# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/SMAOffsetProtectOptV1Mod2.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 ######################################## Warning ######################################## # You won't get a lot of benefits by simply changing to this strategy # # with the HyperOpt values changed. # # # # You should test it closely, trying backtesting and dry running, and we recommend # # customizing the terms of sale and purchase as well. # # # # You should always be careful in real trading! # ######################################################################################### # Modified Buy / Sell params - 20210619 # Buy hyperspace params: entry_params = {'base_nb_candles_entry': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_entry': 59} # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 20, 'high_offset': 1.01} 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__SMAOffsetProtectOptV1Mod2__20260115_122204(IStrategy): INTERFACE_VERSION = 3 # Modified ROI - 20210620 # ROI table: minimal_roi = {'0': 0.028, '10': 0.018, '30': 0.01, '40': 0.005} # Stoploss: stoploss = -0.5 antipump_threshold = DecimalParameter(0, 0.4, default=0.25, space='entry', optimize=True) # 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 = 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 = 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 = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count: int = 200 plot_config = {'main_plot': {'ma_entry_16': {'color': 'orange'}, 'ma_exit_20': {'color': 'purple'}}, 'pump_strength': {'pump_strength': {'color': 'yellow'}}} 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, self.slow_ewo) #pump stregth dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] # 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_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0)) conditions.append((dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'entry'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit'] = 1 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, self.slow_ewo) #pump stregth dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe class Github_DerSalvador_freqtrade_helm_chart__SMAOffsetProtectOptV1Mod2__20260115_122204_antipump(Github_DerSalvador_freqtrade_helm_chart__SMAOffsetProtectOptV1Mod2__20260115_122204): def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dont_entry_conditions = [] dont_entry_conditions.append(dataframe['pump_strength'] > self.antipump_threshold.value) conditions.append((dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0)) conditions.append((dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'entry'] = 1 if dont_entry_conditions: for condition in dont_entry_conditions: dataframe.loc[condition, 'entry'] = 0 return dataframe