benchmarking.py

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

# Define the ticker symbols for the stock and the benchmark index
stock_symbol = 'AAPL'
benchmark_symbol = '^GSPC'

# Fetch historical data for the past year
stock_data = yf.download(stock_symbol, start='2023-01-01', end='2024-01-01')
benchmark_data = yf.download(benchmark_symbol, start='2023-01-01', end='2024-01-01')

# Calculate daily returns
stock_data['Daily Return'] = stock_data['Adj Close'].pct_change()
benchmark_data['Daily Return'] = benchmark_data['Adj Close'].pct_change()

# Calculate cumulative returns
stock_data['Cumulative Return'] = (1 + stock_data['Daily Return']).cumprod()
benchmark_data['Cumulative Return'] = (1 + benchmark_data['Daily Return']).cumprod()

# Plot the cumulative returns
plt.figure(figsize=(14, 7))
plt.plot(stock_data['Cumulative Return'], label=f'{stock_symbol} Cumulative Return')
plt.plot(benchmark_data['Cumulative Return'], label=f'{benchmark_symbol} Cumulative Return')
plt.title('Stock vs. Benchmark Cumulative Returns')
plt.xlabel('Date')
plt.ylabel('Cumulative Return')
plt.legend()
plt.show()