This project was forked from https://github.com/golsun/deep-RL-trading

Playing trading games with deep reinforcement learning

This repo is the code for this paper. Deep reinforcement learing is used to find optimal strategies in these two scenarios:

• Momentum trading: capture the underlying dynamics
• Arbitrage trading: utilize the hidden relation among the inputs

Several neural networks are compared:

• Recurrent Neural Networks (GRU/LSTM)
• Convolutional Neural Network (CNN)
• Multi-Layer Perception (MLP)

Dependencies

You can get all dependencies via the Anaconda environment file, env.yml:

conda env create -f env.yml

Play with it

Just call the main function

python main.py

You can play with model parameters (specified in main.py).