# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ClucHAnixV1.py from functools import reduce from typing import Optional import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, IntParameter from pandas import DataFrame, Series, DatetimeIndex, merge from datetime import datetime def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) class Github_remiotore_freqtrade__ClucHAnixV1__20260111_210550(IStrategy): """ PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom. """ buy_params = { 'bbdelta-close': 0.01965, 'bbdelta-tail': 0.95089, 'close-bblower': 0.00799, 'closedelta-close': 0.00556, 'rocr-1h': 0.54904, "lambo2_ema_14_factor": 0.981, "lambo2_rsi_14_limit": 39, "lambo2_rsi_4_limit": 44, 'adx': 34, 'aroon-down': 33, 'aroon-up': 98 } sell_params = { "pHSL": -0.99, "pPF_1": 0.02, "pPF_2": 0.05, "pSL_1": 0.02, "pSL_2": 0.04, 'sell-fisher': 0.38414, 'sell-bbmiddle-close': 1.07634 } minimal_roi = { "0": 100 } stoploss = -0.99 # use custom stoploss trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.012 trailing_only_offset_is_reached = False """ END HYPEROPT """ timeframe = '1m' use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 168 order_types = { 'buy': 'limit', 'sell': 'limit', 'emergencysell': 'limit', 'forcebuy': "limit", 'forcesell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: 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 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 if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi'] = ta.RSI(dataframe) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['adx'] = ta.ADX(dataframe, timeperiod=90) aroon = ta.AROON(dataframe, timeperiod=60) dataframe['aroon-down'] = aroon['aroondown'] dataframe['aroon-up'] = aroon['aroonup'] dataframe['hma'] = qtpylib.hma(dataframe['close'], 14) dataframe['cci'] = ta.CCI(dataframe, timeperiod=14) rsi = 0.1 * (dataframe['rsi_14'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) dataframe = self.resample(dataframe, self.timeframe, 5) dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170) dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34) dataframe['mfi'] = ta.MFI(dataframe) dataframe['cmf'] = self.chaikin_mf(dataframe) rsi = 0.1 * (dataframe["rsi"] - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.buy_params conditions = [] dataframe.loc[:, 'buy_tag'] = '' lambo2 = ( (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) clucha = ( (dataframe['rocr_1h'].gt(params['rocr-1h'])) & (( (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['ha_close'] * params['bbdelta-close'])) & (dataframe['closedelta'].gt(dataframe['ha_close'] * params['closedelta-close'])) & (dataframe['tail'].lt(dataframe['bbdelta'] * params['bbdelta-tail'])) & (dataframe['ha_close'].lt(dataframe['lower'].shift())) & (dataframe['ha_close'].le(dataframe['ha_close'].shift())) ) | ( (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < params['close-bblower'] * dataframe['bb_lowerband']) )) ) yolo = ( (dataframe['adx'] > params['adx']) & (dataframe['aroon-up'] > params['aroon-up']) & (dataframe['aroon-down'] < params['aroon-down']) & (dataframe['volume'] > 0) ) fishi_hull = ( (dataframe['hma'] < dataframe['hma'].shift()) & (dataframe['cci'] <= -50.0) & (dataframe['fisher_rsi'] < -0.5) & (dataframe['volume'] > 0) ) cci = ( (dataframe['cci_one'] < -100) & (dataframe['cci_two'] < -100) & (dataframe['cmf'] < -0.1) & (dataframe['mfi'] < 25) & (dataframe['resample_medium'] > dataframe['resample_short']) & (dataframe['resample_long'] < dataframe['close']) ) conditions.append(lambo2) dataframe.loc[lambo2, 'buy_tag'] += 'lambo2 ' conditions.append(clucha) dataframe.loc[clucha, 'buy_tag'] += 'clucHA ' conditions.append(yolo) dataframe.loc[yolo, 'buy_tag'] += 'yolo ' conditions.append(fishi_hull) dataframe.loc[fishi_hull, 'buy_tag'] += 'fishi_hull ' conditions.append(cci) dataframe.loc[cci, 'buy_tag'] += 'cci ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.sell_params dataframe.loc[ (dataframe['fisher'] > params['sell-fisher']) & (dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) & (dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) & (dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) & (dataframe['ema_fast'] > dataframe['ha_close']) & ((dataframe['ha_close'] * params['sell-bbmiddle-close']) > dataframe['bb_middleband']) & (dataframe['volume'] > 0) , 'sell' ] = 1 return dataframe @staticmethod def chaikin_mf(df, periods=20): close = df['close'] low = df['low'] high = df['high'] volume = df['volume'] mfv = ((close - low) - (high - close)) / (high - low) mfv = mfv.fillna(0.0) # float division by zero mfv *= volume cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum() return Series(cmf, name='cmf') @staticmethod def resample(dataframe, interval, factor): df = dataframe.copy() df = df.set_index(DatetimeIndex(df['date'])) ohlc_dict = { 'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last' } df = df.resample(str(int(interval[:-1]) * factor) + 'min', label="right").agg(ohlc_dict) df['resample_sma'] = ta.SMA(df, timeperiod=100, price='close') df['resample_medium'] = ta.SMA(df, timeperiod=50, price='close') df['resample_short'] = ta.SMA(df, timeperiod=25, price='close') df['resample_long'] = ta.SMA(df, timeperiod=200, price='close') df = df.drop(columns=['open', 'high', 'low', 'close']) df = df.resample(interval[:-1] + 'min') df = df.interpolate(method='time') df['date'] = df.index df.index = range(len(df)) dataframe = merge(dataframe, df, on='date', how='left') return dataframe class ClucV1DCA(Github_remiotore_freqtrade__ClucHAnixV1__20260111_210550): position_adjustment_enable = True max_rebuy_orders = 1 max_rebuy_multiplier = 2 def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, entry_tag: Optional[str], **kwargs) -> float: if (self.config['position_adjustment_enable'] is True) and (self.config['stake_amount'] == 'unlimited'): return proposed_stake / self.max_rebuy_multiplier else: return proposed_stake def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if (self.config['position_adjustment_enable'] is False) or (current_profit > -0.08): return None filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) if 0 < count_of_buys <= self.max_rebuy_orders: try: stake_amount = filled_buys[0].cost stake_amount = stake_amount return stake_amount except Exception as exception: return None return None