DQN-Trading

심층 강화 학습을 통한 주식 거래 시장 OpenAI GYM 환경

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.

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