BSc IT Project Guide: Stock Trend Classification
1. Introduction
The 'Stock Trend Classification' project involves using machine learning techniques to classify stock market trends as bullish or bearish. This project helps investors and analysts make informed decisions by analyzing historical stock data and identifying trend patterns.
2. Objective
To develop a system that classifies stock market trends based on historical stock data using machine learning models such as Logistic Regression, Support Vector Machines, or Neural Networks.
3. Scope
- Use historical stock price data from sources like Yahoo
Finance
- Preprocess data to extract features such as moving averages, RSI, MACD, etc.
- Train ML models to classify the market trend
- Evaluate performance using metrics such as accuracy, precision, and recall
- Create a simple dashboard to visualize predictions
4. Tools and Technologies
- Programming Language: Python
- Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn
- Data Source: Yahoo Finance API
- IDE: Jupyter Notebook, VS Code
5. System Architecture
The architecture includes data collection, preprocessing, feature extraction, model training and evaluation, and trend classification output.
6. Methodology
1. Collect historical stock data.
2. Clean and preprocess data.
3. Extract technical indicators as features.
4. Label data for trends (e.g., up or down).
5. Split data into training and testing sets.
6. Train ML models (e.g., SVM, Logistic Regression).
7. Evaluate and fine-tune models.
8. Visualize and interpret results.
7. Expected Outcomes
- A functional model that classifies stock trends accurately
- Performance metrics reports
- A visualization dashboard for stock trends
8. Future Enhancements
- Incorporate sentiment analysis of financial news
- Use deep learning techniques like LSTM
- Extend to multi-class classification (bullish, bearish, stagnant)