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feat: Add signal dates to ATR EMA screeners for backtesting

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Bobby (aider) 2025-02-08 12:32:14 -08:00
parent a8dd33c3d9
commit 2f784e5d65
2 changed files with 27 additions and 18 deletions
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14
src/screener/t_atr_ema.py
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@ -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"\n🟢 {ticker} @ ${current_price:.2f}")
print(f"\n🟢 {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}")

31
src/screener/t_atr_ema_v2.py
View File

@ -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"\n🔍 {ticker} @ ${current_price:.2f}")
# Print signal information with date
print(f"\n🔍 {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%)")