This example applies the deep Q-network method to train an agent that will trade a single share to maximize profit. The goal is to demonstrate how to go beyond the Atari games and apply RL to a different practical domain. Based on the setup in chapter 8 of [1], we use one-minute historical share price intervals, and then apply a Double DQN architecture to accommodate a simple set of discrete trading actions: do nothing, buy a single share, and close the position. The customized environment is constructed using Open AI Gym and the RL agents are trained using Amazon SageMaker.
[1] Maxim Lapan. "Deep Reinforcement Learning Hands-On." Packt (2018).
rl_stock_trading_coach_customEnv.ipynb: notebook used for training stock trading agent.src/datasets/YNDX_160101_161231.csv: source data. See notebook for license.config.py: configurations including data selection, data directory.data.py: data functions.trading_env.py: custom environments and simulator.train-coach.py: launcher for coach training.evaluate-coach.py: launcher for coach evaluation.preset-stock-trading-ddqn.py: coach preset for Double DQN.
This notebook is for educational purposes only. Past trading performance does not guarantee future performance. The loss in trading can be substantial, and therefore investors should use all trading strategies at their own risk.