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refactor: standardize ATR EMA scanner to scan entire date range

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Bobby (aider) 2025-02-08 20:05:29 -08:00
parent cea43c4d32
commit 15304106ac
1 changed files with 66 additions and 52 deletions
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114
src/screener/t_atr_ema.py
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@ -7,41 +7,53 @@ from trading.position_calculator import PositionCalculator
from utils.data_utils import get_stock_data, validate_signal_date, print_signal, save_signals_to_csv from utils.data_utils import get_stock_data, validate_signal_date, print_signal, save_signals_to_csv
from indicators.three_atr_ema import ThreeATREMAIndicator from indicators.three_atr_ema import ThreeATREMAIndicator
def check_atr_ema_bullish_signal(df: pd.DataFrame) -> bool: def check_entry_signal(df: pd.DataFrame) -> list:
"""Check for bullish signal based on ATR EMA indicator""" """
# Get latest values from DataFrame Check for entry signals based on Three ATR EMA strategy throughout the date range
last_price = df.iloc[-1]
previous_price = df.iloc[-2] # Get the previous row for comparison 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 []
indicator = ThreeATREMAIndicator() indicator = ThreeATREMAIndicator()
results = indicator.calculate(df) results = indicator.calculate(df)
indicator = ThreeATREMAIndicator()
results = indicator.calculate(df)
last_bands = results.iloc[-1]
print(f"\nSunnyBands Indicators:") if len(results) < 2:
print(f"DMA: ${last_bands['dma']:.2f}") return []
print(f"Upper Band: ${last_bands['upper_band']:.2f}")
print(f"Lower Band: ${last_bands['lower_band']:.2f}")
print(f"Bullish Signal: {'Yes' if last_bands['signal'] else 'No'}")
def check_atr_ema_buy_condition(df: pd.DataFrame) -> tuple: signals = []
"""Check if price is below EMA and moving up through lower ATR band"""
# Get latest values from DataFrame
last_price = df.iloc[-1]
previous_price = df.iloc[-2] # Get the previous row for comparison
results = ThreeATREMAIndicator().calculate(df) # Ensure results are calculated here
# Check if price is below EMA and has started moving up # Start from index 1 to compare with previous
ema = results['ema'].iloc[-1] for i in range(1, len(df)):
lower_band = results['lower_band'].iloc[-1] current = df.iloc[i]
previous = df.iloc[i-1]
signal = ( # Get indicator values
last_price['close'] < ema and ema = results['ema'].iloc[i]
previous_price['close'] <= lower_band and lower_band = results['lower_band'].iloc[i]
last_price['close'] > previous_price['close'] prev_lower_band = results['lower_band'].iloc[i-1]
)
return signal, last_price['date'] if signal else None, results.iloc[-1] # Entry conditions
entry_signal = (
current['close'] < ema and
previous['close'] <= prev_lower_band and
current['close'] > previous['close']
)
if entry_signal:
signal_data = {
'price': current['close'],
'ema': ema,
'lower_band': lower_band
}
signals.append((True, current['date'], signal_data))
return signals
def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None: def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None:
print(f"\nScanning for stocks ${min_price:.2f}-${max_price:.2f} with min volume {min_volume:,}") print(f"\nScanning for stocks ${min_price:.2f}-${max_price:.2f} with min volume {min_volume:,}")
@ -110,30 +122,32 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
results = indicator.calculate(df) results = indicator.calculate(df)
# Check for signals # Check for signals throughout the date range
signal, signal_date, indicator_values = check_atr_ema_buy_condition(df) signals = check_entry_signal(df)
if signal: for signal, signal_date, signal_data in signals:
target_price = indicator_values['upper_band'] entry_data = {
'ticker': ticker,
'entry_price': signal_data['price'],
'target_price': signal_data['ema'],
'volume': current_volume,
'signal_date': signal_date,
'last_update': datetime.fromtimestamp(last_update/1000000000)
}
if calculator: if calculator:
position = calculator.calculate_position_size(current_price, target_price) position = calculator.calculate_position_size(entry_data['entry_price'])
if position['shares'] > 0: potential_profit = (entry_data['target_price'] - entry_data['entry_price']) * position['shares']
signal_data = { entry_data.update({
'ticker': ticker, 'shares': position['shares'],
'entry_price': current_price, 'position_size': position['position_value'],
'target_price': target_price, 'stop_loss': position['stop_loss'],
'signal_date': signal_date, 'risk_amount': position['potential_loss'],
'volume': current_volume, 'profit_amount': potential_profit,
'last_update': datetime.fromtimestamp(last_update/1000000000), 'risk_reward_ratio': abs(potential_profit / position['potential_loss']) if position['potential_loss'] != 0 else 0
'shares': position['shares'], })
'position_size': position['position_value'],
'stop_loss': position['stop_loss'], bullish_signals.append(entry_data)
'risk_amount': position['potential_loss'], print_signal(entry_data, "🟢")
'profit_amount': position['potential_profit'],
'risk_reward_ratio': position['risk_reward_ratio']
}
bullish_signals.append(signal_data)
print_signal(signal_data, "🟢")
except Exception as e: except Exception as e:
print(f"Error processing {ticker}: {str(e)}") print(f"Error processing {ticker}: {str(e)}")