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Albert-Z-Guo/Deep-Reinforcement-Stock-Trading
Lightweight deep reinforcement learning framework for portfolio management
What it is
A lightweight deep reinforcement learning framework for portfolio management. The project explores applying deep RL algorithms to stock trading in a highly modular way, so individual components can be swapped or extended. Researchers and developers experimenting with RL-based allocation strategies are the intended audience. It is an exploratory research project, and its results should be reproduced and stress-tested before drawing conclusions about real trading.
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 | US equities, Multi-market |
| Stack | Python |
| Learning curve | Moderate 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): −3 over the period, now 688. Gaps mean no snapshot was taken that day.
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly mod
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