# -*- coding: utf-8 -*- # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame, Series # -------------------------------- 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 datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt import math import pandas_ta as pta ######################################## Warning ######################################## # You won't get a lot of benefits by simply changing to this strategy # # with the HyperOpt values changed. # # # # You should test it closely, trying backtesting and dry running, and we recommend # # customizing the terms of sale and purchase as well. # # # # You should always be careful in real trading! # ######################################################################################### # Modified Buy / Sell params - 20210619 # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 16, "ewo_high": 5.672, "ewo_low": -19.931, "low_offset": 0.973, "rsi_buy": 59, "ewo_high_bb": 4.86, "for_ma_length": 22, "for_sigma": 1.74, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 20, "high_offset": 1.010, "pHSL": -0.178, "pPF_1": 0.01, "pPF_2": 0.048, "pSL_1": 0.009, "pSL_2": 0.043, "for_ma_length_sell": 65, "for_sigma_sell": 1.895, "rsi_high": 72, } # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) return vwma # Modified Elder Ray Index def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma)) return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend 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 class SMA_BBRSI(IStrategy): INTERFACE_VERSION = 2 # Modified ROI - 20210620 # ROI table: minimal_roi = { "0": 0.028, "10": 0.018, "30": 0.010, "40": 0.005 } # Stoploss: stoploss = -0.5 antipump_threshold = DecimalParameter(0, 0.4, default=0.25, space='buy', optimize=True) # SMAOffset base_nb_candles_buy = IntParameter( 5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter( 5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter( 0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter( 2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 ignore_roi_if_buy_signal = False # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count: int = 200 plot_config = { 'main_plot': { 'ma_buy_16': {'color': 'orange'}, 'ma_sell_20': {'color': 'purple'}, }, 'pump_strength': { 'pump_strength': {'color': 'yellow'} }, } protections = [ # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 1, # "stop_duration_candles": 6, # "only_per_pair": True # }, # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 2, # "stop_duration_candles": 6, # "only_per_pair": False # }, { "method": "LowProfitPairs", "lookback_period_candles": 60, "trade_limit": 1, "stop_duration": 60, "required_profit": -0.05 }, { "method": "MaxDrawdown", "lookback_period_candles": 24, "trade_limit": 1, "stop_duration_candles": 12, "max_allowed_drawdown": 0.2 }, ] ewo_high_bb = DecimalParameter(0, 7.0, default=buy_params['ewo_high_bb'], space='buy', optimize=True) for_sigma = DecimalParameter(0, 10.0, default=buy_params['for_sigma'], space='buy', optimize=True) for_sigma_sell = DecimalParameter(0, 10.0, default=sell_params['for_sigma_sell'], space='sell', optimize=True) rsi_high = IntParameter(60, 100, default=sell_params['rsi_high'], space='sell', optimize=True) for_ma_length = IntParameter(5, 80, default=buy_params['for_ma_length'], space='buy', optimize=True) for_ma_length_sell = IntParameter(5, 80, default=sell_params['for_ma_length_sell'], space='sell', optimize=True) is_optimize_trailing = True pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', optimize=is_optimize_trailing , 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', optimize=is_optimize_trailing , load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=is_optimize_trailing , load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=is_optimize_trailing , load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=is_optimize_trailing , load=True) use_custom_stoploss = True 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 # Only for hyperopt invalid return if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe( pair=metadata['pair'], timeframe=self.informative_timeframe) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # //@version=3 # study(" RSI + BB (EMA) + Dispersion (2.0)", overlay=false) # # // Инициализация параметров # src = input(title="Source", type=source, defval=close) // Устанавливаем тип цены для расчетов src = 'close' # for_rsi = input(title="RSI_period", type=integer, defval=14) // Период для RSI for_rsi = 14 # for_ma = input(title="Basis_BB", type=integer, defval=20) // Период для MA внутри BB # for_ma = 20 # for_mult = input(title="Stdev", type=integer, defval=2, minval=1, maxval=5) // Число стандартных отклонений для BB for_mult = 2 # for_sigma = input(title="Dispersion", type=float, defval=0.1, minval=0.01, maxval=1) // Дисперсия вокруг MA for_sigma = 0.1 # # // Условия работы скрипта # current_rsi = rsi(src, for_rsi) // Текущее положение индикатора RSI dataframe['rsi'] = ta.RSI(dataframe[src], for_rsi) dataframe['rsi_4'] = ta.RSI(dataframe[src], 4) if self.config['runmode'].value == 'hyperopt': for for_ma in range(5, 81): # basis = ema(current_rsi, for_ma) dataframe[f'basis_{for_ma}'] = ta.EMA(dataframe['rsi'], for_ma) # dev = for_mult * stdev(current_rsi, for_ma) dataframe[f'dev_{for_ma}'] = ta.STDDEV(dataframe['rsi'], for_ma) # upper = basis + dev #dataframe[f'upper_{for_ma}'] = (dataframe[f'basis_{for_ma}'] + (dataframe[f'dev_{for_ma}'] * for_mult)) # lower = basis - dev #dataframe[f'lower_{for_ma}'] = dataframe[f'basis_{for_ma}'] - (dataframe[f'dev_{for_ma}'] * for_mult) # disp_up = basis + ((upper - lower) * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (сверху) # dataframe[f'disp_up_{for_ma}'] = dataframe[f'basis_{for_ma}'] + ((dataframe[f'upper_{for_ma}'] - dataframe[f'lower_{for_ma}']) * for_sigma) # disp_down = basis - ((upper - lower) * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (снизу) # dataframe[f'disp_down_{for_ma}'] = dataframe[f'basis_{for_ma}'] - ((dataframe[f'upper_{for_ma}'] - dataframe[f'lower_{for_ma}']) * for_sigma) # color_rsi = current_rsi >= disp_up ? lime : current_rsi <= disp_down ? red : #ffea00 // Текущий цвет RSI, в зависимости от его местоположения внутри BB else: dataframe[f'basis_{self.for_ma_length.value}'] = ta.EMA(dataframe['rsi'], self.for_ma_length.value) dataframe[f'basis_{self.for_ma_length_sell.value}'] = ta.EMA(dataframe['rsi'], self.for_ma_length_sell.value) # dev = for_mult * stdev(current_rsi, for_ma) dataframe[f'dev_{self.for_ma_length.value}'] = ta.STDDEV(dataframe['rsi'], self.for_ma_length.value) dataframe[f'dev_{self.for_ma_length_sell.value}'] = ta.STDDEV(dataframe['rsi'], self.for_ma_length_sell.value) # # // Дополнительные линии и заливка для областей для RSI # h1 = hline(70, color=#d4d4d4, linestyle=dotted, linewidth=1) h1 = 70 # h2 = hline(30, color=#d4d4d4, linestyle=dotted, linewidth=1) h2 = 30 # fill (h1, h2, transp=95) # # // Алерты и условия срабатывания # rsi_Green = crossover(current_rsi, disp_up) # rsi_Red = crossunder(current_rsi, disp_down) # alertcondition(condition=rsi_Green, # title="RSI cross Above Dispersion Area", # message="The RSI line closing crossed above the Dispersion area.") # # alertcondition(condition=rsi_Red, # title="RSI cross Under Dispersion Area", # message="The RSI line closing crossed below the Dispersion area") # # // Результаты и покраска # plot(basis, color=black) # plot(upper, color=#00fff0, linewidth=2) # plot(lower, color=#00fff0, linewidth=2) # s1 = plot(disp_up, color=white) # s2 = plot(disp_down, color=white) # fill(s1, s2, color=white, transp=80) # plot(current_rsi, color=color_rsi, linewidth=2) #ATR dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['atr_high'] = (dataframe['high'] - (dataframe['atr'] * 3.5)).rolling(2).max() dataframe['ema_atr'] = ta.EMA(dataframe['atr'], timeperiod=14) # #HLC3 # dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 # #OTF # dataframe['hlc3OTF'] = OTF(dataframe, source='close') # #ZLEMA BUY # dataframe['zlema_1'] = dataframe['hlc3OTF'] # dataframe['zlema_1_std'] = dataframe['close'] # dataframe['ema_data'] = dataframe['hlc3OTF'] + (dataframe['hlc3OTF'] - dataframe['hlc3OTF'].shift(2)) # dataframe['ema_data_2'] = dataframe['hlc3OTF'] + (dataframe['hlc3OTF'] - dataframe['hlc3OTF'].shift(1)) # dataframe['zlema_4'] = ta.EMA(dataframe['ema_data'], timeperiod = 4) # dataframe['zlema_2'] = ta.EMA(dataframe['ema_data_2'], timeperiod = 2) # dataframe['ema_data_4_std'] = dataframe['close'] + (dataframe['close'] - dataframe['close'].shift(2)) # dataframe['zlema_4_std'] = ta.EMA(dataframe['ema_data_4_std'], timeperiod = 4) # Modified Elder Ray Index dataframe['moderi_96'] = moderi(dataframe, 96) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) #CCI dataframe['cci_slow'] = pta.cci(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=240) dataframe['cci_fast'] = pta.cci(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=20) # Calculate all ma_buy values for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) #pump stregth dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dont_buy_conditions = [] dont_buy_conditions.append( (dataframe['pump_strength'] > self.antipump_threshold.value) ) conditions.append( ( (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( (dataframe['rsi'] < (dataframe[f'basis_{self.for_ma_length.value}'] - (dataframe[f'dev_{self.for_ma_length.value}'] * self.for_sigma.value))) & ( ( (dataframe['EWO'] > self.ewo_high_bb.value) & (dataframe['EWO'] < 10) ) | ( (dataframe['EWO'] >= 10) & (dataframe['rsi'] < 40) ) ) & (dataframe['rsi_4'] < 25) & (dataframe['volume'] > 0) & (dataframe['cci_fast'] < 100) # & # (qtpylib.crossed_above(dataframe['hlc3OTF'], dataframe['zlema_4'])) # & # (dataframe["roc_bbwidth_max"] < 70) ) & (dataframe['moderi_96'] == True) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ]=1 if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( ((( (dataframe['rsi'] > self.rsi_high.value) | # upper = basis + dev # lower = basis - dev # disp_down = basis - ((upper - lower) * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (снизу) # disp_down = basis - ((2* dev * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (снизу) (dataframe['rsi'] > dataframe[f'basis_{self.for_ma_length_sell.value}'] + ((dataframe[f'dev_{self.for_ma_length_sell.value}'] * self.for_sigma_sell.value))) ) & (dataframe['moderi_96'] == True)) | ( (qtpylib.crossed_below(dataframe['close'], dataframe['atr_high'])) ))& (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe