# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/NotAnotherSMAOffsetStrategyModHO.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 from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds 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 # @Rallipanos # Buy hyperspace params: # entry_params = { # "base_nb_candles_entry": 7, # "ewo_high": 3.004, # "ewo_low": -9.551, # "low_offset": 0.984, # "rsi_entry": 56, # } # # Sell hyperspace params: # exit_params = { # "base_nb_candles_exit": 19, # "high_offset": 1.0, # "high_offset_2": 0.998, # } # # Buy hyperspace params: # entry_params = { # "base_nb_candles_entry": 12, # "ewo_high": 2.38, # "ewo_low": -9.496, # "low_offset": 0.986, # "rsi_entry": 65, # } # Sell hyperspace params: # exit_params = { # "base_nb_candles_exit": 11, # "high_offset": 1.0, # "high_offset_2": 0.995, # } # Buy hyperspace params: # entry_params = { # "base_nb_candles_entry": 12, # "ewo_high": 2.303, # "ewo_low": -8.114, # "low_offset": 0.986, # "rsi_entry": 68, # } # Buy hyperspace params: # entry_params = { # "base_nb_candles_entry": 10, # "ewo_high": 3.751, # "ewo_low": -9.735, # "low_offset": 0.984, # "rsi_entry": 68, # } # Buy hyperspace params: # entry_params = { # "base_nb_candles_entry": 10, # "ewo_high": 3.734, # "ewo_low": -9.551, # "low_offset": 0.984, # "rsi_entry": 65, # } # Buy hyperspace params: entry_params = {'base_nb_candles_entry': 10, 'ewo_high': 3.206, 'ewo_low': -10.69, 'low_offset': 0.984, 'rsi_entry': 63} # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 6, 'high_offset': 1.002, 'high_offset_2': 1.0} 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['low'] * 100 return emadif class Github_DerSalvador_freqtrade_helm_chart__NotAnotherSMAOffsetStrategyModHO__20260115_122204(IStrategy): INTERFACE_VERSION = 3 # ROI table: # "20": 0.09, # "40": 0.029, # "90": 0 minimal_roi = {'0': 0.214} # minimal_roi = { # "0": 0.99, # } # Stoploss: stoploss = -0.32 # 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(0.95, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], 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 = True trailing_stop_positive = 0.0075 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True use_custom_stoploss = 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' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 plot_config = {'main_plot': {'ma_entry': {'color': 'orange'}, 'ma_exit': {'color': 'orange'}}} slippage_protection = {'retries': 3, 'max_slippage': -0.02} def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] if last_candle is not None: # if (exit_reason in ['roi','exit_signal','trailing_stop_loss']): if exit_reason in ['exit_signal']: if last_candle['block_trade_exit']: return False if last_candle['di_up'] and last_candle['adx'] > previous_candle_1['adx']: return False if last_candle['hma_50'] * 1.149 > last_candle['ema_100'] and last_candle['close'] < last_candle['ema_100'] * 0.951: # *1.2 return False # slippage try: state = self.slippage_protection['__pair_retries'] except KeyError: state = self.slippage_protection['__pair_retries'] = {} candle = dataframe.iloc[-1].squeeze() slippage = rate / candle['close'] - 1 if slippage < self.slippage_protection['max_slippage']: pair_retries = state.get(pair, 0) if pair_retries < self.slippage_protection['retries']: state[pair] = pair_retries + 1 return False state[pair] = 0 return True def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: stoploss = self.stoploss dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if last_candle is None: return stoploss trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc - timedelta(seconds=timeframe_to_seconds(self.timeframe))) trade_candle = dataframe.loc[dataframe['date'] == trade_date] if trade_candle.empty: return stoploss trade_candle = trade_candle.squeeze() dur_minutes = (current_time - trade.open_date_utc).seconds // 60 slippage_ratio = trade.open_rate / trade_candle['close'] - 1 slippage_ratio = slippage_ratio if slippage_ratio > 0 else 0 current_profit_comp = current_profit + slippage_ratio if current_profit_comp >= self.trailing_stop_positive_offset: return self.trailing_stop_positive for x in self.minimal_roi: dur = int(x) roi = self.minimal_roi[x] if dur_minutes >= dur and current_profit_comp >= roi: return 0.001 return stoploss 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) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) # confirm_trade_exit dataframe['adx'] = ta.ADX(dataframe, timeperiod=2) dataframe['di_up'] = ta.PLUS_DI(dataframe, timeperiod=2) > ta.MINUS_DI(dataframe, timeperiod=2) rsi2 = ta.RSI(dataframe, timeperiod=2) rsi4 = ta.RSI(dataframe, timeperiod=4) dataframe['block_trade_exit'] = rsi2 > rsi4 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['rsi_fast'] < 35) & (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) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value)) conditions.append((dataframe['rsi_fast'] < 35) & (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) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value)) 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['sma_9']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow'])) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe