What is Gym Trading Env?

Gym Trading Env is an innovative environment designed for simulating stock trading and training Reinforcement Learning (RL) agents. It sets out to be fast and customizable, making the implementation of various RL trading algorithms a breeze.

Key Features

This package aims to greatly simplify the research phase by offering:

• Easy and quick download of technical data from multiple exchanges.
• A simple yet powerful environment for both users and AI, capable of supporting complex operations like short selling and margin trading.
• High-performance rendering, allowing the visualization of hundreds of thousands of candles simultaneously, tailored to demonstrate the agent’s actions and results.
• (Coming soon) A user-friendly method for backtesting any RL agents or strategies.

Installation

Gym Trading Env is compatible with Python 3.9 and higher and works on Windows, Mac, and Linux. To get started, you can install it using pip:

pip install gym-trading-env

Alternatively, you can clone the repository using git:

git clone https://github.com/ClementPerroud/Gym-Trading-Env

For comprehensive guidance, check the documentation.

Understanding Gym Trading Env with an Analogy

Imagine Gym Trading Env as a high-tech training ground for aspiring traders, much like a sophisticated video game arena. In this arena, each stock is a character with unique attributes and abilities (like volatility and historical performance), and you, as the player, control a nimble trading agent that has the tools and strategies to outsmart the competition.

Just like in a game, you can quickly download the stats of various characters (stocks) from different realms (exchanges) and test your skills in fast-paced scenarios (complex trading operations). The performance rendering in Gym Trading Env reflects the game graphics: you can see not just your agent’s movements, but the entire battlefield of candles (price movements) flickering before you, helping you analyze your decisions and outcomes in real-time.

Troubleshooting Tips

If you encounter any issues during installation or running the Gym Trading Env, consider the following troubleshooting suggestions:

• Ensure you are using Python version 3.9 or higher; older versions may lead to compatibility issues.
• Check your internet connection, as the package requires downloading external data.
• If pip does not recognize the installation command, ensure that it is correctly installed and added to your PATH.

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