# source: https://raw.githubusercontent.com/joocy75-hash/TradingView-Strategy/6bec559d180e0df1112dde85037b93e21ac5c8f9/freqtrade/user_data/strategies/FreqAIStrategy.py """ FreqAI Strategy - 머신러닝 기반 전략 LightGBM/XGBoost/CatBoost 모델을 사용한 예측 전략 강화학습(RL) 모드도 지원 Generated by TradingView Strategy Research Lab """ import logging from functools import reduce from typing import Optional import numpy as np import pandas as pd import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter logger = logging.getLogger(__name__) class Github_joocy75_hash_TradingView_Strategy__FreqAIStrategy__20260113_034943(IStrategy): """ FreqAI 머신러닝 전략 지원 모델: - LightGBMRegressor (기본, 빠름) - XGBoostRegressor (정확도 높음) - CatBoostRegressor (범주형 데이터에 강함) - ReinforcementLearner (강화학습) """ # 전략 설정 INTERFACE_VERSION = 3 # 타임프레임 timeframe = '5m' # FreqAI 사용 can_short = False # 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 minimal_roi = { "0": 0.1, "30": 0.05, "60": 0.02, "120": 0.01, } # 주문 설정 order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True } # 최적화 파라미터 buy_rsi = IntParameter(20, 40, default=30, space='buy', optimize=True) sell_rsi = IntParameter(60, 80, default=70, space='sell', optimize=True) def feature_engineering_expand_all( self, dataframe: DataFrame, period: int, metadata: dict, **kwargs ) -> DataFrame: """ FreqAI 피처 엔지니어링 - 모든 타임프레임에 적용 """ # RSI dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) # MFI dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) # ADX dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) # SMA dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) # EMA dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) # 볼린저 밴드 bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) # MACD macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["%-macd-period"] = macd["macd"] dataframe["%-macdsignal-period"] = macd["macdsignal"] dataframe["%-macdhist-period"] = macd["macdhist"] # ROC (Rate of Change) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) # 상대 거래량 dataframe["%-relative_volume-period"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) return dataframe def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: """ FreqAI 피처 엔지니어링 - 기본 타임프레임에만 적용 """ # 가격 변화율 dataframe["%-pct-change"] = dataframe["close"].pct_change() # 고가-저가 범위 dataframe["%-high-low"] = ( dataframe["high"] - dataframe["low"] ) / dataframe["close"] # 시가-종가 범위 dataframe["%-open-close"] = ( dataframe["open"] - dataframe["close"] ) / dataframe["close"] # 거래량 변화 dataframe["%-volume-change"] = dataframe["volume"].pct_change() return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: """ FreqAI 피처 엔지니어링 - 표준 피처 """ # 날짜/시간 피처 dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-hour_of_day"] = dataframe["date"].dt.hour return dataframe def set_freqai_targets( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: """ FreqAI 타겟 설정 (예측할 값) """ # 미래 수익률 예측 (24캔들 후) dataframe["&-s_close"] = ( dataframe["close"] .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) .mean() / dataframe["close"] - 1 ) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 지표 계산 """ # FreqAI 예측 수행 dataframe = self.freqai.start(dataframe, metadata, self) # 추가 지표 (FreqAI 외) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 진입 조건 """ enter_long_conditions = [ # FreqAI 예측이 양수 (상승 예측) dataframe["&-s_close"] > 0.01, # 예측 신뢰도 dataframe["do_predict"] == 1, # RSI 필터 dataframe['rsi'] < self.buy_rsi.value, # 거래량 필터 dataframe["volume"] > 0, ] if enter_long_conditions: dataframe.loc[ reduce(lambda x, y: x & y, enter_long_conditions), "enter_long" ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 청산 조건 """ exit_long_conditions = [ # FreqAI 예측이 음수 (하락 예측) dataframe["&-s_close"] < -0.005, # 또는 RSI 과매수 dataframe['rsi'] > self.sell_rsi.value, # 거래량 필터 dataframe["volume"] > 0, ] if exit_long_conditions: dataframe.loc[ reduce(lambda x, y: x & y, exit_long_conditions), "exit_long" ] = 1 return dataframe def get_ticker_indicator(self): """ FreqAI 모델 식별자 """ return int(self.config["freqai"]["identifier"])