# source: https://raw.githubusercontent.com/TheoBrigitte/freqtrade/82c1c1e1f5f3457fa5376f65124042aff88d7493/strategies/smas/SMAOffsetProtectOptV0.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 SMA = 'SMA' EMA = 'EMA' # Buy hyperspace params: #buy_params = { # "base_nb_candles_buy": 20, # "ewo_high": 6, # "fast_ewo": 50, # "slow_ewo": 200, # "low_offset": 0.958, # "buy_trigger": "EMA", # "ewo_high": 2.0, # "ewo_low": -16.062, # "rsi_buy": 51, #} buy_params = { "base_nb_candles_buy": 20, "ewo_high": 5.499, "ewo_low": -19.881, "low_offset": 0.975, "rsi_buy": 67, "buy_trigger": "EMA", # value loaded from strategy "fast_ewo": 50, # value loaded from strategy "slow_ewo": 200, # value loaded from strategy "buy_trigger": "EMA", } # Sell hyperspace params: #sell_params = { # "base_nb_candles_sell": 20, # "high_offset": 1.012, # "sell_trigger": "EMA", #} # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 24, "high_offset": 1.012, "sell_trigger": "EMA", } 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_TheoBrigitte_freqtrade__SMAOffsetProtectOptV0__20241203_205711(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 0.01 } # Stoploss: stoploss = -0.5 # 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) buy_trigger = CategoricalParameter( [SMA, EMA], default=buy_params['buy_trigger'], space='buy', optimize=False) sell_trigger = CategoricalParameter( [SMA, EMA], default=sell_params['sell_trigger'], space='sell', optimize=False) # Protection 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) fast_ewo = IntParameter( 10, 50, default=buy_params['fast_ewo'], space='buy', optimize=False) slow_ewo = IntParameter( 100, 200, default=buy_params['slow_ewo'], space='buy', optimize=False) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # slow_ema = IntParameter( # 10, 50, default=buy_params['fast_ewo'], space='buy', optimize=True) # fast_ema = IntParameter( # 100, 200, default=buy_params['slow_ewo'], 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_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = True # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' use_exit_signal = True exit_profit_only = False process_only_new_candles = True startup_candle_count = 30 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.informative_timeframe) for pair in pairs] # EMA informative_pairs['ema_50'] = ta.EMA(informative_pairs, timeperiod=50) informative_pairs['ema_100'] = ta.EMA(informative_pairs, timeperiod=100) informative_pairs['ema_200'] = ta.EMA(informative_pairs, timeperiod=200) # SMA informative_pairs['sma_200'] = ta.SMA(informative_pairs, timeperiod=200) informative_pairs['sma_200_dec'] = informative_pairs['sma_200'] < informative_pairs['sma_200'].shift( 20) # RSI informative_pairs['rsi'] = ta.RSI(informative_pairs, timeperiod=14) 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: # informative = self.get_informative_indicators(metadata) # dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, # ffill=True) # Calculate all base_nb_candles_buy values for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all base_nb_candles_buy values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) # ---------------- original code ------------------- ##SMAOffset #if self.buy_trigger.value == 'EMA': # dataframe['ma_buy'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_buy.value) #else: # dataframe['ma_buy'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_buy.value) # #if self.sell_trigger.value == 'EMA': # dataframe['ma_sell'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_sell.value) #else: # dataframe['ma_sell'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_sell.value) # #dataframe['ma_offset_buy'] = dataframe['ma_buy'] * self.low_offset.value #dataframe['ma_offset_sell'] = dataframe['ma_sell'] * self.high_offset.value # ------------ end original code -------------------- # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] 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) ) ) # ---------------- original code ------------------- #conditions.append( # ( # (dataframe['close'] < dataframe['ma_offset_buy']) & # (dataframe['EWO'] > self.ewo_high.value) & # (dataframe['rsi'] < self.rsi_buy.value) & # (dataframe['volume'] > 0) # ) #) #conditions.append( # ( # (dataframe['close'] < dataframe['ma_offset_buy']) & # (dataframe['EWO'] < self.ewo_low.value) & # (dataframe['volume'] > 0) # ) #) # ------------ end original code -------------------- if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ]=1 return dataframe def populate_exit_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) ) ) # ---------------- original code ------------------- #conditions.append( # ( # (dataframe['close'] > dataframe['ma_offset_sell']) & # (dataframe['volume'] > 0) # ) #) # ------------ end original code -------------------- if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe