Amey-Thakur/OPTIMIZING-STOCK-TRADING-STRATEGY-WITH-REINFORCEMENT-LEARNING
Machine Learning Project to Optimize Stock Trading Strategies Using Reinforcement Learning.
This project helps individual traders and financial analysts develop and test automated stock trading strategies. It takes historical stock price data and applies a Q-Learning algorithm to recommend 'Buy', 'Sell', or 'Hold' decisions. The output is an optimized trading strategy aimed at maximizing portfolio returns, which can be visualized through an interactive dashboard.
Use this if you are a trader or financial enthusiast looking to explore how machine learning can automate and optimize your stock trading decisions using historical data.
Not ideal if you require a high-frequency trading system, real-time market prediction, or a strategy that incorporates complex macroeconomic factors beyond simple price movements.
Stars
49
Forks
17
Language
Python
License
MIT
Category
Last pushed
Feb 21, 2026
Commits (30d)
0
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