An implementation of Q-learning applied to (short-term) stock trading. The model uses n-day windows of closing prices to determine if the best action to take at a given time is to buy, sell or sit.
As a result of the short-term state representation, the model is not very good at making decisions over long-term trends, but is quite good at predicting peaks and troughs.
We trained the model woth GSPC data of 2010 and tested with the first quarter of 2011.
S&P 500, 2011Q1. Profit of $92.84:
Create virtual env and install dependencies:
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Make models folder and run the code:
mkdir models
python train_app.py
You may change the these parameters in train_app.py:
window_size = 5 episode_count = 30 stock_name = "^GSPC_2011"
Then when training finishes you can evaluate with the test dataset :
python evaluate_app.py
Change these variables in evaluate_app.py accordingly before running:
stock_name = "GSPC_2011-03" model_name = "model_ep30"