# source: https://raw.githubusercontent.com/TheoBrigitte/freqtrade/64016ea71200c26f0d3fc72f1ab0f2fd21dec491/strategies/BinHV45/BinHV27_werkkrew.py from freqtrade.strategy.interface import IStrategy from typing import Dict, List, Tuple, Optional 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_TheoBrigitte_freqtrade__BinHV27_werkkrew__20250101_232537(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 # Probably don't change these 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 * numpy.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 the custom_roi dict is defined and enabled, do it, otherwise fallback to default functionality if custom_roi and custom_roi['enabled']: # linear decay: f(t) = start - (rate * t) 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)) # exponential decay: f(t) = start * e^(-rate*t) - c elif custom_roi['decay-type'] == 'exp': min_roi = max(custom_roi['end-pct'], custom_roi['start-pct'] * numpy.exp(-custom_roi['decay-rate']*trade_dur)) # polynomial or exponential best-fit to points in defined roi table 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: # Get highest entry in ROI dict where key <= trade-duration 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]