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feat: Add Sunny-SMA scanner combining Sunny Bands and 21 SMA strategy

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Bobby (aider) 2025-02-19 23:26:58 -08:00
parent fa078a99c1
commit 3b151ae55a
3 changed files with 129 additions and 2 deletions
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2
src/pages/screener/technical_scanner_page.py
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@ -13,7 +13,7 @@ def technical_scanner_page():
with scanner_tab:
scanner_type = st.selectbox(
"Select Scanner",
["SunnyBands", "ATR-EMA", "ATR-EMA v2", "Heikin-Ashi", "Candlestick"],
["SunnyBands", "ATR-EMA", "ATR-EMA v2", "Heikin-Ashi", "Candlestick", "Sunny-SMA"],
key="tech_scanner_type"
)

4
src/screener/scanner_controller.py
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@ -4,6 +4,7 @@ from screener.t_atr_ema import run_atr_ema_scanner
from screener.t_atr_ema_v2 import run_atr_ema_scanner_v2
from screener.t_heikinashi import run_heikin_ashi_scanner
from screener.t_candlestick import run_candlestick_scanner
from screener.t_sunny_sma import run_sunny_sma_scanner
def run_technical_scanner(scanner_choice: str, start_date: str, end_date: str,
min_price: float, max_price: float, min_volume: int,
@ -31,7 +32,8 @@ def run_technical_scanner(scanner_choice: str, start_date: str, end_date: str,
"atr-ema": lambda: run_atr_ema_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt),
"atr-ema_v2": lambda: run_atr_ema_scanner_v2(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt),
"heikin-ashi": lambda: run_heikin_ashi_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt),
"candlestick": lambda: run_candlestick_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt, selected_patterns)
"candlestick": lambda: run_candlestick_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt, selected_patterns),
"sunny-sma": lambda: run_sunny_sma_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt)
}
scanner_func = scanner_map.get(scanner_choice)

125
src/screener/t_sunny_sma.py Normal file
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@ -0,0 +1,125 @@
import pandas as pd
import talib
from datetime import datetime
from utils.data_utils import (
get_stock_data, validate_signal_date, print_signal,
save_signals_to_csv, get_qualified_stocks
)
from utils.scanner_utils import initialize_scanner, process_signal_data
from indicators.sunny_bands import SunnyBands
def check_entry_signal(df: pd.DataFrame) -> list:
"""
Check for entry signals based on combined Sunny Bands and SMA strategy
Conditions:
1. 21 SMA below lowest Sunny Band
2. Price crosses above 21 SMA
3. Price still below Sunny Bands
Args:
df (pd.DataFrame): DataFrame with price data
Returns:
list: List of tuples (signal, date, signal_data) for each signal found
"""
if len(df) < 21: # Need at least 21 bars for SMA
return []
# Calculate Sunny Bands
sunny = SunnyBands()
sunny_results = sunny.calculate(df)
# Calculate 21 day SMA
sma21 = talib.SMA(df['close'].values, timeperiod=21)
signals = []
# Start from index 21 to ensure we have enough data for SMA
for i in range(21, len(df)):
current = df.iloc[i]
prev = df.iloc[i-1]
current_bands = sunny_results.iloc[i]
current_sma = sma21[i]
# Check conditions:
# 1. SMA below lower band
sma_below_band = current_sma < current_bands['lower_band']
# 2. Price crosses above SMA
price_cross_sma = (current['close'] > current_sma) and (prev['close'] < sma21[i-1])
# 3. Price still below lower band
price_below_band = current['close'] < current_bands['lower_band']
if sma_below_band and price_cross_sma and price_below_band:
signal_data = {
'price': current['close'],
'sma21': current_sma,
'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 run_sunny_sma_scanner(min_price: float, max_price: float, min_volume: int,
portfolio_size: float = None, interval: str = "1d",
start_date: datetime = None, end_date: datetime = None) -> None:
"""
Run scanner combining Sunny Bands and 21 SMA strategy
"""
try:
# Initialize scanner components
interval, start_date, end_date, qualified_stocks, calculator = initialize_scanner(
min_price=min_price,
max_price=max_price,
min_volume=min_volume,
portfolio_size=portfolio_size,
interval=interval,
start_date=start_date,
end_date=end_date
)
if not qualified_stocks:
return
bullish_signals = []
for ticker, current_price, current_volume, last_update, stock_type in qualified_stocks:
try:
df = get_stock_data(ticker, start_date, end_date, interval)
if df.empty or len(df) < 50: # Need at least 50 bars for the indicators
continue
signals = check_entry_signal(df)
for signal, signal_date, signal_data in signals:
# Custom print for Sunny-SMA signals
print(f"🌞 {ticker}: SMA-21 Cross at ${signal_data['price']:.2f} on {signal_date.strftime('%Y-%m-%d')}")
print(f" SMA: ${signal_data['sma21']:.2f}")
print(f" Lower Band: ${signal_data['lower_band']:.2f}")
entry_data = {
'ticker': ticker,
'signal_date': signal_date,
'entry_price': signal_data['price'],
'sma21': signal_data['sma21'],
'lower_band': signal_data['lower_band'],
'volume': current_volume,
'last_update': last_update,
'stock_type': stock_type
}
bullish_signals.append(entry_data)
except Exception as e:
print(f"Error processing {ticker}: {str(e)}")
continue
save_signals_to_csv(bullish_signals, 'sunny_sma')
return bullish_signals
except Exception as e:
print(f"Error during scan: {str(e)}")
return []