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ThibautTheate/An-Application-of-Deep-Reinforcement-Learning-to-Algorithmic-Trading
Experiment code for a deep RL algorithmic trading paper
What it is
Experimental code supporting the research paper An Application of Deep Reinforcement Learning to Algorithmic Trading. It implements the paper's DQN-based trading approach and runs backtests on data from several markets to reproduce the reported results. Researchers and developers who want to study or reproduce this deep RL trading method are the intended audience. It is a paper companion, so it is organised around the experiments rather than as a reusable library.
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 215. Gaps mean no snapshot was taken that day.
Experimental code supporting the results presented in the scientific research paper entitled "An Application of Deep Reinforcement Learning to Algorithmic Trading"
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