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golsun/deep-RL-trading
Deep reinforcement learning on idealised trading games
What it is
A research project that applies deep reinforcement learning to idealised trading games. Agents are trained repeatedly in simplified market settings to explore how RL strategies behave. Researchers and developers interested in the foundations of RL for trading can learn from its setup and code. The games are deliberately idealised, so conclusions do not carry over directly to real markets with costs and noise.
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 | Low 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 360. Gaps mean no snapshot was taken that day.
playing idealized trading games with deep reinforcement learning
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