Benchmark Analytics Engine

A comprehensive desktop application for stock portfolio analysis and benchmark comparison. This project built a robust financial applications with proper error handling, data processing, and quantitative analysis capabilities.

🛠️ Setup and Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation Steps

1. Clone the repository

   git clone <repository-url>

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   python main.py

Dependencies

yfinance==0.2.28      # Yahoo Finance API wrapper
pandas==2.1.4         # Data manipulation and analysis
numpy==1.24.3         # Numerical computing
matplotlib==3.8.2     # Data visualization
tkinter-tooltip==2.1.0 # GUI enhancements

📖 How to Use

Getting Started

1. Launch the Application

   python main.py

2. Basic Analysis (Equal Weights)

   • Enter stock symbols: AAPL,MSFT,GOOGL,AMZN,META
   • Select benchmark: "S&P 500"
   • Choose period: "1 Year"
   • Click "Analyze Portfolio"

3. Custom Portfolio Weights

   • Enter your symbols
   • In the "Portfolio Weights" section, specify percentages for each stock
   • Ensure total equals 100%
   • Click "Analyze Portfolio"

Step 1: Enter Portfolio Symbols

• Manual Entry: Type stock symbols separated by commas

  Example: AAPL,MSFT,GOOGL,AMZN,META,NVDA
  

• Quick Options: Use pre-configured sample portfolios
  • Tech Growth: AAPL,MSFT,GOOGL,AMZN,META
  • Value Stocks: BRK-B,JNJ,PG,KO,WMT
  • Dividend Aristocrats: JNJ,PG,KO,WMT,HD
  • Financial: JPM,BAC,WFC,GS,MS
  • Healthcare: JNJ,PFE,UNH,ABBV,TMO

Step 2: Configure Portfolio Weights (Optional)

• Equal Weights: Click "Set Equal Weights" for automatic distribution
• Custom Weights: Enter percentages for each stock

  Example:
  AAPL: 30%
  MSFT: 25%
  GOOGL: 20%
  AMZN: 15%
  META: 10%
  Total: 100% ✓
  

• Weight Validation:
  • Green: Valid weights (100% total)
  • Red: Invalid weights (≠ 100% total)
  • Blue: No weights set (uses equal weighting)

Step 3: Analysis Settings

• Benchmark Selection:

  • S&P 500 (SPY): Large-cap US stocks
  • Russell 2000 (IWM): Small-cap US stocks
  • NASDAQ 100 (QQQ): Technology-heavy index
  • MSCI World (URTH): Global developed markets
  • Emerging Markets (EEM): International growth markets

• Time Periods:

  • 1 Month: Short-term analysis
  • 3 Months: Quarterly analysis
  • 6 Months: Semi-annual analysis
  • 1 Year: Annual analysis (recommended)
  • 2 Years: Medium-term analysis
  • 5 Years: Long-term analysis

Step 4: View Results

• Summary Tab: Key metrics and portfolio weights
• Detailed Metrics Tab: Comprehensive financial calculations
• Risk Analysis Tab: Risk-specific metrics and analysis
• Charts Tab: Performance, risk-return, and rolling metrics

🚀 Features

• Portfolio Analysis: Compare custom portfolios against market benchmarks
• Real-time Data: Fetch live market data from Yahoo Finance API
• Comprehensive Metrics: Calculate 15+ key financial performance indicators
• Interactive Charts: Generate professional-grade visualizations
• Risk Assessment: Advanced risk metrics including VaR and maximum drawdown
• Robust Error Handling: Retry logic, data validation, graceful failure recovery
• Data Export: Save analysis results and charts for reporting

📊 Financial Metrics

Performance Metrics

• Alpha (Jensen's Alpha): Excess return beyond CAPM prediction
• Beta: Market sensitivity and systematic risk
• Sharpe Ratio: Risk-adjusted return measurement
• Information Ratio: Active management skill assessment
• R-Squared: Correlation with benchmark

Risk Metrics

• Volatility: Annualized standard deviation of returns
• Maximum Drawdown: Largest peak-to-trough decline
• Value at Risk (VaR): Potential loss at confidence levels (95%, 99%)
• Tracking Error: Deviation from benchmark performance

Advanced Analytics

• Up/Down Capture Ratios: Asymmetric market performance
• Calmar Ratio: Return per unit of maximum risk
• Rolling Metrics: Time-varying performance analysis
• Correlation Analysis: Portfolio diversification assessment

📈 Financial Calculations

Returns Calculation

# Daily returns using percentage change
daily_returns = prices.pct_change().dropna()

# Cumulative returns with compounding
cumulative_returns = (1 + daily_returns).cumprod() - 1

# Portfolio returns (equal-weighted or custom-weighted)
portfolio_returns = returns.mean(axis=1)  # Equal-weighted
# OR
portfolio_returns = (returns * weights).sum(axis=1)  # Custom-weighted

Risk Metrics

# Annualized volatility
volatility = returns.std() * sqrt(252)

# Maximum drawdown
running_max = cumulative_returns.cummax()
drawdown = (cumulative_returns / running_max) - 1
max_drawdown = drawdown.min()

# Value at Risk (95%)
var_95 = returns.quantile(0.05)

Performance Metrics

# Beta calculation
covariance = np.cov(portfolio_returns, market_returns)[0,1]
variance = np.var(market_returns)
beta = covariance / variance

# Sharpe ratio
excess_return = portfolio_return - risk_free_rate
sharpe_ratio = excess_return / portfolio_volatility

# Alpha (Jensen's Alpha)
alpha = portfolio_return - (risk_free_rate + beta * (market_return - risk_free_rate))

Demo