# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ClucHAnix_5m_0.py 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, RealParameter from pandas import DataFrame, Series 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_ccxt_freqtrade__ClucHAnix_5m_0__20260111_210550(IStrategy): """ PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom. """ buy_params = { "bbdelta_close": 0.01889, "bbdelta_tail": 0.72235, "close_bblower": 0.0127, "closedelta_close": 0.00916, } sell_params = { "pHSL": -0.10, "pPF_1": 0.025, "pPF_2": 0.035, "pSL_1": 0.02, "pSL_2": 0.03, } minimal_roi = { "0": 0.038, "10": 0.028, "40": 0.015, } 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 = '5m' use_sell_signal = False sell_profit_only = False ignore_roi_if_buy_signal = True 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 } bbdelta_close = RealParameter(0.0005, 0.04, default=0.01965, space='buy', optimize=True) closedelta_close = RealParameter(0.0005, 0.04, default=0.00556, space='buy', optimize=True) bbdelta_tail = RealParameter(0.7, 1.5, default=0.95089, space='buy', optimize=True) close_bblower = RealParameter(0.0005, 0.04, default=0.00799, space='buy', optimize=True) pHSL = DecimalParameter(-0.500, -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(0.010, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.010, 0.070, default=0.040, decimals=3, space='sell', load=True) 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_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'buy_tag'] = 'ClucHA' dataframe.loc[ ( (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['ha_close'] * self.bbdelta_close.value)) & (dataframe['closedelta'].gt(dataframe['ha_close'] * self.closedelta_close.value)) & (dataframe['tail'].lt(dataframe['bbdelta'] * self.bbdelta_tail.value)) & (dataframe['ha_close'].lt(dataframe['lower'].shift())) & (dataframe['ha_close'].le(dataframe['ha_close'].shift())) ) | ( (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < self.close_bblower.value * dataframe['bb_lowerband']) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['volume'] > 0), 'sell' ] = 0 return dataframe class Cluc5mDCA(Github_remiotore_ccxt_freqtrade__ClucHAnix_5m_0__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