feat: Enhance SunnyBand scanner with progress tracking and error handling
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5273b659e1
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@ -21,12 +21,20 @@ def get_stock_data(ticker: str, start_date: datetime, end_date: datetime) -> pd.
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ORDER BY date ASC
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"""
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result = client.query(query)
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return pd.DataFrame(
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result.result_rows,
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columns=['date', 'open', 'high', 'low', 'close', 'volume']
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)
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try:
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result = client.query(query)
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if not result.result_rows:
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print(f"No data found for {ticker}")
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return pd.DataFrame()
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df = pd.DataFrame(
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result.result_rows,
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columns=['date', 'open', 'high', 'low', 'close', 'volume']
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)
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return df
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except Exception as e:
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print(f"Error fetching data for {ticker}: {str(e)}")
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return pd.DataFrame()
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def get_valid_tickers(min_price: float, max_price: float, min_volume: int) -> list:
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"""Get tickers that meet the price and volume criteria"""
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@ -46,32 +54,50 @@ def get_valid_tickers(min_price: float, max_price: float, min_volume: int) -> li
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def run_sunny_scanner(min_price: float, max_price: float, min_volume: int) -> None:
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"""Run the SunnyBand scanner and save results"""
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print(f"\nInitializing scan for stocks between ${min_price:.2f} and ${max_price:.2f}")
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print(f"Minimum volume: {min_volume:,}")
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# Get date range (60 days of data for calculations)
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end_date = datetime.now()
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start_date = end_date - timedelta(days=60)
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# Get valid tickers
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print("\nFetching qualified stocks...")
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tickers = get_valid_tickers(min_price, max_price, min_volume)
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if not tickers:
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print("No stocks found matching your criteria.")
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return
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print(f"\nScanning {len(tickers)} stocks...")
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print(f"\nFound {len(tickers)} stocks to scan")
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print("Looking for SunnyBand crossovers...")
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print("This may take a few minutes...")
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# Initialize results lists
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bullish_signals = []
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bearish_signals = []
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errors = []
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# Initialize SunnyBands indicator
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sunny = SunnyBands()
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# Track progress
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total = len(tickers)
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processed = 0
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# Scan each ticker
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for ticker in tickers:
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processed += 1
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if processed % 10 == 0: # Show progress every 10 stocks
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print(f"Progress: {processed}/{total} stocks processed ({(processed/total)*100:.1f}%)")
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try:
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# Get price data
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df = get_stock_data(ticker, start_date, end_date)
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if df.empty:
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continue
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if len(df) < 50: # Need enough data for the indicator
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continue
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@ -82,37 +108,51 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int) -> No
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last_day = df.iloc[-1]
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if results['bullish_signal'].iloc[-1]:
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bullish_signals.append({
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signal_data = {
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'ticker': ticker,
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'price': last_day['close'],
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'volume': last_day['volume'],
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'date': last_day['date'],
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'dma': results['dma'].iloc[-1],
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'lower_band': results['lower_band'].iloc[-1]
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})
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}
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bullish_signals.append(signal_data)
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print(f"🟢 Bullish Signal: {ticker} at ${last_day['close']:.2f}")
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elif results['bearish_signal'].iloc[-1]:
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bearish_signals.append({
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signal_data = {
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'ticker': ticker,
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'price': last_day['close'],
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'volume': last_day['volume'],
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'date': last_day['date'],
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'dma': results['dma'].iloc[-1],
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'upper_band': results['upper_band'].iloc[-1]
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})
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}
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bearish_signals.append(signal_data)
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print(f"🔴 Bearish Signal: {ticker} at ${last_day['close']:.2f}")
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except Exception as e:
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print(f"Error processing {ticker}: {str(e)}")
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errors.append(f"{ticker}: {str(e)}")
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continue
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# Save and display results
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output_date = datetime.now().strftime("%Y%m%d")
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print(f"\nScan Complete! Processed {total} stocks.")
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if errors:
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print(f"\nEncountered {len(errors)} errors during scan:")
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for error in errors[:5]: # Show first 5 errors
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print(error)
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if len(errors) > 5:
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print(f"...and {len(errors) - 5} more errors")
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if bullish_signals:
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print("\n🟢 Bullish Signals:")
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print(f"\n🟢 Found {len(bullish_signals)} Bullish Signals:")
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df_bullish = pd.DataFrame(bullish_signals)
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bullish_file = f'src/reports/sunny_bullish_{output_date}.csv'
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df_bullish.to_csv(bullish_file, index=False)
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print(f"\nSaved {len(bullish_signals)} bullish signals to {bullish_file}")
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print(f"Saved to {bullish_file}")
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for signal in bullish_signals:
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print(f"\n{signal['ticker']}:")
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@ -122,11 +162,11 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int) -> No
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print(f"Lower Band: ${signal['lower_band']:.2f}")
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if bearish_signals:
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print("\n🔴 Bearish Signals:")
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print(f"\n🔴 Found {len(bearish_signals)} Bearish Signals:")
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df_bearish = pd.DataFrame(bearish_signals)
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bearish_file = f'src/reports/sunny_bearish_{output_date}.csv'
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df_bearish.to_csv(bearish_file, index=False)
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print(f"\nSaved {len(bearish_signals)} bearish signals to {bearish_file}")
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print(f"Saved to {bearish_file}")
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for signal in bearish_signals:
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print(f"\n{signal['ticker']}:")
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