Risk-Metrics-Alpha-Beta-Sharpe-Ratio-Volatility-

This Python script allows users to analyze the performance of a stock portfolio relative to a market index. The analysis includes calculating key financial metrics such as Alpha, Beta, Sharpe Ratio, Volatility, and portfolio-specific metrics like weighted returns and portfolio volatility.

The data is fetched from Yahoo Finance using the yfinance library, and the script offers a comprehensive comparison of the stocks' and portfolio's performance relative to an index over a user-defined date range.

Features

• Fetches stock data and calculates daily returns.
• Calculates Alpha and Beta of individual stocks against a chosen index.
• Computes Sharpe Ratio and Volatility for both individual stocks and portfolio.
• Supports portfolio analysis, including weighted average returns and portfolio volatility.
• Generates visualizations comparing the Volatility and Sharpe Ratio of each stock and index.
• Interactive inputs for stock tickers, index tickers, date range, and portfolio weights.

Requirements

To run this script, you will need the following Python libraries:

• yfinance: For fetching financial data from Yahoo Finance.
• numpy: For numerical calculations.
• pandas: For data manipulation and analysis.
• matplotlib: For generating visualizations.

You can install these libraries using pip:

pip install yfinance numpy pandas matplotlib

Script Description

1. fetch_data(tickers, start_date, end_date)

This function fetches historical data for the given stock tickers from Yahoo Finance. It also calculates daily percentage returns for the stocks by using the adjusted closing price.

Parameters:

• tickers: A list of stock tickers (e.g., ['AAPL', 'MSFT']).
• start_date: The start date for data fetching (e.g., '2020-01-01').
• end_date: The end date for data fetching (e.g., '2020-12-31').

Returns:

• close_data: A DataFrame containing the 'Close' prices for the stocks.
• returns: A DataFrame containing the daily returns for the stocks.

2. calculate_alpha_beta(stock_returns, index_returns)

This function calculates the Alpha and Beta of a stock relative to a market index.

Parameters:

• stock_returns: The daily returns of the stock.
• index_returns: The daily returns of the market index.

Returns:

• alpha: The excess return of the stock compared to the index.
• beta: The stock's sensitivity to market movements (volatility relative to the index).

3. calculate_sharpe_volatility(returns, risk_free_rate=0.01)

This function calculates the Sharpe Ratio and Volatility for a stock or portfolio.

Parameters:

• returns: A DataFrame containing the daily returns of a stock or portfolio.
• risk_free_rate: The risk-free rate (default is 0.01, which represents 1%).

Returns:

• sharpe_ratio: A measure of the risk-adjusted return of the stock or portfolio.
• volatility: The standard deviation of the returns, annualized by multiplying by the square root of 252 (number of trading days in a year).

4. analyze_stocks(stocks, index, start_date, end_date, user_weights)

This is the main function that performs the stock and portfolio analysis. It fetches data, calculates financial metrics for each stock, and computes portfolio performance metrics based on user-defined weights.

Parameters:

• stocks: A list of stock tickers to analyze (e.g., ['AAPL', 'MSFT']).
• index: The market index ticker (e.g., '^GSPC' for the S&P 500).
• start_date: The start date for data fetching (e.g., '2020-01-01').
• end_date: The end date for data fetching (e.g., '2020-12-31').
• user_weights: A list or array of portfolio weights corresponding to each stock.

Returns:

• results_df: A DataFrame containing the Alpha, Beta, Sharpe Ratio, and Volatility for each stock and the index, along with the portfolio performance metrics.

5. User Interaction

The script will prompt users for the following inputs:

• Stock Tickers: Comma-separated list of stock symbols (e.g., AAPL, MSFT, TSLA).
• Index Ticker: The ticker symbol of the market index (e.g., ^GSPC for the S&P 500).
• Start Date: The start date in YYYY-MM-DD format (e.g., '2020-01-01').
• End Date: The end date in YYYY-MM-DD format (e.g., '2020-12-31').
• Portfolio Weights: A comma-separated list of portfolio weights corresponding to each stock (e.g., 0.4, 0.3, 0.3).

Example Input

Enter stock tickers separated by commas (e.g., AAPL, MSFT, TSLA): AAPL, MSFT, TSLA
Enter the index ticker (e.g., ^GSPC for S&P 500): ^GSPC
Enter start date (YYYY-MM-DD): 2020-01-01
Enter end date (YYYY-MM-DD): 2020-12-31
Enter the weights for the stocks separated by commas (e.g., 0.4, 0.3, 0.3 for AAPL, MSFT, TSLA): 0.4, 0.3, 0.3

Visualizations

• A bar chart comparing the Volatility and Sharpe Ratio for each stock and the index will be displayed.

How to Use

1. Clone or download this repository to your local machine.
2. Install the required dependencies:

   pip install yfinance numpy pandas matplotlib

3. Run the script:

   python stock_analysis.py

4. Follow the on-screen prompts to input stock tickers, index ticker, date range, and portfolio weights.
5. View the performance metrics in the terminal and the visualizations.

Example Output

Terminal Output

Stock and Index Performance Metrics:
           Alpha     Beta  Sharpe Ratio  Volatility
Index     0.0000    1.0000       0.8251      0.1875
AAPL      0.0105    1.2456       1.0512      0.2993
MSFT      0.0148    1.0802       1.1754      0.2654
TSLA      0.0123    2.2345       1.9023      0.6487

Portfolio Performance Metrics:
Portfolio Sharpe Ratio: 1.2113
Portfolio Volatility: 0.3225

Visualization

A bar chart will be displayed, comparing Volatility and Sharpe Ratio for each stock and the index.

Contributing

Feel free to fork the repository, make changes, and submit pull requests. Contributions are welcome!

Acknowledgements

• Data provided by Yahoo Finance.
• yfinance library used for downloading stock data.

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