Portfolio Analysis: Backtesting, LSTM Supervised Learning, and DQN Reinforcement Learning Strategies for Coca Cola Stocks
The requirements.txt file should list all Python libraries that your notebooks depend on, and they will be installed using:
- git clone https://github.com/badcoder-cloud/DQN-for-Portfolio-Managment
- pip install -r requirements.txt
The Automated Stock Trading System of Cocal Cola stock powerd by Deep Q Learning that aims automate decision-making processes for buying, selling, and holding stocks in dynamic financial markets.
The primary purpose of this project is to develop an intelligent trading system that adapts to changing market conditions. By utilizing Deep Q-Learning, the system learns optimal trading strategies based on historical data, technical indicators, and market trends. The goal is to enhance portfolio performance and provide users with a systematic approach to investment decision-making.
- Algorithmic Trading: Implement a Deep Q-Learning algorithm to autonomously make trading decisions by learning from historical market data.
- Risk Management: Develop strategies for risk assessment and management to minimize potential losses in various market scenarios.
- Market Adaptability: Enable the system to adapt to changing market conditions, incorporating real-time data for more accurate predictions.
- Portfolio Optimization: Optimize the composition of the stock portfolio to maximize returns while considering risk tolerance and investment goals.
- Backtesting and Evaluation: Implement robust backtesting mechanisms to evaluate the performance of the trading system using historical data.
- Continuous Learning: Implement mechanisms for continuous learning, enabling the system to adapt and improve its strategies over time.
Real-time market data integration. Dynamic portfolio rebalancing. Sentiment analysis for news and social media data. Machine learning models for predicting stock price movements.