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

stock='NVDA'
sector='SMH'
lookback='365d'

s= yf.download(stock, period=lookback, progress=False)
se= yf.download(sector, period=lookback, progress=False)

s['SMA20']=s['Close'].rolling(20).mean()
s['SMA50']=s['Close'].rolling(50).mean()
s['SMA200']=s['Close'].rolling(200).mean()
s['Ret']=s['Close'].pct_change()
s['Vol20']=s['Ret'].rolling(20).std()*np.sqrt(252)

merged=pd.merge(s[['Close','Ret']], se[['Close']], left_index=True, right_index=True, suffixes=('','_etf'))
merged['Ret_etf']=merged['Close_etf'].pct_change()
merged['Corr']=merged['Ret'].rolling(60).corr(merged['Ret_etf'])

# plots
plt.figure(figsize=(12,6))
plt.plot(s['Close'],label='Close')
plt.plot(s['SMA20'],label='SMA20')
plt.plot(s['SMA50'],label='SMA50')
plt.plot(s['SMA200'],label='SMA200')
plt.legend()
plt.title('NVDA price & moving averages')
plt.savefig('nvda_ma.png')

plt.figure(figsize=(12,4))
plt.plot(s['Vol20'],label='20‑Day Vol (annualized)')
plt.legend()
plt.title('NVDA volatility')
plt.savefig('nvda_vol.png')

plt.figure(figsize=(12,4))
plt.plot(merged['Corr'],label='60‑Day Correlation with SMH')
plt.legend()
plt.title('NVDA vs SMH correlation')
plt.savefig('nvda_corr.png')

print('Done')