# source: https://raw.githubusercontent.com/richardjozsa/ft_strategies/464d9ac48e1c46ad3197736bd526ab9ed59fd13f/strategies/cipher1h.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from pandas.core.series import Series from functools import reduce import pandas_ta as pta import technical.indicators as technicali from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib rangeUpper = 60 rangeLower = 5 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'] def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=5) ema2 = ta.EMA(df, timeperiod=35) emadif = (ema1 - ema2) / df['close'] * 100 return emadif def wavetrend(df:DataFrame) -> Series: # inputs n1 = 9 # Channel Length n2 = 12 # Average Length ap = (df['high'] + df['low'] + df['close']) / 3 # HLC3 # wavetrend calculation esa = ta.EMA(ap, timeperiod=3) d = ta.EMA(abs(ap - esa), timeperiod=9) ci = (ap - esa) / (0.015 * d) wt1 = ta.EMA(ci, timeperiod=n2) wt2 = ta.SMA(wt1, timeperiod=3) wtVWAP = wt1 - wt2 return pd.Series(data={ "wt1": wt1, "wt2": wt2, "wtVWAP": wtVWAP }) def hlc3(df): return (df['high'] + df['low'] + df['close']) / 3 def fractalize(osrc): fcopy = osrc.copy() top_fractal = fcopy.shift(4) < fcopy.shift (2) & fcopy.shift(3) < fcopy.shift(2) & fcopy.shift(2) > fcopy.shift(1) & fcopy.shift(2) > fcopy bot_fractal = fcopy.shift(4) > fcopy.shift (2) & fcopy.shift(3) > fcopy.shift(2) & fcopy.shift(2) < fcopy.shift(1) & fcopy.shift(2) < fcopy class github_richardjozsa_ft_strategies__cipher1h__20211214_195922(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # Stoploss: stoploss = -0.329 # Buy hyperspace params: # Buy hyperspace params: buy_params = { "buy_aroondown": 16, "buy_aroonup": 68, "buy_arrondown_cat": False, "buy_arronup_cat": False, "buy_mfi": 36, "buy_schaff_cat": False, "buy_schafff_val": 34, "buy_vwap_cat": False, "buy_wt_oversold": -32, "ewo_high": 0.068, "fast_ewo": 67, "rsi_buy": 66, "slow_ewo": 207, "use_bull": False, "use_hidden_bull": False, } # Sell hyperspace params: sell_params = { "sell_aroondown": 87, "sell_aroonup": 68, "sell_arrondown_cat": False, "sell_arronup_cat": True, "sell_mfi": 81, "sell_schaff_cat": False, "sell_schafff_val": 71, "sell_vwap_cat": True, "sell_wt_overbought": 55, "use_bear": False, "use_hidden_bear": False, } # ROI table: minimal_roi = { "0": 0.469, "202": 0.122, "863": 0.051, "1569": 0 } # Trailing stop: trailing_stop = True trailing_stop_positive = 0.255 trailing_stop_positive_offset = 0.329 trailing_only_offset_is_reached = False use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '1h' #inf_1h = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True startup_candle_count: int = 30 #schaff val #schaff_fast_val= IntParameter(15,30, default = 23, space = 'buy') #schaff_slow_val= IntParameter(40,65, default = 50, space = 'buy') period = 10 #buyval buy_mfi = IntParameter(10, 90, default = 50, space = 'buy' ) fast_ewo = IntParameter(35, 75, default = 50, space = 'buy' ) slow_ewo = IntParameter(130, 210, default = 200, space = 'buy' ) buy_wt_oversold = IntParameter(-65, -30, default = -53, space = 'buy' ) buy_aroonup = IntParameter(40, 100, default = 77, space = 'buy') buy_aroondown = IntParameter(0, 40, default = 24, space = 'buy' ) buy_schafff_val = IntParameter(20, 40, default = 25, space = 'buy' ) #sell val sell_mfi = IntParameter(10, 90, default = 50, space = 'sell' ) sell_wt_overbought = IntParameter(30, 65, default = 53, space = 'sell' ) sell_aroonup = IntParameter(0, 75, default = 24, space = 'sell' ) sell_aroondown = IntParameter(40, 100, default = 68, space = 'sell') sell_schafff_val = IntParameter(65, 90, default = 75, space = 'sell' ) #buy cat #buy_mfi_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_vwap_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_arronup_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) # buy_wt_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_schaff_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) #sell cat # sell_mfi_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) # sell_wt_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_vwap_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_arronup_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_schaff_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) #RSIDIV use_bull = CategoricalParameter([True, False], default = True, space = 'buy' ) use_hidden_bull = CategoricalParameter([True, False], default = True, space = 'buy' ) use_bear = CategoricalParameter([True, False], default = True, space = 'sell' ) use_hidden_bear = CategoricalParameter([True, False], default = True, space = 'sell' ) ewo_high = DecimalParameter(0, 7.0, default=5.835, space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #MFI dataframe['mfi'] = ta.MFI(dataframe, timeperiod = 60) #VWAP dataframe['vwap'] = qtpylib.rolling_vwap(dataframe) #wavtrend wavetrendi = wavetrend(dataframe) dataframe['wt1'] = wavetrendi['wt1'] dataframe['wt2'] = wavetrendi['wt2'] dataframe['wtVWAP'] = wavetrendi['wtVWAP'] ''''' wbl = 60 dataframe['oscwt'] = dataframe['wt2'].fillna(0) dataframe['wtmin'] = dataframe['oscwt'].rolling(wbl).min() dataframe['wtprevmin'] = np.where(dataframe['wtmin'] > dataframe['wtmin'].shift(), dataframe['wtmin'].shift(), dataframe['wtmin']) dataframe.loc[ (dataframe['oscwt '] == dataframe['wtprevmin']), 'wtplfound'] = 1 dataframe['wtplfound'] = dataframe['wtplfound'].fillna(0) dataframe['wtmax'] = dataframe['oscwt'].rolling(wbl).max() dataframe['wtprevmax'] = np.where(dataframe['wtmax'] < dataframe['wtmax'].shift(), dataframe['wtmax'].shift(), dataframe['wtmax']) dataframe.loc[ (dataframe['oscwt'] == dataframe['wtprevmax']), 'wtphfound'] = 1 dataframe['phfound'] = dataframe['phfound'].fillna(0) dataframe['wtvaluwhen_plfoundd_osc'], dataframe['wtinrange_plfound_osc'] = valuewhen(dataframe, 'wtplfound', 'oscwt', 1) dataframe.loc[ ( (dataframe['oscwt'] > dataframe['wtvaluewhen_plfound_osc'])& ( dataframe['wtinrange_plfound_osc'] == 1) ), 'wtoschl'] = 1 dataframe['wtvaluewhen_plfound_low'] ''' #MACD #RSI+MFI #stohrsi dataframe['schaff'] = pta.stc(dataframe) #aaron aroon = ta.AROON(dataframe) dataframe['aroonup'] = aroon['aroonup'] dataframe['aroondown'] = aroon['aroondown'] #Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) #RSIDIV 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 # )""" return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bconditions = [] #if self.buy_mfi_cat.value == True: bconditions.append(qtpylib.crossed_above(dataframe['mfi'], self.buy_mfi.value)) if self.use_bull.value: bconditions.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: bconditions.append( ( (dataframe['hiddenBullCond'] > 0) & (dataframe['EWO'] > self.ewo_high.value) & #(dataframe['osc'] < self.rsi_buy.value) & (dataframe['volume'] > 0) ) ) #if self.buy_wt_cat.value: bconditions.append(qtpylib.crossed_above(dataframe['wt1'], dataframe['wt2'])& ((dataframe['wt2']-dataframe['wt1']) <=0)& (dataframe['wt2']<= self.buy_wt_oversold.value) ) if self.buy_vwap_cat.value: bconditions.append(dataframe["close"] <= dataframe['vwap']) if self.buy_arronup_cat.value == True: bconditions.append(qtpylib.crossed_above(dataframe['aroonup'], self.buy_aroonup.value)) if self.buy_arrondown_cat.value == True: bconditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.buy_aroondown.value)) if self.buy_schaff_cat.value: bconditions.append(qtpylib.crossed_above(dataframe['schaff'], self.buy_schafff_val.value)) bconditions.append(dataframe['volume'] > 0) if bconditions: dataframe.loc[ reduce(lambda x, y: x & y, bconditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: sconditions = [] #if self.sell_mfi_cat.value == True: sconditions.append(qtpylib.crossed_below(dataframe['mfi'], self.sell_mfi.value)) if self.use_bear.value: sconditions.append( ( (dataframe['bearCond'] > 0) & (dataframe['volume'] > 0) ) ) if self.use_hidden_bear.value: sconditions.append( ( (dataframe['hiddenBearCond'] > 0) & (dataframe['volume'] > 0) ) ) #if self.buy_wt_cat.value: sconditions.append(qtpylib.crossed_below(dataframe['wt1'], dataframe['wt2'])& ((dataframe['wt2']-dataframe['wt1']) >=0)& (dataframe['wt2']>= self.sell_wt_overbought.value) ) if self.sell_vwap_cat.value: sconditions.append(dataframe["close"] >= dataframe['vwap']) if self.sell_arrondown_cat.value == True: sconditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.sell_aroondown.value)) if self.sell_arronup_cat.value == True: sconditions.append(qtpylib.crossed_below(dataframe['aroonup'], self.sell_aroonup.value)) if self.sell_schaff_cat.value: sconditions.append(qtpylib.crossed_below(dataframe['schaff'], self.sell_schafff_val.value)) sconditions.append(dataframe['volume'] > 0) if sconditions: dataframe.loc[ reduce(lambda x, y: x & y, sconditions), 'sell'] = 1 return dataframe