# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/GeneTrader_gen10_1734726148_6020.py from datetime import datetime, timedelta import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter, stoploss_from_open from functools import reduce import warnings warnings.simplefilter(action="ignore", category=RuntimeWarning) ## Bull version, No stoploss, Just do it class Github_remiotore_ccxt_freqtrade__GeneTrader_gen10_1734726148_6020__20260111_210550(IStrategy): minimal_roi = { "0": 1 } timeframe = '5m' process_only_new_candles = True startup_candle_count = 240 order_types = { 'entry': 'market', 'exit': 'market', 'emergency_exit': 'market', 'force_entry': 'market', 'force_exit': "market", 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_market_ratio': 0.99 } # Hyperopt Parameters # hard stoploss profit pHSL = DecimalParameter(-0.2, -0.04, default=-0.2, space='sell', optimize=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.015, space='sell', optimize=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.013, space='sell', optimize=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', optimize=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.057, space='sell', optimize=True) stoploss_opt = DecimalParameter(-0.6, -0.1, default=-0.27, space='sell', optimize=True) stoploss = stoploss_opt.value pMinProfit = DecimalParameter(-0.3, 0.0, default=-0.288, space='sell', optimize=True) pCurrentProfit = DecimalParameter(-0.1, 0.2, default=0.092, space='sell', optimize=True) trailing_stop = False trailing_stop_positive = 0.002 trailing_stop_positive_offset = 0.05 trailing_only_offset_is_reached = True use_custom_stoploss = True buy_rsi_fast_32 = IntParameter(20.0, 70.0, default=65, space='buy', optimize=True) buy_rsi_32 = IntParameter(15.0, 50.0, default=36, space='buy', optimize=True) buy_sma15_32 = DecimalParameter(0.9, 1.0, default=0.9, space='buy', optimize=True) buy_cti_32 = DecimalParameter(-1.0, 1.0, default=0.78, space='buy', optimize=True) sell_fastx = IntParameter(50.0, 100.0, default=89, space='sell', optimize=True) sell_loss_cci = IntParameter(0.0, 600.0, default=448, space='sell', optimize=True) sell_loss_cci_profit = DecimalParameter(-0.15, 0.0, default=-0.08, space='sell', optimize=True) buy_new_rsi_fast = IntParameter(20.0, 70.0, default=57, space='buy', optimize=True) buy_new_rsi = IntParameter(15.0, 50.0, default=15, space='buy', optimize=True) buy_new_sma15 = DecimalParameter(0.9, 1.0, default=0.965, space='buy', optimize=True) sell_cci = IntParameter(0.0, 600.0, default=156, space='sell', optimize=True) time_sell_4_3 = IntParameter(2.0, 6.0, default=6, space='sell', optimize=True) time_sell_10_7 = IntParameter(6.0, 12.0, default=6, space='sell', optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # buy_1 indicators dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # profit sell indicators stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastk'] = stoch_fast['fastk'] dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) dataframe['ma120'] = ta.MA(dataframe, timeperiod=120) dataframe['ma240'] = ta.MA(dataframe, timeperiod=240) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' buy_1 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) & (dataframe['rsi'] > self.buy_rsi_32.value) & (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) & (dataframe['cti'] < self.buy_cti_32.value) ) buy_new = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < self.buy_new_rsi_fast.value) & (dataframe['rsi'] > self.buy_new_rsi.value) & (dataframe['close'] < dataframe['sma_15'] * self.buy_new_sma15.value) & (dataframe['cti'] < self.buy_cti_32.value) ) conditions.append(buy_1) dataframe.loc[buy_1, 'enter_tag'] += 'buy_1' conditions.append(buy_new) dataframe.loc[buy_new, 'enter_tag'] += 'buy_new' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 return dataframe def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() min_profit = trade.calc_profit_ratio(trade.min_rate) current_profit_threshold = self.pCurrentProfit.value if current_profit > 0: if current_candle["fastk"] > self.sell_fastx.value: return "fastk_profit_sell" if min_profit <= self.pMinProfit.value: # 使用 pMinProfit 超参数 if current_profit > self.sell_loss_cci_profit.value: if current_candle["cci"] > self.sell_loss_cci.value: return "cci_loss_sell" if current_profit >= current_profit_threshold: # 使用 pCurrentProfit 超参数 if current_candle["cci"] > self.sell_cci.value: return "cci_loss_sell_fast" if current_time - timedelta(hours=self.time_sell_4_3.value) > trade.open_date_utc: if current_profit > -0.03: return "time_loss_sell_4_3" if current_time - timedelta(hours=self.time_sell_10_7.value) > trade.open_date_utc: if current_profit > -0.07: return "time_loss_sell_10_7" return None def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, ['exit_long', 'exit_tag']] = (0, 'long_out') return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. if (current_profit > PF_2): sl_profit = SL_2 + (current_profit - PF_2) elif (current_profit > PF_1): sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL # Only for hyperopt invalid return if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit)