A reinforcement learning environment for training trading agents using real Korean equities orderbook and execution data.
Trading Gym is a reinforcement learning environment specifically designed for training algorithmic trading agents. It provides a simulated market environment using real historical orderbook and execution data from Korean equities markets, allowing agents to learn short-term trading strategies through interaction with realistic market conditions.
Quantitative researchers, algorithmic trading developers, and data scientists who want to train reinforcement learning agents for financial markets using real market data.
Developers choose Trading Gym because it provides a standardized OpenAI Gym-compatible interface specifically for trading, uses real market data for realistic simulations, and simplifies the process of training and comparing trading algorithms.
This trading-gym is the first trading for agent to train with episode of short term trading itself.
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Follows the OpenAI Gym API with reset(), step(), and render() methods, ensuring seamless integration with existing reinforcement learning frameworks and libraries.
Uses actual Korean equities orderbook and execution data at tick-level, providing an authentic environment for training trading agents based on historical market conditions.
Allows configuration of episode types, profit goals, stop-loss percentages, and durations, enabling flexible and tailored reinforcement learning scenarios.
Easy installation via git clone and straightforward Python integration, as demonstrated in the quick run example, reducing initial setup complexity.
Specifically designed for Korean equities data in strict formats, making it impractical for other markets without significant data preparation and adaptation efforts.
The project only provides the environment; users must develop their own reinforcement learning agents from scratch, which increases development time and expertise required.
As noted in future plans, it lacks proper pip packaging and may have structural issues, making it less polished for production use compared to more mature libraries.
The README has typos and sparse details, with few practical examples beyond basic usage, which could hinder adoption for developers new to trading or RL.