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README
Gym Trading Env is an Gymnasium environment for simulating stocks and training Reinforcement Learning (RL) trading agents. It was designed to be fast and customizable for easy RL trading algorithms implementation.
| Documentation |
Key features
This package aims to greatly simplify the research phase by offering :
- Easy and quick download technical data on several exchanges
- A simple and fast environment for the user and the AI, but which allows complex operations (Short, Margin trading).
- A high performance rendering (can display several hundred thousand candles simultaneously), customizable to visualize the actions of its agent and its results.
- (Coming soon) An easy way to backtest any RL-Agents or any kind
Installation
Gym Trading Env supports Python 3.9+ on Windows, Mac, and Linux. You can install it using pip:
pip install gym-trading-env
Or using git :
git clone https://github.com/ClementPerroud/Gym-Trading-Env
Documentation available here
Core symbols most depended-on inside this repo
addcalled by 7
src/gym_trading_env/utils/history.py
log
called by 6
src/gym_trading_env/environments.py
index
called by 5
src/gym_trading_env/renderer.py
reset
called by 5
src/gym_trading_env/environments.py
valorisation
called by 5
src/gym_trading_env/utils/portfolio.py
step
called by 4
src/gym_trading_env/environments.py
run
called by 3
src/gym_trading_env/renderer.py
_get_price
called by 3
src/gym_trading_env/environments.py
Shape
Languages
Python100%
Modules by API surface
src/gym_trading_env/environments.py25 symbols
src/gym_trading_env/utils/portfolio.py12 symbols
src/gym_trading_env/renderer.py12 symbols
src/gym_trading_env/utils/history.py7 symbols
src/gym_trading_env/downloader.py6 symbols
examples/example_vectorized_multi_environment.py2 symbols
examples/example_multi_environnement.py2 symbols
src/gym_trading_env/utils/charts.py1 symbols
examples/example_vectorized_environment.py1 symbols
examples/example_environnement.py1 symbols
For agents
$ claude mcp add Gym-Trading-Env \
-- python -m otcore.mcp_server <graph>