Finance App

Overview

The Finance App is an interactive Streamlit-based web application that enables users to analyze stock performance, calculate key financial metrics, and optimize portfolio allocations. The app fetches real-time stock data from Yahoo Finance and provides intuitive visualizations for investment decision-making.

Features

Stock Data Analysis

• Multi-ticker Support: Analyze multiple stocks simultaneously with comma-separated inputs
• Flexible Date Range: Select custom date ranges for historical analysis
• Real-time Data: Fetch current stock data through Yahoo Finance API
• Price Visualization: Interactive charts showing price movements over time

Performance Metrics

• Sharpe Ratio: Calculate risk-adjusted returns to measure performance efficiency
• Sortino Ratio: Evaluate downside risk-adjusted performance
• Returns Analysis: Visualize and analyze stock return patterns
• Comparative Analysis: Compare performance across multiple securities

Portfolio Management

• Portfolio Optimization: Generate optimal portfolio weights (equal-weighted implementation)
• Weight Visualization: Bar charts showing allocation across different securities
• Data Export: Download stock data as CSV for further analysis
• Interactive Interface: User-friendly controls for customizing analysis parameters

Technical Implementation

Core Components

def fetch_data(tickers, start_date, end_date):
    data_frames = []
    for ticker in tickers:
        stock_data = yf.download(ticker, start=start_date, end=end_date)['Adj Close']
        data_frames.append(stock_data)
    
    data = pd.concat(data_frames, axis=1, keys=tickers)
    data.columns = tickers
    return data

The application fetches adjusted close prices using the Yahoo Finance API, processes the data with Pandas, and presents it through multiple interactive visualizations.

Performance Calculations

def calculate_sharpe_ratio(returns, risk_free_rate=0.02):
    portfolio_return = returns.mean().mean()
    portfolio_volatility = returns.std().mean()
    return (portfolio_return - risk_free_rate) / portfolio_volatility

def calculate_sortino_ratio(returns, risk_free_rate=0.02):
    portfolio_return = returns.mean().mean()
    downside_returns = returns[returns < 0]
    downside_deviation = downside_returns.std().mean()
    return (portfolio_return - risk_free_rate) / downside_deviation

These implementations calculate crucial risk-adjusted performance metrics that help users understand investment efficiency.

User Interface

The Streamlit interface provides:

• Text inputs for stock ticker entry
• Date selectors for time range customization
• Interactive charts for price and returns visualization
• Performance metrics display
• Portfolio weight visualization
• Data download capabilities

Technology Stack

• Streamlit: Powers the interactive web interface
• Pandas: Handles financial data processing
• yfinance: Provides stock market data access
• NumPy: Performs numerical operations
• Matplotlib: Creates financial charts and visualizations

Future Enhancements

Planned improvements include:

• Advanced portfolio optimization using Modern Portfolio Theory
• Additional performance metrics (Jensen’s Alpha, Information Ratio)
• Technical indicator integration (Moving Averages, RSI, MACD)
• Fundamental analysis using financial statement data
• Backtesting capabilities for trading strategies