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This project provides a general environment for stock market trading simulation usingOpenAI 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 fromKarpathy'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.

• 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 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.

• 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