diff --git a/src/screener/t_atr_ema.py b/src/screener/t_atr_ema.py index 3b6e495..a4f54c0 100644 --- a/src/screener/t_atr_ema.py +++ b/src/screener/t_atr_ema.py @@ -24,7 +24,7 @@ def check_atr_ema_bullish_signal(df: pd.DataFrame) -> bool: 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) -> bool: +def check_atr_ema_buy_condition(df: pd.DataFrame) -> tuple: """Check if price is below EMA and moving up through lower ATR band""" # Get latest values from DataFrame last_price = df.iloc[-1] @@ -35,11 +35,13 @@ def check_atr_ema_buy_condition(df: pd.DataFrame) -> bool: ema = results['ema'].iloc[-1] lower_band = results['lower_band'].iloc[-1] - return ( + signal = ( last_price['close'] < ema and previous_price['close'] <= lower_band and last_price['close'] > previous_price['close'] ) + + return signal, last_price['date'] if signal else None, results.iloc[-1] 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:,}") @@ -108,8 +110,9 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por results = indicator.calculate(df) # Check for signals - if results['bullish_signal'].iloc[-1]: - target_price = results['upper_band'].iloc[-1] + signal, signal_date, indicator_values = check_atr_ema_buy_condition(df) + if signal: + target_price = indicator_values['upper_band'] if calculator: position = calculator.calculate_position_size(current_price, target_price) @@ -118,6 +121,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por 'ticker': ticker, 'entry': current_price, 'target': target_price, + 'signal_date': signal_date, 'volume': current_volume, 'last_update': datetime.fromtimestamp(last_update/1000000000), 'shares': position['shares'], @@ -129,7 +133,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por } bullish_signals.append(signal_data) dollar_risk = signal_data['risk'] * -1 - print(f"\nšŸŸ¢ {ticker} @ ${current_price:.2f}") + print(f"\nšŸŸ¢ {ticker} @ ${current_price:.2f} on {signal_date.strftime('%Y-%m-%d %H:%M')}") print(f" Size: {signal_data['shares']} shares (${signal_data['position_size']:.2f})") print(f" Stop: ${signal_data['stop_loss']:.2f} (7%) | Target: ${target_price:.2f}") print(f" Risk/Reward: 1:{signal_data['r_r']:.1f} | Risk: ${dollar_risk:.2f}") diff --git a/src/screener/t_atr_ema_v2.py b/src/screener/t_atr_ema_v2.py index 4b20adc..c879c2f 100644 --- a/src/screener/t_atr_ema_v2.py +++ b/src/screener/t_atr_ema_v2.py @@ -7,7 +7,7 @@ from utils.data_utils import get_stock_data from screener.user_input import get_interval_choice, get_date_range from indicators.three_atr_ema import ThreeATREMAIndicator -def check_entry_signal(df: pd.DataFrame) -> bool: +def check_entry_signal(df: pd.DataFrame) -> tuple: """ Check for entry signal based on Three ATR EMA strategy @@ -15,16 +15,16 @@ def check_entry_signal(df: pd.DataFrame) -> bool: df (pd.DataFrame): DataFrame with price data Returns: - bool: True if entry signal is present, False otherwise + tuple: (bool, datetime, dict) Entry signal, signal date, and signal data """ if len(df) < 2: # Need at least 2 bars for comparison - return False + return False, None, None indicator = ThreeATREMAIndicator() results = indicator.calculate(df) if len(results) < 2: - return False + return False, None, None # Get latest values current = df.iloc[-1] @@ -36,16 +36,19 @@ def check_entry_signal(df: pd.DataFrame) -> bool: prev_lower_band = results['lower_band'].iloc[-2] # Entry conditions from Pine script: - # 1. Price is below EMA - # 2. Previous close was at or below lower band - # 3. Current close is higher than previous close entry_signal = ( current['close'] < ema and previous['close'] <= prev_lower_band and current['close'] > previous['close'] ) - return entry_signal + signal_data = { + 'price': current['close'], + 'ema': ema, + 'lower_band': lower_band + } if entry_signal else None + + return entry_signal, current['date'] if entry_signal else None, signal_data def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None: """ @@ -118,16 +121,18 @@ def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int, if df.empty or len(df) < 21: # Need at least 21 bars for EMA continue - if check_entry_signal(df): + signal, signal_date, signal_data = check_entry_signal(df) + if signal: signal_data = { 'ticker': ticker, - 'price': current_price, + 'price': signal_data['price'], 'volume': current_volume, + 'signal_date': signal_date, 'last_update': datetime.fromtimestamp(last_update/1000000000) } if calculator: - position = calculator.calculate_position_size(current_price) + position = calculator.calculate_position_size(signal_data['price']) signal_data.update({ 'shares': position['shares'], 'position_size': position['position_value'], @@ -137,8 +142,8 @@ def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int, entry_signals.append(signal_data) - # Print signal information - print(f"\nšŸ” {ticker} @ ${current_price:.2f}") + # Print signal information with date + print(f"\nšŸ” {ticker} @ ${signal_data['price']:.2f} on {signal_date.strftime('%Y-%m-%d %H:%M')}") if calculator: print(f" Size: {signal_data['shares']} shares (${signal_data['position_size']:.2f})") print(f" Stop: ${signal_data['stop_loss']:.2f} (7%)")