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pyqstrat
Fast, transparent Python framework for backtesting quantitative strategies
What it is
A Python library for backtesting quantitative trading strategies, built around goals of speed, transparency, and extensibility. Performance-critical components are written at the numpy level or in Cython and C++, and users can define indicators, signals, trading rules, contract groups, and custom market simulation and metrics. Parameter optimization can use all available CPUs. Installation is easiest through mamba since the package compiles C++ and depends on compiled numpy, scipy, and pandas, which may add setup friction for traders used to pure-Python tools.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Backtesting, Strategy research |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Steep learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | No commits in over six months |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): −1 over the period, now 371. Gaps mean no snapshot was taken that day.
A fast, extensible, transparent python library for backtesting quantitative strategies.
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