An implementation of Q-learning applied to short-swing 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.

To get some real stock data from Alpha Vantage, run the following code:

python fetchdata.py

To train the model, make a directory called model first, and then train. Here, I give an example of training on the Google stock data with 10-day windows of closing prices and 200 episodes of simulation.

mkdir model
python train.py GOOGL_20190516 10 200

Then evaluate the model and get the total-profit:

python evaluate.py GOOGL_20190516 model_ep200

Deep Q-Learning with Keras and Gym - Q-learning overview and Agent skeleton code