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refactor: Optimize sunny scanner with direct database query and simplified processing

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Bobby Abellana (aider) 2025-02-07 00:24:08 -08:00 committed by Bobby Abellana
parent e7a32dd9ab
commit 7faf56a712
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1 changed files with 81 additions and 71 deletions
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64
src/screener/t_sunnyband.py
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@ -277,15 +277,31 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
interval = get_interval_choice() interval = get_interval_choice()
end_date = datetime.now() end_date = datetime.now()
start_date = end_date - timedelta(days=1) start_date = end_date - timedelta(days=1)
lookback_start = end_date - timedelta(days=60)
tickers = get_valid_tickers(min_price, max_price, min_volume, interval) # First get the data from database
if not tickers: client = create_client()
# Query to get stocks meeting criteria
query = f"""
SELECT DISTINCT ticker, close
FROM stock_db.stock_prices_daily
WHERE date = (SELECT max(date) FROM stock_db.stock_prices_daily)
AND close BETWEEN {min_price} AND {max_price}
AND volume >= {min_volume}
ORDER BY ticker
"""
try:
result = client.query(query)
qualified_stocks = [(row[0], row[1]) for row in result.result_rows]
if not qualified_stocks:
print("No stocks found matching criteria.") print("No stocks found matching criteria.")
return return
print(f"\nScanning {len(tickers)} qualified stocks...") print(f"\nFound {len(qualified_stocks)} stocks matching criteria")
# Initialize indicators
sunny = SunnyBands() sunny = SunnyBands()
calculator = None calculator = None
if portfolio_size and portfolio_size > 0: if portfolio_size and portfolio_size > 0:
@ -294,56 +310,48 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
bullish_signals = [] bullish_signals = []
bearish_signals = [] bearish_signals = []
for ticker in tickers: # Process each qualified stock
for ticker, current_price in qualified_stocks:
try: try:
df = get_stock_data(ticker, start_date, end_date, interval) df = get_stock_data(ticker, start_date, end_date, interval)
if df.empty or len(df) < 50: if df.empty or len(df) < 50:
continue continue
results = sunny.calculate(df) results = sunny.calculate(df)
last_day = df.iloc[-1]
if results['bullish_signal'].iloc[-1]: if results['bullish_signal'].iloc[-1]:
entry_price = last_day['close'] target_price = results['upper_band'].iloc[-1]
dma = results['dma'].iloc[-1]
upper_band = results['upper_band'].iloc[-1]
band_range = upper_band - dma
target_price = upper_band + band_range
signal_data = {
'ticker': ticker,
'entry': entry_price,
'target': target_price
}
if calculator: if calculator:
position = calculator.calculate_position_size(entry_price, target_price) position = calculator.calculate_position_size(current_price, target_price)
signal_data.update({ if position['shares'] > 0:
signal_data = {
'ticker': ticker,
'entry': current_price,
'target': target_price,
'shares': position['shares'], 'shares': position['shares'],
'position_size': position['position_value'], 'position_size': position['position_value'],
'stop_loss': position['stop_loss'], 'stop_loss': position['stop_loss'],
'risk': position['potential_loss'], 'risk': position['potential_loss'],
'reward': position['potential_profit'], 'reward': position['potential_profit'],
'r_r': position['risk_reward_ratio'] 'r_r': position['risk_reward_ratio']
}) }
bullish_signals.append(signal_data) bullish_signals.append(signal_data)
print(f"\n🟢 {ticker} Entry: ${entry_price:.2f} Target: ${target_price:.2f}") print(f"\n🟢 {ticker} Entry: ${current_price:.2f} Target: ${target_price:.2f}")
if calculator:
print(f" Shares: {signal_data['shares']} | Risk: ${abs(signal_data['risk']):.2f} | " print(f" Shares: {signal_data['shares']} | Risk: ${abs(signal_data['risk']):.2f} | "
f"Reward: ${signal_data['reward']:.2f} | R/R: {signal_data['r_r']:.2f}") f"Reward: ${signal_data['reward']:.2f} | R/R: {signal_data['r_r']:.2f}")
elif results['bearish_signal'].iloc[-1]: elif results['bearish_signal'].iloc[-1]:
bearish_signals.append({ bearish_signals.append({
'ticker': ticker, 'ticker': ticker,
'price': last_day['close'] 'price': current_price
}) })
print(f"\n🔴 {ticker} at ${last_day['close']:.2f}") print(f"\n🔴 {ticker} at ${current_price:.2f}")
except Exception as e: except Exception as e:
continue continue
# Save results more concisely # Save results
output_date = datetime.now().strftime("%Y%m%d") output_date = datetime.now().strftime("%Y%m%d")
if bullish_signals: if bullish_signals:
df_bullish = pd.DataFrame(bullish_signals) df_bullish = pd.DataFrame(bullish_signals)
@ -354,4 +362,6 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
df_bearish.to_csv(f'reports/sunny_bearish_{output_date}.csv', index=False) df_bearish.to_csv(f'reports/sunny_bearish_{output_date}.csv', index=False)
print(f"\nFound {len(bullish_signals)} bullish and {len(bearish_signals)} bearish signals") print(f"\nFound {len(bullish_signals)} bullish and {len(bearish_signals)} bearish signals")
print("Results saved to reports directory")
except Exception as e:
print(f"Error during scan: {str(e)}")