AutomatedStockTrading-DeepQ-Learning
A Deep Q-learning reinforcement learning agent for automated stock trading using historical market data.
What is AutomatedStockTrading-DeepQ-Learning?
AutomatedStockTrading-DeepQ-Learning is a reinforcement learning project that implements a Deep Q-learning agent for algorithmic stock trading. It trains an AI model to make automated trading decisions (buy, sell, hold) based on historical stock market data, with the goal of maximizing portfolio value. The system processes real stock data, trains through multiple episodes, and evaluates performance on test datasets.
Data scientists, quantitative researchers, and machine learning enthusiasts interested in applying reinforcement learning to financial markets and algorithmic trading systems.
Provides a practical implementation of Deep Q-learning specifically adapted for stock trading, with reusable models and testing notebooks. It demonstrates how reinforcement learning can be applied to financial decision-making without requiring complex trading infrastructure.
Overview
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
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