Ready for first real test

state/sandbox/tmpwly2d8oo/script.py

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