NSE Nifty 50 Portfolio Optimization using Modern Portfolio Theory

📊 Project Overview

This project implements a comprehensive data-driven stock portfolio optimization model using Modern Portfolio Theory (MPT) specifically designed for the Indian stock market (NSE). The system determines the most efficient allocation of capital across Nifty 50 companies to maximize risk-adjusted returns measured by the Sharpe Ratio[1].

🎯 Key Features

• Real-time NSE Data Integration: Automated extraction of historical stock data for all Nifty 50 companies using multiple data sources
• Advanced Portfolio Optimization: Implementation of mean-variance optimization with SLSQP algorithm for constrained optimization[2]
• Monte Carlo Simulation: Generation of 15,000+ random portfolios to visualize the efficient frontier
• Multi-objective Optimization:
  • Maximum Sharpe Ratio optimization
  • Minimum Variance optimization
  • Risk-constrained optimization
• Comprehensive Risk Analysis: Correlation matrices, beta calculations, and diversification metrics
• Professional Visualizations: Interactive plots showing efficient frontier, portfolio allocations, and risk-return profiles
• Automated Reporting: Export results to multiple CSV files and Excel workbooks

🚀 Technical Implementation

Core Algorithms

• Optimization Engine: Sequential Least Squares Programming (SLSQP) for constrained optimization
• Risk Modeling: Covariance matrix calculations with annualized volatility measures
• Performance Metrics: Sharpe ratio, Treynor ratio, and risk-adjusted return calculations
• Efficient Frontier: Quadratic programming for optimal risk-return combinations

📚 Research & Methodology

• Data Period: 4+ years of historical data (2020-2024)
• Frequency: Daily price data with 252 trading days annualization
• Risk Model: Full covariance matrix with correlation analysis
• Constraints: Long-only portfolio with concentration limits
• Validation: Monte Carlo simulation with 15,000+ scenarios

🤝 Contributing

Contributions are welcome! Please feel free to submit pull requests for:

• Additional optimization algorithms (Black-Litterman, Risk Parity)
• Enhanced data sources and real-time feeds
• Advanced risk models (GARCH, Factor models)
• Machine learning integration
• Performance attribution analysis

🏷️ Tags

#PortfolioOptimization #ModernPortfolioTheory #NSEIndia #QuantitativeFinance #Python #AlgorithmicTrading #RiskManagement #DataScience #FinTech #InvestmentStrategy