# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-michael-k8s-namespace/YorganStrategy.py # --- Do not remove these libs --- # --- Do not remove these libs --- import datetime from datetime import datetime, timedelta from functools import reduce from typing import Dict, List import numpy as np # -------------------------------- import talib.abstract as ta import technical.indicators as ftt from pandas import DataFrame from technical.util import resample_to_interval, resampled_merge import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter, merge_informative_pair, stoploss_from_open from freqtrade.strategy.interface import IStrategy # @Rallipanos # # Buy hyperspace params: entry_params = {'base_nb_candles_entry': 14, 'ewo_high': 2.327, 'ewo_high_2': -2.327, 'ewo_low': -20.988, 'low_offset': 0.975, 'low_offset_2': 0.955, 'rsi_entry': 69} # # Buy hyperspace params: entry_params = {'base_nb_candles_entry': 18, 'ewo_high': 3.422, 'ewo_high_2': -3.436, 'ewo_low': -8.562, 'low_offset': 0.966, 'low_offset_2': 0.959, 'rsi_entry': 66} # # # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 17, 'high_offset': 0.997, 'high_offset_2': 1.01} # # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 7, 'high_offset': 1.014, 'high_offset_2': 0.995} # # Buy hyperspace params: # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy entry_params = {'ewo_high_2': -5.642, 'low_offset_2': 0.951, 'rsi_entry': 54, 'base_nb_candles_entry': 16, 'ewo_high': 3.422, 'ewo_low': -8.562, 'low_offset': 0.966} # # Sell hyperspace params: # value loaded from strategy exit_params = {'base_nb_candles_exit': 8, 'high_offset_2': 1.002, 'high_offset': 1.014} # Buy hyperspace params: # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy entry_params = {'base_nb_candles_entry': 8, 'ewo_high': 4.179, 'ewo_low': -16.917, 'ewo_high_2': -2.609, 'low_offset': 0.986, 'low_offset_2': 0.944, 'rsi_entry': 58} # Sell hyperspace params: # value loaded from strategy exit_params = {'base_nb_candles_exit': 16, 'high_offset': 1.054, 'high_offset_2': 1.018} 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__YorganStrategy__20260416_224245(IStrategy): INTERFACE_VERSION = 3 # ROI table: minimal_roi = {'0': 0.283, '40': 0.086, '99': 0.036, '0': 10} # Stoploss: stoploss = -0.1 # SMAOffset base_nb_candles_entry = IntParameter(2, 20, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True) base_nb_candles_exit = IntParameter(10, 40, 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) low_offset_2 = DecimalParameter(0.9, 0.99, default=entry_params['low_offset_2'], 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) ewo_high_2 = DecimalParameter(-6.0, 12.0, default=entry_params['ewo_high_2'], 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 = 0.0033 trailing_stop_positive_offset = 0.03 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 # Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = False 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] if last_candle is not None: if exit_reason in ['exit_signal']: 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 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['ema_100'] = ta.EMA(dataframe, timeperiod=100) 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) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(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), ['enter_long', 'enter_tag']] = (1, 'ewo1') dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.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) & (dataframe['rsi'] < 25), ['enter_long', 'enter_tag']] = (1, 'ewo2') dataframe.loc[(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), ['enter_long', 'enter_tag']] = (1, 'ewolow') 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