SmaCrossoverStrategy

"""
Simple Moving Average Crossover Strategy - 20/50 SMA

Generated by TradingView Strategy Research Lab
Date: 2026-01-19 13:42:17
Original Strategy: SMA Crossover

Backtest Results:
- Win Rate: 55.0%
- Profit Factor: 1.80
- Max Drawdown: 12.0%
"""

from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
from pandas import DataFrame
import talib.abstract as ta


class SmaCrossoverStrategy(IStrategy):
    """
    Simple Moving Average Crossover Strategy - 20/50 SMA
    """
    
    # 전략 설정
    INTERFACE_VERSION = 3
    
    # 타임프레임
    timeframe = '1h'
    
    # 리스크 관리
    stoploss = -0.05
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True
    
    # ROI 테이블 (시간별 목표 수익률)
    minimal_roi = {
        "0": 0.10,    # 즉시 10% 수익 시 청산
        "30": 0.05,   # 30분 후 5% 수익 시 청산
        "60": 0.025,  # 1시간 후 2.5% 수익 시 청산
        "120": 0.01,  # 2시간 후 1% 수익 시 청산
    }
    
    # 주문 설정
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': True
    }
    
    # 최적화 가능한 파라미터
    sma_fast_period = IntParameter(10, 40, default=20, space='buy')
    sma_slow_period = IntParameter(40, 70, default=50, space='buy')
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        지표 계산
        """
        dataframe['sma_fast'] = ta.SMA(dataframe["close"], timeperiod=20)
        dataframe['sma_slow'] = ta.SMA(dataframe["close"], timeperiod=50)
        
        return dataframe
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        진입 조건
        """
        dataframe.loc[
            (dataframe['sma_fast'] > dataframe['sma_slow'])
            & (dataframe['sma_fast'].shift(1) <= dataframe['sma_slow'].shift(1))
            & (dataframe['volume'] > dataframe['volume'].rolling(20).mean())
            ,
            'enter_long'] = 1
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        청산 조건
        """
        dataframe.loc[
            (dataframe['sma_fast'] < dataframe['sma_slow'])
            & (dataframe['sma_fast'].shift(1) >= dataframe['sma_slow'].shift(1))
            ,
            'exit_long'] = 1
        
        return dataframe