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Investing algorithm framework
Python framework for building, backtesting, and deploying trading algorithms
What it is
An open-source Python framework covering the full quantitative trading workflow, from strategy definition to live deployment. It supports vector backtesting with Polars, event-driven simulation, over 80 performance metrics, Monte Carlo testing, an interactive dashboard, and paper or live trading of the same strategy code. Quantitative developers who want to backtest at scale and deploy strategies without rewriting them will find it a good fit. The framework targets crypto and traditional markets through configurable portfolio and credential management, though users will need to supply their own market data and broker or exchange accounts to run live strategies.
At a glance
Worth watchingOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Developers |
|---|---|
| Used for | Backtesting, Live trading |
| Markets | Crypto, Multi-market |
| Stack | Python |
| Learning curve | Moderate learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Last commit 11 days ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): +104 over the period, now 2,168. Gaps mean no snapshot was taken that day.
Framework for developing, backtesting, and deploying automated trading algorithms.
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