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.

• Python2.7 or higher
• Numpy
• HDF5
• Keras with Beckend (Theano or/and Tensorflow)
• OpenAI Gym

Note that the most sample training data in this repo is from Chinese stocks. You need to re-download your own training data depending on your problem.

If all the above requirements are satisfied you can begin training both the 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:

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

Please note that the provided neural network architecture in this repo is too small to learn. So, it may under-fit if you try to learn al stock data. It's just fitted for 10-100 stocks worth of data for a few years.This was the first project before I worked on the propreitary version for the fund I'm working with.
Thus you need to re-design your own architecture! Have fun with this one, and
let me know if you have better one!

Below is training curve for Top-10 SSE stock datas for 4 years using Policy Gradient. Clearly you need to train more!!

• Test environment to check overfitting.
• Elaborate the PG's train interface.

[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