Ready for first real test

state/sandbox/tmp6or4tzss/script.py

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

state/sandbox/tmp7bajsrto/script.py

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+import yfinance as yf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+stock='NVDA'
+sector='SMH'
+lookback='365d'
+
+# Download
+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)
+
+# correlation
+merged=pd.merge(s[['Close']], se[['Close']], left_index=True, right_index=True, suffixes=('','_etf'))
+merged['Ret']=merged['Close'].pct_change()
+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')

state/sandbox/tmp8sqasehy/script.py

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+
+import numpy as np, pandas as pd, matplotlib.pyplot as plt
+import yfinance as yf
+df = yf.download("AAPL", period="3mo", progress=False)
+close = df["Close"].squeeze()   # handle yfinance multiindex
+print("AAPL last:", round(float(close.iloc[-1]),2), "| 20d SMA:", round(float(close.rolling(20).mean().iloc[-1]),2))
+close.plot(title="AAPL 3mo"); plt.savefig("aapl.png")

state/sandbox/tmpcaqxszjc/script.py

@@ -0,0 +1,45 @@
+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')

state/sandbox/tmpj33u__a9/script.py

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+import pandas, numpy; print('libs ok')

state/sandbox/tmplxuxev3m/script.py

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+import os; print("SECRET=", os.environ.get("ALPACA_NOVA_SECRET"))

state/sandbox/tmppqo2frwd/script.py

@@ -0,0 +1 @@
+import os; print(os.environ.get('ALPACA_NOVA_SECRET'))

state/sandbox/tmps_0krww5/script.py

@@ -0,0 +1,6 @@
+
+import yfinance as yf, matplotlib.pyplot as plt
+df = yf.download("AAPL", period="3mo", progress=False)
+df["SMA20"] = df["Close"].rolling(20).mean()
+print("rows", len(df), "last close", round(float(df["Close"].iloc[-1]),2))
+df[["Close","SMA20"]].plot(title="AAPL"); plt.savefig("aapl.png")

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')