Deep-Reinforcement-Stock-Trading
A modular deep reinforcement learning framework for portfolio management, enabling algorithmic stock trading with DQN and DDPG agents.
What is Deep-Reinforcement-Stock-Trading?
Deep-Reinforcement-Stock-Trading is a Python framework that applies deep reinforcement learning to stock trading and portfolio management. It enables developers and researchers to train agents using algorithms like DQN and DDPG to make trading decisions based on historical price data. The framework addresses the challenge of directly mapping market observations to actions (buy, hold, sell) rather than predicting prices.
Researchers and developers interested in applying reinforcement learning to financial markets, algorithmic trading, or portfolio optimization. It's suitable for those with a background in machine learning who want to experiment with DRL in trading scenarios.
It offers a modular and extensible structure that simplifies implementing and testing DRL algorithms for trading, with built-in evaluation and visualization tools. Unlike price prediction models, it directly outputs trading actions, providing an end-to-end approach to automated portfolio management.
Overview
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
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