# source: https://raw.githubusercontent.com/ShahAnuj2610/my-freqtrade/744f698dbbf3cb10ce1cd820d31fe357de388914/user_data/strategies/RSIDivergence.py # --- Do not remove these libs --- 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_ShahAnuj2610_my_freqtrade__RSIDivergence__20220214_083345(IStrategy): INTERFACE_VERSION = 2 # Buy hyperspace params: buy_params = { 'use_bull': True, 'use_hidden_bull': False, "ewo_high": 5.835, "rsi_buy": 55, } # Sell hyperspace params: sell_params = { 'use_bear': True, 'use_hidden_bear': True } # ROI table: minimal_roi = { "0": 0.05, } # Stoploss: stoploss = -0.05 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True # Optimal timeframe for the strategy 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) # Protection 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) # plFound = na(pivotlow(osc, lbL, lbR)) ? false : true 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) # phFound = na(pivothigh(osc, lbL, lbR)) ? false : true 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) #------------------------------------------------------------------------------ # Regular Bullish # Osc: Higher Low # oscHL = osc[lbR] > valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[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) ) , 'oscHL'] = 1 # Price: Lower Low # priceLL = low[lbR] < valuewhen(plFound, low[lbR], 1) dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe, 'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] < dataframe['valuewhen_plFound_low']) , 'priceLL'] = 1 #bullCond = plotBull and priceLL and oscHL and plFound dataframe.loc[ ( (dataframe['priceLL'] == 1) & (dataframe['oscHL'] == 1) & (dataframe['plFound'] == 1) ) , 'bullCond'] = 1 # plot( # plFound ? osc[lbR] : na, # offset=-lbR, # title="Regular Bullish", # linewidth=2, # color=(bullCond ? bullColor : noneColor) # ) # # plotshape( # bullCond ? osc[lbR] : na, # offset=-lbR, # title="Regular Bullish Label", # text=" Bull ", # style=shape.labelup, # location=location.absolute, # color=bullColor, # textcolor=textColor # ) # //------------------------------------------------------------------------------ # // Hidden Bullish # // Osc: Lower Low # # oscLL = osc[lbR] < valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[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 # # // Price: Higher Low # # priceHL = low[lbR] > valuewhen(plFound, low[lbR], 1) dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe,'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] > dataframe['valuewhen_plFound_low']) , 'priceHL'] = 1 # hiddenBullCond = plotHiddenBull and priceHL and oscLL and plFound dataframe.loc[ ( (dataframe['priceHL'] == 1) & (dataframe['oscLL'] == 1) & (dataframe['plFound'] == 1) ) , 'hiddenBullCond'] = 1 # # plot( # plFound ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bullish", # linewidth=2, # color=(hiddenBullCond ? hiddenBullColor : noneColor) # ) # # plotshape( # hiddenBullCond ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bullish Label", # text=" H Bull ", # style=shape.labelup, # location=location.absolute, # color=bullColor, # textcolor=textColor # ) # # //------------------------------------------------------------------------------ # // Regular Bearish # // Osc: Lower High # # oscLH = osc[lbR] < valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[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 # # // Price: Higher High # # priceHH = high[lbR] > valuewhen(phFound, high[lbR], 1) dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] > dataframe['valuewhen_phFound_high']) , 'priceHH'] = 1 # # bearCond = plotBear and priceHH and oscLH and phFound dataframe.loc[ ( (dataframe['priceHH'] == 1) & (dataframe['oscLH'] == 1) & (dataframe['phFound'] == 1) ) , 'bearCond'] = 1 # # plot( # phFound ? osc[lbR] : na, # offset=-lbR, # title="Regular Bearish", # linewidth=2, # color=(bearCond ? bearColor : noneColor) # ) # # plotshape( # bearCond ? osc[lbR] : na, # offset=-lbR, # title="Regular Bearish Label", # text=" Bear ", # style=shape.labeldown, # location=location.absolute, # color=bearColor, # textcolor=textColor # ) # # //------------------------------------------------------------------------------ # // Hidden Bearish # // Osc: Higher High # # oscHH = osc[lbR] > valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[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 # # // Price: Lower High # # priceLH = high[lbR] < valuewhen(phFound, high[lbR], 1) dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] < dataframe['valuewhen_phFound_high']) , 'priceLH'] = 1 # # hiddenBearCond = plotHiddenBear and priceLH and oscHH and phFound dataframe.loc[ ( (dataframe['priceLH'] == 1) & (dataframe['oscHH'] == 1) & (dataframe['phFound'] == 1) ) , 'hiddenBearCond'] = 1 # # plot( # phFound ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bearish", # linewidth=2, # color=(hiddenBearCond ? hiddenBearColor : noneColor) # ) # # plotshape( # hiddenBearCond ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bearish Label", # text=" H Bear ", # style=shape.labeldown, # location=location.absolute, # color=bearColor, # textcolor=textColor # )""" # Elliot 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['EWO'] > self.ewo_high.value) & #(dataframe['osc'] < self.rsi_buy.value) & (dataframe['volume'] > 0) ) ) if self.use_hidden_bull.value: conditions.append( ( (dataframe['hiddenBullCond'] > 0) & #(dataframe['EWO'] > self.ewo_high.value) & #(dataframe['osc'] < self.rsi_buy.value) & (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