Home / LLM agents & research
saeed349/Deep-Reinforcement-Learning-in-Trading
RL trading agent and environment for simulated or historical data
What it is
Code for a reinforcement learning trading agent together with its trading environment, which works with both simulated and historical market data. The environment design takes inspiration from OpenAI Gym. Developers and researchers learning how RL agents are built for trading can use it for experiments and code study. It is an educational project, so strategies learned in it need validation before any real deployment.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Developers |
|---|---|
| Used for | Strategy research, Backtesting |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Moderate learning curve |
| Practical value | Low practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| 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 219. Gaps mean no snapshot was taken that day.
This repository provides the code for a Reinforcement Learning trading agent with its trading environment that works with both simulated and historical market data. This was inspired by OpenAI Gym fra
Similar tools
Deep reinforcement learning library for automated quantitative trading
Python algorithmic trading framework with machine learning support
Python backtesting and live trading platform for strategies
Fast limit order book simulator for trading strategy research
Python reinforcement learning framework for training trading agents
Modular schedule-driven backtesting engine for equities and ETFs