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refactor: Remove position sizing, stops, and targets from candlestick scanner

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Bobby (aider) 2025-02-19 22:30:39 -08:00
parent 0a7d1afcde
commit e8bf5282cd
1 changed files with 16 additions and 39 deletions
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55
src/screener/t_candlestick.py
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@ -44,14 +44,11 @@ def check_entry_signal(df: pd.DataFrame, selected_patterns: list = None) -> list
Returns:
list: List of tuples (signal, date, signal_data) for each signal found
"""
if len(df) < 14: # Need at least 14 bars for ATR calculation
if len(df) < 14: # Need minimum bars for pattern recognition
return []
signals = []
# Calculate ATR first
atr = talib.ATR(df['high'].values, df['low'].values, df['close'].values, timeperiod=14)
# Use selected patterns or all patterns if none selected
patterns_to_scan = {k: v for k, v in CANDLESTICK_PATTERNS.items()
if selected_patterns is None or k in selected_patterns}
@ -67,7 +64,7 @@ def check_entry_signal(df: pd.DataFrame, selected_patterns: list = None) -> list
)
# Look for signals across all candles
for i in range(14, len(df)): # Start after ATR lookback period
for i in range(14, len(df)): # Start after lookback period
found_patterns = []
for pattern_name, pattern_values in pattern_signals.items():
@ -76,25 +73,10 @@ def check_entry_signal(df: pd.DataFrame, selected_patterns: list = None) -> list
found_patterns.append(CANDLESTICK_PATTERNS[pattern_name]['description'])
if found_patterns:
# Calculate basic target and stop levels using recent price action
current_price = df.iloc[i]['close']
recent_low = df.iloc[max(0, i-5):i+1]['low'].min() # Look back 5 bars for stop
# Default to percentage-based calculations
atr_value = atr[i]
if pd.isna(atr_value):
target_price = current_price * 1.02 # 2% target
stop_loss = current_price * 0.99 # 1% stop
else:
target_price = current_price + (2 * atr_value)
stop_loss = max(recent_low, current_price - atr_value)
signal_data = {
'price': current_price,
'price': df.iloc[i]['close'],
'patterns': ', '.join(found_patterns),
'pattern_count': len(found_patterns),
'target_price': target_price,
'stop_loss': stop_loss
'pattern_count': len(found_patterns)
}
signals.append((True, df.iloc[i]['date'], signal_data))
@ -106,16 +88,6 @@ def run_candlestick_scanner(min_price: float, max_price: float, min_volume: int,
selected_patterns: list = None) -> None:
"""
Run candlestick pattern scanner to find bullish patterns
Args:
min_price (float): Minimum stock price
max_price (float): Maximum stock price
min_volume (int): Minimum volume
portfolio_size (float, optional): Portfolio size for position sizing
interval (str, optional): Time interval for data. Defaults to "1d"
start_date (datetime, optional): Start date for scanning
end_date (datetime, optional): End date for scanning
selected_patterns (list, optional): List of patterns to scan for
"""
try:
# Initialize scanner components
@ -138,16 +110,21 @@ def run_candlestick_scanner(min_price: float, max_price: float, min_volume: int,
try:
df = get_stock_data(ticker, start_date, end_date, interval)
if df.empty or len(df) < 5: # Need at least 5 bars
if df.empty or len(df) < 14: # Need minimum bars
continue
signals = check_entry_signal(df, selected_patterns) # Pass selected_patterns here
signals = check_entry_signal(df, selected_patterns)
for signal, signal_date, signal_data in signals:
signal_data['date'] = signal_date
entry_data = process_signal_data(
ticker, signal_data, current_volume,
last_update, stock_type, calculator
)
entry_data = {
'ticker': ticker,
'date': signal_date,
'entry_price': signal_data['price'],
'patterns': signal_data['patterns'],
'pattern_count': signal_data['pattern_count'],
'volume': current_volume,
'last_update': last_update,
'type': stock_type
}
bullish_signals.append(entry_data)
print_signal(entry_data, "🕯️") # Candlestick emoji