Deep Reinforcement Learning driven trading agent.

This project is an implementation of Q-learning applied to stock trading. The agent uses n-day windows of closing prices to determine the best action at a given time is to buy, sell or sit.

This is agent is good as predicting peaks and troughs in short-term and not very good at making decisions over long-term trends, but is quite good at predicting peaks and troughs.

Some of the results are shown below:

Total profit: $ 972.43

Total profit: $1243.72

Total profit: $1945.50

Total profit: -$178.58

setup the data folder and download historical dataset from Yahoo/finance into data folder

mkdir data

setup the model folder where we save our trained models, starting training with python train.py [stock_name] [window_size] [epochs]

mkdir model
python train.py ^GSPC 10 1000

setup plots folder where we save our evaluate plots, run evaluate script with command python evaulate.py [stock_name] [model_name]

mkdir plots
python evaluate.py ^GSPC_2018 model_ep500

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