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Stock Trading using Reinforcement Learning

Playing with actor critic deep reinforcement learning models for automating and optimizing stock trading strategies to maximize profit in a custom OpenAI gym. We will use pretrained models using the stable_baselines library (A2C, PPO2, TRPO) and a custom DDPG model in Keras (buggy)

Project Structure

  • Stock Trading with RL.ipynb: Jupyter Notebook for interacting with the different components
  • env.py: StockTradingEnv OpenAI gym environment, where we define the observation space, agent actions (BUY, SELL, HOLD and percentage of shares (continuous action space)).
  • graph.py: Used to render live trades from the agent
  • agent.py: Implementation of a DDPG (Deep Deterministic Policy Gradient) RL agent.
  • models.py: Contains the Actor and Critic models used by the DDPG agent (actor maps states to actions, critic returns Q value of the state action mapping)
  • utils.py: Utility functions used by the project