# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ImpulseMACD.py import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair) from functools import reduce import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_remiotore_freqtrade__ImpulseMACD__20260111_210550(IStrategy): ''' author@: Bryant Suen github@: https://github.com/BryantSuen Originally designed by @lazybear: https://www.tradingview.com/script/qt6xLfLi-Impulse-MACD-LazyBear/ ''' INTERFACE_VERSION = 3 timeframe = '1h' can_short: bool = True order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True } order_time_in_force = { "entry": "GTC", "exit": "GTC" } macd_ma_period = IntParameter(20, 50, default=20, space='buy', optimize=True, load=True) macd_signal_period = IntParameter(5, 15, default=14, space='buy', optimize=True, load=True) check_macd_position = BooleanParameter(default=False, space='buy', optimize=True, load=True) minimal_roi = { "0": 0.8 } stoploss = -0.5 trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.09 trailing_only_offset_is_reached = True process_only_new_candles = False use_exit_signal = True startup_candle_count: int = 100 def _cal_smma(self, series:pd.Series, period: int) -> pd.Series: return series.ewm(alpha=1/period, adjust=False, min_periods=period).mean() def _cal_zero_lag_ema(self, series:pd.Series, period: int) -> pd.Series: ema_1 = ta.EMA(series, timeperiod=period) ema_2 = ta.EMA(ema_1, timeperiod=period) return 2 * ema_1 - ema_2 def impulsive_macd(self, dataframe: DataFrame, length_ma: int, length_signal: int) -> tuple: mean_hlc = dataframe[['high', 'low', 'close']].mean(axis=1) high_smma = self._cal_smma(dataframe['high'], length_ma) low_smma = self._cal_smma(dataframe['low'], length_ma) middle_zlema = self._cal_zero_lag_ema(mean_hlc, length_ma) impulse_macd = np.where(middle_zlema > high_smma, middle_zlema - high_smma, 0) impulse_macd = np.where(middle_zlema < low_smma, middle_zlema - low_smma, impulse_macd) impulse_macd_signal = ta.SMA(impulse_macd, timeperiod=length_signal) impulse_macd_hist = impulse_macd - impulse_macd_signal return impulse_macd, impulse_macd_signal, impulse_macd_hist def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: for ma_period in self.macd_ma_period.range: for signal_period in self.macd_signal_period.range: macd, macdsignal, macdhist = self.impulsive_macd(dataframe, ma_period, signal_period) dataframe[f'impulse_macd_{ma_period}_{signal_period}'] = macd dataframe[f'impulse_macdsignal_{ma_period}_{signal_period}'] = macdsignal dataframe[f'impulse_macdhist_{ma_period}_{signal_period}'] = macdhist return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: triggers_long = [] triggers_short = [] guards_long = [] guards_short = [] triggers_long.append(qtpylib.crossed_above(dataframe[f'impulse_macd_{self.macd_ma_period.value}_{self.macd_signal_period.value}'], dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'])) triggers_short.append(qtpylib.crossed_below(dataframe[f'impulse_macd_{self.macd_ma_period.value}_{self.macd_signal_period.value}'], dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'])) if self.check_macd_position.value: guards_long.append(dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'] < 0) guards_short.append(dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'] > 0) guards_long.append(dataframe['volume'] > 0) guards_short.append(dataframe['volume'] > 0) if triggers_long: dataframe.loc[ reduce(lambda x, y: x | y, triggers_long) & reduce(lambda x, y: x & y, guards_long), 'enter_long'] = 1 if triggers_short: dataframe.loc[ reduce(lambda x, y: x | y, triggers_short) & reduce(lambda x, y: x & y, guards_short), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: triggers_long = [] triggers_short = [] guards_long = [] guards_short = [] triggers_short.append(qtpylib.crossed_above(dataframe[f'impulse_macd_{self.macd_ma_period.value}_{self.macd_signal_period.value}'], dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'])) triggers_long.append(qtpylib.crossed_below(dataframe[f'impulse_macd_{self.macd_ma_period.value}_{self.macd_signal_period.value}'], dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'])) if self.check_macd_position.value: guards_short.append(dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'] < 0) guards_long.append(dataframe[f'impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}'] > 0) guards_long.append(dataframe['volume'] > 0) guards_short.append(dataframe['volume'] > 0) if triggers_long: dataframe.loc[ reduce(lambda x, y: x | y, triggers_long) & reduce(lambda x, y: x & y, guards_long), 'exit_long'] = 1 if triggers_short: dataframe.loc[ reduce(lambda x, y: x | y, triggers_short) & reduce(lambda x, y: x & y, guards_short), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: return 5 @property def plot_config(self): return { 'main_plot': {}, 'subplots': { "IMPULSE_MACD": { f"impulse_macd_{self.macd_ma_period.value}_{self.macd_signal_period.value}": {'color': 'blue'}, f"impulse_macdsignal_{self.macd_ma_period.value}_{self.macd_signal_period.value}": {'color': 'orange'}, f"impulse_macdhist_{self.macd_ma_period.value}_{self.macd_signal_period.value}": {'type': 'bar', 'plotly': {'opacity': 0.9}} } } }