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evgps/a3c_trading
Trading with recurrent actor-critic reinforcement learning
What it is
A research project that trades using recurrent actor-critic reinforcement learning based on the A3C algorithm. The recurrent architecture lets the agent carry information across time steps of market data while it learns a trading policy. Researchers and developers exploring deep RL for trading can use it as a reference implementation. It is an experiment from a research setting, and its results depend on the data and assumptions used in training.
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 | Crypto, 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 437. Gaps mean no snapshot was taken that day.
Trading with recurrent actor-critic reinforcement learning
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