llSourcell/Q-Learning-for-Trading

Overview

This is the code for this video on Youtube by Siraj Raval on Q Learning for Trading as part of the Move 37 course at School of AI. Credits for this code go to ShuaiW.

Related post: Teach Machine to Trade

Dependencies

Python 2.7. To install all the libraries, run pip install -r requirements.txt

Table of content

• agent.py: a Deep Q learning agent
• envs.py: a simple 3-stock trading environment
• model.py: a multi-layer perceptron as the function approximator
• utils.py: some utility functions
• run.py: train/test logic
• requirement.txt: all dependencies
• data/: 3 csv files with IBM, MSFT, and QCOM stock prices from Jan 3rd, 2000 to Dec 27, 2017 (5629 days). The data was retrieved using Alpha Vantage API

How to run

To train a Deep Q agent, run python run.py --mode train. There are other parameters and I encourage you look at the run.py script. After training, a trained model as well as the portfolio value history at episode end would be saved to disk.

To test the model performance, run python run.py --mode test --weights <trained_model>, where <trained_model> points to the local model weights file. Test data portfolio value history at episode end would be saved to disk.