# --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List import numpy as np from functools import reduce from pandas import DataFrame, Series from technical.indicators import zema, VIDYA, RMI from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class pmaxTest(IStrategy): """ author@: Gert Wohlgemuth just a skeleton """ # Minimal ROI designed for the strategy. # adjust based on market conditions. We would recommend to keep it low for quick turn arounds # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { "0": 100, #"0": 0.053, #"23": 0.039, #"62": 0.022, #"186": 0 } # sell space sell_params = { "pHSL": -0.04, "pPF_1": 0.011, "pPF_2": 0.069, "pSL_1": 0.011, "pSL_2": 0.068, } # Optimal stoploss designed for the strategy stoploss = -0.99 use_custom_stoploss = True # Optimal timeframe for the strategy timeframe = '5m' ## Trailing params # hard stoploss profit pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) ## Custom Trailing stoploss ( credit to Perkmeister for this custom stoploss to help the strategy ride a green candle ) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. if (current_profit > PF_2): sl_profit = SL_2 + (current_profit - PF_2) elif (current_profit > PF_1): sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL # For hyperopt only if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] # Profit Maximizer - PMAX dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # trima dataframe['trima_buy'] = ta.TRIMA(dataframe, 10) # zema dataframe['zema_buy'] = zema(dataframe, 30) dataframe['rmi'] = RMI(dataframe, length=9, mom=4) dataframe['cci'] = ta.CCI(dataframe, 46) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( qtpylib.crossed_above(dataframe['trima_buy'], dataframe['zema_buy']) & (dataframe['trima_buy'] > dataframe['pm']) & (dataframe['zema_buy'] > dataframe['pm']) & # add check (dataframe['rmi'] < 50) & (dataframe['cci'] <= -91) & (dataframe['srsi_fk'] < 41) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.10 )) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0) ) ,'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['volume'] > 0) # Make sure Volume is not 0 ),'sell'] = 0 return dataframe # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier/10) * df[atr]) df['basic_lb'] = mavalue - ((multiplier/10) * df[atr]) basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if ( basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] final_lb[i] = basic_lb[i] if ( basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx