Q-Learning in short-swing trading
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.
How to run
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
References
Deep Q-Learning with Keras and Gym - Q-learning overview and Agent skeleton code