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Jérôme Delacotte
191
SMAOffsetProtectOptV1Mod2.py
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191
SMAOffsetProtectOptV1Mod2.py
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from typing import Dict, List
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from functools import reduce
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from pandas import DataFrame
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# --------------------------------
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import talib.abstract as ta
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import numpy as np
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import datetime
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from technical.util import resample_to_interval, resampled_merge
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
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import technical.indicators as ftt
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######################################## Warning ########################################
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# You won't get a lot of benefits by simply changing to this strategy #
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# with the HyperOpt values changed. #
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# #
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# You should test it closely, trying backtesting and dry running, and we recommend #
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# customizing the terms of sale and purchase as well. #
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# #
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# You should always be careful in real trading! #
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#########################################################################################
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# Modified Buy / Sell params - 20210619
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# Buy hyperspace params:
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buy_params = {
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"base_nb_candles_buy": 16,
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"ewo_high": 5.672,
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"ewo_low": -19.931,
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"low_offset": 0.973,
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"rsi_buy": 59,
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}
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# Sell hyperspace params:
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sell_params = {
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"base_nb_candles_sell": 20,
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"high_offset": 1.010,
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}
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def EWO(dataframe, ema_length=5, ema2_length=35):
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df = dataframe.copy()
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ema1 = ta.EMA(df, timeperiod=ema_length)
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ema2 = ta.EMA(df, timeperiod=ema2_length)
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emadif = (ema1 - ema2) / df['close'] * 100
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return emadif
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class SMAOffsetProtectOptV1Mod2(IStrategy):
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INTERFACE_VERSION = 2
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# Modified ROI - 20210620
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# ROI table:
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minimal_roi = {
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"0": 0.028,
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"10": 0.018,
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"30": 0.010,
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"40": 0.005
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}
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# Stoploss:
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stoploss = -0.5
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# SMAOffset
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base_nb_candles_buy = IntParameter(
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5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
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base_nb_candles_sell = IntParameter(
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5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
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low_offset = DecimalParameter(
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0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
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high_offset = DecimalParameter(
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0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
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# Protection
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fast_ewo = 50
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slow_ewo = 200
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ewo_low = DecimalParameter(-20.0, -8.0,
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default=buy_params['ewo_low'], space='buy', optimize=True)
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ewo_high = DecimalParameter(
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2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
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rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
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# Trailing stop:
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trailing_stop = False
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trailing_stop_positive = 0.001
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trailing_stop_positive_offset = 0.01
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trailing_only_offset_is_reached = True
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# Sell signal
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use_sell_signal = True
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sell_profit_only = False
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sell_profit_offset = 0.01
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ignore_roi_if_buy_signal = False
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# Optimal timeframe for the strategy
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timeframe = '5m'
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informative_timeframe = '1h'
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process_only_new_candles = True
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startup_candle_count: int = 30
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plot_config = {
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'main_plot': {
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'ma_buy': {'color': 'orange'},
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'ma_sell': {'color': 'orange'},
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},
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}
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use_custom_stoploss = False
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def informative_pairs(self):
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pairs = self.dp.current_whitelist()
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informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
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return informative_pairs
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def get_informative_indicators(self, metadata: dict):
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dataframe = self.dp.get_pair_dataframe(
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pair=metadata['pair'], timeframe=self.informative_timeframe)
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Calculate all ma_buy values
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for val in self.base_nb_candles_buy.range:
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dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
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# Calculate all ma_sell values
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for val in self.base_nb_candles_sell.range:
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dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
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# Elliot
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dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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conditions.append(
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(
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(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
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(dataframe['EWO'] > self.ewo_high.value) &
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(dataframe['rsi'] < self.rsi_buy.value) &
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(dataframe['volume'] > 0)
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)
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)
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conditions.append(
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(
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(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
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(dataframe['EWO'] < self.ewo_low.value) &
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(dataframe['volume'] > 0)
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)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x | y, conditions),
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'buy'
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]=1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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conditions.append(
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(
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(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
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(dataframe['volume'] > 0)
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)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x | y, conditions),
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'sell'
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]=1
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return dataframe
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