# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/RSIDivergence.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 numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from freqtrade.strategy import DecimalParameter, IntParameter, BooleanParameter rangeUpper = 60 rangeLower = 5 def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif def valuewhen(dataframe, condition, source, occurrence): copy = dataframe.copy() copy['colFromIndex'] = copy.index copy = copy.sort_values(by=[condition, 'colFromIndex'], ascending=False).reset_index(drop=True) copy['valuewhen'] = np.where(copy[condition] > 0, copy[source].shift(-occurrence), 100) copy['valuewhen'] = copy['valuewhen'].fillna(100) copy['barrsince'] = copy['colFromIndex'] - copy['colFromIndex'].shift(-occurrence) copy.loc[ ( (rangeLower <= copy['barrsince']) & (copy['barrsince'] <= rangeUpper) ) , "in_range"] = 1 copy['in_range'] = copy['in_range'].fillna(0) copy = copy.sort_values(by=['colFromIndex'], ascending=True).reset_index(drop=True) return copy['valuewhen'], copy['in_range'] class Github_remiotore_freqtrade__RSIDivergence__20260111_210550(IStrategy): INTERFACE_VERSION = 2 buy_params = { 'use_bull': True, 'use_hidden_bull': False, "ewo_high": 5.835, "rsi_buy": 55, } sell_params = { 'use_bear': True, 'use_hidden_bear': True } minimal_roi = { "0": 0.05, } stoploss = -0.05 trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True timeframe = '5m' use_custom_stoploss = False use_bull = BooleanParameter(default=buy_params['use_bull'], space='buy', optimize=True) use_hidden_bull = BooleanParameter(default=buy_params['use_hidden_bull'], space='buy', optimize=True) use_bear = BooleanParameter(default=sell_params['use_bear'], space='sell', optimize=True) use_hidden_bear = BooleanParameter(default=sell_params['use_hidden_bear'], space='sell', optimize=True) fast_ewo = 50 slow_ewo = 200 ewo_high = DecimalParameter(0, 7.0, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ study(title="Divergence Indicator", format=format.price, resolution="") len = input(title="RSI Period", minval=1, defval=14) src = input(title="RSI Source", defval=close) lbR = input(title="Pivot Lookback Right", defval=5) # lookahead lbL = input(title="Pivot Lookback Left", defval=5) rangeUpper = input(title="Max of Lookback Range", defval=60) rangeLower = input(title="Min of Lookback Range", defval=5) plotBull = input(title="Plot Bullish", defval=true) plotHiddenBull = input(title="Plot Hidden Bullish", defval=false) plotBear = input(title="Plot Bearish", defval=true) plotHiddenBear = input(title="Plot Hidden Bearish", defval=false) bearColor = color.red bullColor = color.green hiddenBullColor = color.new(color.green, 80) hiddenBearColor = color.new(color.red, 80) textColor = color.white noneColor = color.new(color.white, 100) osc = rsi(src, len) """ len = 14 src = dataframe['close'] lbL = 10#5 dataframe['osc'] = ta.RSI(src, len) dataframe['osc'] = dataframe['osc'].fillna(0) dataframe['min'] = dataframe['osc'].rolling(lbL).min() dataframe['prevMin'] = np.where(dataframe['min'] > dataframe['min'].shift(), dataframe['min'].shift(), dataframe['min']) dataframe.loc[ (dataframe['osc'] == dataframe['prevMin']) , 'plFound'] = 1 dataframe['plFound'] = dataframe['plFound'].fillna(0) dataframe['max'] = dataframe['osc'].rolling(lbL).max() dataframe['prevMax'] = np.where(dataframe['max'] < dataframe['max'].shift(), dataframe['max'].shift(), dataframe['max']) dataframe.loc[ (dataframe['osc'] == dataframe['prevMax']) , 'phFound'] = 1 dataframe['phFound'] = dataframe['phFound'].fillna(0) dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] > dataframe['valuewhen_plFound_osc']) & (dataframe['inrange_plFound_osc'] == 1) ) , 'oscHL'] = 1 dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe, 'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] < dataframe['valuewhen_plFound_low']) , 'priceLL'] = 1 dataframe.loc[ ( (dataframe['priceLL'] == 1) & (dataframe['oscHL'] == 1) & (dataframe['plFound'] == 1) ) , 'bullCond'] = 1 dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] < dataframe['valuewhen_plFound_osc']) & (dataframe['inrange_plFound_osc'] == 1) ) , 'oscLL'] = 1 dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe,'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] > dataframe['valuewhen_plFound_low']) , 'priceHL'] = 1 dataframe.loc[ ( (dataframe['priceHL'] == 1) & (dataframe['oscLL'] == 1) & (dataframe['plFound'] == 1) ) , 'hiddenBullCond'] = 1 dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] < dataframe['valuewhen_phFound_osc']) & (dataframe['inrange_phFound_osc'] == 1) ) , 'oscLH'] = 1 dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] > dataframe['valuewhen_phFound_high']) , 'priceHH'] = 1 dataframe.loc[ ( (dataframe['priceHH'] == 1) & (dataframe['oscLH'] == 1) & (dataframe['phFound'] == 1) ) , 'bearCond'] = 1 dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] > dataframe['valuewhen_phFound_osc']) & (dataframe['inrange_phFound_osc'] == 1) ) , 'oscHH'] = 1 dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] < dataframe['valuewhen_phFound_high']) , 'priceLH'] = 1 dataframe.loc[ ( (dataframe['priceLH'] == 1) & (dataframe['oscHH'] == 1) & (dataframe['phFound'] == 1) ) , 'hiddenBearCond'] = 1 dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.use_bull.value: conditions.append( ( (dataframe['bullCond'] > 0) & (dataframe['volume'] > 0) ) ) if self.use_hidden_bull.value: conditions.append( ( (dataframe['hiddenBullCond'] > 0) & (dataframe['volume'] > 0) ) ) 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: conditions = [] if self.use_bear.value: conditions.append( ( (dataframe['bearCond'] > 0) & (dataframe['volume'] > 0) ) ) if self.use_hidden_bear.value: conditions.append( ( (dataframe['hiddenBearCond'] > 0) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 dataframe.to_csv('user_data/csvs/%s_%s.csv' % (self.__class__.__name__, metadata["pair"].replace("/", "_"))) return dataframe