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- OpenClaw
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- OpenClaw
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- SKILL.md
Tencent SkillHub Β· AI
Options Spread Conviction Engine
Multi-regime options spread analysis engine with Kelly Criterion Position Sizing. Scores vertical spreads (bull put, bear call, bull call, bear put) and mult...
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Multi-regime options spread analysis engine with Kelly Criterion Position Sizing. Scores vertical spreads (bull put, bear call, bull call, bear put) and mult...
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Documentation
Options Spread Conviction Engine
Multi-regime options spread scoring using technical indicators and IV term structure analysis.
Install
brew install jq npm install yahoo-finance2 sudo ln -s /opt/homebrew/bin/yahoo-finance /usr/local/bin/yf
Overview
This engine analyzes any ticker and scores seven options strategies across two categories:
Vertical Spreads (Directional)
StrategyTypePhilosophyIdeal Setupbull_putCreditMean ReversionBullish trend + oversold dipbear_callCreditMean ReversionBearish trend + overbought ripbull_callDebitBreakoutStrong bullish momentumbear_putDebitBreakoutStrong bearish momentum
Multi-Leg Strategies (Non-Directional / Theta)
StrategyTypePhilosophyIdeal Setupiron_condorCreditPremium SellingIV Rank >70, RSI neutral, range-boundbutterflyDebitPinning PlayBB squeeze, RSI center, low ADXcalendarDebitTheta HarvestInverted IV term structure (front > back)
Vertical Spreads
Weights vary by strategy type (Credit = Mean Reversion, Debit = Breakout): Credit Spreads (bull_put, bear_call) IndicatorWeightPurposeIchimoku Cloud25 ptsTrend structure & equilibriumRSI20 ptsEntry timing (mean-reversion)MACD15 ptsMomentum confirmationBollinger Bands25 ptsVolatility regimeADX15 ptsTrend strength validation Debit Spreads (bull_call, bear_put) IndicatorWeightPurposeIchimoku Cloud20 ptsTrend confirmationRSI10 ptsDirectional momentumMACD30 ptsBreakout accelerationBollinger Bands25 ptsBandwidth expansionADX15 ptsTrend strength validation
Multi-Leg Strategies
Iron Condor (Credit / Range-Bound) ComponentWeightRationaleIV Rank (BBW %)25 ptsRich premiums to sellRSI Neutrality20 ptsNo directional momentumADX Range-Bound20 ptsWeak trend = range structurePrice Position20 ptsCentered in range = safe marginsMACD Neutrality15 ptsNo acceleration in any direction Triggers: IV Rank > 70: Premium-rich environment RSI 40-60: Neutral momentum ADX < 25: Weak/no trend Price near %B center: Max profit zone maximized Strike Selection: SELL put at 1-sigma below price (short put) BUY put at 2-sigma below (long put β wing) SELL call at 1-sigma above price (short call) BUY call at 2-sigma above (long call β wing) Output: All 4 strikes (put_long, put_short, call_short, call_long) Max profit zone (width between short strikes) Wing width Butterfly (Debit / Volatility Compression) ComponentWeightRationaleBB Squeeze30 ptsVol compression = narrow rangeRSI Neutrality25 ptsPrice at equilibriumADX Weakness20 ptsNo directional trend at allPrice Centering15 ptsAt center of range for max profitMACD Flatness10 ptsNo momentum Triggers: BBW percentile < 25: Squeeze active RSI 45-55: Dead-center (tighter than condor) ADX < 20: Very weak trend MACD histogram near zero Price at %B = 0.50 Strike Selection: BUY 1 call at strike below center (lower wing) SELL 2 calls at center strike (body) BUY 1 call at strike above center (upper wing) Output: 3 strikes (lower_long, middle_short, upper_long) Max profit price (= middle strike) Profit zone (approximate breakevens) Calendar Spread (Debit / Theta Harvesting) ComponentWeightRationaleIV Term Structure30 ptsFront IV > Back IV = theta edgePrice Stability20 ptsPrice stays near strikeRSI Neutrality20 ptsNot trending away from strikeADX Moderate15 ptsSome structure, not trending hardMACD Neutrality15 ptsNo directional acceleration Triggers: Front-month IV > Back-month IV by > 5%: Inverted term structure Low recent volatility: Price stability RSI neutral: No directional momentum ADX 18-25: Moderate trend structure (not chaos) Data Sources: Primary: Live options chain IV from Yahoo Finance Fallback: Historical volatility proxy (HV 10-day vs 30-day) Strike Selection: ATM strike (rounded to standard interval) Front expiry: nearest available Back expiry: 25+ days after front Output: Single strike (both legs) Front and back expiry dates IV differential (%) Theta advantage description
Conviction Tiers
ScoreTierAction80-100EXECUTEHigh conviction β Enter the spread60-79PREPAREFavorable β Size the trade40-59WATCHInteresting β Add to watchlist0-39WAITPoor conditions β Avoid / No setup
Vertical Spreads
# Basic analysis (auto-detects best strategy) conviction-engine AAPL # Specific strategy conviction-engine SPY --strategy bear_call conviction-engine QQQ --strategy bull_call --period 2y
Multi-Leg Strategies
# Iron Condor β high IV, range-bound conviction-engine SPY --strategy iron_condor # Butterfly β volatility compression, pinning play conviction-engine AAPL --strategy butterfly # Calendar β inverted IV term structure, theta harvest conviction-engine TSLA --strategy calendar
Multiple Tickers
conviction-engine AAPL MSFT GOOGL --strategy bull_put conviction-engine SPY QQQ IWM --strategy iron_condor
JSON Output (for automation)
conviction-engine TSLA --strategy butterfly --json conviction-engine SPY --strategy calendar --json | jq '.[0].iv_term_structure'
Full Options
conviction-engine <ticker> [ticker...] --strategy {bull_put,bear_call,bull_call,bear_put,iron_condor,butterfly,calendar} --period {1y,2y,3y,5y} --interval {1h,1d,1wk} --json
Iron Condor
================================================================================ SPY β Iron Condor (Credit) ================================================================================ Price: $681.27 | Score: 31.8/100 β WAIT [IV Rank +2.5/25] IV Rank (BBW proxy): 5% (VERY_LOW) BBW: 3.17 (1Y range: 2.37 - 18.13) Premiums are THIN β poor risk/reward for credit Strikes: BUY 680.0P | SELL 685.0P SELL 695.0C | BUY 700.0C Max Profit Zone: $685.0 - $695.0 Wing Width: $5.00
Butterfly
================================================================================ SPY β Long Butterfly (Debit) ================================================================================ Price: $681.27 | Score: 64.5/100 β PREPARE [BB Squeeze +27.0/30] Bandwidth: 3.1701 (percentile: 21%) SQUEEZE ACTIVE β 19 consecutive bars Strikes: BUY 1x 685.0C | SELL 2x 690.0C | BUY 1x 695.0C Max Profit Price: $690.0 Profit Zone: ~$685.0 - $695.0
Calendar Spread
================================================================================ SPY β Calendar Spread (Debit) ================================================================================ Price: $681.27 | Score: 67.2/100 β PREPARE [IV Term Structure +30.0/30] Front IV: 27.5% | Back IV: 19.4% Differential: +41.7% INVERTED TERM STRUCTURE β calendar opportunity confirmed Strikes: Strike: $680.0 SELL 2026-02-13 | BUY 2026-03-13 Theta Advantage: Front IV > Back IV by 41.7%
IV Rank Approximation
IV Rank is approximated using Bollinger Bandwidth (BBW) percentile over 252 trading days: IV Rank β (Current BBW - 52wk Low BBW) / (52wk High BBW - 52wk Low BBW) Γ 100 This correlation is well-documented: realized volatility (BBW) and implied volatility rank move with ~0.7-0.8 correlation (Sinclair, "Volatility Trading", 2013).
IV Term Structure
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.
Quantitative Modules (v2.3.0)
The engine now includes four quantitative modules for rigorous strategy validation and optimization:
1. Regime Detector (regime_detector.py)
Market regime classification using VIX percentiles: CRISIS: VIX > 80th percentile β favors premium selling (iron condors) HIGH_VOL: VIX 60-80th β elevated IV benefits credit spreads NORMAL: VIX 40-60th β balanced environment, all strategies viable LOW_VOL: VIX 20-40th β cheap options favor debit spreads EUPHORIA: VIX < 20th β momentum continues, mean reversion brewing # Detect current regime python3 scripts/regime_detector.py # Get regime-adjusted weights for specific strategy python3 scripts/regime_detector.py --strategy iron_condor --json Integration: from regime_detector import RegimeDetector detector = RegimeDetector() regime, confidence = detector.detect_regime() weights = detector.get_regime_weights(regime) adjusted_score, reasoning = detector.regime_aware_score(75, regime, 'bull_put')
2. Volatility Forecaster (vol_forecaster.py)
GARCH-based realized volatility forecasting with VRP analysis: Fits GARCH(1,1) to historical returns Forecasts realized volatility over configurable horizon Calculates volatility risk premium (IV - RV forecast) Provides conviction adjustments based on VRP # Analyze AAPL volatility python3 scripts/vol_forecaster.py AAPL # Compare IV = 25% vs forecast RV python3 scripts/vol_forecaster.py SPY --iv 0.25 --horizon 5 Interpretation: VRP > 5%: Favorable for selling premium (credit spreads) VRP < -5%: Favorable for buying premium (debit spreads) VRP near 0: No volatility edge, focus on directional setup Integration: from vol_forecaster import VolatilityForecaster forecaster = VolatilityForecaster("AAPL") params = forecaster.fit_garch() # Returns omega, alpha, beta forecast = forecaster.forecast_vol(horizon=5) vrp, strength, rec = forecaster.vol_risk_premium(iv=0.25, rv_forecast=forecast.annualized_vol) adjusted_score, reasoning = forecaster.add_to_conviction(70, vrp_signal, 'bull_put')
3. Enhanced Kelly Sizer (enhanced_kelly.py)
Drawdown-constrained, correlation-aware position sizing: Full Kelly criterion calculation Drawdown constraint: f_dd = f_kelly Γ (1 - target_dd / max_dd) Conviction-based Kelly scaling: 90-100: Half Kelly 80-89: Quarter Kelly 60-79: Eighth Kelly <60: No position Correlation penalty for portfolio context # Calculate position with $390 account python3 scripts/enhanced_kelly.py --loss 80 --win 40 --pop 0.65 --conviction 85 # Include correlation with existing position python3 scripts/enhanced_kelly.py --loss 80 --win 40 --pop 0.65 --conviction 85 --correlation 0.3 Integration: from enhanced_kelly import EnhancedKellySizer sizer = EnhancedKellySizer(account_value=390, max_drawdown=0.20) result = sizer.calculate_position( spread_cost=80, max_loss=80, win_amount=40, conviction=85, pop=0.65, existing_correlation=0.0 ) # Returns: contracts, total_risk, kelly_fraction, recommendation
4. Backtest Validator (backtest_validator.py)
Walk-forward validation of conviction scores: Simulates historical trades across ticker universe Validates tier separation (EXECUTE vs WAIT performance) Statistical tests (t-tests, ANOVA) Tier separation scoring (0-1) Weight calibration suggestions # Backtest bull_put on AAPL, MSFT, SPY (2022-2024) python3 scripts/backtest_validator.py --tickers AAPL MSFT SPY --start 2022-01-01 --end 2024-01-01 --strategy bull_put # JSON output for analysis python3 scripts/backtest_validator.py --tickers SPY --json Output Metrics: Win rate per tier Expectancy per tier: (win_rate Γ avg_win) - (loss_rate Γ avg_loss) Sharpe ratio per tier P-values for tier differences Separation score (0-1, higher = better discrimination) Integration: from backtest_validator import BacktestValidator validator = BacktestValidator(engine, "2022-01-01", "2024-01-01") results_df = validator.run_walk_forward(["AAPL", "MSFT"], hold_days=5) report = validator.validate_tiers(results_df) print(f"Separation score: {report.tier_separation_score:.2f}") print(f"EXECUTE vs WAIT p-value: {report.p_values['execute_vs_wait']:.4f}")
5. Quantitative Integration (quantitative_integration.py)
Unified interface combining all quantitative modules: # Full quantitative analysis with regime and VRP python3 scripts/quantitative_integration.py AAPL --regime-aware --vol-aware # With Kelly sizing python3 scripts/quantitative_integration.py SPY --regime-aware --pop 0.65 --max-loss 80 --win-amount 40 # Run backtest validation python3 scripts/quantitative_integration.py --backtest SPY QQQ --start 2022-01-01 --end 2024-01-01 Integration: from quantitative_integration import QuantConvictionEngine engine = QuantConvictionEngine(account_value=390, max_drawdown=0.20) # Analyze with regime and VRP adjustments result = engine.analyze("AAPL", "bull_put", regime_aware=True, vol_aware=True) print(f"Final score: {result.final_score}") print(f"Regime: {result.regime}") print(f"VRP: {result.vrp_signal.vrp if result.vrp_signal else 'N/A'}") # Calculate position size sizing = engine.calculate_position(result, pop=0.65, max_loss=80, win_amount=40) print(f"Contracts: {sizing['contracts']}") # Run backtest validation report = engine.run_backtest(["SPY", "QQQ"], "2022-01-01", "2024-01-01") print(f"Recommendation: {report.recommendation}")
Academic Foundation
Ichimoku Cloud β Trend structure (Hosoda, 1968) RSI β Momentum oscillator (Wilder, 1978) MACD β Trend momentum (Appel, 1979) Bollinger Bands β Volatility envelopes (Bollinger, 2001) IV Rank / Term Structure β Options market microstructure (Sinclair, 2013) Combining orthogonal signals reduces false-positive rate compared to single-indicator strategies (Pring, 2002; Murphy, 1999).
Architecture
conviction-engine/ βββ scripts/ β βββ conviction-engine # CLI wrapper (bash) β βββ spread_conviction_engine.py # Core engine (vertical spreads) β βββ multi_leg_strategies.py # Multi-leg extensions β βββ quantitative_integration.py # Unified quantitative interface β βββ regime_detector.py # VIX-based regime classification β βββ vol_forecaster.py # GARCH volatility forecasting β βββ enhanced_kelly.py # Drawdown-constrained Kelly sizing β βββ backtest_validator.py # Walk-forward validation β βββ quant_scanner.py # Quantitative options scanner β βββ market_scanner.py # Technical market scanner β βββ calculator.py # Black-Scholes & POP calculator β βββ position_sizer.py # Kelly position sizing β βββ chain_analyzer.py # IV surface analyzer β βββ options_math.py # Core mathematical models β βββ setup-venv.sh # Environment setup βββ tests/ # Unit tests β βββ test_regime_detector.py β βββ test_vol_forecaster.py β βββ test_enhanced_kelly.py β βββ test_backtest_validator.py β βββ run_tests.py βββ SKILL.md # This documentation
Module Separation
spread_conviction_engine.py: Vertical spreads, shared infrastructure (data fetching, indicator computation) multi_leg_strategies.py: Iron condors, butterflies, calendars (imports from main engine) quantitative_integration.py: Unified interface for regime/vol/Kelly/backtest modules regime_detector.py: Market regime classification using VIX percentiles vol_forecaster.py: GARCH-based realized volatility forecasting enhanced_kelly.py: Drawdown-constrained, correlation-aware position sizing backtest_validator.py: Walk-forward validation of conviction scores This separation keeps concerns clean while avoiding duplication.
IV Data
Yahoo Finance Limitations: Options chains may be unavailable after market hours or for low-volume tickers Fallback: Historical volatility (HV) proxy is less accurate than live IV but provides signal IV Rank: Approximated from BBW; actual IV Rank requires options chain data
Strike Selection
Approximation: Strikes derived from Bollinger Band levels (1-sigma / 2-sigma) Rounding: Rounded to standard option strike intervals based on stock price No Live Pricing: Does not fetch live option premiums; strike selection is structural, not value-optimized
Data Quality
Minimum 180 trading days required for full Ichimoku cloud population Multi-leg strategies require options chains (calendar spreads especially) After-hours analysis may have reduced data quality
Market Assumptions
Assumes normal options market conditions (not extreme volatility events) Strike intervals assume US equity options conventions Not tested on futures, commodities, or non-US markets
Requirements
Python 3.10+ (Python 3.14+ supported via pure-python mode) Isolated virtual environment (auto-created on first run) Internet connection (fetches data from Yahoo Finance)
Installation
clawhub install options-spread-conviction-engine The skill automatically creates a virtual environment and installs: pandas >= 2.0 pandas_ta >= 0.4.0 (pure Python mode on 3.14+) yfinance >= 1.0 scipy, tqdm Note: On Python 3.14+, the engine runs in pure Python mode without numba. Performance is slightly reduced but all functionality works correctly.
Market Scanners
The engine includes two distinct scanning tools for different trading philosophies:
1. Technical Scanner (market_scanner.py)
Automates the search for high-conviction plays across entire stock universes using technical indicators (Ichimoku, RSI, MACD, BB). Features Scans S&P 500, Nasdaq 100, or custom ticker lists. Filters for EXECUTE tier (conviction β₯80). Runs position sizing to ensure trades fit account guardrails. Usage # Scan S&P 500 for high-conviction technical setups python3 scripts/market_scanner.py --universe sp500
2. Quantitative Scanner (quant_scanner.py)
A mathematically-rigorous scanner that ignores technical indicators in favor of market microstructure and probability. Features IV Surface Analysis: Analyzes skew and term structure. Monte Carlo POP: 10,000-run simulations for true Probability of Profit. EV Optimization: Finds trades with the highest risk-adjusted mathematical expectancy. Account-Aware: Enforces small-account constraints ($100 max risk). Usage # Maximize POP (Probability of Profit) for SPY python3 scripts/quant_scanner.py SPY --mode pop # High-expectancy (EV) plays with specific DTE python3 scripts/quant_scanner.py AAPL TSLA --mode ev --min-dte 30
Calculator & Position Sizer
The integrated toolchain includes:
calculator.py
Black-Scholes options pricing with support for: Single options: calls, puts Vertical spreads: bull call, bear put Multi-leg: iron condors, butterflies Greeks calculation (delta, gamma, theta, vega, rho) Monte Carlo POP simulation
position_sizer.py
Kelly criterion position sizing adapted for small accounts: Full Kelly and fractional Kelly (default 0.25) Account guardrails ($390 default, $100 max risk) Trade screening and ranking Strike adjustment suggestions from position_sizer import calculate_position result = calculate_position( account_value=390, max_loss_per_spread=80, win_amount=40, pop=0.65, ) # Returns: contracts, total_risk, recommendation, reason
Files
scripts/conviction-engine β Main CLI wrapper for conviction engine scripts/spread_conviction_engine.py β Core engine (vertical spreads) scripts/multi_leg_strategies.py β Multi-leg extensions (v2.0.0) scripts/market_scanner.py β Automated market scanner for EXECUTE plays scripts/calculator.py β Black-Scholes pricing, Greeks, Monte Carlo POP scripts/position_sizer.py β Kelly criterion position sizing scripts/setup-venv.sh β Environment setup data/sp500_tickers.txt β S&P 500 constituents data/ndx100_tickers.txt β Nasdaq 100 constituents assets/ β Documentation and examples
Version History
v2.3.0 (2026-02-13): Quantitative rigor upgrade Regime Detector: VIX-based market regime classification Volatility Forecaster: GARCH-based RV forecasting with VRP analysis Enhanced Kelly Sizer: Drawdown-constrained, correlation-aware position sizing Backtest Validator: Walk-forward validation with tier separation testing Quantitative Integration: Unified interface for all quantitative modules Comprehensive unit test suite for all new modules v2.2.0 (2026-02-13): Kelly Criterion position sizing with full/half Kelly, edge calculation, and account-aware contract sizing v2.1.0 (2026-02-12): Added market scanner, integrated calculator and position sizer v2.0.0 (2026-02-12): Added multi-leg strategies (iron condor, butterfly, calendar) v1.2.1 (2026-02-09): Volume multiplier, dynamic strike suggestions v1.1.0 (2026-02-08): Cross-signal weighting, multi-strategy support v1.0.0 (2026-02-07): Initial bull put spread engine
License
MIT β Part of the Financial Toolkit for OpenClaw
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs1 Config
- SKILL.md
- CODE_REVIEW_REPORT.md
- MULTI_LEG_REPORT.md
- QUANT_SCANNER.md
- README.md
- _meta.json