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refactor: Update Sunny Bands scanner to match ATR EMA pattern and focus on bullish signals

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Bobby (aider) 2025-02-08 20:06:53 -08:00
parent 15304106ac
commit c610bc7d4a
1 changed files with 50 additions and 33 deletions
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83
src/screener/t_sunnyband.py
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@ -10,6 +10,44 @@ from utils.data_utils import get_stock_data, validate_signal_date, print_signal,
def check_entry_signal(df: pd.DataFrame) -> list:
"""
Check for entry signals based on Sunny Bands strategy throughout the date range
Args:
df (pd.DataFrame): DataFrame with price data
Returns:
list: List of tuples (signal, date, signal_data) for each signal found
"""
if len(df) < 2: # Need at least 2 bars for comparison
return []
sunny = SunnyBands()
results = sunny.calculate(df)
if len(results) < 2:
return []
signals = []
# Start from index 1 to compare with previous
for i in range(1, len(df)):
current = df.iloc[i]
current_bands = results.iloc[i]
# Check for bullish signal
if current_bands['bullish_signal']:
signal_data = {
'price': current['close'],
'upper_band': current_bands['upper_band'],
'lower_band': current_bands['lower_band'],
'dma': current_bands['dma']
}
signals.append((True, current['date'], signal_data))
return signals
def get_valid_tickers(min_price: float, max_price: float, min_volume: int, interval: str) -> list:
"""Get tickers that meet the price and volume criteria"""
client = create_client()
@ -235,57 +273,36 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
if df.empty or len(df) < 50: # Need at least 50 bars for the indicator
continue
# Calculate SunnyBands
results = sunny.calculate(df)
# Check for signals
if results['bullish_signal'].iloc[-1]:
target_price = results['upper_band'].iloc[-1]
# Check for signals throughout the date range
signals = check_entry_signal(df)
for signal, signal_date, signal_data in signals:
if calculator:
try:
position = calculator.calculate_position_size(
entry_price=current_price,
target_price=target_price
entry_price=signal_data['price'],
target_price=signal_data['upper_band']
)
if position['shares'] > 0:
# Update signal data with proper stop loss calculation
# Get signal date from latest data point
signal_date = df.iloc[-1]['date']
signal_data = {
entry_data = {
'ticker': ticker,
'entry_price': current_price,
'target_price': target_price,
'entry_price': signal_data['price'],
'target_price': signal_data['upper_band'],
'signal_date': signal_date,
'volume': current_volume,
'last_update': datetime.fromtimestamp(last_update/1000000000),
'shares': position['shares'],
'position_size': position['position_value'],
'stop_loss': current_price * 0.93, # 7% stop loss
'stop_loss': signal_data['price'] * 0.93, # 7% stop loss
'risk_amount': position['potential_loss'],
'profit_amount': position['potential_profit'],
'risk_reward_ratio': position['risk_reward_ratio']
}
bullish_signals.append(signal_data)
# Update print output format
dollar_risk = position['potential_loss'] * -1
signal_date = validate_signal_date(df.iloc[-1]['date']) # Get and validate the date
signal_data['signal_date'] = signal_date # Add to signal data
print_signal(signal_data, "🟢")
bullish_signals.append(entry_data)
print_signal(entry_data, "🟢")
except ValueError as e:
print(f"Skipping {ticker} position: {str(e)}")
continue
elif results['bearish_signal'].iloc[-1]:
signal_date = df.iloc[-1]['date']
bearish_signals.append({
'ticker': ticker,
'price': current_price,
'volume': current_volume,
'signal_date': signal_date,
'last_update': datetime.fromtimestamp(last_update/1000000000)
})
print(f"\n🔴 {ticker} @ ${current_price:.2f} on {signal_date.strftime('%Y-%m-%d %H:%M')}")
except Exception as e:
print(f"Error processing {ticker}: {str(e)}")