This project is a modified version of the original Trading Gym, which is an open-source project for the development of reinforcement learning algorithms in the context of trading. It is currently composed of a single environment and implements a generic way of feeding this trading environment different type of price data.

Perfect Sine-Function Trading

Semi-Perfect Sine-Function Trading

1. Make sure you have Python 3

2. Then install trading-gym

   pip install tgym

   We strongly recommend using virtual environments. A very good guide can be found at https://python-guide-pt-br.readthedocs.io/pt_BR/latest/dev/virtualenvs.html.

3. Then install keras

   pip install keras

   By default, the backend will be set to Theano. You can also run it with Tensorflow by installing it with pip install tensorflow. You then need to edit ~/.keras/keras.json and make sure "backend": "tensorflow" is specified.

python examples/dqn_agent.py

Stored in config/config.json

To plot properly with matlibplot, you should disable "interactive mode" by adding this in your python scripts (I've added it to examples/dqn_agent.py already)

plt.interactive(False)

Our environments API is strongly inspired by OpenAI Gym. We aim to entirely base it upon OpenAI Gym architecture and propose Trading Gym as an additional OpenAI environment.

MIT License