BSc IT Project Guide: Anomaly Detection in Stocks
Objective
To develop a system that detects anomalies in stock market data using machine learning techniques, helping identify unusual market behavior indicative of fraud, crises, or other significant events.
Tools and Technologies
- Programming Language: Python
- Libraries: Scikit-learn, Pandas, Numpy, Matplotlib, Seaborn
- Machine Learning Models: Isolation Forest, One-Class SVM, Autoencoders
- Data Sources: Yahoo Finance, Alpha Vantage API, Kaggle Datasets
- IDE: Jupyter Notebook / VS Code
Modules
1. Data Collection and Preprocessing
2. Feature Engineering
3. Model Selection and Training
4. Anomaly Detection and Interpretation
5. Visualization Dashboard
6. Report Generation
Methodology
1. Collect historical stock market data from reliable
sources.
2. Preprocess the data (handling missing values, normalization, etc.).
3. Engineer features to highlight trends and patterns.
4. Use anomaly detection algorithms like Isolation Forest or Autoencoders.
5. Visualize results to show anomalies and explain the model’s decision.
6. Evaluate the system with backtesting against known market anomalies.
Expected Outcome
A system that can highlight potential anomalous behavior in stock data, serving as a tool for analysts to identify potential fraud, market manipulation, or early signs of financial crises.