This repository provides a reinforcement learning environment for algorithmic trading, specifically designed for single-instrument trading using historical bar data. It caters to researchers and developers building and testing trading strategies with RL agents.

How It Works

The environment simulates a trading scenario where an RL agent can interact with historical market data. It likely uses a state-action-reward loop common in RL, allowing agents to learn optimal trading policies by observing market conditions and executing buy, sell, or hold actions.

Quick Start & Requirements

• Primary install / run command: pip install gym-trading
• Prerequisites: Python 3.6+, OpenAI Gym.
• Links: Jupyter Notebook

Highlighted Details

• Designed for single-instrument trading.
• Includes a basic usage example with a policy gradient strategy using TensorFlow.

Maintenance & Community

• No specific contributors, sponsorships, or community links (Discord/Slack, roadmap) are mentioned in the README.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

The environment is focused on single-instrument trading and the provided example strategy is illustrative, not guaranteed to be profitable. The lack of explicit licensing information may pose compatibility concerns for commercial use.