57 lines
1.6 KiB
Python
57 lines
1.6 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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# Tickers
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main = 'MRVL'
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peers = ['AVGO','AMD','NVDA']
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lookback = '180d'
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# Download data
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df_main = yf.download(main, period=lookback, progress=False)
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df_peers = {t: yf.download(t, period=lookback, progress=False) for t in peers}
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# Technical indicators for main
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for ma in [20,50,200]:
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df_main[f'SMA{ma}'] = df_main['Close'].rolling(ma).mean()
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# Return and volatility
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df_main['Ret'] = df_main['Close'].pct_change()
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df_main['Vol20'] = df_main['Ret'].rolling(20).std() * np.sqrt(252)
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# Correlation with peers
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corr_df = pd.DataFrame(index=df_main.index)
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for t in peers:
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corr_df[f'{t}_corr'] = df_main['Ret'].rolling(60).corr(df_peers[t]['Close'].pct_change())
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# Plot price + MA
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plt.figure(figsize=(12,6))
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plt.plot(df_main['Close'], label='Close')
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plt.plot(df_main['SMA20'], label='SMA20')
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plt.plot(df_main['SMA50'], label='SMA50')
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plt.plot(df_main['SMA200'], label='SMA200')
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plt.title(f'{main} price with moving averages')
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plt.legend()
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plt.tight_layout()
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plt.savefig('mrvl_ma.png')
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# Plot volatility
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plt.figure(figsize=(12,4))
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plt.plot(df_main['Vol20'], label='20‑Day Vol (annualized)')
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plt.title(f'{main} 20‑Day volatility')
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plt.legend()
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plt.tight_layout()
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plt.savefig('mrvl_vol.png')
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# Plot correlation heatmap
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plt.figure(figsize=(8,6))
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corr_vals = corr_df.mean().values
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plt.bar(peers, corr_vals)
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plt.title('Average 60‑day correlation of MRVL with peers')
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plt.ylabel('Correlation')
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plt.tight_layout()
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plt.savefig('mrvl_corr.png')
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print('Charts generated: mrvl_ma.png, mrvl_vol.png, mrvl_corr.png')
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