# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ClucHAnix_5m_8.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta 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 from freqtrade.persistence import Trade 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_8__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, "rocr_1h": 0.79492, } sell_params = { "pHSL": -0.35, "pPF_1": 0.011, "pPF_2": 0.064, "pSL_1": 0.011, "pSL_2": 0.062, 'sell_fisher': 0.39075, 'sell_bbmiddle_close': 0.99754 } buy_params = { "bbdelta_close": 0.00774, "bbdelta_tail": 0.99061, "close_bblower": 0.01027, "closedelta_close": 0.00871, "rocr_1h": 0.52805, } sell_params = { "pHSL": -0.291, "pPF_1": 0.009, "pPF_2": 0.1, "pSL_1": 0.012, "pSL_2": 0.068, "sell_bbmiddle_close": 1.09653, "sell_fisher": 0.37332, } @property def plot_config(self): """ There are a lot of solutions how to build the return dictionary. The only important point is the return value. Example: plot_config = {'main_plot': {}, 'subplots': {}} """ plot_config = {} plot_config["main_plot"] = { "bb_lowerband", "bb_middleband", "ema_fast", "ema_slow", } plot_config["subplots"] = { "Mean Volume": { "volume_mean_slow": {"color": "#A1A2CF"} }, "ROCR_1h" : { "rocr_1h": {"color": "#7D2C34"} } } 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 = '5m' use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False use_custom_stoploss = False process_only_new_candles = True startup_candle_count = 168 order_types = { 'buy': 'market', 'sell': 'market', 'emergencysell': 'market', 'forcebuy': 'market', 'forcesell': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } rocr_1h = RealParameter(0.5, 1.0, default=buy_params['rocr_1h'], space='buy', optimize=True) bbdelta_close = RealParameter(0.0005, 0.02, default=buy_params['bbdelta_close'], space='buy', optimize=True) closedelta_close = RealParameter(0.0005, 0.02, default=buy_params['closedelta_close'], space='buy', optimize=True) bbdelta_tail = RealParameter(0.7, 1.0, default=buy_params['bbdelta_tail'], space='buy', optimize=True) close_bblower = RealParameter(0.0005, 0.02, default=buy_params['close_bblower'], space='buy', optimize=True) sell_fisher = RealParameter(0.1, 0.5, default=sell_params['sell_fisher'], space='sell', optimize=True) sell_bbmiddle_close = RealParameter(0.97, 1.1, default=sell_params['sell_bbmiddle_close'], space='sell', optimize=True) pHSL = DecimalParameter(-0.500, -0.040, default=sell_params['pHSL'], decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=sell_params['pPF_1'], decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=sell_params['pSL_1'], decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=sell_params['pPF_2'], decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=sell_params['pSL_2'], decimals=3, space='sell', load=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) rsi = ta.RSI(dataframe) dataframe["rsi"] = rsi rsi = 0.1 * (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 custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # print(dir(dataframe)) last_candle = dataframe.iloc[-1].squeeze() if current_profit >= 0.02: return 'RR' def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( dataframe['rocr_1h'].gt(self.rocr_1h.value) ) & (( (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['fisher'] > self.sell_fisher.value) & # (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'] * self.sell_bbmiddle_close.value) > dataframe['bb_middleband']) & # (dataframe['volume'] > 0), # 'sell' # ] = 1 return dataframe