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Finance
Python toolkit for market data, indicators and backtesting research
What it is
A collection of 150+ quantitative finance Python programs built on NumPy and pandas. It downloads and normalizes OHLCV data, computes technical indicators, runs stock screens, and includes a modest backtester with next-open execution, cash accounting, commission and slippage handling. Additional modules cover portfolio optimization, statistical models such as ARIMA and PCA, and reporting. Research code intended for quant traders and learners, with the caveat that fundamentals are snapshots rather than point-in-time inputs and public data providers may throttle or change schemas.
At a glance
Worth watchingOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Beginners |
|---|---|
| Used for | Data analysis, Strategy research |
| Markets | US equities, Multi-market |
| Stack | Python |
| Learning curve | Easy to start |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Last commit 29 days ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): +90 over the period, now 4,310. Gaps mean no snapshot was taken that day.
150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.
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