# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/SMAOffsetProtectOpt.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 # Buy hyperspace params: orginal # entry_params = { # "base_nb_candles_entry": 16, # "ewo_high": 5.638, # "ewo_low": -19.993, # "low_offset": 0.978, # "rsi_entry": 61, # "fast_ewo": 50, # value loaded from strategy # "slow_ewo": 200, # value loaded from strategy # } # Buy hyperspace params: from v0 # value loaded from strategy # value loaded from strategy entry_params = {'base_nb_candles_entry': 20, 'ewo_high': 5.499, 'ewo_low': -19.881, 'low_offset': 0.975, 'rsi_entry': 50, 'fast_ewo': 50, 'slow_ewo': 200} # Sell hyperspace params:orginal # exit_params = { # "base_nb_candles_exit": 49, # "high_offset": 1.006, # } # Sell hyperspace params: from v0 exit_params = {'base_nb_candles_exit': 24, 'high_offset': 1.012} 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_DerSalvador_freqtrade_helm_chart__SMAOffsetProtectOpt__20260115_122204(IStrategy): INTERFACE_VERSION = 3 # ROI table: minimal_roi = {'0': 0.2, '38': 0.074, '78': 0.025, '194': 0} # Stoploss: stoploss = -0.228 # SMAOffset base_nb_candles_entry = IntParameter(5, 80, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True) base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=True) high_offset = DecimalParameter(0.99, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) # Protection fast_ewo = IntParameter(10, 50, default=entry_params['fast_ewo'], space='entry', optimize=False) slow_ewo = IntParameter(100, 200, default=entry_params['slow_ewo'], space='entry', optimize=False) # fast_ewo = 50 # slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=True) rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.049 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = True ## Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'ioc'} # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count = 50 plot_config = {'main_plot': {'ma_entry': {'color': 'green'}, 'ma_exit': {'color': 'red'}}, 'subplots': {'RSI': {'rsi': {'color': '#fe2e34', 'type': 'line'}}, 'EWO': {'EWO': {'color': '#c7d729', 'type': 'line'}}}} 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: # Calculate all ma_entry values for val in self.base_nb_candles_entry.range: dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_exit values for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) # 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 = [] dataframe['ma_entry'] = dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value # (dataframe['close'].shift(1) < dataframe['ma_entry']) & # (dataframe['low'] < dataframe['ma_entry']) & # (dataframe['close'] > dataframe['ma_entry']) & # (qtpylib.crossed_above(dataframe['close'], dataframe['ma_entry'])) & conditions.append((dataframe['close'] < dataframe['ma_entry']) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0)) # (dataframe['close'].shift(1) < dataframe['ma_entry']) & # (dataframe['low'] < dataframe['ma_entry']) & # (dataframe['close'] > dataframe['ma_entry']) & # (qtpylib.crossed_above(dataframe['close'], dataframe['ma_entry'])) & conditions.append((dataframe['close'] < dataframe['ma_entry']) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe['ma_exit'] = dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value # (dataframe['close'] > (dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value)) & conditions.append(qtpylib.crossed_below(dataframe['close'], dataframe['ma_exit']) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe