# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/macdPlus.py import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.persistence import Trade 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__macdPlus__20260111_210550(IStrategy): 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: cond_long = reduce(lambda x, y: x | y, triggers_long) & reduce(lambda x, y: x & y, guards_long) dataframe.loc[cond_long, 'enter_long'] = 1 dataframe.loc[cond_long, 'enter_tag'] = 'long' if triggers_short: cond_short = reduce(lambda x, y: x | y, triggers_short) & reduce(lambda x, y: x & y, guards_short) dataframe.loc[cond_short, 'enter_short'] = 1 dataframe.loc[cond_short, 'enter_tag'] = 'short' 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 confirm_trade_entry( self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], side: str, **kwargs ) -> bool: open_trades = Trade.get_open_trades() num_shorts, num_longs = 0, 0 for trade in open_trades: if "short" in trade.enter_tag: num_shorts += 1 elif "long" in trade.enter_tag: num_longs += 1 if side == "long" and num_longs >= 5: return False if side == "short" and num_shorts >= 5: return False return True 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}} } } }