56 lines
1.9 KiB
Python
56 lines
1.9 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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# Define tickers
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stock = 'NVDA'
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sector_etf = 'SMH'
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lookback = 365 # days
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# Download daily data
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stock_df = yf.download(stock, period=f'{lookback}d', progress=False)
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sector_df = yf.download(sector_etf, period=f'{lookback}d', progress=False)
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# Calculate technical indicators
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stock_df['SMA20'] = stock_df['Close'].rolling(window=20).mean()
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stock_df['SMA50'] = stock_df['Close'].rolling(window=50).mean()
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stock_df['SMA200'] = stock_df['Close'].rolling(window=200).mean()
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stock_df['DailyRet'] = stock_df['Close'].pct_change()
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stock_df['Vol20'] = stock_df['DailyRet'].rolling(window=20).std() * np.sqrt(252) # annualized
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# Correlation with sector ETF
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merged = pd.merge(stock_df[['Close']], sector_df[['Close']], left_index=True, right_index=True, suffixes=('_stock', '_etf'))
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merged['DailyRet_stock'] = merged['Close_stock'].pct_change()
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merged['DailyRet_etf'] = merged['Close_etf'].pct_change()
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corr = merged['DailyRet_stock'].rolling(window=60).corr(merged['DailyRet_etf'])
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# Plot price with SMAs
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plt.figure(figsize=(12,6))
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plt.plot(stock_df['Close'], label=f'{stock} Close')
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plt.plot(stock_df['SMA20'], label='SMA20')
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plt.plot(stock_df['SMA50'], label='SMA50')
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plt.plot(stock_df['SMA200'], label='SMA200')
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plt.title(f'{stock} Price with Moving Averages')
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plt.legend()
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plt.tight_layout()
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plt.savefig('nvda_ma_chart.png')
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# Plot volatility
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plt.figure(figsize=(12,4))
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plt.plot(stock_df['Vol20'], label='20‑Day Vol (annualized)')
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plt.title(f'{stock} 20‑Day Volatility')
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plt.legend()
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plt.tight_layout()
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plt.savefig('nvda_vol_chart.png')
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# Plot correlation
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plt.figure(figsize=(12,4))
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plt.plot(corr, label='60‑Day Correlation with SMH')
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plt.title(f'{stock} vs SMH Correlation')
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plt.legend()
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plt.tight_layout()
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plt.savefig('nvda_corr_chart.png')
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print('Charts generated: nvda_ma_chart.png, nvda_vol_chart.png, nvda_corr_chart.png')
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