goldspanlabs/optopsy

Health v2 · maintenance only

Adoption not part of the score

1462 stars · 223 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Optopsy is a Python backtesting and statistics library for options strategies, offering 38 built-in strategies, per-leg delta targeting, trade and portfolio simulation, risk metrics, and slippage/commission modeling. It works natively with pandas DataFrames and any options data source, and includes a data CLI and plugin system.

Use cases

• backtest iron condors on SPX with profit targets and stop losses
• compare 45-DTE options strategies vs holding to expiration
• compute Sharpe, Sortino, VaR and other risk metrics for option spreads
• filter strategy entries with technical indicators like RSI and MACD
• simulate multi-strategy options portfolios with capital tracking
• download and cache historical options chain data
• model commissions and slippage in options backtests

When to choose

• you need to research and backtest options spread strategies in Python
• you want pandas-native results that fit an existing data workflow
• you need per-leg delta targeting, early exits, and realistic fill modeling
• you want quick statistics on options strategies without building a spreadsheet

When to avoid

• you need live order execution or broker integration rather than research/backtesting
• you trade equities, futures, or crypto rather than options
• you require a GUI-first trading platform
• you need a license more permissive than AGPL-3.0

Facets

library · maturity active

trading simulation data-science cli plugin-system fintech data-science developer-tools python cli options-trading backtesting options-strategies pandas risk-metrics slippage-modeling algorithmic-trading trading

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