文件列表:

kospi_10.csv (204, 2016-12-23)
market_dqn.py (4716, 2016-12-23)
market_env.py (5060, 2016-12-23)
market_model_builder.py (3329, 2016-12-23)
market_pg.py (4813, 2016-12-23)
model_builder.py (687, 2016-12-23)
pg_over_top_10.png (14800, 2016-12-23)
sample_data (0, 2016-12-23)
sample_data\000660.csv (172130, 2016-12-23)
sample_data\005380.csv (178627, 2016-12-23)
sample_data\005930.csv (188535, 2016-12-23)
sample_data\005935.csv (182329, 2016-12-23)
sample_data\012330.csv (177509, 2016-12-23)
sample_data\015760.csv (172568, 2016-12-23)
sample_data\028260.csv (23544, 2016-12-23)
sample_data\032830.csv (82623, 2016-12-23)
sample_data\035420.csv (103260, 2016-12-23)
sample_data\090430.csv (130606, 2016-12-23)

# 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 [model filename] Train Policy Gradient: $ python market_pg.py [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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