portfolio-diversification.py

import yfinance as yf
import pandas as pd
import numpy as np

# Define the tickers of the stocks in the portfolio
tickers = ['AAPL', 'MSFT', 'AMZN']

# Fetch historical data for these stocks
data = yf.download(tickers, start='2023-01-01', end='2024-01-01')['Adj Close']

# Calculate daily returns
daily_returns = data.pct_change().dropna()

# Define weights for the stocks in the portfolio
weights = np.array([0.4, 0.3, 0.3])  # For example, 40% AAPL, 30% MSFT, 30% AMZN

# Calculate portfolio daily returns
portfolio_daily_returns = daily_returns.dot(weights)

# Portfolio variance
portfolio_variance = np.dot(weights.T, np.dot(daily_returns.cov() * 252, weights))

# Portfolio standard deviation (volatility)
portfolio_std_dev = np.sqrt(portfolio_variance)

# Correlation matrix of the returns
correlation_matrix = daily_returns.corr()

print(f"Portfolio Variance: {portfolio_variance}")
print(f"Portfolio Standard Deviation (Volatility): {portfolio_std_dev}")
print("Correlation Matrix of the Returns:")
print(correlation_matrix)