# source: https://raw.githubusercontent.com/jameszaa52/freqtrade/28f9dad9ebcc1e2d56d7286488ac02148ab2358c/user_data/strategies/BinHModWhiteHOV0.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 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 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 github_jameszaa52_freqtrade__BinHModWhiteHOV0__20210820_105637(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 10 } # Buy hyperspace params: buy_params = { "bb_bottom_delta_diff": 0.006, "bb_delta_diff": 0.022, "bb_tail_diff": 0.132, "bb_top_delta_diff": 0.013, "bb_width": 0.069, "ema_rise": 54, "ema_slow": 160, "ema_trend": 90, "ewo_base": 70, "ewo_high": 2.9, "ewo_step": 90, "buy_protection_one": True, # value loaded from strategy "buy_protection_three": True, # value loaded from strategy "buy_protection_two": True, # value loaded from strategy } # Sell hyperspace params: sell_params = { "high_offset": 1.0, # value loaded from strategy } # Stoploss: stoploss = -0.3 # Protections buy_protection_one = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_protection_two = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_protection_three = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) # Buy ema_trend = CategoricalParameter([50, 70, 90, 110], default=buy_params['ema_trend'], space='buy', optimize=True, load=True) ema_slow = CategoricalParameter([140, 160, 180, 200, 220, 240, 260, 280, 300], default=buy_params['ema_slow'], space='buy', optimize=True, load=True) ema_rise = CategoricalParameter([6, 12, 18, 24, 30, 36, 42, 48, 54, 60], default=buy_params['ema_rise'], space='buy', optimize=True, load=True) # Strat bb_width = DecimalParameter(0.001, 0.070, default=buy_params['bb_width'], decimals=3, space='buy', optimize=True) bb_delta_diff = DecimalParameter(0.001, 0.06, default=buy_params['bb_delta_diff'], decimals=3, space='buy', optimize=True) bb_tail_diff = DecimalParameter(0.050, 0.450, default=buy_params['bb_tail_diff'], decimals=3, space='buy', optimize=True) bb_bottom_delta_diff = DecimalParameter(0.005, 0.040, default=buy_params['bb_bottom_delta_diff'], decimals=3, space='buy', optimize=True) bb_top_delta_diff = DecimalParameter(0.005, 0.090, default=buy_params['bb_top_delta_diff'], decimals=3, space='buy', optimize=True) ewo_base = CategoricalParameter([50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150], default=buy_params['ewo_base'], space='buy', optimize=True, load=True) ewo_step = CategoricalParameter([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150], default=buy_params['ewo_step'], space='buy', optimize=True, load=True) ewo_high = DecimalParameter(1.0, 6.0, default=buy_params['ewo_high'], decimals=1, space='buy', optimize=True) # Sell high_offset = DecimalParameter(1.000, 1.020, default=sell_params['high_offset'], decimals=3, space='sell', optimize=False, load=True) # Trailing stop: trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.05 # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 ignore_roi_if_buy_signal = True # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count = 500 use_custom_stoploss = False 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: # Bottoms and Tops dataframe['bottom'] = dataframe[['open', 'close']].min(axis=1) dataframe['top'] = dataframe[['open', 'close']].max(axis=1) # BB 40 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbwidth'] = (bb_40['upper'] - bb_40['lower']) / bb_40['mid'] dataframe['space'] = dataframe['bbwidth'].rolling(60).mean() dataframe['bbdelta'] = (bb_40['mid'] - bb_40['lower']).abs() dataframe['bottomdelta'] = (dataframe['bottom'] - dataframe['bottom'].shift()).abs() dataframe['topdelta'] = (dataframe['top'] - dataframe['top'].shift()).abs() dataframe['tail'] = (dataframe['bottom'] - dataframe['low']).abs() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # EMA protect dataframe[f'ema_trend_{self.ema_trend.value}'] = ta.EMA(dataframe, timeperiod=int(self.ema_trend.value)) dataframe[f'ema_slow_{self.ema_slow.value}'] = ta.EMA(dataframe, timeperiod=int(self.ema_slow.value)) # Elliot dataframe[f'EWO_{self.ewo_base.value}_{self.ewo_step.value}'] = EWO(dataframe, (self.ewo_base.value), (self.ewo_base.value + self.ewo_step.value)) if self.buy_protection_one.value: conditions.append( ( # Bull trend (dataframe[f'ema_trend_{self.ema_trend.value}'] > dataframe[f'ema_slow_{self.ema_slow.value}']) ) ) if self.buy_protection_two.value: conditions.append( ( # Rising trend (dataframe[f'ema_slow_{self.ema_slow.value}'] > dataframe[f'ema_slow_{self.ema_slow.value}'].shift(self.ema_rise.value)) & (dataframe[f'ema_slow_{self.ema_slow.value}'].shift(self.ema_rise.value) > dataframe[f'ema_slow_{self.ema_slow.value}'].shift(self.ema_rise.value + self.ema_rise.value)) ) ) if self.buy_protection_three.value: conditions.append( ( # EWO (dataframe[f'EWO_{self.ewo_base.value}_{self.ewo_step.value}'] > self.ewo_high.value) ) ) # Main conditions.append( ( # Strat (dataframe['space'] > self.bb_width.value) & (dataframe['bottom'] < dataframe['lower'].shift()) & (dataframe['bottom'] < dataframe['bottom'].shift()) & (dataframe['bbdelta'] > dataframe['bottom'] * self.bb_delta_diff.value) & (dataframe['tail'] < dataframe['bbdelta'] * self.bb_tail_diff.value) & (dataframe['bottomdelta'] > (dataframe['bottom'] * self.bb_bottom_delta_diff.value)) & (dataframe['topdelta'] < (dataframe['top'] * self.bb_top_delta_diff.value)) & (dataframe['lower'].shift() > 0) & # Volume (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 = [] conditions.append( ( (dataframe['close'] > dataframe['mid'] * self.high_offset.value) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe