risk-analysis.py

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

# Fetch historical data for AAPL and MSFT
tickers = ['AAPL', 'MSFT']
data = yf.download(tickers, start='2022-01-01', end='2023-01-01')['Adj Close']

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

# Define weights of AAPL and MSFT in the portfolio
weights = np.array([0.5, 0.5])  # 50% AAPL, 50% MSFT

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

# Calculate the 95% VaR using the historical method
VaR_95 = np.percentile(portfolio_returns, 5)
print(f"95% Value at Risk (VaR): {VaR_95*100:.2f}%")

# Calculate Conditional Value at Risk (CVaR) at the 95% level
CVaR_95 = portfolio_returns[portfolio_returns <= VaR_95].mean()
print(f"95% Conditional Value at Risk (CVaR): {CVaR_95*100:.2f}%")