# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/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: #entry_params = { # "base_nb_candles_entry": 20, # "ewo_high": 6, # "fast_ewo": 50, # "slow_ewo": 200, # "low_offset": 0.958, # "entry_trigger": "EMA", # "ewo_high": 2.0, # "ewo_low": -16.062, # "rsi_entry": 51, #} # value loaded from strategy # 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': 67, 'entry_trigger': 'EMA', 'fast_ewo': 50, 'slow_ewo': 200, 'entry_trigger': 'EMA'} # Sell hyperspace params: #exit_params = { # "base_nb_candles_exit": 20, # "high_offset": 1.012, # "exit_trigger": "EMA", #} # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 24, 'high_offset': 1.012, 'exit_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_DerSalvador_freqtrade_helm_chart__SMAOffsetProtectOptV0__20260416_224245(IStrategy): INTERFACE_VERSION = 3 # ROI table: minimal_roi = {'0': 0.01} # Stoploss: stoploss = -0.5 # 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) entry_trigger = CategoricalParameter([SMA, EMA], default=entry_params['entry_trigger'], space='entry', optimize=False) exit_trigger = CategoricalParameter([SMA, EMA], default=exit_params['exit_trigger'], space='exit', optimize=False) # Protection 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) 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) rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True) # slow_ema = IntParameter( # 10, 50, default=entry_params['fast_ewo'], space='entry', optimize=True) # fast_ema = IntParameter( # 100, 200, default=entry_params['slow_ewo'], 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 = 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_entry': {'color': 'orange'}, 'ma_offset_exit': {'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_entry values for val in self.base_nb_candles_entry.range: dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all base_nb_candles_entry values for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) # ---------------- original code ------------------- ##SMAOffset #if self.entry_trigger.value == 'EMA': # dataframe['ma_entry'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_entry.value) #else: # dataframe['ma_entry'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_entry.value) # #if self.exit_trigger.value == 'EMA': # dataframe['ma_exit'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_exit.value) #else: # dataframe['ma_exit'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_exit.value) # #dataframe['ma_offset_entry'] = dataframe['ma_entry'] * self.low_offset.value #dataframe['ma_offset_exit'] = dataframe['ma_exit'] * 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_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)) # ---------------- original code ------------------- #conditions.append( # ( # (dataframe['close'] < dataframe['ma_offset_entry']) & # (dataframe['EWO'] > self.ewo_high.value) & # (dataframe['rsi'] < self.rsi_entry.value) & # (dataframe['volume'] > 0) # ) #) #conditions.append( # ( # (dataframe['close'] < dataframe['ma_offset_entry']) & # (dataframe['EWO'] < self.ewo_low.value) & # (dataframe['volume'] > 0) # ) #) # ------------ end original code -------------------- 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 = [] conditions.append((dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0)) # ---------------- original code ------------------- #conditions.append( # ( # (dataframe['close'] > dataframe['ma_offset_exit']) & # (dataframe['volume'] > 0) # ) #) # ------------ end original code -------------------- if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe