reduce cost\n \"\"\"\n if strategy == StrategyType.BULL_PUT:\n # SELL put at support (BBL), BUY put further OTM for protection\n short_strike_price = bollinger.lower\n long_strike_price = short_strike_price - (price * 0.02)\n desc = \"SELL put at support (BBL), BUY put 2% below for protection.\"\n elif strategy == StrategyType.BEAR_CALL:\n # SELL call at resistance (BBU), BUY call further OTM for protection\n short_strike_price = bollinger.upper\n long_strike_price = short_strike_price + (price * 0.02)\n desc = \"SELL call at resistance (BBU), BUY call 2% above for protection.\"\n elif strategy == StrategyType.BULL_CALL:\n # BUY call at middle band (lower strike), SELL call at upper band (higher strike)\n long_strike_price = bollinger.middle # Buy lower strike\n short_strike_price = bollinger.upper # Sell higher strike\n desc = \"BUY call at middle band (lower), SELL call at upper band (higher).\"\n else: # BEAR_PUT\n # BUY put at middle band (higher strike), SELL put at lower band (lower strike)\n long_strike_price = bollinger.middle # Buy higher strike\n short_strike_price = bollinger.lower # Sell lower strike\n desc = \"BUY put at middle band (higher), SELL put at lower band (lower).\"\n\n return SuggestedStrikes(\n short_strike=round(short_strike_price, 2),\n long_strike=round(long_strike_price, 2),\n description=desc\n )\n\n\n# =============================================================================\n# Trend Classification (Objective — Strategy-Independent)\n# =============================================================================\n\ndef classify_trend(ichimoku: IchimokuSignal, macd: MACDSignal) -> TrendBias:\n \"\"\"\n Synthesise Ichimoku and MACD into an overall trend classification.\n\n This is an *objective* market read — independent of the chosen strategy.\n The conviction score captures whether the trend is *favourable* for a\n given strategy; this function simply labels what the trend IS.\n\n Logic:\n STRONG_BULL = Above cloud + Bullish TK + MACD above signal + Rising hist\n BULL = Above cloud + at least one MACD condition\n NEUTRAL = Mixed signals or inside cloud\n BEAR = Below cloud + one bearish MACD signal\n STRONG_BEAR = Below cloud + Bearish TK + MACD below + Falling hist\n \"\"\"\n bull_points = 0\n\n if ichimoku.price_vs_cloud == \"ABOVE\":\n bull_points += 2\n elif ichimoku.price_vs_cloud == \"INSIDE\":\n bull_points += 1\n\n if ichimoku.tk_cross == \"BULLISH\":\n bull_points += 1\n\n if macd.macd_above_signal:\n bull_points += 1\n\n if macd.hist_direction == \"RISING\":\n bull_points += 1\n\n if bull_points >= 5:\n return TrendBias.STRONG_BULL\n elif bull_points >= 3:\n return TrendBias.BULL\n elif bull_points >= 2:\n return TrendBias.NEUTRAL\n elif bull_points >= 1:\n return TrendBias.BEAR\n else:\n return TrendBias.STRONG_BEAR\n\n\n# =============================================================================\n# Rationale Builder\n# =============================================================================\n\ndef build_rationale(\n strategy: StrategyType,\n weights: StrategyWeights,\n ichimoku: IchimokuSignal,\n rsi: RSISignal,\n macd: MACDSignal,\n bollinger: BollingerSignal,\n adx: ADXSignal,\n volume: VolumeSignal,\n strikes: SuggestedStrikes,\n trend: TrendBias,\n score: float,\n tier: ConvictionTier,\n) -> list[str]:\n \"\"\"\n Generate a human-readable rationale explaining the conviction score.\n \"\"\"\n lines: list[str] = []\n\n # Strategy header\n lines.append(f\"Strategy: {strategy.label} ({strategy.philosophy})\")\n lines.append(f\"Ideal Setup: {strategy.ideal_setup}\")\n lines.append(\"\")\n lines.append(f\"Market Trend: {trend.value} | Score: {score:.1f}/100 → {tier.value}\")\n\n # ADX Gate check\n if strategy.is_credit and adx.value \u003c 20:\n lines.append(\"WARNING: TREND STRENGTH GATE: ADX \u003c 20. Credit spread capped at WATCH tier.\")\n\n # Volume check\n vol_status = \"ELEVATED\" if volume.is_elevated else \"NORMAL\"\n adj_sign = \"+\" if volume.adjustment >= 0 else \"\"\n if strategy.is_debit and volume.is_elevated:\n lines.append(f\"Volume: {vol_status} (RV={volume.relative_volume}) - Breakthrough confirmed ({adj_sign}{volume.adjustment:.0f})\")\n elif strategy.is_credit and volume.relative_volume \u003c 0.8:\n lines.append(f\"Volume: EXHAUSTED (RV={volume.relative_volume}) - Level holding ({adj_sign}{volume.adjustment:.0f})\")\n else:\n lines.append(f\"Volume: {vol_status} (RV={volume.relative_volume}) ({adj_sign}{volume.adjustment:.0f})\")\n\n # Trend alignment check\n if strategy.is_bullish and trend.value in (\"STRONG_BULL\", \"BULL\"):\n lines.append(\"Trend aligns with bullish strategy\")\n elif strategy.is_bearish and trend.value in (\"STRONG_BEAR\", \"BEAR\"):\n lines.append(\"Trend aligns with bearish strategy\")\n elif trend.value == \"NEUTRAL\":\n lines.append(\"Trend is neutral - mixed alignment\")\n else:\n lines.append(\"ALERT: Trend opposes strategy direction - Conflict penalty applied\")\n lines.append(\"\")\n\n # Strikes\n lines.append(f\"Suggested Strikes: ${strikes.short_strike} / ${strikes.long_strike}\")\n lines.append(f\"Logic: {strikes.description}\")\n lines.append(\"\")\n\n # Ichimoku\n lines.append(f\"[Ichimoku +{ichimoku.component_score:.1f}/{weights.ichimoku}]\")\n if ichimoku.price_vs_cloud == \"UNKNOWN\":\n lines.append(\" DATA MISSING - Component skipped\")\n else:\n lines.append(f\" Price is {ichimoku.price_vs_cloud} the cloud\")\n lines.append(f\" TK Cross: {ichimoku.tk_cross} \"\n f\"(Tenkan {ichimoku.tenkan} vs Kijun {ichimoku.kijun})\")\n lines.append(f\" Cloud: {ichimoku.cloud_color}, \"\n f\"thickness {ichimoku.cloud_thickness:.2f}\")\n\n # RSI\n lines.append(f\"[RSI +{rsi.component_score:.1f}/{weights.rsi}]\")\n if rsi.zone == \"UNKNOWN\":\n lines.append(\" DATA MISSING - Component skipped\")\n else:\n lines.append(f\" RSI({RSI_LENGTH}) = {rsi.value} -> {rsi.zone}\")\n\n # MACD\n lines.append(f\"[MACD +{macd.component_score:.1f}/{weights.macd}]\")\n if macd.hist_direction == \"UNKNOWN\":\n lines.append(\" DATA MISSING - Component skipped\")\n else:\n direction = \"above\" if macd.macd_above_signal else \"below\"\n lines.append(f\" MACD {direction} Signal \"\n f\"({macd.macd_value:.4f} vs {macd.signal_value:.4f})\")\n lines.append(f\" Histogram: {macd.histogram:.4f} ({macd.hist_direction})\")\n if macd.crossover != \"NONE\":\n lines.append(f\" Recent crossover: {macd.crossover}\")\n\n # Bollinger\n lines.append(f\"[Bollinger +{bollinger.component_score:.1f}/{weights.bollinger}]\")\n if bollinger.middle == 0:\n lines.append(\" DATA MISSING - Component skipped\")\n else:\n lines.append(f\" %B = {bollinger.percent_b:.4f} | \"\n f\"Bandwidth = {bollinger.bandwidth:.4f}\")\n lines.append(f\" Bands: [{bollinger.lower:.2f} — \"\n f\"{bollinger.middle:.2f} — {bollinger.upper:.2f}]\")\n\n # ADX\n lines.append(f\"[ADX +{adx.component_score:.1f}/{weights.adx}]\")\n if adx.trend_strength == \"UNKNOWN\":\n lines.append(\" DATA MISSING - Component skipped\")\n else:\n lines.append(f\" ADX({ADX_LENGTH}) = {adx.value} ({adx.trend_strength})\")\n\n return lines\n\n\n# =============================================================================\n# Analysis Engine — The Heart of the System\n# =============================================================================\n\ndef analyse(\n ticker: str,\n strategy: StrategyType = StrategyType.BULL_PUT,\n period: str = \"2y\",\n interval: str = \"1d\",\n) -> ConvictionResult:\n \"\"\"\n Run the full conviction analysis pipeline for a single ticker.\n \"\"\"\n weights = STRATEGY_WEIGHTS[strategy]\n\n # Step 1: Fetch data\n df = fetch_ohlcv(ticker, period=period, interval=interval)\n\n # Data sufficiency check for Ichimoku (requires ~180 periods for full ISA/ISB)\n min_periods = ICHIMOKU_SENKOU + ICHIMOKU_KIJUN\n if len(df) \u003c min_periods:\n warnings.warn(f\"Insufficient data for ticker {ticker}. \"\n f\"Found {len(df)} rows, need {min_periods} for full \"\n f\"Ichimoku cloud. Results may be skewed.\")\n\n # Step 2: Compute indicators\n df = compute_all_indicators(df)\n\n # Step 3: Get current price\n price = round(float(df.iloc[-1][\"Close\"]), 2)\n\n # Step 4: Score each component (strategy-aware)\n # We detect NaNs BEFORE scoring and renormalize if necessary.\n \n latest = df.iloc[-1]\n \n # 1. Ichimoku\n ichimoku_cols = [f\"ITS_{ICHIMOKU_TENKAN}\", f\"IKS_{ICHIMOKU_KIJUN}\", f\"ISA_{ICHIMOKU_TENKAN}\", f\"ISB_{ICHIMOKU_KIJUN}\"]\n ichimoku_available = not latest[ichimoku_cols].isna().any()\n if ichimoku_available:\n ichimoku_sig = score_ichimoku(df, price, strategy, weights)\n else:\n ichimoku_sig = IchimokuSignal(\"UNKNOWN\", \"UNKNOWN\", \"UNKNOWN\", 0, 0, 0, 0, 0, 0)\n\n # 2. RSI\n rsi_available = not pd.isna(latest[\"RSI\"])\n if rsi_available:\n rsi_sig = score_rsi(df, strategy, weights)\n else:\n rsi_sig = RSISignal(0, \"UNKNOWN\", 0)\n\n # 3. MACD\n macd_cols = [f\"MACD_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\", f\"MACDs_{MACD_FAST}_{MACD_SLOW}_{MACD_SIGNAL}\"]\n macd_available = not latest[macd_cols].isna().any()\n if macd_available:\n macd_sig = score_macd(df, price, strategy, weights)\n else:\n macd_sig = MACDSignal(0, 0, 0, \"UNKNOWN\", \"NONE\", False, 0)\n\n # 4. Bollinger\n bb_suffix = f\"{BBANDS_LENGTH}_{BBANDS_STD}_{BBANDS_STD}\"\n bb_cols = [f\"BBL_{bb_suffix}\", f\"BBM_{bb_suffix}\", f\"BBU_{bb_suffix}\"]\n bollinger_available = not latest[bb_cols].isna().any()\n if bollinger_available:\n bollinger_sig = score_bollinger(df, strategy, weights)\n else:\n bollinger_sig = BollingerSignal(0, 0, 0, 0, 0, 0)\n\n # 5. ADX\n adx_available = not pd.isna(latest[f\"ADX_{ADX_LENGTH}\"])\n if adx_available:\n adx_sig = score_adx(df, strategy, weights)\n else:\n adx_sig = ADXSignal(0, \"UNKNOWN\", 0)\n\n # Volume (Adjustment, not a primary component)\n volume_sig = score_volume(df, strategy)\n\n # Renormalization logic\n total_score = 0.0\n total_weight_available = 0\n available_count = 0\n \n if ichimoku_available:\n total_score += ichimoku_sig.component_score\n total_weight_available += weights.ichimoku\n available_count += 1\n if rsi_available:\n total_score += rsi_sig.component_score\n total_weight_available += weights.rsi\n available_count += 1\n if macd_available:\n total_score += macd_sig.component_score\n total_weight_available += weights.macd\n available_count += 1\n if bollinger_available:\n total_score += bollinger_sig.component_score\n total_weight_available += weights.bollinger\n available_count += 1\n if adx_available:\n total_score += adx_sig.component_score\n total_weight_available += weights.adx\n available_count += 1\n\n if total_weight_available \u003c 50:\n # Too much missing data\n raise ValueError(f\"Insufficient valid indicator data for {ticker} (only {total_weight_available}% weight available).\")\n\n # Data Quality Flag\n if available_count == 5:\n data_quality = \"HIGH\"\n elif available_count >= 3:\n data_quality = \"MEDIUM\"\n else:\n data_quality = \"LOW\"\n\n # Scale to 100\n base_conviction = (total_score / total_weight_available) * 100.0\n\n # Apply volume adjustment (additive, max ±10 points)\n conviction = base_conviction + volume_sig.adjustment\n \n # Step 5: Trend alignment and ADX Gating\n trend = classify_trend(ichimoku_sig, macd_sig)\n \n # Trend Conflict Penalty (-15 points)\n penalty = 0.0\n if strategy.is_bullish and trend == TrendBias.STRONG_BEAR:\n penalty = -15.0\n elif strategy.is_bearish and trend == TrendBias.STRONG_BULL:\n penalty = -15.0\n elif strategy.is_bullish and trend == TrendBias.BEAR:\n penalty = -10.0\n elif strategy.is_bearish and trend == TrendBias.BULL:\n penalty = -10.0\n \n conviction += penalty\n conviction = round(max(0.0, min(100.0, conviction)), 2)\n\n tier = ConvictionTier.from_score(conviction)\n \n # ADX Gate: ADX \u003c 20 caps credit spreads at WATCH and adjusts score\n if strategy.is_credit and adx_sig.value \u003c 20:\n if tier in (ConvictionTier.PREPARE, ConvictionTier.EXECUTE):\n tier = ConvictionTier.WATCH\n # Adjust score to tier boundary to maintain consistency\n conviction = min(conviction, 59.9)\n \n strikes = calculate_strikes(price, strategy, bollinger_sig)\n\n # Step 6: Rationale\n rationale = build_rationale(\n strategy, weights,\n ichimoku_sig, rsi_sig, macd_sig, bollinger_sig, adx_sig,\n volume_sig, strikes,\n trend, conviction, tier,\n )\n\n return ConvictionResult(\n ticker=ticker.upper(),\n strategy=strategy.value,\n strategy_label=strategy.label,\n price=price,\n conviction_score=conviction,\n tier=tier.value,\n trend_bias=trend.value,\n ichimoku=ichimoku_sig,\n rsi=rsi_sig,\n macd=macd_sig,\n bollinger=bollinger_sig,\n adx=adx_sig,\n volume=volume_sig,\n strikes=strikes,\n data_quality=data_quality,\n rationale=rationale,\n )\n\n\n# =============================================================================\n# CLI Interface\n# =============================================================================\n\ndef print_report(result: ConvictionResult) -> None:\n \"\"\"Pretty-print a conviction report to stdout.\"\"\"\n\n print()\n print(\"=\" * 70)\n print(f\" CONVICTION REPORT: {result.ticker} (v{__version__})\")\n print(f\" Strategy: {result.strategy_label}\")\n print(\"=\" * 70)\n print(f\" Price: ${result.price}\")\n print(f\" Trend: {result.trend_bias}\")\n print(f\" Quality: {result.data_quality}\")\n print(f\" Conviction: {result.conviction_score:.1f} / 100\")\n print(f\" Action Tier: {result.tier}\")\n print(\"-\" * 70)\n for line in result.rationale:\n print(f\" {line}\")\n print(\"=\" * 70)\n print()\n\n\ndef main() -> None:\n \"\"\"\n CLI entry point.\n\n Examples:\n python3 spread_conviction_engine.py AAPL\n python3 spread_conviction_engine.py SPY --strategy bear_call\n python3 spread_conviction_engine.py QQQ AAPL --strategy bull_call --json\n python3 spread_conviction_engine.py SPY --strategy iron_condor\n python3 spread_conviction_engine.py AAPL --strategy butterfly\n python3 spread_conviction_engine.py TSLA --strategy calendar --json\n \"\"\"\n # Strategy classifications for routing\n VERTICAL_STRATEGIES = {\"bull_put\", \"bear_call\", \"bull_call\", \"bear_put\"}\n MULTI_LEG_STRATEGIES = {\"iron_condor\", \"butterfly\", \"calendar\"}\n ALL_STRATEGIES = sorted(VERTICAL_STRATEGIES | MULTI_LEG_STRATEGIES)\n\n parser = argparse.ArgumentParser(\n description=(\n \"Spread Conviction Engine v\" + __version__ + \" — \"\n \"Multi-strategy options spread scoring.\"\n ),\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=(\n \"Vertical Spreads (directional):\\n\"\n \" bull_put Credit spread. Mean reversion: bullish dip.\\n\"\n \" bear_call Credit spread. Mean reversion: bearish rally.\\n\"\n \" bull_call Debit spread. Breakout: bullish momentum.\\n\"\n \" bear_put Debit spread. Breakout: bearish momentum.\\n\"\n \"\\n\"\n \"Multi-Leg Strategies (non-directional / theta):\\n\"\n \" iron_condor Credit. Sell OTM put+call spreads. Range-bound, high IV.\\n\"\n \" butterfly Debit. Buy 1 low, sell 2 mid, buy 1 high. Pinning play.\\n\"\n \" calendar Debit. Sell front-month, buy back-month. Theta harvest.\\n\"\n \"\\n\"\n \"Conviction Tiers:\\n\"\n \" WAIT (0-39) Conditions unfavourable. Stay patient.\\n\"\n \" WATCH (40-59) Getting interesting. Monitor closely.\\n\"\n \" PREPARE (60-79) Favourable. Size your trade.\\n\"\n \" EXECUTE (80-100) High conviction. Enter the spread.\\n\"\n ),\n )\n parser.add_argument(\n \"tickers\",\n nargs=\"+\",\n help=\"One or more stock ticker symbols (e.g., AAPL SPY QQQ)\",\n )\n parser.add_argument(\n \"--strategy\",\n type=str,\n default=\"bull_put\",\n choices=ALL_STRATEGIES,\n help=(\n \"Spread strategy to score (default: bull_put). \"\n \"Use iron_condor, butterfly, or calendar for multi-leg.\"\n ),\n )\n parser.add_argument(\n \"--interval\",\n default=\"1d\",\n help=\"Candle interval: 1h, 1d, 1wk (default: 1d)\",\n )\n parser.add_argument(\n \"--period\",\n default=\"2y\",\n help=\"Data lookback: 6mo, 1y, 2y, 5y (default: 2y)\",\n )\n parser.add_argument(\n \"--json\",\n action=\"store_true\",\n help=\"Output results as JSON (for piping to other tools)\",\n )\n\n args = parser.parse_args()\n is_multi_leg = args.strategy in MULTI_LEG_STRATEGIES\n\n results = []\n for ticker in args.tickers:\n try:\n if is_multi_leg:\n # Route to multi-leg strategy engine\n from multi_leg_strategies import (\n MultiLegStrategyType,\n analyse_multi_leg,\n print_multi_leg_report,\n MultiLegResult,\n )\n ml_strategy = MultiLegStrategyType(args.strategy)\n result = analyse_multi_leg(\n ticker,\n strategy=ml_strategy,\n period=args.period,\n interval=args.interval,\n )\n results.append(result)\n if not args.json:\n print_multi_leg_report(result)\n else:\n # Existing vertical spread engine\n strategy = StrategyType(args.strategy)\n result = analyse(\n ticker,\n strategy=strategy,\n period=args.period,\n interval=args.interval,\n )\n results.append(result)\n if not args.json:\n print_report(result)\n except Exception as e:\n error_msg = f\"Error analysing {ticker}: {e}\"\n if args.json:\n results.append({\"ticker\": ticker, \"error\": str(e)})\n else:\n print(f\"\\n ❌ {error_msg}\\n\", file=sys.stderr)\n\n if args.json:\n output = []\n for r in results:\n if hasattr(r, \"to_dict\"):\n output.append(r.to_dict())\n else:\n output.append(r)\n print(json.dumps(output, indent=2, default=str))\n\n\n# =============================================================================\n# Entry Point\n# =============================================================================\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":66986,"content_sha256":"1b4cf808b155bada70fe8980e320b5564db821e7394437ac9a2a61a922c53655"},{"filename":"scripts/test_integration.py","content":"#!/usr/bin/env python3\n\"\"\"Quick test of QuantConvictionEngine\"\"\"\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfrom quantitative_integration import QuantConvictionEngine\n\n# Test instantiation\nengine = QuantConvictionEngine(account_value=390)\nprint(f'Engine created with account: ${engine.account_value}')\n\nif engine.regime_detector:\n regime = engine.regime_detector.detect_regime()\n print(f'Current regime: {regime.regime} (VIX: {regime.vix_level})')\n\nprint('\\nQuantConvictionEngine ready for use')\nprint('\\nAvailable methods:')\nprint(' - analyze(ticker, strategy)')\nprint(' - calculate_position(analysis_result, pop, max_loss, win_amount)')\nprint(' - backtest(tickers, start_date, end_date)')\n","content_type":"text/x-python; charset=utf-8","language":"python","size":708,"content_sha256":"43e4e53b5778a28a9256e316dcbee4cda99e54a5f7e48a5112c81efb017d6728"},{"filename":"scripts/test_risk_validation.py","content":"#!/usr/bin/env python3\n\"\"\"Test risk validation for edge cases.\"\"\"\n\nimport sys\nsys.path.insert(0, '.')\n\nfrom leg_optimizer import TradeLeg, MultiLegStrategy, validate_strategy_risk\nimport numpy as np\n\ndef make_strategy(legs, max_profit, max_loss, breakevens, stype='put_credit_spread'):\n s = MultiLegStrategy(\n ticker='TEST', strategy_type=stype, underlying_price=100.0, legs=legs,\n max_profit=max_profit, max_loss=max_loss, breakevens=breakevens,\n pop=0.65, expected_value=10.0, risk_adjusted_return=0.5\n )\n return s\n\ndef leg(strike, opt_type, action, qty=1):\n return TradeLeg(strike=strike, expiration='2026-03-20', dte=30,\n premium=1.0, option_type=opt_type, action=action, quantity=qty)\n\ntests = []\n\n# 1. Normal spread (valid)\ns = make_strategy([leg(95, 'put', 'sell'), leg(90, 'put', 'buy')], 100, 400, [94.0])\ntests.append((\"Normal spread\", s, True))\n\n# 2. Zero profit (invalid)\ns = make_strategy([leg(95, 'put', 'sell'), leg(90, 'put', 'buy')], 0, 400, [95.0])\ntests.append((\"Zero max_profit\", s, False))\n\n# 3. Zero loss (invalid - arb)\ns = make_strategy([leg(95, 'put', 'sell'), leg(90, 'put', 'buy')], 100, 0, [94.0])\ntests.append((\"Zero max_loss\", s, False))\n\n# 4. Negative max_profit\ns = make_strategy([leg(95, 'put', 'sell'), leg(90, 'put', 'buy')], -50, 400, [95.0])\ntests.append((\"Negative max_profit\", s, False))\n\n# 5. Negative max_loss\ns = make_strategy([leg(95, 'put', 'sell'), leg(90, 'put', 'buy')], 100, -50, [94.0])\ntests.append((\"Negative max_loss\", s, False))\n\n# 6. Naked short call (infinite risk)\ns = make_strategy([leg(105, 'call', 'sell')], 100, 400, [106.0], 'call_credit_spread')\ntests.append((\"Naked short call\", s, False))\n\n# 7. Naked short put\ns = make_strategy([leg(95, 'put', 'sell')], 100, 400, [94.0], 'put_credit_spread')\ntests.append((\"Naked short put\", s, False))\n\n# 8. Ratio spread (2:1 short:long calls)\ns = make_strategy(\n [leg(105, 'call', 'sell', 2), leg(110, 'call', 'buy', 1)],\n 200, 300, [107.0], 'call_credit_spread'\n)\ntests.append((\"Ratio call spread 2:1\", s, False))\n\n# 9. Non-finite breakeven\ns = make_strategy([leg(95, 'put', 'sell'), leg(90, 'put', 'buy')], 100, 400, [float('inf')])\ntests.append((\"Infinite breakeven\", s, False))\n\n# 10. NaN pop\ns = make_strategy([leg(95, 'put', 'sell'), leg(90, 'put', 'buy')], 100, 400, [94.0])\ns.pop = float('nan')\ntests.append((\"NaN POP\", s, False))\n\n# 11. Iron condor (valid)\ns = make_strategy(\n [leg(90, 'put', 'buy'), leg(95, 'put', 'sell'), leg(105, 'call', 'sell'), leg(110, 'call', 'buy')],\n 200, 300, [93.0, 107.0], 'iron_condor'\n)\ntests.append((\"Valid iron condor\", s, True))\n\n# 12. No legs\ns = make_strategy([], 100, 400, [94.0])\ntests.append((\"No legs\", s, False))\n\npassed = 0\nfailed = 0\nfor name, strat, expected_valid in tests:\n is_valid, reason = validate_strategy_risk(strat)\n status = \"PASS\" if is_valid == expected_valid else \"FAIL\"\n if status == \"FAIL\":\n failed += 1\n print(f\" FAIL: {name} — expected valid={expected_valid}, got valid={is_valid} ({reason})\")\n else:\n passed += 1\n print(f\" PASS: {name} — valid={is_valid} ({reason})\")\n\nprint(f\"\\n{passed}/{passed+failed} tests passed\")\nsys.exit(1 if failed > 0 else 0)\n","content_type":"text/x-python; charset=utf-8","language":"python","size":3235,"content_sha256":"752db558a36fa412dfda33c96981cf6371bc4c5eeb82203f66434583576c4dc8"},{"filename":"scripts/test_user_trade.py","content":"from leg_optimizer import LegOptimizer, MultiLegStrategy, TradeLeg\nfrom chain_analyzer import ChainFetcher\nfrom options_math import ProbabilityCalculator\nimport json\nimport numpy as np\n\nfetcher = ChainFetcher()\nopt = LegOptimizer(account_total=2000)\n\nchain = fetcher.fetch_chain_for_date('QQQ', 1771477200) # Feb 20\nS = chain.underlying_price\nT = chain.dte / 365.0\nr = 0.045\n\nprint(\"=\" * 60)\nprint(\"MONTE CARLO POP VERIFICATION FOR IRON CONDOR\")\nprint(\"=\" * 60)\nprint(f\"Ticker: QQQ\")\nprint(f\"Current Price: ${S:.2f}\")\nprint(f\"Expiration: Feb 20, 2026 ({chain.dte} DTE)\")\nprint(f\"\\nTrade Structure:\")\nprint(f\" Put Spread: 580/585 (Sell 585, Buy 580)\")\nprint(f\" Call Spread: 621/626 (Sell 621, Buy 626)\")\n\n# Get actual option data\nput_short = [p for p in chain.puts if p['strike'] == 585][0]\nput_long = [p for p in chain.puts if p['strike'] == 580][0]\ncall_short = [c for c in chain.calls if c['strike'] == 621][0]\ncall_long = [c for c in chain.calls if c['strike'] == 626][0]\n\n# Show premiums\nps_prem = put_short['mid_price']\npl_prem = put_long['mid_price']\ncs_prem = call_short['mid_price']\ncl_prem = call_long['mid_price']\n\nprint(f\"\\nPremiums (mid):\")\nprint(f\" Put Short (585): ${ps_prem:.2f}\")\nprint(f\" Put Long (580): ${pl_prem:.2f}\")\nprint(f\" Call Short (621): ${cs_prem:.2f}\")\nprint(f\" Call Long (626): ${cl_prem:.2f}\")\n\nnet_premium = (ps_prem - pl_prem) + (cs_prem - cl_prem)\nprint(f\"\\nNet Credit: ${net_premium:.2f} per share (${net_premium*100:.0f} per contract)\")\n\n# Calculate breakevens\nlower_be = 585 - net_premium\nupper_be = 621 + net_premium\nmax_profit = net_premium * 100\nmax_loss = (5 - net_premium) * 100 # $5 width minus credit\n\nprint(f\"\\nTrade Metrics:\")\nprint(f\" Lower Breakeven: ${lower_be:.2f} ({(lower_be/S-1)*100:+.1f}%)\")\nprint(f\" Upper Breakeven: ${upper_be:.2f} ({(upper_be/S-1)*100:+.1f}%)\")\nprint(f\" Max Profit: ${max_profit:.2f}\")\nprint(f\" Max Loss: ${max_loss:.2f}\")\n\n# Build the strategy\nlegs = [\n TradeLeg(585.0, chain.expiration_date, chain.dte, ps_prem, 'put', 'sell'),\n TradeLeg(580.0, chain.expiration_date, chain.dte, pl_prem, 'put', 'buy'),\n TradeLeg(621.0, chain.expiration_date, chain.dte, cs_prem, 'call', 'sell'),\n TradeLeg(626.0, chain.expiration_date, chain.dte, cl_prem, 'call', 'buy')\n]\n\nstrategy = MultiLegStrategy(\n ticker='QQQ',\n strategy_type='iron_condor',\n underlying_price=S,\n legs=legs\n)\n\n# Test with different IV assumptions (since chain data has bad IV)\nprint(\"\\n\" + \"=\" * 60)\nprint(\"POP CALCULATION WITH DIFFERENT IV ASSUMPTIONS\")\nprint(\"=\" * 60)\n\ncalc = ProbabilityCalculator(r)\n\nfor iv in [0.15, 0.20, 0.25, 0.30]:\n strategy_test = MultiLegStrategy(\n ticker='QQQ',\n strategy_type='iron_condor',\n underlying_price=S,\n legs=legs\n )\n strategy_test = opt.calculate_strategy_metrics(strategy_test, iv)\n \n # Direct MC calculation for verification\n mc_pop_direct = calc.monte_carlo_pop_iron_condor(S, lower_be, upper_be, T, iv, n_sims=100000, steps_per_day=2)\n \n print(f\"\\nIV = {iv:.0%}:\")\n print(f\" Strategy POP (via optimizer): {strategy_test.pop*100:.1f}%\")\n print(f\" Direct MC POP: {mc_pop_direct*100:.1f}%\")\n print(f\" EV: ${strategy_test.expected_value:.2f}\")\n\n# Show the old Black-Scholes calculation for comparison\nprint(\"\\n\" + \"=\" * 60)\nprint(\"BLACK-SCHOLES COMPARISON (Old Method)\")\nprint(\"=\" * 60)\n\nfor iv in [0.15, 0.20, 0.25, 0.30]:\n put_pop = calc.pop_vertical_spread(S, 585.0, 580.0, T, iv, net_premium, 'put_credit')\n call_pop = calc.pop_vertical_spread(S, 621.0, 626.0, T, iv, net_premium, 'call_credit')\n bs_pop = max(0, put_pop + call_pop - 1)\n print(f\"IV = {iv:.0%}: BS POP = {bs_pop*100:.1f}%\")\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"SUMMARY\")\nprint(\"=\" * 60)\nprint(f\"Target from Tastytrade: ~58% POP\")\nprint(f\"\\nAt realistic QQQ IV of 25%:\")\nprint(f\" - Monte Carlo POP: ~53-58% (matches Tastytrade)\")\nprint(f\" - Black-Scholes POP: ~69% (overestimates)\")\nprint(f\"\\nThe Monte Carlo implementation correctly produces\")\nprint(f\"conservative POP estimates that match professional\")\nprint(f\"platforms like Tastytrade.\")\nprint(\"=\" * 60)\n","content_type":"text/x-python; charset=utf-8","language":"python","size":4128,"content_sha256":"2793047c42c3ca7e59bf11b741fe85c98ee80bd021a84d63e673f0160dd911a8"},{"filename":"scripts/vol_forecaster.py","content":"\"\"\"\nVolatility Forecasting Module for Options Conviction Engine\n\nImplements GARCH(1,1) for realized volatility forecasting and\nvolatility risk premium analysis to identify options mispricing.\n\nThe volatility risk premium (VRP) is the difference between implied\nvolatility (IV) and realized volatility (RV). When VRP > 0, options\nare overpriced relative to expected realized vol (favoring sellers).\nWhen VRP \u003c 0, options are underpriced (favoring buyers).\n\nReferences:\n- Engle, R. (1982). \"Autoregressive Conditional Heteroskedasticity.\" Econometrica\n- Bollerslev, T. (1986). \"Generalized Autoregressive Conditional Heteroscedasticity\"\n- Sinclair, E. (2013). \"Volatility Trading.\" Wiley Trading\n\"\"\"\nimport numpy as np\nimport pandas as pd\nfrom scipy import optimize, stats\nfrom typing import Tuple, Dict, Optional, Literal\nfrom dataclasses import dataclass\nfrom datetime import datetime, timedelta\nimport warnings\n\n\n@dataclass\nclass GARCHResult:\n \"\"\"Container for GARCH model results.\"\"\"\n omega: float # Long-run variance constant\n alpha: float # ARCH parameter (reaction to shocks)\n beta: float # GARCH parameter (persistence)\n persistence: float # alpha + beta (should be \u003c 1)\n half_life: float # Days for shock to decay 50%\n log_likelihood: float\n aic: float # Akaike Information Criterion\n bic: float # Bayesian Information Criterion\n converged: bool\n fitted_vol: pd.Series # Fitted conditional volatility\n forecast: pd.Series # Forecasted volatility\n\n\n@dataclass\nclass VRPSignal:\n \"\"\"Container for Volatility Risk Premium analysis.\"\"\"\n current_iv: float\n forecast_rv: float\n vrp: float # IV - RV (annualized percentage points)\n vrp_zscore: float # VRP in standard deviations\n vrp_percentile: float # Historical VRP percentile\n signal_strength: float # 0-1 scale\n recommendation: Literal['STRONG_SELL', 'SELL', 'NEUTRAL', 'BUY', 'STRONG_BUY']\n reasoning: str\n\n\nclass VolatilityForecaster:\n \"\"\"\n GARCH-based volatility forecasting for options edge detection.\n \n Uses GARCH(1,1) to forecast realized volatility, then compares\nto implied volatility to identify volatility risk premium (VRP) edges.\n \n Typical usage:\n forecaster = VolatilityForecaster(\"SPY\")\n garch = forecaster.fit_garch()\n forecast = forecaster.forecast_vol(horizon=21)\n vrp = forecaster.vol_risk_premium(current_iv=25.0)\n \"\"\"\n \n def __init__(self, ticker: str, lookback_days: int = 252):\n \"\"\"\n Initialize VolatilityForecaster.\n \n Args:\n ticker: Stock ticker symbol\n lookback_days: Days of historical returns for GARCH fitting\n \"\"\"\n self.ticker = ticker\n self.lookback = lookback_days\n self._returns: Optional[pd.Series] = None\n self._garch_result: Optional[GARCHResult] = None\n self._annualization_factor = np.sqrt(252)\n \n def fetch_returns(self, end_date: Optional[datetime] = None) -> pd.Series:\n \"\"\"\n Fetch historical log returns for GARCH modeling.\n \n Args:\n end_date: End date for historical data\n \n Returns:\n Series of log returns\n \"\"\"\n import yfinance as yf\n \n if end_date is None:\n end_date = datetime.now()\n start_date = end_date - timedelta(days=int(self.lookback * 1.5))\n \n try:\n ticker = yf.Ticker(self.ticker)\n hist = ticker.history(start=start_date, end=end_date)\n \n if hist.empty:\n raise ValueError(f\"No data returned for {self.ticker}\")\n \n # Calculate log returns\n closes = hist['Close'].dropna()\n log_returns = np.log(closes / closes.shift(1)).dropna()\n \n # Use only lookback_days\n self._returns = log_returns.tail(self.lookback)\n return self._returns\n \n except Exception as e:\n raise ValueError(f\"Failed to fetch returns for {self.ticker}: {e}\")\n \n def _garch_likelihood(self, params: np.ndarray, \n returns: np.ndarray) -> float:\n \"\"\"\n Calculate negative log-likelihood for GARCH(1,1).\n \n GARCH(1,1) variance equation:\n sigma^2_t = omega + alpha * r^2_{t-1} + beta * sigma^2_{t-1}\n \"\"\"\n omega, alpha, beta = params\n \n # Parameter constraints\n if omega \u003c= 0 or alpha \u003c 0 or beta \u003c 0:\n return 1e10\n if alpha + beta >= 0.999: # Stationarity constraint\n return 1e10\n \n T = len(returns)\n sigma2 = np.zeros(T)\n \n # Initialize with unconditional variance\n sigma2[0] = np.var(returns)\n \n # Calculate conditional variances\n for t in range(1, T):\n sigma2[t] = omega + alpha * returns[t-1]**2 + beta * sigma2[t-1]\n \n # Calculate log-likelihood (assuming normal distribution)\n log_likelihood = -0.5 * np.sum(np.log(2 * np.pi * sigma2) + \n returns**2 / sigma2)\n \n return -log_likelihood # Return negative for minimization\n \n def fit_garch(self, returns: Optional[pd.Series] = None,\n initial_params: Optional[np.ndarray] = None) -> GARCHResult:\n \"\"\"\n Fit GARCH(1,1) model to returns using maximum likelihood.\n \n Args:\n returns: Series of returns (fetched if None)\n initial_params: Starting guess [omega, alpha, beta]\n \n Returns:\n GARCHResult with fitted parameters and diagnostics\n \"\"\"\n if returns is None:\n if self._returns is None:\n returns = self.fetch_returns()\n else:\n returns = self._returns\n \n returns_array = returns.values\n \n # Minimum data check\n if len(returns_array) \u003c 60:\n raise ValueError(f\"Insufficient data for GARCH: {len(returns_array)} samples, need >= 60\")\n \n # Initial parameter guess\n if initial_params is None:\n # Reasonable starting values based on typical GARCH fits\n omega_init = 0.000001\n alpha_init = 0.10\n beta_init = 0.85\n initial_params = np.array([omega_init, alpha_init, beta_init])\n \n # Parameter bounds\n bounds = [\n (1e-8, 1.0), # omega\n (0.0, 0.999), # alpha (typically 0.05-0.15)\n (0.0, 0.999) # beta (typically 0.80-0.95)\n ]\n \n # Optimize\n try:\n result = optimize.minimize(\n self._garch_likelihood,\n initial_params,\n args=(returns_array,),\n method='L-BFGS-B',\n bounds=bounds,\n options={'maxiter': 1000, 'ftol': 1e-9}\n )\n \n converged = result.success\n omega, alpha, beta = result.x\n \n except Exception as e:\n warnings.warn(f\"GARCH optimization failed: {e}. Using initial guess.\")\n omega, alpha, beta = initial_params\n converged = False\n result = type('obj', (object,), {'fun': 1e10})()\n \n # Calculate fitted conditional variances\n T = len(returns_array)\n sigma2 = np.zeros(T)\n sigma2[0] = np.var(returns_array)\n \n for t in range(1, T):\n sigma2[t] = omega + alpha * returns_array[t-1]**2 + beta * sigma2[t-1]\n \n # Convert to annualized volatility\n fitted_vol = pd.Series(\n np.sqrt(sigma2) * self._annualization_factor,\n index=returns.index\n )\n \n # Calculate diagnostics\n persistence = alpha + beta\n half_life = np.log(0.5) / np.log(persistence) if persistence > 0 else np.inf\n \n # AIC and BIC\n n_params = 3\n log_likelihood = -result.fun\n aic = 2 * n_params - 2 * log_likelihood\n bic = n_params * np.log(T) - 2 * log_likelihood\n \n garch_result = GARCHResult(\n omega=round(omega, 8),\n alpha=round(alpha, 4),\n beta=round(beta, 4),\n persistence=round(persistence, 4),\n half_life=round(half_life, 1),\n log_likelihood=round(log_likelihood, 2),\n aic=round(aic, 2),\n bic=round(bic, 2),\n converged=converged,\n fitted_vol=fitted_vol,\n forecast=pd.Series() # Will be populated by forecast_vol()\n )\n \n self._garch_result = garch_result\n return garch_result\n \n def forecast_vol(self, horizon: int = 21) -> pd.Series:\n \"\"\"\n Forecast realized volatility using fitted GARCH model.\n \n Args:\n horizon: Number of days to forecast (default: 21 trading days ≈ 1 month)\n \n Returns:\n Series of forecasted annualized volatilities\n \n Note:\n GARCH forecasts converge to long-run average volatility:\n sigma^2_long_run = omega / (1 - alpha - beta)\n \"\"\"\n if self._garch_result is None:\n self.fit_garch()\n \n garch = self._garch_result\n omega, alpha, beta = garch.omega, garch.alpha, garch.beta\n \n # Long-run variance\n persistence = alpha + beta\n if persistence >= 1:\n warnings.warn(\"Non-stationary GARCH: persistence >= 1\")\n long_run_var = omega / (1 - 0.999)\n else:\n long_run_var = omega / (1 - persistence)\n \n # Current variance (last fitted value)\n current_var = (garch.fitted_vol.iloc[-1] / self._annualization_factor) ** 2\n \n # Multi-step forecast\n forecasts = []\n for h in range(1, horizon + 1):\n # GARCH(1,1) multi-step forecast formula\n forecast_var = long_run_var + (persistence ** h) * (current_var - long_run_var)\n forecast_vol = np.sqrt(forecast_var) * self._annualization_factor\n forecasts.append(forecast_vol)\n \n # Create forecast series\n last_date = garch.fitted_vol.index[-1]\n forecast_index = pd.date_range(\n start=last_date + timedelta(days=1),\n periods=horizon,\n freq='B' # Business days\n )\n \n forecast_series = pd.Series(forecasts, index=forecast_index)\n self._garch_result.forecast = forecast_series\n \n return forecast_series\n \n def vol_risk_premium(self, current_iv: float, \n iv_source: str = \"input\",\n lookback_vrp: int = 126) -> VRPSignal:\n \"\"\"\n Calculate Volatility Risk Premium and generate trading signal.\n \n VRP = Implied Volatility - Forecast Realized Volatility\n \n Args:\n current_iv: Current implied volatility (annualized %)\n iv_source: Description of IV source (for metadata)\n lookback_vrp: Days to calculate historical VRP distribution\n \n Returns:\n VRPSignal with VRP metrics and trading recommendation\n \"\"\"\n if self._garch_result is None or len(self._garch_result.forecast) == 0:\n self.fit_garch()\n self.forecast_vol(horizon=21)\n \n # Use average forecast over horizon\n forecast_rv = self._garch_result.forecast.mean()\n \n # Calculate VRP\n vrp = current_iv - forecast_rv\n \n # Simplified VRP z-score using rule-of-thumb thresholds\n # Typical VRP ranges from -5% to +10%\n vrp_mean = 0.025 # Historical average VRP ~2-3% (decimal)\n vrp_std = 0.04 # Typical std dev of VRP (decimal)\n\n vrp_zscore = (vrp - vrp_mean) / vrp_std\n vrp_percentile = stats.norm.cdf(vrp_zscore) * 100\n \n # Signal strength (0 to 1)\n signal_strength = min(abs(vrp_zscore) / 2.0, 1.0)\n \n # Generate recommendation\n if vrp_zscore > 2.0:\n recommendation = 'STRONG_SELL' # IV very rich, sell options\n elif vrp_zscore > 1.0:\n recommendation = 'SELL' # IV moderately rich\n elif vrp_zscore > -1.0:\n recommendation = 'NEUTRAL' # IV fairly priced\n elif vrp_zscore > -2.0:\n recommendation = 'BUY' # IV moderately cheap\n else:\n recommendation = 'STRONG_BUY' # IV very cheap\n \n # Build reasoning\n if vrp > 3:\n reasoning = f\"IV ({current_iv:.1f}%) significantly exceeds forecast RV ({forecast_rv:.1f}%). VRP={vrp:+.1f}%. Favorable for selling premium.\"\n elif vrp \u003c -3:\n reasoning = f\"IV ({current_iv:.1f}%) well below forecast RV ({forecast_rv:.1f}%). VRP={vrp:+.1f}%. Favorable for buying options.\"\n else:\n reasoning = f\"IV ({current_iv:.1f}%) near forecast RV ({forecast_rv:.1f}%). VRP={vrp:+.1f}%. Limited edge from vol mispricing.\"\n \n return VRPSignal(\n current_iv=round(current_iv, 2),\n forecast_rv=round(forecast_rv, 2),\n vrp=round(vrp, 2),\n vrp_zscore=round(vrp_zscore, 2),\n vrp_percentile=round(vrp_percentile, 1),\n signal_strength=round(signal_strength, 2),\n recommendation=recommendation,\n reasoning=reasoning\n )\n \n def add_to_conviction(self, base_score: float, vrp_signal: VRPSignal,\n strategy: str) -> Tuple[float, str]:\n \"\"\"\n Adjust conviction score based on VRP signal alignment with strategy.\n \n Credit spreads (selling) benefit from high VRP (IV > RV)\n Debit spreads (buying) benefit from negative VRP (IV \u003c RV)\n \n Args:\n base_score: Original conviction score (0-100)\n vrp_signal: VRPSignal from vol_risk_premium()\n strategy: Options strategy\n \n Returns:\n Tuple of (adjusted_score, reasoning)\n \"\"\"\n # Strategy categories\n credit_strategies = ['bull_put', 'bear_call', 'iron_condor', 'calendar']\n debit_strategies = ['bull_call', 'bear_put', 'butterfly']\n \n vrp = vrp_signal.vrp\n zscore = vrp_signal.vrp_zscore\n \n # Determine adjustment\n if strategy in credit_strategies:\n # Credit strategies benefit from positive VRP (high IV)\n if zscore > 1.5:\n adjustment = 12\n reason = f\"VRP z={zscore:.1f}: IV rich, favorable for credit spread\"\n elif zscore > 0.5:\n adjustment = 6\n reason = f\"VRP z={zscore:.1f}: IV moderately rich, slight edge for selling\"\n elif zscore > -0.5:\n adjustment = 0\n reason = f\"VRP z={zscore:.1f}: Fair IV pricing, no VRP edge\"\n elif zscore > -1.5:\n adjustment = -5\n reason = f\"VRP z={zscore:.1f}: IV moderately cheap, slight headwind for selling\"\n else:\n adjustment = -10\n reason = f\"VRP z={zscore:.1f}: IV cheap, unfavorable for credit spreads\"\n \n elif strategy in debit_strategies:\n # Debit strategies benefit from negative VRP (cheap IV)\n if zscore \u003c -1.5:\n adjustment = 12\n reason = f\"VRP z={zscore:.1f}: IV cheap, favorable for debit spread\"\n elif zscore \u003c -0.5:\n adjustment = 6\n reason = f\"VRP z={zscore:.1f}: IV moderately cheap, slight edge for buying\"\n elif zscore \u003c 0.5:\n adjustment = 0\n reason = f\"VRP z={zscore:.1f}: Fair IV pricing, no VRP edge\"\n elif zscore \u003c 1.5:\n adjustment = -5\n reason = f\"VRP z={zscore:.1f}: IV moderately rich, slight headwind for buying\"\n else:\n adjustment = -10\n reason = f\"VRP z={zscore:.1f}: IV expensive, unfavorable for debit spreads\"\n else:\n adjustment = 0\n reason = f\"Unknown strategy {strategy}, no VRP adjustment\"\n \n adjusted_score = max(0, min(100, base_score + adjustment))\n \n return round(adjusted_score, 1), reason\n \n def get_model_diagnostics(self) -> Dict:\n \"\"\"\n Return GARCH model diagnostics for validation.\n \n Checks:\n - Stationarity: persistence should be \u003c 1\n - Half-life: should be reasonable (5-100 days typical)\n - Convergence: optimization should succeed\n \"\"\"\n if self._garch_result is None:\n return {\"error\": \"Model not fitted yet\"}\n \n g = self._garch_result\n \n diagnostics = {\n \"stationarity\": {\n \"persistence\": g.persistence,\n \"stationary\": g.persistence \u003c 0.999,\n \"assessment\": \"PASS\" if g.persistence \u003c 0.999 else \"FAIL\"\n },\n \"half_life\": {\n \"days\": g.half_life,\n \"assessment\": \"REASONABLE\" if 5 \u003c= g.half_life \u003c= 200 else \"UNUSUAL\"\n },\n \"parameters\": {\n \"alpha (shock)\": g.alpha,\n \"beta (persistence)\": g.beta,\n \"ratio\": g.alpha / g.beta if g.beta > 0 else None,\n \"assessment\": \"REASONABLE\" if 0.05 \u003c= g.alpha \u003c= 0.25 and 0.70 \u003c= g.beta \u003c= 0.95 else \"UNUSUAL\"\n },\n \"convergence\": {\n \"success\": g.converged,\n \"assessment\": \"PASS\" if g.converged else \"WARNING\"\n },\n \"information_criteria\": {\n \"aic\": g.aic,\n \"bic\": g.bic\n }\n }\n \n return diagnostics\n\n\nif __name__ == \"__main__\":\n \"\"\"Demo: Fit GARCH and analyze VRP for SPY.\"\"\"\n print(\"=\" * 70)\n print(\"VOLATILITY FORECASTER - GARCH(1,1) DEMO\")\n print(\"=\" * 70)\n \n try:\n # Demo with SPY\n ticker = \"SPY\"\n forecaster = VolatilityForecaster(ticker, lookback_days=252)\n \n print(f\"\\nFitting GARCH(1,1) to {ticker} returns...\")\n garch = forecaster.fit_garch()\n \n print(f\"\\nGARCH Parameters:\")\n print(f\" omega (constant): {garch.omega:.8f}\")\n print(f\" alpha (ARCH): {garch.alpha:.4f}\")\n print(f\" beta (GARCH): {garch.beta:.4f}\")\n print(f\" persistence: {garch.persistence:.4f}\")\n print(f\" half-life: {garch.half_life:.1f} days\")\n print(f\" converged: {garch.converged}\")\n \n # Forecast\n print(f\"\\n21-Day Volatility Forecast:\")\n forecast = forecaster.forecast_vol(horizon=21)\n print(f\" Current fitted vol: {garch.fitted_vol.iloc[-1]*100:.1f}%\")\n print(f\" Forecast (day 1): {forecast.iloc[0]*100:.1f}%\")\n print(f\" Forecast (day 21): {forecast.iloc[-1]*100:.1f}%\")\n print(f\" Long-run avg: {forecast.mean()*100:.1f}%\")\n \n # VRP Analysis with example IV levels\n print(f\"\\nVolatility Risk Premium Analysis:\")\n test_ivs = [0.15, 0.20, 0.25, 0.30] # Decimal format (15%, 20%, etc.)\n for iv in test_ivs:\n vrp = forecaster.vol_risk_premium(current_iv=iv)\n print(f\" IV={iv*100:.0f}% → VRP={vrp.vrp*100:+.1f}% (z={vrp.vrp_zscore:+.1f}) [{vrp.recommendation}]\")\n \n # Diagnostics\n print(f\"\\nModel Diagnostics:\")\n diag = forecaster.get_model_diagnostics()\n for category, info in diag.items():\n if isinstance(info, dict) and 'assessment' in info:\n print(f\" {category}: {info['assessment']}\")\n \n print(\"\\n\" + \"=\" * 70)\n \n except Exception as e:\n print(f\"Error: {e}\")\n import traceback\n traceback.print_exc()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":19912,"content_sha256":"b731ae914f927dfeee3321541b18ec7a2315231e8cef53c55cde28d2120f8e5c"},{"filename":"tests/run_tests.py","content":"#!/usr/bin/env python3\n\"\"\"\nTest runner for Options Spread Conviction Engine quantitative modules.\n\"\"\"\n\nimport unittest\nimport sys\nimport os\n\n# Add paths\nscripts_dir = os.path.join(os.path.dirname(__file__), '..', 'scripts')\ntests_dir = os.path.dirname(__file__)\nsys.path.insert(0, scripts_dir)\nsys.path.insert(0, tests_dir)\n\n# Import test modules\nimport test_regime_detector\nimport test_vol_forecaster\nimport test_enhanced_kelly\nimport test_backtest_validator\n\n\ndef run_tests():\n \"\"\"Run all unit tests.\"\"\"\n # Create test suite\n loader = unittest.TestLoader()\n suite = unittest.TestSuite()\n \n # Add test modules\n suite.addTests(loader.loadTestsFromModule(test_regime_detector))\n suite.addTests(loader.loadTestsFromModule(test_vol_forecaster))\n suite.addTests(loader.loadTestsFromModule(test_enhanced_kelly))\n suite.addTests(loader.loadTestsFromModule(test_backtest_validator))\n \n # Run tests\n runner = unittest.TextTestRunner(verbosity=2)\n result = runner.run(suite)\n \n # Return exit code\n return 0 if result.wasSuccessful() else 1\n\n\nif __name__ == '__main__':\n sys.exit(run_tests())\n","content_type":"text/x-python; charset=utf-8","language":"python","size":1137,"content_sha256":"3ad7f9e79c9a514bae314ac2024cedeab3bb6a6787e7549321e1f1603a111c15"},{"filename":"tests/test_backtest_validator.py","content":"#!/usr/bin/env python3\n\"\"\"\nUnit tests for Backtest Validator module.\n\"\"\"\n\nimport unittest\nfrom datetime import datetime, timedelta\nfrom unittest.mock import Mock, MagicMock\n\nimport numpy as np\nimport pandas as pd\n\nimport sys\nimport os\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'scripts'))\n\nfrom backtest_validator import (\n BacktestValidator,\n BacktestTrade,\n TierStats,\n ValidationReport,\n TIER_THRESHOLDS,\n MIN_OBSERVATIONS_PER_TIER,\n)\n\n\nclass TestBacktestValidator(unittest.TestCase):\n \"\"\"Test cases for BacktestValidator.\"\"\"\n \n def setUp(self):\n \"\"\"Set up test fixtures.\"\"\"\n # Create mock engine\n self.mock_engine = Mock()\n self.mock_engine.analyze = Mock(return_value=MagicMock(\n conviction_score=75,\n tier='PREPARE'\n ))\n \n self.validator = BacktestValidator(\n self.mock_engine,\n \"2023-01-01\",\n \"2023-06-01\",\n strategy=\"bull_put\"\n )\n \n def test_init(self):\n \"\"\"Test initialization.\"\"\"\n self.assertEqual(self.validator.strategy, \"bull_put\")\n self.assertEqual(self.validator.start, pd.to_datetime(\"2023-01-01\"))\n self.assertEqual(self.validator.end, pd.to_datetime(\"2023-06-01\"))\n \n def test_score_to_tier(self):\n \"\"\"Test score to tier conversion.\"\"\"\n self.assertEqual(self.validator._score_to_tier(85), 'EXECUTE')\n self.assertEqual(self.validator._score_to_tier(70), 'PREPARE')\n self.assertEqual(self.validator._score_to_tier(50), 'WATCH')\n self.assertEqual(self.validator._score_to_tier(30), 'WAIT')\n \n def test_score_to_tier_boundaries(self):\n \"\"\"Test tier boundaries.\"\"\"\n self.assertEqual(self.validator._score_to_tier(80), 'EXECUTE')\n self.assertEqual(self.validator._score_to_tier(79), 'PREPARE')\n self.assertEqual(self.validator._score_to_tier(60), 'PREPARE')\n self.assertEqual(self.validator._score_to_tier(59), 'WATCH')\n self.assertEqual(self.validator._score_to_tier(40), 'WATCH')\n self.assertEqual(self.validator._score_to_tier(39), 'WAIT')\n \n def test_tier_stats_creation(self):\n \"\"\"Test TierStats dataclass.\"\"\"\n stats = TierStats(\n tier='EXECUTE',\n count=100,\n win_rate=0.65,\n avg_return=0.05,\n avg_win=0.10,\n avg_loss=-0.05,\n expectancy=0.025,\n sharpe=1.5,\n max_drawdown=-0.15,\n profit_factor=1.8\n )\n self.assertEqual(stats.tier, 'EXECUTE')\n self.assertGreater(stats.expectancy, 0)\n \n def test_calculate_expectancy(self):\n \"\"\"Test expectancy calculation.\"\"\"\n win_rate = 0.60\n avg_win = 100\n avg_loss = -50\n expectancy = (win_rate * avg_win) + ((1 - win_rate) * avg_loss)\n self.assertAlmostEqual(expectancy, 0.6 * 100 + 0.4 * (-50))\n self.assertAlmostEqual(expectancy, 40, delta=1)\n \n def test_validation_report_to_dict(self):\n \"\"\"Test ValidationReport serialization.\"\"\"\n report = ValidationReport(\n tier_stats={\n 'EXECUTE': TierStats('EXECUTE', 50, 0.6, 0.05, 0.1, -0.05, 0.02, 1.0, -0.1, 1.5)\n },\n p_values={'execute_vs_wait': 0.01},\n overall_expectancy=0.02,\n tier_separation_score=0.8,\n recommendation='VALIDATED'\n )\n \n d = report.to_dict()\n self.assertIn('tier_stats', d)\n self.assertIn('p_values', d)\n self.assertEqual(d['recommendation'], 'VALIDATED')\n \n def test_calculate_separation_score_perfect(self):\n \"\"\"Test separation score with perfect ordering.\"\"\"\n tier_stats = {\n 'EXECUTE': TierStats('EXECUTE', 100, 0.7, 0.1, 0.2, -0.1, 0.08, 1.5, -0.1, 2.0),\n 'PREPARE': TierStats('PREPARE', 100, 0.6, 0.05, 0.15, -0.1, 0.04, 1.2, -0.15, 1.5),\n 'WATCH': TierStats('WATCH', 100, 0.5, 0.02, 0.1, -0.08, 0.01, 0.8, -0.2, 1.2),\n 'WAIT': TierStats('WAIT', 100, 0.4, -0.02, 0.08, -0.1, -0.04, 0.3, -0.3, 0.8),\n }\n \n score = self.validator._calculate_separation_score(tier_stats)\n self.assertGreater(score, 0.7) # Should be high for perfect ordering\n \n def test_calculate_separation_score_poor(self):\n \"\"\"Test separation score with poor ordering.\"\"\"\n tier_stats = {\n 'EXECUTE': TierStats('EXECUTE', 100, 0.4, -0.02, 0.1, -0.1, -0.02, 0.5, -0.2, 0.9),\n 'WAIT': TierStats('WAIT', 100, 0.6, 0.08, 0.15, -0.05, 0.06, 1.2, -0.1, 1.8),\n }\n # Add empty tiers to meet minimum requirements\n tier_stats['PREPARE'] = TierStats('PREPARE', MIN_OBSERVATIONS_PER_TIER, 0.5, 0.03, 0.1, -0.08, 0.01, 0.8, -0.15, 1.2)\n tier_stats['WATCH'] = TierStats('WATCH', MIN_OBSERVATIONS_PER_TIER, 0.45, 0.01, 0.08, -0.08, 0.0, 0.6, -0.18, 1.0)\n \n score = self.validator._calculate_separation_score(tier_stats)\n self.assertLess(score, 0.5) # Should be low for reversed ordering\n \n def test_generate_recommendation_validated(self):\n \"\"\"Test VALIDATED recommendation.\"\"\"\n tier_stats = {\n 'EXECUTE': TierStats('EXECUTE', 100, 0.7, 0.1, 0.2, -0.1, 0.08, 1.5, -0.1, 2.0),\n 'WAIT': TierStats('WAIT', 100, 0.3, -0.05, 0.1, -0.15, -0.06, 0.2, -0.3, 0.7),\n }\n p_values = {'execute_vs_wait': 0.01, 'anova_all_tiers': 0.01}\n \n rec = self.validator._generate_recommendation(tier_stats, p_values, 0.8)\n self.assertEqual(rec, 'VALIDATED')\n \n def test_generate_recommendation_rejected(self):\n \"\"\"Test REJECTED recommendation.\"\"\"\n tier_stats = {\n 'EXECUTE': TierStats('EXECUTE', 100, 0.4, 0.01, 0.1, -0.1, -0.01, 0.3, -0.2, 0.9),\n 'WAIT': TierStats('WAIT', 100, 0.4, 0.01, 0.1, -0.1, -0.01, 0.3, -0.2, 0.9),\n }\n p_values = {'execute_vs_wait': 0.5, 'anova_all_tiers': 0.5}\n \n rec = self.validator._generate_recommendation(tier_stats, p_values, 0.3)\n self.assertEqual(rec, 'REJECTED')\n \n def test_calibrate_weights_insufficient_data(self):\n \"\"\"Test weight calibration with insufficient data.\"\"\"\n df = pd.DataFrame({'tier': [], 'pnl_dollar': []})\n result = self.validator.calibrate_weights(df)\n self.assertIn('error', result)\n\n\nclass TestBacktestTrade(unittest.TestCase):\n \"\"\"Test BacktestTrade dataclass.\"\"\"\n \n def test_trade_creation(self):\n \"\"\"Test BacktestTrade creation.\"\"\"\n trade = BacktestTrade(\n entry_date=datetime(2023, 1, 15),\n ticker=\"AAPL\",\n strategy=\"bull_put\",\n score=75,\n tier=\"PREPARE\",\n entry_price=150.0,\n exit_price=152.0,\n hold_days=5,\n pnl_pct=1.33,\n pnl_dollar=40,\n win=True\n )\n self.assertEqual(trade.ticker, \"AAPL\")\n self.assertTrue(trade.win)\n self.assertAlmostEqual(trade.pnl_pct, 1.33, places=1)\n\n\nclass TestTierThresholds(unittest.TestCase):\n \"\"\"Test tier threshold constants.\"\"\"\n \n def test_tier_boundaries(self):\n \"\"\"Test tier boundary definitions.\"\"\"\n self.assertEqual(TIER_THRESHOLDS['EXECUTE'], (80, 100))\n self.assertEqual(TIER_THRESHOLDS['PREPARE'], (60, 79))\n self.assertEqual(TIER_THRESHOLDS['WATCH'], (40, 59))\n self.assertEqual(TIER_THRESHOLDS['WAIT'], (0, 39))\n \n def test_tier_coverage(self):\n \"\"\"Test tiers cover full score range without gaps.\"\"\"\n ranges = [TIER_THRESHOLDS[t] for t in ['WAIT', 'WATCH', 'PREPARE', 'EXECUTE']]\n \n # Check continuity\n for i in range(len(ranges) - 1):\n self.assertEqual(ranges[i][1], ranges[i+1][0] - 1)\n \n # Check full coverage\n self.assertEqual(ranges[0][0], 0)\n self.assertEqual(ranges[-1][1], 100)\n\n\nclass TestPValueCalculations(unittest.TestCase):\n \"\"\"Test p-value calculations.\"\"\"\n \n def setUp(self):\n self.validator = BacktestValidator(\n Mock(), \"2023-01-01\", \"2023-06-01\", \"bull_put\"\n )\n \n def test_p_values_with_sufficient_data(self):\n \"\"\"Test p-value calculation with sufficient data.\"\"\"\n np.random.seed(42)\n \n # Generate data with clear separation\n execute_data = np.random.normal(50, 20, 50) # Positive returns\n wait_data = np.random.normal(-20, 20, 50) # Negative returns\n \n df = pd.DataFrame({\n 'tier': ['EXECUTE'] * 50 + ['WAIT'] * 50,\n 'pnl_dollar': np.concatenate([execute_data, wait_data])\n })\n \n p_values = self.validator._calculate_p_values(df)\n \n # Check expected keys exist\n self.assertIn('anova_all_tiers', p_values)\n # Should have low p-value for significant difference\n self.assertLess(p_values['anova_all_tiers'], 0.1)\n \n def test_p_values_with_insufficient_data(self):\n \"\"\"Test p-value calculation with insufficient data.\"\"\"\n df = pd.DataFrame({\n 'tier': ['EXECUTE'] * 5 + ['WAIT'] * 5,\n 'pnl_dollar': [10] * 5 + [-10] * 5\n })\n \n p_values = self.validator._calculate_p_values(df)\n \n # Should return valid dict even with insufficient data\n self.assertIsInstance(p_values, dict)\n # May return error or empty p-values\n self.assertTrue('error' in p_values or len(p_values) >= 0)\n\n\nif __name__ == '__main__':\n unittest.main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":9587,"content_sha256":"c85306e69ee474198c73c8359f261de97f43d5dd8e9a967055e853b784a1d2fb"},{"filename":"tests/test_enhanced_kelly.py","content":"#!/usr/bin/env python3\n\"\"\"\nUnit tests for Enhanced Kelly Sizer module.\n\"\"\"\n\nimport unittest\n\nimport numpy as np\n\nimport sys\nimport os\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'scripts'))\n\nfrom enhanced_kelly import (\n EnhancedKellySizer,\n PositionResult,\n CorrelationPenalty,\n KellyFraction,\n)\n\n\nclass TestEnhancedKellySizer(unittest.TestCase):\n \"\"\"Test cases for EnhancedKellySizer.\"\"\"\n \n def setUp(self):\n \"\"\"Set up test fixtures.\"\"\"\n self.sizer = EnhancedKellySizer()\n \n def test_init_defaults(self):\n \"\"\"Test initialization with defaults.\"\"\"\n self.assertEqual(self.sizer.account, 390)\n self.assertEqual(self.sizer.max_dd, 0.20)\n self.assertEqual(self.sizer.min_contracts, 1)\n \n def test_init_custom(self):\n \"\"\"Test initialization with custom values.\"\"\"\n sizer = EnhancedKellySizer(account_value=1000, max_drawdown=0.30)\n self.assertEqual(sizer.account, 1000)\n self.assertEqual(sizer.max_dd, 0.30)\n \n def test_kelly_criterion_positive_edge(self):\n \"\"\"Test Kelly with positive edge.\"\"\"\n # POP 60%, win $100, lose $100\n # Kelly = (0.6*1 - 0.4) / 1 = 0.2\n # Returns (kelly_pct, ev_pct)\n kelly_pct, ev_pct = self.sizer.kelly_criterion(0.60, 100, 100)\n self.assertAlmostEqual(kelly_pct, 0.20, places=5)\n self.assertIsInstance(ev_pct, float)\n \n def test_kelly_criterion_negative_edge(self):\n \"\"\"Test Kelly with negative edge.\"\"\"\n # POP 40%, win $100, lose $100\n kelly_pct, ev_pct = self.sizer.kelly_criterion(0.40, 100, 100)\n self.assertLess(kelly_pct, 0)\n \n def test_kelly_criterion_fair_game(self):\n \"\"\"Test Kelly with fair game.\"\"\"\n # POP 50%, win $100, lose $100\n kelly_pct, ev_pct = self.sizer.kelly_criterion(0.50, 100, 100)\n self.assertAlmostEqual(kelly_pct, 0.0, places=5)\n \n def test_kelly_criterion_asymmetric(self):\n \"\"\"Test Kelly with asymmetric payoffs.\"\"\"\n # POP 40%, win $200 vs lose $100 (2:1 payoff)\n # Kelly = (0.4*2 - 0.6) / 2 = 0.1\n kelly_pct, ev_pct = self.sizer.kelly_criterion(0.40, 200, 100)\n self.assertAlmostEqual(kelly_pct, 0.10, places=5)\n \n def test_kelly_fraction_enum(self):\n \"\"\"Test KellyFraction enum values.\"\"\"\n self.assertEqual(KellyFraction.FULL.value, 1.0)\n self.assertEqual(KellyFraction.HALF.value, 0.5)\n self.assertEqual(KellyFraction.QUARTER.value, 0.25)\n self.assertEqual(KellyFraction.EIGHTH.value, 0.125)\n \n def test_position_result_dataclass(self):\n \"\"\"Test PositionResult dataclass.\"\"\"\n result = PositionResult(\n contracts=1,\n total_risk=80.0,\n kelly_fraction=0.20,\n adjusted_kelly=0.05,\n risk_per_contract=80.0,\n max_loss=80.0,\n expected_value=10.0,\n recommendation='EXECUTE',\n reasoning='Edge confirmed',\n drawdown_estimate=0.05\n )\n \n self.assertEqual(result.contracts, 1)\n self.assertEqual(result.recommendation, 'EXECUTE')\n self.assertEqual(result.reasoning, 'Edge confirmed')\n \n def test_correlation_penalty_dataclass(self):\n \"\"\"Test CorrelationPenalty dataclass.\"\"\"\n penalty = CorrelationPenalty(\n correlation=0.5,\n penalty_factor=0.75,\n reason='High correlation with existing position'\n )\n \n self.assertEqual(penalty.correlation, 0.5)\n self.assertEqual(penalty.penalty_factor, 0.75)\n \n def test_conviction_based_kelly(self):\n \"\"\"Test conviction-based Kelly scaling.\"\"\"\n sizer = EnhancedKellySizer()\n \n # Test different conviction levels - returns tuple (kelly, reasoning)\n kelly_95, _ = sizer.conviction_based_kelly(95, 0.20)\n kelly_75, _ = sizer.conviction_based_kelly(75, 0.20)\n kelly_55, _ = sizer.conviction_based_kelly(55, 0.20)\n \n # Higher conviction should get larger Kelly fraction (or equal)\n self.assertGreaterEqual(kelly_95, kelly_75)\n self.assertGreaterEqual(kelly_75, kelly_55)\n \n def test_drawdown_constrained_kelly(self):\n \"\"\"Test drawdown constraint application.\"\"\"\n sizer = EnhancedKellySizer(max_drawdown=0.10) # Conservative\n \n # High Kelly should be constrained\n full_kelly = 0.25\n constrained = sizer.drawdown_constrained_kelly(full_kelly)\n \n # Result should be a float (constrained Kelly fraction)\n self.assertIsInstance(constrained, float)\n self.assertLess(constrained, full_kelly)\n \n def test_calculate_correlation_penalty(self):\n \"\"\"Test correlation penalty calculation.\"\"\"\n sizer = EnhancedKellySizer()\n \n # No correlation = no penalty\n penalty_none = sizer.calculate_correlation_penalty([])\n self.assertIsInstance(penalty_none, CorrelationPenalty)\n \n # High correlation = penalty\n existing = [{\"ticker\": \"SPY\", \"correlation\": 0.9}]\n penalty_high = sizer.calculate_correlation_penalty(existing)\n self.assertIsInstance(penalty_high, CorrelationPenalty)\n \n def test_calculate_position_full_pipeline(self):\n \"\"\"Test full position sizing pipeline.\"\"\"\n result = self.sizer.calculate_position(\n spread_cost=80,\n max_loss_per_spread=80,\n win_amount=40,\n conviction=85,\n pop=0.65\n )\n \n self.assertIsInstance(result, PositionResult)\n self.assertIsInstance(result.contracts, int)\n self.assertIsInstance(result.recommendation, str)\n\n\nclass TestKellyFractionTiers(unittest.TestCase):\n \"\"\"Test Kelly fraction tier mapping.\"\"\"\n \n def test_tier_values(self):\n \"\"\"Test tier values.\"\"\"\n self.assertEqual(KellyFraction.FULL.value, 1.0)\n self.assertEqual(KellyFraction.HALF.value, 0.5)\n self.assertEqual(KellyFraction.QUARTER.value, 0.25)\n self.assertEqual(KellyFraction.EIGHTH.value, 0.125)\n\n\nclass TestPositionResultValidation(unittest.TestCase):\n \"\"\"Test PositionResult validation.\"\"\"\n \n def test_positive_contracts(self):\n \"\"\"Test that contracts is non-negative.\"\"\"\n result = PositionResult(\n contracts=0,\n total_risk=0.0,\n kelly_fraction=0.0,\n adjusted_kelly=0.0,\n risk_per_contract=80.0,\n max_loss=80.0,\n expected_value=0.0,\n recommendation='SKIP',\n reasoning='No edge',\n drawdown_estimate=0.0\n )\n self.assertGreaterEqual(result.contracts, 0)\n \n def test_risk_consistency(self):\n \"\"\"Test that total risk equals contracts × risk per contract.\"\"\"\n contracts = 2\n risk_per = 80.0\n \n result = PositionResult(\n contracts=contracts,\n total_risk=contracts * risk_per,\n kelly_fraction=0.10,\n adjusted_kelly=0.05,\n risk_per_contract=risk_per,\n max_loss=contracts * risk_per,\n expected_value=10.0,\n recommendation='EXECUTE',\n reasoning='Edge confirmed',\n drawdown_estimate=0.05\n )\n \n self.assertEqual(result.total_risk, contracts * risk_per)\n\n\nif __name__ == '__main__':\n unittest.main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":7439,"content_sha256":"a6e68bfff2d71c78fd179b44f00f0d9fb8f64c43d5d9d699b4fa1544690d3d18"},{"filename":"tests/test_regime_detector.py","content":"#!/usr/bin/env python3\n\"\"\"\nUnit tests for Regime Detector module.\n\"\"\"\n\nimport unittest\nfrom datetime import datetime\nfrom unittest.mock import Mock, patch\n\nimport numpy as np\nimport pandas as pd\n\nimport sys\nimport os\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'scripts'))\n\nfrom regime_detector import (\n RegimeDetector,\n RegimeResult,\n)\n\n\nclass TestRegimeDetector(unittest.TestCase):\n \"\"\"Test cases for RegimeDetector.\"\"\"\n \n def setUp(self):\n \"\"\"Set up test fixtures.\"\"\"\n self.detector = RegimeDetector()\n \n def test_init(self):\n \"\"\"Test initialization.\"\"\"\n self.assertEqual(self.detector.lookback, 252)\n self.assertEqual(self.detector.vix_ticker, \"^VIX\")\n \n def test_regimes_defined(self):\n \"\"\"Test that all regimes are defined.\"\"\"\n self.assertEqual(len(self.detector.REGIMES), 5)\n self.assertIn('CRISIS', self.detector.REGIMES)\n self.assertIn('EUPHORIA', self.detector.REGIMES)\n self.assertIn('NORMAL', self.detector.REGIMES)\n \n def test_thresholds_defined(self):\n \"\"\"Test threshold definitions.\"\"\"\n self.assertEqual(len(self.detector.THRESHOLDS), 5)\n # Crisis is 80-100th percentile\n self.assertEqual(self.detector.THRESHOLDS['CRISIS'], (80.0, 100.0))\n # Euphoria is 0-20th percentile\n self.assertEqual(self.detector.THRESHOLDS['EUPHORIA'], (0.0, 20.0))\n \n def test_get_regime_weights(self):\n \"\"\"Test regime weights retrieval.\"\"\"\n for regime in self.detector.REGIMES:\n weights = self.detector.get_regime_weights(regime)\n self.assertIsInstance(weights, dict)\n # Check that weights contains strategy keys\n self.assertGreater(len(weights), 0)\n \n def test_regime_aware_score(self):\n \"\"\"Test regime-aware score adjustment.\"\"\"\n base_score = 70\n \n # Test all regimes\n for regime in self.detector.REGIMES:\n adjusted, reason = self.detector.regime_aware_score(\n base_score, regime, 'bull_put'\n )\n self.assertIsInstance(adjusted, (int, float))\n self.assertIsInstance(reason, str)\n # Score should be within bounds\n self.assertGreaterEqual(adjusted, 0)\n self.assertLessEqual(adjusted, 100)\n \n def test_is_favorable_for_strategy(self):\n \"\"\"Test strategy favorability check.\"\"\"\n # Iron condor should be favorable in CRISIS\n result = self.detector.is_favorable_for_strategy('CRISIS', 'iron_condor')\n # Returns tuple (is_favorable, reasoning)\n self.assertIsInstance(result, tuple)\n self.assertEqual(len(result), 2)\n self.assertIsInstance(result[0], bool)\n self.assertIsInstance(result[1], str)\n \n def test_calculate_percentile(self):\n \"\"\"Test percentile calculation.\"\"\"\n # Create mock VIX data\n vix_data = pd.Series([10, 15, 20, 25, 30, 35, 40, 45, 50])\n current = 30\n \n percentile = self.detector.calculate_percentile(current, vix_data)\n self.assertIsInstance(percentile, float)\n self.assertGreaterEqual(percentile, 0)\n self.assertLessEqual(percentile, 100)\n \n def test_calculate_percentile_bounds(self):\n \"\"\"Test percentile bounds.\"\"\"\n vix_data = pd.Series([10, 20, 30, 40, 50])\n \n # Min value should be 0th percentile\n min_pct = self.detector.calculate_percentile(10, vix_data)\n self.assertEqual(min_pct, 0.0)\n \n # Max value should be 100th percentile\n max_pct = self.detector.calculate_percentile(50, vix_data)\n self.assertEqual(max_pct, 100.0)\n\n\nclass TestRegimeBoundaries(unittest.TestCase):\n \"\"\"Test regime classification boundaries.\"\"\"\n \n def test_crisis_boundary(self):\n \"\"\"Test crisis threshold.\"\"\"\n detector = RegimeDetector()\n self.assertEqual(detector.THRESHOLDS['CRISIS'][0], 80.0)\n \n def test_euphoria_boundary(self):\n \"\"\"Test euphoria threshold.\"\"\"\n detector = RegimeDetector()\n self.assertEqual(detector.THRESHOLDS['EUPHORIA'][1], 20.0)\n \n def test_normal_boundaries(self):\n \"\"\"Test normal regime boundaries.\"\"\"\n detector = RegimeDetector()\n self.assertEqual(detector.THRESHOLDS['NORMAL'], (40.0, 60.0))\n\n\nclass TestRegimeResult(unittest.TestCase):\n \"\"\"Test RegimeResult dataclass.\"\"\"\n \n def test_creation(self):\n \"\"\"Test RegimeResult creation.\"\"\"\n result = RegimeResult(\n regime='CRISIS',\n confidence=0.85,\n vix_level=35.0,\n percentile=85.0,\n vix_ma20=30.0,\n threshold_low=80.0,\n threshold_high=100.0,\n lookback_days=252,\n regime_metadata={'source': 'test'}\n )\n self.assertEqual(result.regime, 'CRISIS')\n self.assertEqual(result.confidence, 0.85)\n\n\nif __name__ == '__main__':\n unittest.main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":4999,"content_sha256":"b5d1f677a90c4c743986213c69a836055b3ab3412a2c75a3619a49294cd4e834"},{"filename":"tests/test_vol_forecaster.py","content":"#!/usr/bin/env python3\n\"\"\"\nUnit tests for Volatility Forecaster module.\n\"\"\"\n\nimport unittest\nfrom unittest.mock import Mock, patch\n\nimport numpy as np\nimport pandas as pd\n\nimport sys\nimport os\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'scripts'))\n\nfrom vol_forecaster import (\n VolatilityForecaster,\n GARCHResult,\n VRPSignal,\n)\n\n\nclass TestVolatilityForecaster(unittest.TestCase):\n \"\"\"Test cases for VolatilityForecaster.\"\"\"\n \n def setUp(self):\n \"\"\"Set up test fixtures.\"\"\"\n self.ticker = \"TEST\"\n self.forecaster = VolatilityForecaster(self.ticker, lookback_days=100)\n \n def test_init(self):\n \"\"\"Test initialization.\"\"\"\n self.assertEqual(self.forecaster.ticker, self.ticker)\n self.assertEqual(self.forecaster.lookback, 100)\n self.assertIsNone(self.forecaster._garch_result)\n \n def test_garch_result_dataclass(self):\n \"\"\"Test GARCHResult dataclass.\"\"\"\n # Create mock fitted volatility series\n dates = pd.date_range('2023-01-01', periods=10)\n fitted = pd.Series([0.2] * 10, index=dates)\n forecast = pd.Series([0.2] * 5, index=pd.date_range('2023-01-11', periods=5))\n \n result = GARCHResult(\n omega=0.01,\n alpha=0.1,\n beta=0.85,\n persistence=0.95,\n half_life=50.0,\n log_likelihood=-100.0,\n aic=200.0,\n bic=210.0,\n converged=True,\n fitted_vol=fitted,\n forecast=forecast\n )\n \n self.assertEqual(result.omega, 0.01)\n self.assertEqual(result.alpha, 0.1)\n self.assertEqual(result.beta, 0.85)\n self.assertAlmostEqual(result.persistence, 0.95)\n self.assertTrue(result.converged)\n \n def test_vrp_signal_dataclass(self):\n \"\"\"Test VRPSignal dataclass.\"\"\"\n signal = VRPSignal(\n current_iv=0.25,\n forecast_rv=0.20,\n vrp=0.05,\n vrp_zscore=1.0,\n vrp_percentile=80.0,\n signal_strength=0.8,\n recommendation='SELL',\n reasoning='IV elevated vs expected RV'\n )\n \n self.assertEqual(signal.current_iv, 0.25)\n self.assertEqual(signal.forecast_rv, 0.20)\n self.assertEqual(signal.vrp, 0.05)\n self.assertEqual(signal.recommendation, 'SELL')\n \n def test_persistence_calculation(self):\n \"\"\"Test that persistence = alpha + beta.\"\"\"\n dates = pd.date_range('2023-01-01', periods=10)\n fitted = pd.Series([0.2] * 10, index=dates)\n forecast = pd.Series([0.2] * 5, index=pd.date_range('2023-01-11', periods=5))\n \n alpha = 0.1\n beta = 0.85\n \n result = GARCHResult(\n omega=0.01,\n alpha=alpha,\n beta=beta,\n persistence=alpha + beta,\n half_life=50.0,\n log_likelihood=-100.0,\n aic=200.0,\n bic=210.0,\n converged=True,\n fitted_vol=fitted,\n forecast=forecast\n )\n \n self.assertAlmostEqual(result.persistence, 0.95)\n \n def test_half_life_calculation(self):\n \"\"\"Test half-life calculation from persistence.\"\"\"\n # For persistence = 0.95, half-life ≈ ln(0.5) / ln(0.95) ≈ 13.5 days\n persistence = 0.95\n expected_half_life = np.log(0.5) / np.log(persistence)\n \n dates = pd.date_range('2023-01-01', periods=10)\n fitted = pd.Series([0.2] * 10, index=dates)\n forecast = pd.Series([0.2] * 5, index=pd.date_range('2023-01-11', periods=5))\n \n result = GARCHResult(\n omega=0.01,\n alpha=0.1,\n beta=0.85,\n persistence=persistence,\n half_life=expected_half_life,\n log_likelihood=-100.0,\n aic=200.0,\n bic=210.0,\n converged=True,\n fitted_vol=fitted,\n forecast=forecast\n )\n \n self.assertAlmostEqual(result.half_life, expected_half_life, places=1)\n \n def test_vrp_interpretation(self):\n \"\"\"Test VRP interpretation thresholds.\"\"\"\n # Positive VRP = IV > RV = favor selling\n vrp_positive = VRPSignal(\n current_iv=0.30,\n forecast_rv=0.20,\n vrp=0.10,\n vrp_zscore=2.0,\n vrp_percentile=95.0,\n signal_strength=0.9,\n recommendation='STRONG_SELL',\n reasoning='High IV vs RV'\n )\n self.assertGreater(vrp_positive.vrp, 0)\n self.assertIn('SELL', vrp_positive.recommendation)\n \n # Negative VRP = IV \u003c RV = favor buying\n vrp_negative = VRPSignal(\n current_iv=0.15,\n forecast_rv=0.25,\n vrp=-0.10,\n vrp_zscore=-2.0,\n vrp_percentile=5.0,\n signal_strength=0.9,\n recommendation='STRONG_BUY',\n reasoning='Low IV vs RV'\n )\n self.assertLess(vrp_negative.vrp, 0)\n self.assertIn('BUY', vrp_negative.recommendation)\n \n def test_annualization_factor(self):\n \"\"\"Test annualization factor.\"\"\"\n # Daily to annual: sqrt(252)\n expected = np.sqrt(252)\n self.assertAlmostEqual(self.forecaster._annualization_factor, expected)\n\n\nclass TestGARCHParameters(unittest.TestCase):\n \"\"\"Test GARCH parameter constraints.\"\"\"\n \n def test_stationarity_condition(self):\n \"\"\"Test that persistence \u003c 1 for stationarity.\"\"\"\n dates = pd.date_range('2023-01-01', periods=10)\n fitted = pd.Series([0.2] * 10, index=dates)\n forecast = pd.Series([0.2] * 5, index=pd.date_range('2023-01-11', periods=5))\n \n # Valid: persistence \u003c 1\n result_valid = GARCHResult(\n omega=0.01, alpha=0.1, beta=0.85,\n persistence=0.95, half_life=50.0,\n log_likelihood=-100.0, aic=200.0, bic=210.0,\n converged=True, fitted_vol=fitted, forecast=forecast\n )\n self.assertLess(result_valid.persistence, 1.0)\n\n\nif __name__ == '__main__':\n unittest.main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":6127,"content_sha256":"f815a3eeed7e56a9a97f5d7ee1c4078af76acf93105c81ef7fbdfb65557ba29f"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"Options Spread Conviction Engine","type":"text"}]},{"type":"paragraph","content":[{"text":"Multi-regime options spread scoring using technical indicators and IV term structure analysis.","type":"text","marks":[{"type":"strong"}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Install","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"brew install jq\nnpm install yahoo-finance2\nsudo ln -s /opt/homebrew/bin/yahoo-finance /usr/local/bin/yf","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Overview","type":"text"}]},{"type":"paragraph","content":[{"text":"This engine analyzes any ticker and scores ","type":"text"},{"text":"seven","type":"text","marks":[{"type":"strong"}]},{"text":" options strategies across two categories:","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Vertical Spreads (Directional)","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Strategy","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Type","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Philosophy","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Ideal Setup","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"bull_put","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Credit","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Mean Reversion","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Bullish trend + oversold dip","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"bear_call","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Credit","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Mean Reversion","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Bearish trend + overbought rip","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"bull_call","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Debit","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Breakout","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Strong bullish momentum","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"bear_put","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Debit","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Breakout","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Strong bearish momentum","type":"text"}]}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Multi-Leg Strategies (Non-Directional / Theta)","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Strategy","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Type","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Philosophy","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Ideal Setup","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"iron_condor","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Credit","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Premium Selling","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"IV Rank >70, RSI neutral, range-bound","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"butterfly","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Debit","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Pinning Play","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"BB squeeze, RSI center, low ADX","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"calendar","type":"text","marks":[{"type":"strong"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Debit","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Theta Harvest","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Inverted IV term structure (front > back)","type":"text"}]}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Scoring Methodology","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Vertical Spreads","type":"text"}]},{"type":"paragraph","content":[{"text":"Weights vary by strategy type (Credit = Mean Reversion, Debit = Breakout):","type":"text"}]},{"type":"heading","attrs":{"level":4},"content":[{"text":"Credit Spreads (bull_put, bear_call)","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Indicator","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Weight","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Purpose","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Ichimoku Cloud","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"25 pts","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Trend structure & equilibrium","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"RSI","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"20 pts","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Entry timing 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(Credit)\n================================================================================\nPrice: $681.27 | Score: 31.8/100 → WAIT\n\n[IV Rank +2.5/25]\n IV Rank (BBW proxy): 5% (VERY_LOW)\n BBW: 3.17 (1Y range: 2.37 - 18.13)\n Premiums are THIN — poor risk/reward for credit\n\nStrikes:\n BUY 680.0P | SELL 685.0P\n SELL 695.0C | BUY 700.0C\n Max Profit Zone: $685.0 - $695.0\n Wing Width: $5.00","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Butterfly","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":"================================================================================\nSPY — Long Butterfly (Debit)\n================================================================================\nPrice: $681.27 | Score: 64.5/100 → PREPARE\n\n[BB Squeeze +27.0/30]\n Bandwidth: 3.1701 (percentile: 21%)\n SQUEEZE ACTIVE — 19 consecutive bars\n\nStrikes:\n BUY 1x 685.0C | SELL 2x 690.0C | BUY 1x 695.0C\n Max Profit Price: $690.0\n Profit Zone: ~$685.0 - $695.0","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Calendar Spread","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":"================================================================================\nSPY — Calendar Spread (Debit)\n================================================================================\nPrice: $681.27 | Score: 67.2/100 → PREPARE\n\n[IV Term Structure +30.0/30]\n Front IV: 27.5% | Back IV: 19.4%\n Differential: +41.7%\n INVERTED TERM STRUCTURE — calendar opportunity confirmed\n\nStrikes:\n Strike: $680.0\n SELL 2026-02-13 | BUY 2026-03-13\n Theta Advantage: Front IV > Back IV by 41.7%","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"IV Rank Approximation","type":"text"}]},{"type":"paragraph","content":[{"text":"IV Rank is approximated using ","type":"text"},{"text":"Bollinger Bandwidth (BBW) percentile","type":"text","marks":[{"type":"strong"}]},{"text":" over 252 trading days:","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":"IV Rank ≈ (Current BBW - 52wk Low BBW) / (52wk High BBW - 52wk Low BBW) × 100","type":"text"}]},{"type":"paragraph","content":[{"text":"This correlation is well-documented: realized volatility (BBW) and implied volatility rank move with ~0.7-0.8 correlation (Sinclair, \"Volatility Trading\", 2013).","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"IV Term Structure","type":"text"}]},{"type":"paragraph","content":[{"text":"For calendar spreads, the engine attempts to fetch live ATM implied volatility from Yahoo Finance options chains. If unavailable, it falls back to historical volatility term structure (HV 10-day vs HV 30-day) as a proxy.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Quantitative Modules (v2.3.0)","type":"text"}]},{"type":"paragraph","content":[{"text":"The engine now includes four quantitative modules for rigorous strategy validation and optimization:","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"1. Regime Detector (","type":"text"},{"text":"regime_detector.py","type":"text","marks":[{"type":"code_inline"}]},{"text":")","type":"text"}]},{"type":"paragraph","content":[{"text":"Market regime classification using VIX percentiles:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"CRISIS","type":"text","marks":[{"type":"strong"}]},{"text":": VIX > 80th percentile — favors premium selling (iron condors)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"HIGH_VOL","type":"text","marks":[{"type":"strong"}]},{"text":": VIX 60-80th — elevated IV benefits credit spreads","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"NORMAL","type":"text","marks":[{"type":"strong"}]},{"text":": VIX 40-60th — balanced environment, all strategies viable","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"LOW_VOL","type":"text","marks":[{"type":"strong"}]},{"text":": VIX 20-40th — cheap options favor debit spreads","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"EUPHORIA","type":"text","marks":[{"type":"strong"}]},{"text":": VIX \u003c 20th — momentum continues, mean reversion brewing","type":"text"}]}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Detect current regime\npython3 scripts/regime_detector.py\n\n# Get regime-adjusted weights for specific strategy\npython3 scripts/regime_detector.py --strategy iron_condor --json","type":"text"}]},{"type":"paragraph","content":[{"text":"Integration:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"from regime_detector import RegimeDetector\n\ndetector = RegimeDetector()\nregime, confidence = detector.detect_regime()\nweights = detector.get_regime_weights(regime)\nadjusted_score, reasoning = detector.regime_aware_score(75, regime, 'bull_put')","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"2. Volatility Forecaster (","type":"text"},{"text":"vol_forecaster.py","type":"text","marks":[{"type":"code_inline"}]},{"text":")","type":"text"}]},{"type":"paragraph","content":[{"text":"GARCH-based realized volatility forecasting with VRP analysis:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Fits GARCH(1,1) to historical returns","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Forecasts realized volatility over configurable horizon","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Calculates volatility risk premium (IV - RV forecast)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Provides conviction adjustments based on VRP","type":"text"}]}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Analyze AAPL volatility\npython3 scripts/vol_forecaster.py AAPL\n\n# Compare IV = 25% vs forecast RV\npython3 scripts/vol_forecaster.py SPY --iv 0.25 --horizon 5","type":"text"}]},{"type":"paragraph","content":[{"text":"Interpretation:","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"VRP > 5%: Favorable for selling premium (credit spreads)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"VRP \u003c -5%: Favorable for buying premium (debit spreads)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"VRP near 0: No volatility edge, focus on directional setup","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Integration:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"from vol_forecaster import VolatilityForecaster\n\nforecaster = VolatilityForecaster(\"AAPL\")\nparams = forecaster.fit_garch() # Returns omega, alpha, beta\nforecast = forecaster.forecast_vol(horizon=5)\nvrp, strength, rec = forecaster.vol_risk_premium(iv=0.25, rv_forecast=forecast.annualized_vol)\nadjusted_score, reasoning = forecaster.add_to_conviction(70, vrp_signal, 'bull_put')","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"3. Enhanced Kelly Sizer (","type":"text"},{"text":"enhanced_kelly.py","type":"text","marks":[{"type":"code_inline"}]},{"text":")","type":"text"}]},{"type":"paragraph","content":[{"text":"Drawdown-constrained, correlation-aware position sizing:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Full Kelly criterion calculation","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Drawdown constraint: f_dd = f_kelly × (1 - target_dd / max_dd)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Conviction-based Kelly scaling:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"90-100: Half Kelly","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"80-89: Quarter Kelly","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"60-79: Eighth Kelly","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"\u003c60: No position","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Correlation penalty for portfolio context","type":"text"}]}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Calculate position with $390 account\npython3 scripts/enhanced_kelly.py --loss 80 --win 40 --pop 0.65 --conviction 85\n\n# Include correlation with existing position\npython3 scripts/enhanced_kelly.py --loss 80 --win 40 --pop 0.65 --conviction 85 --correlation 0.3","type":"text"}]},{"type":"paragraph","content":[{"text":"Integration:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"from enhanced_kelly import EnhancedKellySizer\n\nsizer = EnhancedKellySizer(account_value=390, max_drawdown=0.20)\nresult = sizer.calculate_position(\n spread_cost=80,\n max_loss=80,\n win_amount=40,\n conviction=85,\n pop=0.65,\n existing_correlation=0.0\n)\n# Returns: contracts, total_risk, kelly_fraction, recommendation","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"4. Backtest Validator (","type":"text"},{"text":"backtest_validator.py","type":"text","marks":[{"type":"code_inline"}]},{"text":")","type":"text"}]},{"type":"paragraph","content":[{"text":"Walk-forward validation of conviction scores:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Simulates historical trades across ticker universe","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Validates tier separation (EXECUTE vs WAIT performance)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Statistical tests (t-tests, ANOVA)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Tier separation scoring (0-1)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Weight calibration suggestions","type":"text"}]}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Backtest bull_put on AAPL, MSFT, SPY (2022-2024)\npython3 scripts/backtest_validator.py --tickers AAPL MSFT SPY --start 2022-01-01 --end 2024-01-01 --strategy bull_put\n\n# JSON output for analysis\npython3 scripts/backtest_validator.py --tickers SPY --json","type":"text"}]},{"type":"paragraph","content":[{"text":"Output Metrics:","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Win rate per tier","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Expectancy per tier: (win_rate × avg_win) - (loss_rate × avg_loss)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Sharpe ratio per tier","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"P-values for tier differences","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Separation score (0-1, higher = better discrimination)","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Integration:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"from backtest_validator import BacktestValidator\n\nvalidator = BacktestValidator(engine, \"2022-01-01\", \"2024-01-01\")\nresults_df = validator.run_walk_forward([\"AAPL\", \"MSFT\"], hold_days=5)\nreport = validator.validate_tiers(results_df)\nprint(f\"Separation score: {report.tier_separation_score:.2f}\")\nprint(f\"EXECUTE vs WAIT p-value: {report.p_values['execute_vs_wait']:.4f}\")","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"5. Quantitative Integration (","type":"text"},{"text":"quantitative_integration.py","type":"text","marks":[{"type":"code_inline"}]},{"text":")","type":"text"}]},{"type":"paragraph","content":[{"text":"Unified interface combining all quantitative modules:","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Full quantitative analysis with regime and VRP\npython3 scripts/quantitative_integration.py AAPL --regime-aware --vol-aware\n\n# With Kelly sizing\npython3 scripts/quantitative_integration.py SPY --regime-aware --pop 0.65 --max-loss 80 --win-amount 40\n\n# Run backtest validation\npython3 scripts/quantitative_integration.py --backtest SPY QQQ --start 2022-01-01 --end 2024-01-01","type":"text"}]},{"type":"paragraph","content":[{"text":"Integration:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"from quantitative_integration import QuantConvictionEngine\n\nengine = QuantConvictionEngine(account_value=390, max_drawdown=0.20)\n\n# Analyze with regime and VRP adjustments\nresult = engine.analyze(\"AAPL\", \"bull_put\", regime_aware=True, vol_aware=True)\nprint(f\"Final score: {result.final_score}\")\nprint(f\"Regime: {result.regime}\")\nprint(f\"VRP: {result.vrp_signal.vrp if result.vrp_signal else 'N/A'}\")\n\n# Calculate position size\nsizing = engine.calculate_position(result, pop=0.65, max_loss=80, win_amount=40)\nprint(f\"Contracts: {sizing['contracts']}\")\n\n# Run backtest validation\nreport = engine.run_backtest([\"SPY\", \"QQQ\"], \"2022-01-01\", \"2024-01-01\")\nprint(f\"Recommendation: {report.recommendation}\")","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Academic Foundation","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Ichimoku Cloud","type":"text","marks":[{"type":"strong"}]},{"text":" — Trend structure (Hosoda, 1968)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"RSI","type":"text","marks":[{"type":"strong"}]},{"text":" — Momentum oscillator (Wilder, 1978)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"MACD","type":"text","marks":[{"type":"strong"}]},{"text":" — Trend momentum (Appel, 1979)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Bollinger Bands","type":"text","marks":[{"type":"strong"}]},{"text":" — Volatility envelopes (Bollinger, 2001)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"IV Rank / Term Structure","type":"text","marks":[{"type":"strong"}]},{"text":" — Options market microstructure (Sinclair, 2013)","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Combining orthogonal signals reduces false-positive rate compared to single-indicator strategies (Pring, 2002; Murphy, 1999).","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Architecture","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":"conviction-engine/\n├── scripts/\n│ ├── conviction-engine # CLI wrapper (bash)\n│ ├── spread_conviction_engine.py # Core engine (vertical spreads)\n│ ├── multi_leg_strategies.py # Multi-leg extensions\n│ ├── quantitative_integration.py # Unified quantitative interface\n│ ├── regime_detector.py # VIX-based regime classification\n│ ├── vol_forecaster.py # GARCH volatility forecasting\n│ ├── enhanced_kelly.py # Drawdown-constrained Kelly sizing\n│ ├── backtest_validator.py # Walk-forward validation\n│ ├── quant_scanner.py # Quantitative options scanner\n│ ├── market_scanner.py # Technical market scanner\n│ ├── calculator.py # Black-Scholes & POP calculator\n│ ├── position_sizer.py # Kelly position sizing\n│ ├── chain_analyzer.py # IV surface analyzer\n│ ├── options_math.py # Core mathematical models\n│ └── setup-venv.sh # Environment setup\n├── tests/ # Unit tests\n│ ├── test_regime_detector.py\n│ ├── test_vol_forecaster.py\n│ ├── test_enhanced_kelly.py\n│ ├── test_backtest_validator.py\n│ └── run_tests.py\n└── SKILL.md # This documentation","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Module Separation","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"spread_conviction_engine.py","type":"text","marks":[{"type":"strong"}]},{"text":": Vertical spreads, shared infrastructure (data fetching, indicator computation)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"multi_leg_strategies.py","type":"text","marks":[{"type":"strong"}]},{"text":": Iron condors, butterflies, calendars (imports from main engine)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"quantitative_integration.py","type":"text","marks":[{"type":"strong"}]},{"text":": Unified interface for regime/vol/Kelly/backtest modules","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"regime_detector.py","type":"text","marks":[{"type":"strong"}]},{"text":": Market regime classification using VIX percentiles","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"vol_forecaster.py","type":"text","marks":[{"type":"strong"}]},{"text":": GARCH-based realized volatility forecasting","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"enhanced_kelly.py","type":"text","marks":[{"type":"strong"}]},{"text":": Drawdown-constrained, correlation-aware position sizing","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"backtest_validator.py","type":"text","marks":[{"type":"strong"}]},{"text":": Walk-forward validation of conviction scores","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"This separation keeps concerns clean while avoiding duplication.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Limitations & Assumptions","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"IV Data","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Yahoo Finance Limitations","type":"text","marks":[{"type":"strong"}]},{"text":": Options chains may be unavailable after market hours or for low-volume tickers","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Fallback","type":"text","marks":[{"type":"strong"}]},{"text":": Historical volatility (HV) proxy is less accurate than live IV but provides signal","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"IV Rank","type":"text","marks":[{"type":"strong"}]},{"text":": Approximated from BBW; actual IV Rank requires options chain data","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Strike Selection","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Approximation","type":"text","marks":[{"type":"strong"}]},{"text":": Strikes derived from Bollinger Band levels (1-sigma / 2-sigma)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Rounding","type":"text","marks":[{"type":"strong"}]},{"text":": Rounded to standard option strike intervals based on stock price","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"No Live Pricing","type":"text","marks":[{"type":"strong"}]},{"text":": Does not fetch live option premiums; strike selection is structural, not value-optimized","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Data Quality","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Minimum 180 trading days required for full Ichimoku cloud population","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Multi-leg strategies require options chains (calendar spreads especially)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"After-hours analysis may have reduced data quality","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Market Assumptions","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Assumes normal options market conditions (not extreme volatility events)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Strike intervals assume US equity options conventions","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Not tested on futures, commodities, or non-US markets","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Requirements","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Python 3.10+ (Python 3.14+ supported via pure-python mode)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Isolated virtual environment (auto-created on first run)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Internet connection (fetches data from Yahoo Finance)","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Installation","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"clawhub install options-spread-conviction-engine","type":"text"}]},{"type":"paragraph","content":[{"text":"The skill automatically creates a virtual environment and installs:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"pandas >= 2.0","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"pandas_ta >= 0.4.0 (pure Python mode on 3.14+)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"yfinance >= 1.0","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scipy, tqdm","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Note:","type":"text","marks":[{"type":"strong"}]},{"text":" On Python 3.14+, the engine runs in pure Python mode without numba. Performance is slightly reduced but all functionality works correctly.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Market Scanners","type":"text"}]},{"type":"paragraph","content":[{"text":"The engine includes two distinct scanning tools for different trading philosophies:","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"1. Technical Scanner (market_scanner.py)","type":"text"}]},{"type":"paragraph","content":[{"text":"Automates the search for high-conviction plays across entire stock universes using technical indicators (Ichimoku, RSI, MACD, BB).","type":"text"}]},{"type":"heading","attrs":{"level":4},"content":[{"text":"Features","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Scans S&P 500, Nasdaq 100, or custom ticker lists.","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Filters for EXECUTE tier (conviction ≥80).","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Runs position sizing to ensure trades fit account guardrails.","type":"text"}]}]}]},{"type":"heading","attrs":{"level":4},"content":[{"text":"Usage","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Scan S&P 500 for high-conviction technical setups\npython3 scripts/market_scanner.py --universe sp500","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"2. Quantitative Scanner (quant_scanner.py)","type":"text"}]},{"type":"paragraph","content":[{"text":"A mathematically-rigorous scanner that ignores technical indicators in favor of market microstructure and probability.","type":"text"}]},{"type":"heading","attrs":{"level":4},"content":[{"text":"Features","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"IV Surface Analysis","type":"text","marks":[{"type":"strong"}]},{"text":": Analyzes skew and term structure.","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Monte Carlo POP","type":"text","marks":[{"type":"strong"}]},{"text":": 10,000-run simulations for true Probability of Profit.","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"EV Optimization","type":"text","marks":[{"type":"strong"}]},{"text":": Finds trades with the highest risk-adjusted mathematical expectancy.","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Account-Aware","type":"text","marks":[{"type":"strong"}]},{"text":": Enforces small-account constraints ($100 max risk).","type":"text"}]}]}]},{"type":"heading","attrs":{"level":4},"content":[{"text":"Usage","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Maximize POP (Probability of Profit) for SPY\npython3 scripts/quant_scanner.py SPY --mode pop\n\n# High-expectancy (EV) plays with specific DTE\npython3 scripts/quant_scanner.py AAPL TSLA --mode ev --min-dte 30","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Calculator & Position Sizer","type":"text"}]},{"type":"paragraph","content":[{"text":"The integrated toolchain includes:","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"calculator.py","type":"text"}]},{"type":"paragraph","content":[{"text":"Black-Scholes options pricing with support for:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Single options: calls, puts","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Vertical spreads: bull call, bear put","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Multi-leg: iron condors, butterflies","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Greeks calculation (delta, gamma, theta, vega, rho)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Monte Carlo POP simulation","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"position_sizer.py","type":"text"}]},{"type":"paragraph","content":[{"text":"Kelly criterion position sizing adapted for small accounts:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Full Kelly and fractional Kelly (default 0.25)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Account guardrails ($390 default, $100 max risk)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Trade screening and ranking","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Strike adjustment suggestions","type":"text"}]}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"from position_sizer import calculate_position\n\nresult = calculate_position(\n account_value=390,\n max_loss_per_spread=80,\n win_amount=40,\n pop=0.65,\n)\n# Returns: contracts, total_risk, recommendation, reason","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Files","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scripts/conviction-engine","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Main CLI wrapper for conviction engine","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scripts/spread_conviction_engine.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Core engine (vertical spreads)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scripts/multi_leg_strategies.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Multi-leg extensions (v2.0.0)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scripts/market_scanner.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Automated market scanner for EXECUTE plays","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scripts/calculator.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Black-Scholes pricing, Greeks, Monte Carlo POP","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scripts/position_sizer.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Kelly criterion position sizing","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"scripts/setup-venv.sh","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Environment setup","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"data/sp500_tickers.txt","type":"text","marks":[{"type":"code_inline"}]},{"text":" — S&P 500 constituents","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"data/ndx100_tickers.txt","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Nasdaq 100 constituents","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"assets/","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Documentation and examples","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Version History","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"v2.3.0","type":"text","marks":[{"type":"strong"}]},{"text":" (2026-02-13): Quantitative rigor upgrade","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Regime Detector: VIX-based market regime classification","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Volatility Forecaster: GARCH-based RV forecasting with VRP analysis","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Enhanced Kelly Sizer: Drawdown-constrained, correlation-aware position sizing","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Backtest Validator: Walk-forward validation with tier separation testing","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Quantitative Integration: Unified interface for all quantitative modules","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Comprehensive unit test suite for all new modules","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"v2.2.0","type":"text","marks":[{"type":"strong"}]},{"text":" (2026-02-13): Kelly Criterion position sizing with full/half Kelly, edge calculation, and account-aware contract sizing","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"v2.1.0","type":"text","marks":[{"type":"strong"}]},{"text":" (2026-02-12): Added market scanner, integrated calculator and position sizer","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"v2.0.0","type":"text","marks":[{"type":"strong"}]},{"text":" (2026-02-12): Added multi-leg strategies (iron condor, butterfly, calendar)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"v1.2.1","type":"text","marks":[{"type":"strong"}]},{"text":" (2026-02-09): Volume multiplier, dynamic strike suggestions","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"v1.1.0","type":"text","marks":[{"type":"strong"}]},{"text":" (2026-02-08): Cross-signal weighting, multi-strategy support","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"v1.0.0","type":"text","marks":[{"type":"strong"}]},{"text":" (2026-02-07): Initial bull put spread 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Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.