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javifalces/HFTFramework
Research framework for RL-enhanced Avellaneda-Stoikov market making
What it is
A high-frequency trading research framework used for a study on improving the Avellaneda-Stoikov market-making algorithm with reinforcement learning. It combines the classic market-making model with an RL agent and supports backtests and experiments around that idea. Researchers working on market making and reinforcement learning are the intended audience. It was built to support specific research, so adapting it to other strategies or venues takes work.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Strategy research, Backtesting |
| 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 | Last commit 9 days ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): +2 over the period, now 309. Gaps mean no snapshot was taken that day.
HFTFramework utilized for research on " A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm "
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