options-strategy
options-strategy lets you backtest complex option portfolios—from covered calls and protective puts to iron condors and butterflies—by synthesizing theoretical prices via Black-Scholes and tracking Greeks exposure across time. The engine accepts daily underlying data, applies historical volatility, and outputs trade-by-trade records, daily Greeks aggregates, and performance metrics for strategy validation.
options-strategy backtests multi-leg option portfolios using Black-Scholes pricing and Greeks analysis.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-27
options-strategy backtests multi-leg option portfolios using Black-Scholes pricing and Greeks analysis. options-strategy lets you backtest complex option portfolios—from covered calls and protective puts to iron condors and butterflies—by synthesizing theoretical prices via Black-Scholes and tracking Greeks exposure across time. The engine accepts daily underlying data, applies historical volatility, and outputs trade-by-trade records, daily Greeks aggregates, and performance metrics for strategy validation.
Use it when
- options-strategy computes Greeks using Black-Scholes pricing theory.
- Yes.
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Install
HKUDS/Vibe-Trading/options-strategy · repository language: Python
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is options-strategy and what can I backtest with it?
options-strategy is a MIT-licensed backtesting framework for complex option portfolios. It lets you test covered calls, protective puts, iron condors, butterflies, straddles, and other multi-leg strategies by synthesizing theoretical prices via Black-Scholes and tracking Greeks exposure across time. The engine accepts daily underlying data, applies historical volatility, and outputs trade-by-trade records, daily Greeks aggregates, and performance metrics for strategy validation.
How does options-strategy calculate option greeks delta gamma theta vega?
options-strategy computes Greeks using Black-Scholes pricing theory. It derives delta, gamma, theta, and vega sensitivities for each leg of your multi-leg strategies, then aggregates them across your entire portfolio. This lets you analyze volatility sensitivity and time decay effects at both the individual trade and portfolio level, enabling you to understand how your Greeks exposure evolves as market conditions and time change.
Can options-strategy backtest covered call iron condor straddle strategies?
Yes. options-strategy supports backtesting of covered calls, iron condors, straddles, and other multi-leg option strategies. You define your legs, specify entry and exit rules, and the framework synthesizes theoretical prices using Black-Scholes, tracks Greeks exposure, and generates trade-by-trade records and daily aggregates so you can validate strategy performance across historical periods.
What data inputs does options-strategy require for backtesting?
options-strategy requires daily underlying price data (for equity or crypto assets). The engine calculates historical volatility from this data and uses it to synthesize option prices via Black-Scholes. You also define your strategy legs—strike prices, expiration dates, and position types—and the framework handles the rest, outputting Greeks aggregates and performance metrics.
How does options-strategy handle hedging and spread strategy simulation?
options-strategy simulates hedging strategies like protective puts and covered calls, as well as spread strategies such as butterflies and calendar spreads. The framework tracks how each leg's Greeks and P&L contribute to your overall portfolio, allowing you to measure hedge effectiveness and spread profitability across historical scenarios and volatility regimes.
What outputs and metrics does options-strategy provide?
options-strategy delivers trade-by-trade records showing entry, exit, and Greeks for each leg; daily Greeks aggregates (delta, gamma, theta, vega) for your full portfolio; and strategy performance metrics including P&L, win rate, and volatility sensitivity. These outputs enable you to validate strategy logic, understand risk exposure, and optimize your option trading approach.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
Purpose
Backtesting of option portfolio strategies. Starting from the underlying price, the engine synthesizes theoretical option prices with the Black-Scholes model, then simulates PnL, Greeks exposure, and expiration exercise for multi-leg option portfolios.
Applicable scenarios:
- Hedging strategies (covered call, protective put)
- Volatility trading (straddle, strangle)
- Spread strategies (iron condor, butterfly, calendar spread)
- Option pricing analysis and Greeks sensitivity research
Supported Strategy Types
| Strategy | Structure | Applicable Market View |
|---|---|---|
| Covered Call | Hold underlying + short call | Mildly bullish, collect premium |
| Protective Put | Hold underlying + long put | Bullish but wants downside protection |
| Straddle | Buy same-strike call + put | Expect large movement, direction uncertain |
| Strangle | Buy different-strike call + put | Expect large movement, lower cost |
| Iron Condor |
(truncated - see the full file via the links below)
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agent/src/skills/options-strategy/SKILL.md
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Related skills
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