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Stock Trading Market OpenAI Gym Environment with Deep Reinforcement Learning using Keras

Overview

This project provides a general environment for stock market trading simulation using OpenAI Gym. Training data is a close price of each day, which is downloaded from Google Finance, but you can apply any data if you want. Also, it contains simple Deep Q-learning and Policy Gradient from Karpathy's post.

In fact, the purpose of this project is not only providing a best RL solution for stock trading, but also building a general open environment for further research.
So, please, manipulate the model architecture and features to get your own better solution.

Requirements

Usage

Note that the most sample training data in this repo is Korean stock. You may need to re-download your own training data to fit your purpose.

After meet those requirements in above, you can begin the training both algorithms, Deep Q-learning and Policy Gradient.

Train Deep Q-learning:

$ python market_dqn.py <list filename> [model filename]

Train Policy Gradient:

$ python market_pg.py <list filename> [model filename]

For example, you can do like this:

$ python market_pg.py ./kospi_10.csv pg.h5

Aware that the provided neural network architecture in this repo is too small to learn. So, it may under-fitting if you try to learn every stock data. It just fitted for 10 to 100 stock data for a few years. (I checked!!)
Thus you need to re-design your own architecture and
let me know if you have better one!

Below is training curve for Top-10 KOSPI stock datas for 4 years using Policy Gradient.
Training Curve

To do

Reference

[1] Playing Atari with Deep Reinforcement Learning
[2] Deep Reinforcement Learning: Pong from Pixels
[3] KEras Reinforcement Learning gYM agents, KeRLym
[4] Keras plays catch, a single file Reinforcement Learning example