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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A nimble options research and backtesting library for Python
| Date | Stars |
|---|---|
| 2026-07-24 | 1423 |
| 2026-07-25 | 1423 |
| 2026-07-28 | 1423 |
| 2026-07-30 | 1423 |
| 2026-08-06 | 1423 |
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growth rate 0.00%/day
  [](https://github.com/goldspanlabs/optopsy/actions/workflows/ci.yml) [](https://badge.fury.io/py/optopsy) [](https://pypi.org/project/optopsy/) <img src="docs/assets/logo.png" alt="Optopsy" width="150"> A nimble backtesting and statistics library for options strategies. Optopsy is a Python backtesting engine that lets you go from *"How do 45-DTE iron condors on SPX perform with a 50% profit target and 2x stop loss vs holding to expiration?"* to detailed performance statistics in seconds, not spreadsheets. [Full Documentation](https://goldspanlabs.github.io/optopsy/) | [API Reference](https://goldspanlabs.github.io/optopsy/api-reference/) | [Examples](https://goldspanlabs.github.io/optopsy/examples/) ## 🤖 Looking for AI/LLM integration? [optopsy-mcp](https://github.com/goldspanlabs/optopsy-mcp) provides a high-performance MCP server for strategy screening and backtesting. Powered by a complete Rust rewrite of the Optopsy engine, it is specifically built for seamless interaction with Large Language Models. ## Features - **38 Built-in Strategies** - From simple calls/puts to iron condors, butterflies, condors, ratio spreads, collars, calendars, and diagonals - **Per-Leg Delta Targeting** - Select strikes by delta with `target, min, max` per leg - **Trade Simulator** - Chronological simulation with capital tracking, position limits, and equity curves via `simulate()` - **Portfolio Simulation** - Weighted multi-strategy portfolio backtesting via `simulate_portfolio()` - **Early Exits** - Stop-loss, take-profit, and max-hold-days rules for automatic position management - **Commissions** - Model broker fees with per-contract, base fee, and min fee structures - **Risk Metrics** - Sharpe, Sortino, VaR, CVaR, Calmar, Omega, tail ratio, and more via `compute_risk_metrics()` - **80+ Entry Signals** - Filter entries with TA indicators (RSI, MACD, Bollinger Bands, EMA, ATR, IV Rank) via [pandas-ta-classic](https://github.com/xgboosted/pandas-ta-classic) - **Custom Signals** - Use `custom_signal()` to drive entries from any DataFrame with a boolean flag column - **Slippage Modeling** - Realistic fills with mid, spread, liquidity-based, or per-leg slippage - **Live Data Providers** - Fetch options chains and stock prices directly from supported data sources (e.g. EODHD) - **Smart Caching** - Automatic local caching of fetched data with gap detection for efficient re-fetches - **Plugin System** - Extend with custom strategies, signals, and data providers via entry points ## Installation ```bash # Core library only (latest stable release) pip install optopsy # With Data CLI (download & cache market data) pip install optopsy[data] ``` **Requirements:** Python 3.12-3.13, Pandas 2.0+, NumPy 1.26+ ## Data Management Optopsy includes a standalone data CLI for downloading and caching historical market data. ```bash pip install optopsy[data] # Download historical options data (requires EODHD_API_KEY) optopsy-data download SPY optopsy-data download SPY AAPL TSLA # Download stock price history optopsy-data download SPY --stocks # List available symbols optopsy-data symbols optopsy-data symbols -q SPY # Cache management optopsy-data cache size optopsy-data cache clear ``` Data is cached locally as Parquet files at `~/.optopsy/cache/`. Re-running download only fetches new data since your last download. See the [Data Management documentation](https://goldspanlabs.github.io/optopsy/data/) for ful
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e96bd8583a099823, topic:trading