This project provides a custom OpenAI Gym environment for simulating stock trading using historical price data. It is designed for researchers and developers interested in applying reinforcement learning techniques to financial markets. The environment allows for training trading agents and evaluating their performance on unseen data.

How It Works

The environment simulates a stock market by loading historical price data. Agents interact with the environment by taking actions (buy, sell, hold) and receive observations (current price, portfolio status) and rewards (profit/loss). This approach allows for standardized testing and comparison of different reinforcement learning algorithms in a simulated trading context.

Quick Start & Requirements

• Install via pip: pip install stock-trading-environment
• Requires Python 3.6+ and OpenAI Gym.
• Official documentation and a Medium article explaining custom Gym environment creation are available: https://medium.com/@adamjking3/creating-a-custom-openai-gym-environment-for-stock-trading-be532be3910e

Highlighted Details

• Custom OpenAI Gym environment for stock trading simulation.
• Utilizes historical price data for training and evaluation.
• Facilitates reinforcement learning agent development for financial markets.

Maintenance & Community

• The project appears to be maintained by a single author, notadamking.
• No community links (Discord, Slack) or roadmap are provided in the README.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

The project's lack of a specified license raises concerns about commercial use and redistribution. The absence of community channels or a roadmap suggests limited ongoing development or support.