The Portfolio Management System leverages machine learning to forecast stock prices, addressing the economic uncertainties faced by individual investors. This innovative tool aims to simplify the complexities of the stock market, making investment more accessible and informed.
- Stock Price Prediction: Utilizes advanced algorithms to provide accurate estimates of stock prices.
- Market Trends Visualization: Offers real-time insights into the stock market through detailed charts and graphs.
- Reliable Data Collection: Ensures consistent retrieval of accurate data from trusted sources.
- User-Friendly Interface: Designed for ease of use, helping new investors understand and navigate the stock market effectively.
The system is composed of four key components:
- User: The primary actor who interacts with the system.
- Portfolio: A customizable collection of stocks selected by the user.
- Company: The various entities whose stocks are available for investment.
- Company Data: Date-specific data for each company, crucial for analysis and predictions.
- Python: The primary programming language for backend development.
- MySQL: Used for database management and storage.
- Data Analysis Libraries: Includes NumPy, Pandas, and Matplotlib.
- Machine Learning: Employs Keras library with LSTM RNN for predictive analytics.
- Frontend: Implemented using Streamlit for an interactive user experience.
The system is designed to store and manage data efficiently, allowing users to create and edit their portfolios, monitor stock performance, and access detailed company information. The use of neural networks, specifically LSTM RNN, ensures sophisticated data processing and accurate stock market predictions.
For more information on installation, usage, and contribution, please refer to the subsequent sections of this README.