# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BinHV27_747.py from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Dict, List from functools import reduce from pandas import DataFrame, DatetimeIndex, merge import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa class Github_remiotore_freqtrade__BinHV27_747__20260111_210550(IStrategy): timeframe = '5m' buy_params = { 'adx1': 25, 'emarsi1': 20, 'adx2': 30, 'emarsi2': 20, 'adx3': 35, 'emarsi3': 20, 'adx4': 30, 'emarsi4': 25 } sell_params = { 'emarsi1': 75, 'adx2': 30, 'emarsi2': 80, 'emarsi3': 75, } minimal_roi = { "0": 0.05, "10": 0.025, "20": 0.015, "30": 0.01, "720": 0.005, "1440": 0 } custom_minimal_roi = { 'enabled': True, 'decay-type': 'exp', # linear (lin) or exponential (exp) 'decay-rate': 0.015, # bigger is faster, recommended to graph f(t) = start-pct * e(-rate*t) 'decay-time': 1440, # amount of time to reach zero, only relevant for linear decay 'start-pct': 0.10, # starting percentage 'end-pct': 0, # ending percentage 'fit-type': 'poly' # best-fit a shape to the standard ROI table points using a polynomial (linear) or exponential shape } stoploss = -0.50 use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5)) dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(dataframe['rsi'], timeperiod=5)) dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe)) dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe)) dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(dataframe['minusdi'], timeperiod=25)) dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe)) dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(dataframe['plusdi'], timeperiod=5)) dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60)) dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120)) dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120)) dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240)) dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & ((dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300) dataframe['bigdown'] = ~dataframe['bigup'] dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma'] dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift()) dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(dataframe['trend'].shift(2)) dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe['slowsma'].shift().gt(dataframe['slowsma'].shift(2)) dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift() dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift()) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.buy_params dataframe.loc[ dataframe['slowsma'].gt(0) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ( ( ~dataframe['preparechangetrend'] & ~dataframe['continueup'] & dataframe['adx'].gt(params['adx1']) & dataframe['bigdown'] & dataframe['emarsi'].le(params['emarsi1']) ) | ( ~dataframe['preparechangetrend'] & dataframe['continueup'] & dataframe['adx'].gt(params['adx2']) & dataframe['bigdown'] & dataframe['emarsi'].le(params['emarsi2']) ) | ( ~dataframe['continueup'] & dataframe['adx'].gt(params['adx3']) & dataframe['bigup'] & dataframe['emarsi'].le(params['emarsi3']) ) | ( dataframe['continueup'] & dataframe['adx'].gt(params['adx4']) & dataframe['bigup'] & dataframe['emarsi'].le(params['emarsi4']) ) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.sell_params dataframe.loc[ ( ( ~dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & (dataframe['close'].gt(dataframe['lowsma']) | dataframe['close'].gt(dataframe['highsma'])) & dataframe['highsma'].gt(0) & dataframe['bigdown'] ) | ( ~dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & dataframe['close'].gt(dataframe['highsma']) & dataframe['highsma'].gt(0) & (dataframe['emarsi'].ge(params['emarsi1']) | dataframe['close'].gt(dataframe['slowsma'])) & dataframe['bigdown'] ) | ( ~dataframe['preparechangetrendconfirm'] & dataframe['close'].gt(dataframe['highsma']) & dataframe['highsma'].gt(0) & dataframe['adx'].gt(params['adx2']) & dataframe['emarsi'].ge(params['emarsi2']) & dataframe['bigup'] ) | ( dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & dataframe['slowingdown'] & dataframe['emarsi'].ge(params['emarsi3']) & dataframe['slowsma'].gt(0) ) | ( dataframe['preparechangetrendconfirm'] & dataframe['minusdi'].lt(dataframe['plusdi']) & dataframe['close'].gt(dataframe['lowsma']) & dataframe['slowsma'].gt(0) ) ), 'sell'] = 1 return dataframe """ Override for default Freqtrade ROI table functionality """ def poly(x, a, b): return a + (x * b) def exp(x, a, b, c): return a * np.exp(-b * x) + c def make_estimator(func, data): xs, ys = zip(*sorted(data.items())) popt, pcov = curve_fit(func, xs, ys) return lambda x: func(x, *popt) def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]: custom_roi = self.custom_minimal_roi if custom_roi and custom_roi['enabled']: if custom_roi['decay-type'] == 'lin': rate = (custom_roi['start-pct'] - custom_roi['end-pct']) / custom_roi['decay-time'] min_roi = max(custom_roi['end-pct'], custom_roi['start-pct'] - (rate * trade_dur)) elif custom_roi['decay-type'] == 'exp': min_roi = max(custom_roi['end-pct'], custom_roi['start-pct'] * np.exp(-custom_roi['decay-rate']*time)) elif custom_roi['decay-type'] == 'fit': roi = self.minimal_roi estimator = self.make_estimator(custom_roi['fit-type'], roi) min_roi = estimator(trade_dur) else: min_roi = 0 return trade_dur, min_roi else: roi_list = list(filter(lambda x: x <= trade_dur, self.minimal_roi.keys())) if not roi_list: return None, None roi_entry = max(roi_list) return roi_entry, self.minimal_roi[roi_entry]