llSourcell / Reinforcement_learning_for_stock_prediction

Overview

This is the code for this video on Youtube by Siraj Raval. The author of this code is edwardhdlu . It's 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.

Results

Some examples of results on test sets:

!^GSPC 2015 S&P 500, 2015. Profit of $431.04.

Alibaba Group Holding Ltd, 2015. Loss of $351.59.

Apple, Inc, 2016. Profit of $162.73.

Google, Inc, August 2017. Profit of $19.37.

Running the Code

To train the model, download a training and test csv files from Yahoo! Finance into data/

mkdir model
python train ^GSPC 10 1000

Then when training finishes (minimum 200 episodes for results):

python evaluate.py ^GSPC_2011 model_ep1000

References

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