satroan / deep-RL-trading

playing idealized trading games with deep reinforcement learning

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), if you get good results or any trouble, please contact me at [e-mail]