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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.
| Date | Stars |
|---|---|
| 2026-07-31 | 795 |
| 2026-08-06 | 795 |
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0.0
growth rate 0.00%/day
# 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](https://gym.openai.com/).
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](http://karpathy.github.io/2016/05/31/rl/).
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
- Python2.7 or higher
- Numpy
- HDF5
- Keras with Beckend (Theano or/and Tensorflow)
- OpenAI Gym
## 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.
## To do
- Test environment to check overfitting.
- Elaborate the PG's train interface.
## Reference
[1] [Playing Atari with Deep Reinforcement Learning](http://arxiv.org/abs/1312.5602)
[2] [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)
[3] [KEras Reinforcement Learning gYM agents, KeRLym](https://github.com/osh/kerlym)
[4] [Keras plays catch, a single file Reinforcement Learning example](http://edersantana.github.io/articles/keras_rl/)
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matched fp:ad5e173b4556e658, name:reinforcement learning, desc:policy gradient, desc:q-learning