Deep-Reinforcement-Learning-in-Trading
Basic Information
This repository provides code and resources to implement and experiment with deep reinforcement learning applied to trading. It contains an RL-based trading agent and a dedicated trading environment intended to let developers and researchers train and evaluate policies in simulated market settings. The project is aimed at people who want a codebase to explore algorithmic trading strategies using reinforcement learning techniques, to prototype agents that learn from market observations, and to study agent performance within a controlled environment. Documentation and code organization in the repository help users set up experiments and understand the architecture.
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App Details
Features
Provides an implementation of a reinforcement learning trading agent alongside a companion trading environment designed for simulation-based experimentation. The repository consolidates core components required to connect an agent to market-like observations and actions and to perform training and evaluation workflows. It emphasizes a reusable agent-environment scaffold suitable for extending with custom observation spaces, action schemes, or reward functions. The README and repository structure act as the primary guide to available code and how to run or adapt the examples included in the project.
Use Cases
This project helps researchers, data scientists, and developers learn and prototype the application of deep reinforcement learning to algorithmic trading by supplying an agent and environment scaffold that reduces setup overhead. Users can focus on algorithm design, reward engineering, and performance assessment in simulated markets rather than building infrastructure from scratch. The codebase can serve as an instructional example for understanding agent-environment interaction in trading contexts and as a foundation for customization, experimentation, or integration with external market data sources.
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