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yuriak/RLQuant
Research code applying reinforcement learning to quant trading
What it is
A research project applying reinforcement learning to quantitative trading. It includes code for setting up trading environments, training reinforcement learning models and backtesting the resulting strategies, covering the full workflow in one place. Quant researchers and developers who want a worked example of that end-to-end process can study and adapt it. It is research code, so results depend on its data and settings and need independent checking.
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 | GPU optional |
| Maintenance | No commits in over six months |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): 0 over the period, now 359. Gaps mean no snapshot was taken that day.
Applying Reinforcement Learning in Quantitative Trading
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