Backtest multi-leg options portfolios with Black-Scholes pricing and daily Greeks.
npx skills add https://github.com/loanntc/Paave --skill options-strategy-loanntc
Please help me install this Agent Skill. Skill: options-strategy Source: https://github.com/loanntc/Paave/tree/main/skills/options-strategy Command: npx skills add https://github.com/loanntc/Paave --skill options-strategy-loanntc
It helps you evaluate how multi-leg option strategies might perform by turning underlying price history into synthetic option pricing and daily Greeks and PnL, reducing guesswork before you trade.
Use the SignalEngine interface in code/signal_engine.py to emit multi-leg open/close instructions, then run the backtest with engine set to "options" in config.json.
High-intent search queries and answers about installing and using this skill.
Backtest multi-leg options strategies by emitting open/close instructions through a SignalEngine interface, which the options-configured runner processes using Black-Scholes pricing to estimate daily PnL and Greeks.
Black-Scholes pricing calculates portfolio Greeks by computing theoretical option values for each leg and aggregating delta, gamma, theta, and vega exposures to estimate overall portfolio risk and PnL.
Yes, options backtesting supports crypto underlyings and volatility trading by applying Black-Scholes theoretical pricing to historical price data, requiring historical-volatility and risk-free rate inputs.
To start simulating covered calls and protective puts, implement SignalEngine.generate(data_map) to output dated instructions with per-leg type, strike, expiry, and signed quantity, then configure an options runner.
Yes, options backtesting supports calendar spreads across different expiries by simulating open and close instructions for portfolios composed of multiple option legs with varying expiration dates.
Black-Scholes options backtesting relies on theoretical pricing and requires accurate historical-volatility inputs, meaning it estimates rather than replicates exact market fills, which may limit modeling extreme market conditions.
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