BSc IT Project Guide: Portfolio Optimization
1. Introduction
Portfolio Optimization is a financial application that helps investors maximize returns and minimize risks by recommending optimized stock portfolios based on historical data, risk appetite, and investment goals. This project uses statistical models and optimization algorithms like the Modern Portfolio Theory (MPT), Sharpe Ratio, and machine learning techniques for recommendation.
2. Objective
The main objective is to design a system that suggests an optimized portfolio of stocks, balancing risk and return. The tool will help users make informed investment decisions based on their risk tolerance and investment horizon.
3. Tools and Technologies
- Python
- Pandas, NumPy, Matplotlib, Seaborn
- Scikit-learn, CVXPY, PyPortfolioOpt
- Yahoo Finance API / Alpha Vantage API
- Jupyter Notebook or Streamlit (for UI)
4. System Features
- Fetch historical stock data from online APIs
- Calculate returns, volatility, and Sharpe ratio
- Perform optimization using MPT or LSTM
- Visualize the efficient frontier
- Recommend portfolio allocations based on constraints
5. Methodology
1. Data Collection: Pull historical stock data using APIs.
2. Data Processing: Clean, normalize, and calculate returns.
3. Modeling: Apply Modern Portfolio Theory and use risk-return metrics.
4. Optimization: Use mathematical optimization libraries to compute ideal
allocations.
5. Visualization: Use plots for risk-return scatter and efficient frontier.
6. Deployment: Host the system using Streamlit or Flask for user interaction.
6. Expected Output
The user will receive:
- A visualized efficient frontier.
- Portfolio allocations with expected return and risk metrics.
- Option to rebalance based on market updates.
7. Future Scope
- Integrate real-time stock market data.
- Use reinforcement learning for dynamic portfolio adjustment.
- Include multi-asset class optimization.