# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/SMAoffset_antipump_div.py 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 rangeUpper = 60 rangeLower = 5 buy_params = { "antipump_threshold": 0.257, "base_nb_candles_buy": 14, "ewo_high": 2.327, "ewo_high_2": -2.327, "ewo_low": -20.988, "low_offset": 0.975, "low_offset_2": 0.955, "rsi_buy": 40, "base_nb_candles_buy2": 16, "ewo_high2": 5.638, "ewo_low2": -19.993, "low_offset2": 0.978, "rsi_buy2": 61, } sell_params = { "base_nb_candles_sell": 24, "high_offset": 0.991, "high_offset_2": 0.997, "base_nb_candles_sell2": 49, "high_offset2": 1.006, "cstp_bail_how": "roc", "cstp_bail_roc": -0.032, "cstp_bail_time": 1108, "cstp_bb_trailing_input": "bb_lowerband_neutral_inf", "cstp_threshold": -0.036, "cstp_trailing_max_stoploss": 0.054, "cstp_trailing_only_offset_is_reached": 0.06, "cstp_trailing_stop_profit_devider": 2, "droi_pullback": True, "droi_pullback_amount": 0.03, "droi_pullback_respect_table": False, "droi_trend_type": "any", } class Github_remiotore_freqtrade__SMAoffset_antipump_div__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.028, "10": 0.018, "30": 0.010, "40": 0.005 } custom_trade_info = {} stoploss = -0.11 antipump_threshold = DecimalParameter(0, 0.4, default=0.25, space='buy', optimize=True) base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) low_offset_2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) low_offset2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset2'], space='buy', optimize=True) base_nb_candles_buy2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy2'], space='buy', optimize=True) base_nb_candles_sell2 = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) high_offset2 = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(2.00, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) ewo_high_2 = DecimalParameter(-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) ewo_low2 = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low2'], space='buy', optimize=True) ewo_high2 = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high2'], space='buy', optimize=True) rsi_buy2 = IntParameter(30, 70, default=buy_params['rsi_buy2'], space='buy', optimize=True) trailing_stop = False use_sell_signal = False sell_profit_only = False sell_profit_offset = 0.01 ignore_roi_if_buy_signal = False timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count: int = 100 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } custom_trade_info = {} def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] 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: len = 14 src = dataframe['close'] lbL = 10 #5 dataframe['osc'] = ta.RSI(src, len) dataframe['osc'] = dataframe['osc'].fillna(0) dataframe['min'] = dataframe['osc'].rolling(lbL).min() dataframe['prevMin'] = np.where(dataframe['min'] > dataframe['min'].shift(), dataframe['min'].shift(), dataframe['min']) dataframe.loc[ (dataframe['osc'] == dataframe['prevMin']) , 'plFound'] = 1 dataframe['plFound'] = dataframe['plFound'].fillna(0) dataframe['max'] = dataframe['osc'].rolling(lbL).max() dataframe['prevMax'] = np.where(dataframe['max'] < dataframe['max'].shift(), dataframe['max'].shift(), dataframe['max']) dataframe.loc[ (dataframe['osc'] == dataframe['prevMax']) , 'phFound'] = 1 dataframe['phFound'] = dataframe['phFound'].fillna(0) dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] > dataframe['valuewhen_plFound_osc']) & (dataframe['inrange_plFound_osc'] == 1) ) , 'oscHL'] = 1 dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe, 'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] < dataframe['valuewhen_plFound_low']) , 'priceLL'] = 1 dataframe.loc[ ( (dataframe['priceLL'] == 1) & (dataframe['oscHL'] == 1) & (dataframe['plFound'] == 1) ) , 'bullCond'] = 1 dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] < dataframe['valuewhen_plFound_osc']) & (dataframe['inrange_plFound_osc'] == 1) ) , 'oscLL'] = 1 dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe,'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] > dataframe['valuewhen_plFound_low']) , 'priceHL'] = 1 dataframe.loc[ ( (dataframe['priceHL'] == 1) & (dataframe['oscLL'] == 1) & (dataframe['plFound'] == 1) ) , 'hiddenBullCond'] = 1 dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] < dataframe['valuewhen_phFound_osc']) & (dataframe['inrange_phFound_osc'] == 1) ) , 'oscLH'] = 1 dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] > dataframe['valuewhen_phFound_high']) , 'priceHH'] = 1 dataframe.loc[ ( (dataframe['priceHH'] == 1) & (dataframe['oscLH'] == 1) & (dataframe['phFound'] == 1) ) , 'bearCond'] = 1 dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] > dataframe['valuewhen_phFound_osc']) & (dataframe['inrange_phFound_osc'] == 1) ) , 'oscHH'] = 1 dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] < dataframe['valuewhen_phFound_high']) , 'priceLH'] = 1 dataframe.loc[ ( (dataframe['priceLH'] == 1) & (dataframe['oscHH'] == 1) & (dataframe['phFound'] == 1) ) , 'hiddenBearCond'] = 1 for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_buy2.range: dataframe[f'ma_buy2_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_sell2.range: dataframe[f'ma_sell2_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['hma_9'] = qtpylib.hull_moving_average(dataframe['close'], window=9) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['ema_9'] = ta.EMA(dataframe, timeperiod=9) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rmi'] = RMI(dataframe, length=24, mom=5) dataframe['roc'] = dataframe['close'].pct_change(12).rolling(12).max() * 100 informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative['1d-high'] = informative['close'].rolling(24).max() informative['1d-low'] = informative['close'].rolling(24).min() dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dont_buy_conditions = [] dont_buy_conditions.append( (dataframe['pump_strength'] > self.antipump_threshold.value) & (dataframe['bearCond'] < 1) & (dataframe['hiddenBearCond'] < 1) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))& (dataframe['rsi']<25) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] < 35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['close'] < (dataframe[f'ma_buy2_{self.base_nb_candles_buy2.value}'] * self.low_offset2.value)) & (dataframe['EWO'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['close'] < (dataframe[f'ma_buy2_{self.base_nb_candles_buy2.value}'] * self.low_offset2.value)) & (dataframe['EWO'] < self.ewo_low2.value) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ]=1 if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'sell'] = 0 """ conditions.append( ( (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( (dataframe['close'] > (dataframe[f'ma_sell2_{self.base_nb_candles_sell2.value}'] * self.high_offset2.value)) & (dataframe['volume'] > 0) ) ) """ if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if current_profit > 0: if last_candle['hiddenBearCond'] == 1: return 'Hidden_Bear_div_profit' if current_profit > 0: if last_candle['bearCond'] == 1: return 'Bear_div_profit' return None class Github_remiotore_freqtrade__SMAoffset_antipump_div__20260111_210550(Github_remiotore_freqtrade__SMAoffset_antipump_div__20260111_210550): def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dont_buy_conditions = [] dont_buy_conditions.append( (dataframe['pump_strength'] > self.antipump_threshold.value) & (dataframe['bearCond'] < 1) & (dataframe['hiddenBearCond'] < 1) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))& (dataframe['rsi']<25) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] < 35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['close'] < (dataframe[f'ma_buy2_{self.base_nb_candles_buy2.value}'] * self.low_offset2.value)) & (dataframe['EWO'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( ( (dataframe['bullCond'] > 0) | (dataframe['hiddenBullCond'] > 0) )& (dataframe['close'] < (dataframe[f'ma_buy2_{self.base_nb_candles_buy2.value}'] * self.low_offset2.value)) & (dataframe['EWO'] < self.ewo_low2.value) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ]=1 if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe """ Custom Stoploss """ def custom_stoploss_test(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if not self.config['runmode'].value in ('backtest', 'hyperopt'): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) >= 1): last_candle = dataframe.iloc[-1] if((last_candle['sell_copy'] == 1) & (last_candle['buy_copy'] == 0)): sl_new = 0.001 return sl_new def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) if self.config['runmode'].value in ('live', 'dry_run'): dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) sroc = dataframe['sroc'].iat[-1] else: sroc = self.custom_trade_info[trade.pair]['sroc'].loc[current_time]['sroc'] if current_profit < self.cstp_threshold.value: if self.cstp_bail_how.value == 'roc' or self.cstp_bail_how.value == 'any': if (sroc/100) <= self.cstp_bail_roc.value: return 0.001 if self.cstp_bail_how.value == 'time' or self.cstp_bail_how.value == 'any': if trade_dur > self.cstp_bail_time.value: return 0.001 return 1 def valuewhen(dataframe, condition, source, occurrence): copy = dataframe.copy() copy['colFromIndex'] = copy.index copy = copy.sort_values(by=[condition, 'colFromIndex'], ascending=False).reset_index(drop=True) copy['valuewhen'] = np.where(copy[condition] > 0, copy[source].shift(-occurrence), 100) copy['valuewhen'] = copy['valuewhen'].fillna(100) copy['barrsince'] = copy['colFromIndex'] - copy['colFromIndex'].shift(-occurrence) copy.loc[ ( (rangeLower <= copy['barrsince']) & (copy['barrsince'] <= rangeUpper) ) , "in_range"] = 1 copy['in_range'] = copy['in_range'].fillna(0) copy = copy.sort_values(by=['colFromIndex'], ascending=True).reset_index(drop=True) return copy['valuewhen'], copy['in_range'] 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 def RMI(dataframe, *, length=20, mom=5): df = dataframe.copy() df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0) df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0) df.fillna(0, inplace=True) df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length) df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length) df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"])) return df["RMI"] def SROC(dataframe, roclen=21, emalen=13, smooth=21): df = dataframe.copy() roc = ta.ROC(df, timeperiod=roclen) ema = ta.EMA(df, timeperiod=emalen) sroc = ta.ROC(ema, timeperiod=smooth) return sroc