49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
import yfinance as yf
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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stock='NVDA'
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sector='SMH'
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lookback='365d'
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# Download
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s= yf.download(stock, period=lookback, progress=False)
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se= yf.download(sector, period=lookback, progress=False)
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s['SMA20']=s['Close'].rolling(20).mean()
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s['SMA50']=s['Close'].rolling(50).mean()
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s['SMA200']=s['Close'].rolling(200).mean()
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s['Ret']=s['Close'].pct_change()
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s['Vol20']=s['Ret'].rolling(20).std()*np.sqrt(252)
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# correlation
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merged=pd.merge(s[['Close']], se[['Close']], left_index=True, right_index=True, suffixes=('','_etf'))
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merged['Ret']=merged['Close'].pct_change()
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merged['Ret_etf']=merged['Close_etf'].pct_change()
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merged['Corr']=merged['Ret'].rolling(60).corr(merged['Ret_etf'])
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# plots
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plt.figure(figsize=(12,6))
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plt.plot(s['Close'],label='Close')
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plt.plot(s['SMA20'],label='SMA20')
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plt.plot(s['SMA50'],label='SMA50')
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plt.plot(s['SMA200'],label='SMA200')
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plt.legend()
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plt.title('NVDA price & moving averages')
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plt.savefig('nvda_ma.png')
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plt.figure(figsize=(12,4))
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plt.plot(s['Vol20'],label='20‑Day Vol (annualized)')
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plt.legend()
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plt.title('NVDA volatility')
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plt.savefig('nvda_vol.png')
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plt.figure(figsize=(12,4))
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plt.plot(merged['Corr'],label='60‑Day Correlation with SMH')
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plt.legend()
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plt.title('NVDA vs SMH correlation')
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plt.savefig('nvda_corr.png')
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print('Done')
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