# source: https://raw.githubusercontent.com/secondlinger/WinnieBari/73bedc96c87439cd3ca5ae33aca9cb01ed125468/user_data/strategies/CustomRLStrategy.py # user_data/strategies/Github_secondlinger_WinnieBari__CustomRLStrategy__20250919_011108.py from datetime import datetime import pandas as pd import talib.abstract as ta from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy buy_params = { "base_nb_candles_buy": 8, "ewo_high": 2.403, "ewo_high_2": -5.585, "ewo_low": -14.378, "lookback_candles": 3, "low_offset": 0.984, "low_offset_2": 0.942, "profit_threshold": 1.008, "rsi_buy": 72 } sell_params = { "base_nb_candles_sell": 16, "high_offset": 1.084, "high_offset_2": 1.401, "pHSL": -0.15, "pPF_1": 0.016, "pPF_2": 0.024, "pSL_1": 0.014, "pSL_2": 0.022 } def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif class Github_secondlinger_WinnieBari__CustomRLStrategy__20250919_011108(IStrategy): timeframe = "5m" can_short = True stoploss = -0.99 # より現実的なROI設定 minimal_roi = { "0": 0.20, "10": 0.10, "30": 0.05, "60": 0.03, "120": 0.01, "240": 0 } # トレーリングストップの改善 trailing_stop = True trailing_stop_positive = 0.015 # より厳しく trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True fast_ewo = 50 slow_ewo = 200 use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = True plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } slippage_protection = { 'retries': 3, 'max_slippage': -0.02 } def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) return dataframe def populate_indicators(self, dataframe, metadata): dataframe = self.normal_tf_indicators(dataframe, metadata) dataframe = self.freqai.start(dataframe, metadata, self) # RL予測を注入 return dataframe def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame: dataframe["&-action"] = 0 return dataframe def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame: # 既存のフィーチャー dataframe["%-raw_close"] = dataframe["close"] dataframe["%-raw_open"] = dataframe["open"] dataframe["%-raw_high"] = dataframe["high"] dataframe["%-raw_low"] = dataframe["low"] dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # 追加のフィーチャー # ボラティリティ指標 dataframe["%-volatility"] = dataframe['close'].rolling(20).std() # トレンド強度 dataframe["%-trend_strength"] = (dataframe['close'] - dataframe['close'].rolling(20).mean()) / dataframe[ "%-volatility"] # 出来高関連 if 'volume' in dataframe.columns: dataframe["%-volume_sma"] = dataframe['volume'].rolling(20).mean() dataframe["%-volume_ratio"] = dataframe['volume'] / dataframe["%-volume_sma"] # リターン関連 dataframe["%-returns"] = dataframe['close'].pct_change() dataframe["%-returns_1h"] = dataframe['close'].pct_change(12) # 5分足なので12期間=1時間 return dataframe def populate_entry_trend(self, df, metadata): if "do_predict" not in df.columns: df["do_predict"] = 1 if "&-action" not in df.columns: df["&-action"] = 0 # エントリーシグナルの統計をログ出力 long_signals = (df["do_predict"] == 1) & (df["&-action"] == 1) short_signals = (df["do_predict"] == 1) & (df["&-action"] == 3) if long_signals.sum() > 0: print(f"{metadata['pair']}: Long signals: {long_signals.sum()}") if short_signals.sum() > 0: print(f"{metadata['pair']}: Short signals: {short_signals.sum()}") df.loc[long_signals, "enter_long"] = 1 df.loc[short_signals, "enter_short"] = 1 return df def populate_exit_trend(self, df, metadata): if "do_predict" not in df.columns: df["do_predict"] = 1 if "&-action" not in df.columns: df["&-action"] = 0 df.loc[(df["do_predict"] == 1) & (df["&-action"] == 2), "exit_long"] = 1 df.loc[(df["do_predict"] == 1) & (df["&-action"] == 4), "exit_short"] = 1 return df def custom_entry_price(self, pair: str, current_time, proposed_rate: float, entry_tag: str, side: str, **kwargs) -> float: try: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty or 'atr' not in dataframe.columns: return proposed_rate latest_atr = dataframe.iloc[-1]['atr'] # ATRが異常値でないかチェック if pd.isna(latest_atr) or latest_atr <= 0: return proposed_rate latebuy = 0.1 atr_offset = latest_atr * latebuy if side == 'long': return max(dataframe.iloc[-1]['close'] - atr_offset, proposed_rate * 0.999) # 最大0.1%まで下げる else: return min(dataframe.iloc[-1]['close'] + atr_offset, proposed_rate * 1.001) # 最大0.1%まで上げる except Exception as e: print(f"Entry price calculation failed: {e}") return proposed_rate def custom_exit_price(self, pair: str, trade: Trade, current_time: datetime, proposed_rate: float, current_profit: float, exit_tag: str | None, **kwargs) -> float: try: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # 최신 ATR 값 가져오기 latest_atr = dataframe.iloc[-1]['atr'] current_close = dataframe.iloc[-1]['close'] # ATR * 0.1 오프셋 atr_offset = latest_atr * 0.01 if trade.is_short: # 숏: 현재가에서 ATR * 0.1만큼 아래 return current_close - atr_offset else: # 롱: 현재가에서 ATR * 0.1만큼 위 return current_close + atr_offset except: return proposed_rate