# --- 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 datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter # author @tirail ma_types = { 'SMA': ta.SMA, 'EMA': ta.EMA, } class SMAOffsetBZed(IStrategy): INTERFACE_VERSION = 2 # Buy hyperspace params: # buy_params = { # "buy_dip_candles_1": 2, # "buy_dip_candles_2": 12, # "buy_dip_candles_3": 130, # "buy_dip_threshold_1": 0.12, # "buy_dip_threshold_2": 0.28, # "buy_dip_threshold_3": 0.36, # "base_nb_candles_buy": 19, # value loaded from strategy # "buy_trigger": "SMA", # value loaded from strategy # "low_offset": 0.969, # value loaded from strategy # } # # Sell hyperspace params: # sell_params = { # "base_nb_candles_sell": 30, # value loaded from strategy # "high_offset": 1.012, # value loaded from strategy # "sell_trigger": "EMA", # value loaded from strategy # } # Buy hyperspace params: buy_params = { "buy_dip_candles_1": 3, "buy_dip_candles_2": 29, "buy_dip_candles_3": 130, "buy_dip_threshold_1": 0.13, "buy_dip_threshold_2": 0.2, "buy_dip_threshold_3": 0.25, "base_nb_candles_buy": 19, # value loaded from strategy "buy_trigger": "SMA", # value loaded from strategy "low_offset": 0.969, # value loaded from strategy } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 30, # value loaded from strategy "high_offset": 1.012, # value loaded from strategy "sell_trigger": "EMA", # value loaded from strategy } # Stoploss: stoploss = -0.23 # ROI table: minimal_roi = { "0": 1, } inf_1h = '1h' # informative tf optimize_dip = False optimize_non_dip = True base_nb_candles_buy = IntParameter(5, 80, default=30, space='buy', optimize=optimize_non_dip, load=True) low_offset = DecimalParameter(0.8, 0.99, default=0.958, space='buy', optimize=optimize_non_dip, load=True) buy_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=optimize_dip, load=True) buy_dip_threshold_2 = DecimalParameter(0.02, 0.5, default=0.28, space='buy', decimals=2, optimize=optimize_dip, load=True) buy_dip_threshold_3 = DecimalParameter(0.02, 0.5, default=0.28, space='buy', decimals=2, optimize=optimize_dip, load=True) buy_dip_candles_1 = IntParameter(1, 20, default=2, space='buy', optimize=optimize_dip, load=True) buy_dip_candles_2 = IntParameter(1, 40, default=10, space='buy', optimize=optimize_dip, load=True) buy_dip_candles_3 = IntParameter(40, 140, default=132, space='buy', optimize=optimize_dip, load=True) # sell params base_nb_candles_sell = IntParameter(5, 80, default=30, load=True, optimize=True, space='sell') high_offset = DecimalParameter(0.8, 1.1, default=1.012, load=True, optimize=True, space='sell') # they are fine as they are. sell_trigger = CategoricalParameter(ma_types.keys(), default='EMA', space='sell', load=True, optimize=False) buy_trigger = CategoricalParameter(ma_types.keys(), default='SMA', space='buy', optimize=False, load=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.1 trailing_only_offset_is_reached = True # Optimal timeframe for the strategy timeframe = '5m' use_sell_signal = True sell_profit_only = False process_only_new_candles = True startup_candle_count = 200 plot_config = { 'main_plot': { 'ma_offset_buy': {'color': 'orange'}, 'ma_offset_sell': {'color': 'orange'}, }, } use_custom_stoploss = False def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_1h) for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) return informative_1h def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: return 1 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.config['runmode'].value == 'hyperopt': dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value == 'hyperopt': dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value dataframe.loc[ ( (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (((dataframe['open'].rolling(int(self.buy_dip_candles_1.value)).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) & (((dataframe['open'].rolling(int(self.buy_dip_candles_1.value + self.buy_dip_candles_2.value)).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) & (((dataframe['open'].rolling(int(self.buy_dip_candles_1.value + self.buy_dip_candles_2.value + self.buy_dip_candles_3.value)).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value) & (dataframe['close'] < dataframe['ma_offset_buy']) & (dataframe['volume'] > 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value == 'hyperopt': dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value dataframe.loc[ ( (dataframe['close'] > dataframe['ma_offset_sell']) & (dataframe['volume'] > 0) ), 'sell'] = 1 return dataframe