# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BB_RTR_dca_1.py import freqtrade.vendor.qtpylib.indicators as qtpylib import logging import math import numpy as np import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series, DatetimeIndex, merge from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open from functools import reduce from technical.indicators import RMI, zema logger = logging.getLogger(__name__) def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) def is_support(row_data) -> bool: conditions = [] for row in range(len(row_data) - 1): if row < len(row_data) / 2: conditions.append(row_data[row] > row_data[row + 1]) else: conditions.append(row_data[row] < row_data[row + 1]) return reduce(lambda x, y: x & y, conditions) def is_resistance(row_data) -> bool: conditions = [] for row in range(len(row_data) - 1): if row < len(row_data) / 2: conditions.append(row_data[row] < row_data[row + 1]) else: conditions.append(row_data[row] > row_data[row + 1]) return reduce(lambda x, y: x & y, conditions) 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 def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) return vwma def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma)) return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 def VWAPB(dataframe, window_size=20, num_of_std=1): df = dataframe.copy() df['vwap'] = qtpylib.rolling_vwap(df,window=window_size) rolling_std = df['vwap'].rolling(window=window_size).std() df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std) df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std) return df['vwap_low'], df['vwap'], df['vwap_high'] def top_percent_change(dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] class Github_remiotore_freqtrade__BB_RTR_dca_1__20260111_210550(IStrategy): ''' BB_RPB_TSL_RNG with conditions from true_lambo and dca (1) add btc protection to conditions prone to buy high ''' buy_params = { "buy_pump_1_factor": 1.096, "buy_pump_2_factor": 1.125, "buy_threshold": 0.003, "buy_bb_factor": 0.999, "buy_bb_delta": 0.025, "buy_bb_width": 0.095, "buy_cci": -116, "buy_cci_length": 25, "buy_rmi": 49, "buy_rmi_length": 17, "buy_srsi_fk": 32, "buy_closedelta": 12.148, "buy_ema_diff": 0.022, "buy_adx": 20, "buy_fastd": 20, "buy_fastk": 22, "buy_ema_cofi": 0.98, "buy_ewo_high": 4.179, "buy_ema_high": 0.968, "buy_ema_low": 0.935, "buy_ewo": -5.001, "buy_rsi": 23, "buy_rsi_fast": 44, "buy_ema_high_2": 1.087, "buy_ema_low_2": 0.970, "buy_no_trend_cti_4": -0.597, "buy_no_trend_factor_4": 0.024, "buy_no_trend_r14_4": -44.062, "buy_V_bb_width_5": 0.063, "buy_V_cti_5": -0.086, "buy_V_mfi_5": 38.158, "buy_V_r14_5": -41.493, "buy_vwap_closedelta": 26.941, "buy_vwap_closedelta_2": 20.099, "buy_vwap_closedelta_3": 27.654, "buy_vwap_cti": -0.087, "buy_vwap_cti_2": -0.748, "buy_vwap_cti_3": -0.2, "buy_vwap_width": 1.308, "buy_vwap_width_2": 3.212, "buy_vwap_width_3": 0.49, "buy_ada_cti": -0.715, "buy_ada_mama_diff": -0.025, "buy_ada_mama_offset": 0.981, "buy_ada_r_14": -61.294, } sell_params = { "pHSL": -0.998, # Disable ? "pPF_1": 0.019, "pPF_2": 0.065, "pSL_1": 0.019, "pSL_2": 0.062, "sell_cti_r_cti": 0.844, "sell_cti_r_r": -19.99, "sell_u_e_2_cmf": -0.0, "sell_u_e_2_ema_close_delta": 0.016, "sell_u_e_2_rsi": 10, "sell_deadfish_profit": -0.063, "sell_deadfish_bb_factor": 0.954, "sell_deadfish_bb_width": 0.043, "sell_deadfish_volume_factor": 2.37, "sell_cmf_div_1_cmf": 0.442, "sell_cmf_div_1_profit": 0.02, } minimal_roi = { "0": 0.10, } timeframe = '5m' inf_1h = '1h' stoploss = -0.998 use_custom_stoploss = True use_sell_signal = True process_only_new_candles = True startup_candle_count: int = 400 is_optimize_dip = False buy_rmi = IntParameter(30, 50, default=35, optimize= is_optimize_dip) buy_cci = IntParameter(-135, -90, default=-133, optimize= is_optimize_dip) buy_srsi_fk = IntParameter(30, 50, default=25, optimize= is_optimize_dip) buy_cci_length = IntParameter(25, 45, default=25, optimize = is_optimize_dip) buy_rmi_length = IntParameter(8, 20, default=8, optimize = is_optimize_dip) is_optimize_break = False buy_bb_width = DecimalParameter(0.05, 0.2, default=0.15, optimize = is_optimize_break) buy_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize = is_optimize_break) is_optimize_local_dip = False buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_dip) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = False) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_dip) is_optimize_ewo = False buy_rsi_fast = IntParameter(35, 50, default=45, optimize = False) buy_rsi = IntParameter(15, 30, default=35, optimize = False) buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize = is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942 , optimize = is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084 , optimize = is_optimize_ewo) is_optimize_ewo_2 = False buy_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96 , optimize = is_optimize_ewo_2) buy_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09 , optimize = is_optimize_ewo_2) is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi) is_optimize_vwap = False buy_vwap_width = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap) buy_vwap_closedelta = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap) buy_vwap_cti = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap) is_optimize_vwap_2 = False buy_vwap_width_2 = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap_2) buy_vwap_closedelta_2 = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap_2) buy_vwap_cti_2 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap_2) is_optimize_vwap_3 = False buy_vwap_width_3 = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap_3) buy_vwap_closedelta_3 = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap_3) buy_vwap_cti_3 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap_3) is_optimize_no_trend_4 = False buy_no_trend_factor_4 = DecimalParameter(0.01, 0.05, default=0.030 , optimize = is_optimize_no_trend_4) buy_no_trend_cti_4 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_no_trend_4) buy_no_trend_r14_4 = DecimalParameter(-100, -44, default=-80 , optimize = is_optimize_no_trend_4) is_optimize_V_5 = False buy_V_bb_width_5 = DecimalParameter(0.01, 0.1, default=0.01 , optimize = is_optimize_V_5) buy_V_cti_5 = DecimalParameter(-0.95, -0.0, default=-0.6 , optimize = is_optimize_V_5) buy_V_r14_5 = DecimalParameter(-100, 0, default=-60 , optimize = is_optimize_V_5) buy_V_mfi_5 = DecimalParameter(10, 40, default=30 , optimize = is_optimize_V_5) is_optimize_ada = True buy_ada_mama_offset = DecimalParameter(0.9, 1.2, default=1 , optimize = is_optimize_ada) buy_ada_r_14 = DecimalParameter(-100, -44, default=-82 , optimize = is_optimize_ada) buy_ada_mama_diff = DecimalParameter(-0.05, -0.01, default=-0.019 , optimize = is_optimize_ada) buy_ada_cti = DecimalParameter(-1, -0.4, default=-0.82 , optimize = is_optimize_ada) is_optimize_gumbo = False buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_gumbo) buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize = is_optimize_gumbo) buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_gumbo) buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_gumbo) is_optimize_gumbo_protection = False buy_gumbo_tpct_0 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection) buy_gumbo_tpct_3 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection) buy_gumbo_tpct_9 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection) buy_is_dip_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_is_break_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) is_optimize_pump_1 = False buy_pump_1_factor = DecimalParameter(1.0, 1.25, default= 1.1 , optimize = is_optimize_pump_1) is_optimize_pump_2 = False buy_pump_2_factor = DecimalParameter(1.0, 1.20, default= 1.1 , optimize = is_optimize_pump_2) is_optimize_sell_u_e_2 = False sell_u_e_2_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize = is_optimize_sell_u_e_2) sell_u_e_2_ema_close_delta = DecimalParameter(0.001, 0.027, default= 0.024, optimize = is_optimize_sell_u_e_2) sell_u_e_2_rsi = IntParameter(10, 30, default=24, optimize = is_optimize_sell_u_e_2) is_optimize_deadfish = False sell_deadfish_bb_width = DecimalParameter(0.010, 0.025, default=0.05 , optimize = is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.10, -0.05, default=-0.05 , optimize = is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , optimize = is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1.5, 3, default=1.5 , optimize = is_optimize_deadfish) is_optimize_cti_r = False sell_cti_r_cti = DecimalParameter(0.55, 1, default=0.5 , optimize = is_optimize_cti_r) sell_cti_r_r = DecimalParameter(-15, 0, default=-20 , optimize = is_optimize_cti_r) is_optimize_cmf_div = False sell_cmf_div_1_profit = DecimalParameter(0.005, 0.02, default=0.005 , optimize = is_optimize_cmf_div) sell_cmf_div_1_cmf = DecimalParameter(0.0, 0.5, default=0.0 , optimize = is_optimize_cmf_div) sell_cmf_div_2_profit = DecimalParameter(0.005, 0.02, default=0.005 , optimize = is_optimize_cmf_div) sell_cmf_div_2_cmf = DecimalParameter(0.0, 0.5, default=0.0 , optimize = is_optimize_cmf_div) is_optimize_trailing = True pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True, optimize=False) pPF_1 = DecimalParameter(0.008, 0.030, default=0.016, decimals=3, space='sell', load=True, optimize=False) pSL_1 = DecimalParameter(0.008, 0.030, default=0.011, decimals=3, space='sell', load=True, optimize=is_optimize_trailing) pPF_2 = DecimalParameter(0.050, 0.200, default=0.080, decimals=3, space='sell', load=True, optimize=is_optimize_trailing) pSL_2 = DecimalParameter(0.030, 0.200, default=0.040, decimals=3, space='sell', load=True, optimize=is_optimize_trailing) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] informative_pairs += [("BTC/USDT", "5m")] return informative_pairs def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if (current_profit > PF_2): sl_profit = SL_2 + (current_profit - PF_2) elif (current_profit > PF_1): sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit) def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) max_slip = 0.983 if(len(dataframe) < 1): return False dataframe = dataframe.iloc[-1].squeeze() if ((rate > dataframe['close'])) : slippage = ( (rate / dataframe['close']) - 1 ) * 100 if slippage < max_slip: return True else: return False return True 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] previous_candle_1 = dataframe.iloc[-2] max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate) buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() pump_tags = ['adaptive '] if current_profit > 0.02: if (last_candle['momdiv_sell_1h'] == True): return f"signal_profit_q_momdiv_1h( {buy_tag})" if (last_candle['momdiv_sell'] == True): return f"signal_profit_q_momdiv( {buy_tag})" if (last_candle['momdiv_coh'] == True): return f"signal_profit_q_momdiv_coh( {buy_tag})" if (last_candle['cti_40_1h'] > 0.844) and (last_candle['r_84_1h'] > -20): return f"signal_profit_cti_r( {buy_tag})" if 0.012 > current_profit >= 0.0 : if (last_candle['cti'] > self.sell_cti_r_cti.value) and (last_candle['r_14'] > self.sell_cti_r_r.value): return f"sell_profit_cti_r_1( {buy_tag})" if last_candle['close'] > last_candle['ema_200']: if (current_profit > 0.01) and (last_candle['rsi'] > 83): return f"sell_profit_o_1 ( {buy_tag})" if (0.06 > current_profit > 0.02) and (last_candle['rsi'] > 80.0): return f"signal_profit_q_1( {buy_tag})" if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95): return f"signal_profit_q_2( {buy_tag})" if (max_loss > 0.06) and (0.05 > current_profit > 0.01) and (last_candle['rsi'] < 46): return f"signal_profit_r_1( {buy_tag})" if ( (0.02 > current_profit > 0.005) and (last_candle['ema_vwap_diff_50'] > 0.0) and (last_candle['ema_vwap_diff_50'] < 0.012) ): return f"sell_vwap_dump( {buy_tag})" if ( (0.02 > current_profit > 0.005) and (last_candle['cmf'] > 0) and (last_candle['cmf_div_slow'] == 1) ): return f"sell_cmf_div( {buy_tag})" if ( (current_profit < -0.025) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['cmf'] < self.sell_u_e_2_cmf.value) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_u_e_2_ema_close_delta.value) and last_candle['rsi'] > previous_candle_1['rsi'] and (last_candle['rsi'] > (last_candle['rsi_1h'] + self.sell_u_e_2_rsi.value)) ): return f"sell_stoploss_u_e_2( {buy_tag})" if ( (current_profit < self.sell_deadfish_profit.value) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) and (last_candle['cmf'] < 0.0) ): return f"sell_stoploss_deadfish( {buy_tag})" def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']) dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int') for val in self.buy_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) dataframe['cci'] = ta.CCI(dataframe, 26) dataframe['cci_long'] = ta.CCI(dataframe, 170) for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['cti'] = pta.cti(dataframe["close"], length=20) crsi_closechange = dataframe['close'] / dataframe['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3 dataframe['cmf'] = chaikin_money_flow(dataframe, 20) dataframe['mfi'] = ta.MFI(dataframe) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_24'] = ta.EMA(dataframe, timeperiod=24) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) dataframe['vwap_upperband'] = vwap_high dataframe['vwap_middleband'] = vwap dataframe['vwap_lowerband'] = vwap_low dataframe['vwap_width'] = ( (dataframe['vwap_upperband'] - dataframe['vwap_lowerband']) / dataframe['vwap_middleband'] ) * 100 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) dataframe['EWO'] = EWO(dataframe, 50, 200) stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2) dataframe['bb_lowerband2_40'] = bollinger2_40['lower'] dataframe['bb_middleband2_40'] = bollinger2_40['mid'] dataframe['bb_upperband2_40'] = bollinger2_40['upper'] dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_32'] = williams_r(dataframe, period=32) dataframe['r_480'] = williams_r(dataframe, period=480) dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['ema_vwap_diff_50'] = ( ( dataframe['ema_50'] - dataframe['vwap_lowerband'] ) / dataframe['ema_50'] ) dataframe['tpct_change_1'] = top_percent_change(dataframe, 1) dataframe['tpct_change_2'] = top_percent_change(dataframe, 2) dataframe['tpct_change_4'] = top_percent_change(dataframe, 4) mom = momdiv(dataframe) dataframe['momdiv_buy'] = mom['momdiv_buy'] dataframe['momdiv_sell'] = mom['momdiv_sell'] dataframe['momdiv_coh'] = mom['momdiv_coh'] dataframe['momdiv_col'] = mom['momdiv_col'] dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2 dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.25, 0.025) dataframe['mama_diff'] = ( ( dataframe['mama'] - dataframe['fama'] ) / dataframe['hl2'] ) dataframe['kama'] = ta.KAMA(dataframe['close'], 84) dataframe['cmf_div_fast'] = ( ( dataframe['cmf'].rolling(12).max() >= dataframe['cmf'] * 1.025 ) ) dataframe['cmf_div_slow'] = ( ( dataframe['cmf'].rolling(20).max() >= dataframe['cmf'] * 1.025 ) ) dataframe['moderi_96'] = moderi(dataframe, 96) """ Only applied to conditions prone to buy high such as high EWO conditions """ inf_tf = '5m' informative = self.dp.get_pair_dataframe('BTC/USDT', timeframe=inf_tf) informative_btc = informative.copy().shift(1) dataframe['btc_close'] = informative_btc['close'] inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2) informative['bb_lowerband2'] = bollinger2['lower'] informative['bb_middleband2'] = bollinger2['mid'] informative['bb_upperband2'] = bollinger2['upper'] informative['bb_width'] = ((informative['bb_upperband2'] - informative['bb_lowerband2']) / informative['bb_middleband2']) informative['rsi'] = ta.RSI(informative, timeperiod=14) informative['rsi_28'] = ta.RSI(informative, timeperiod=28) informative['rsi_42'] = ta.RSI(informative, timeperiod=42) informative['ema_20'] = ta.EMA(informative, timeperiod=20) informative['ema_26'] = ta.EMA(informative, timeperiod=26) informative['ema_50'] = ta.EMA(informative, timeperiod=50) informative['ema_100'] = ta.EMA(informative, timeperiod=100) informative['ema_200'] = ta.EMA(informative, timeperiod=200) informative['r_84'] = williams_r(informative, period=84) informative['r_480'] = williams_r(informative, period=480) informative['cti'] = pta.cti(informative["close"], length=20) informative['cti_40'] = pta.cti(informative["close"], length=40) crsi_closechange = informative['close'] / informative['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) informative['crsi'] = (ta.RSI(informative['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative['close'], 100)) / 3 informative['cmf'] = chaikin_money_flow(informative, 20) mom = momdiv(informative) informative['momdiv_buy'] = mom['momdiv_buy'] informative['momdiv_sell'] = mom['momdiv_sell'] informative['momdiv_coh'] = mom['momdiv_coh'] informative['momdiv_col'] = mom['momdiv_col'] res_series = informative['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2) sup_series = informative['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2) informative['res_level'] = Series(np.where(res_series, np.where(informative['close'] > informative['open'], informative['close'], informative['open']), float('NaN'))).ffill() informative['res_hlevel'] = Series(np.where(res_series, informative['high'], float('NaN'))).ffill() informative['sup_level'] = Series(np.where(sup_series, np.where(informative['close'] < informative['open'], informative['close'], informative['open']), float('NaN'))).ffill() dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' is_pump_1 = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * self.buy_pump_1_factor.value )) ) pump_protection_strict = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.125 )) & ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.225 )) ) ) pump_protection_loose = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.05 )) & ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.125 )) ) ) pump_protection_mid = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.1 )) & ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.1 )) ) ) is_pump_4 = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.075 )) & ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.175 )) ) ) is_crash_1 = ( (dataframe['tpct_change_1'] < 0.08) & (dataframe['tpct_change_2'] < 0.08) ) is_crash_2 = ( (dataframe['tpct_change_1'] < 0.06) & (dataframe['tpct_change_2'] < 0.06) ) is_crash_3 = ( (dataframe['tpct_change_1'] < 0.055) & (dataframe['tpct_change_2'] < 0.055) ) btc_dump = ( (dataframe['btc_close'].rolling(24).max() >= (dataframe['btc_close'] * 1.03 )) ) rsi_check = ( (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) ) min_EWO_check = ( (dataframe['EWO'] > -5.585) ) max_EWO_check = ( (dataframe['EWO'] < 11.8) ) if self.buy_is_dip_enabled.value: is_dip = ( (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) & (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) & (dataframe['srsi_fk'] < self.buy_srsi_fk.value) ) if self.buy_is_break_enabled.value: is_break = ( ( (dataframe['bb_delta'] > self.buy_bb_delta.value) #"buy_bb_delta": 0.025 0.036 & #"buy_bb_width": 0.095 0.133 (dataframe['bb_width'] > self.buy_bb_width.value) ) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) & # from BinH (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value) & (is_crash_1) ) is_local_uptrend = ( # from NFI next gen (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) & (dataframe['EWO'] < 4) & (dataframe['EWO'] > -2.5) ) is_ewo = ( # from SMA offset (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value) ) is_ewo_2 = ( (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) & (dataframe['EWO'] > self.buy_ewo_high.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) & (dataframe['rsi'] < self.buy_rsi.value) & (rsi_check) & (btc_dump == 0) ) is_vwap = ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta.value / 1000 ) & (dataframe['cti'] < self.buy_vwap_cti.value) & (dataframe['EWO'] > 8) & (rsi_check) & (pump_protection_strict) & (btc_dump == 0) ) is_vwap_2 = ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width_2.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_2.value / 1000 ) & (dataframe['cti'] < self.buy_vwap_cti_2.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & (rsi_check) & (pump_protection_strict) & (btc_dump == 0) ) is_vwap_3 = ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width_3.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_3.value / 1000 ) & (dataframe['cti'] < self.buy_vwap_cti_3.value) & (dataframe['EWO'] < 4) & (dataframe['EWO'] > -2.5) & (dataframe['rsi_28_1h'] < 46) & (pump_protection_loose) & (rsi_check) & (btc_dump == 0) ) is_VWAP = ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['tpct_change_1'] > 0.04) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (rsi_check) & (btc_dump == 0) ) is_no_trend_4 = ( (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_no_trend_factor_4.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['cti'] < self.buy_no_trend_cti_4.value) & (dataframe['r_14'] < self.buy_no_trend_r14_4.value) & (dataframe['EWO'] < -4) & (min_EWO_check) & (rsi_check) ) is_V_5 = ( (dataframe['bb_width'] > self.buy_V_bb_width_5.value) & (dataframe['cti'] < self.buy_V_cti_5.value) & (dataframe['r_14'] < self.buy_V_r14_5.value) & (dataframe['mfi'] < self.buy_V_mfi_5.value) & (dataframe['ema_vwap_diff_50'] > 0.215) & (dataframe['EWO'] < -10) & (rsi_check) ) is_insta = ( (dataframe['bb_width_1h'] > 0.131) & (dataframe['r_14'] < -51) & (dataframe['r_84_1h'] < -70) & (dataframe['cti'] < -0.845) & (dataframe['cti_40_1h'] < -0.735) & ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.1 )) ) & (btc_dump == 0) ) is_adaptive = ( (dataframe['kama'] > dataframe['fama']) & (dataframe['fama'] > dataframe['mama'] * self.buy_ada_mama_offset.value) & (dataframe['r_14'] < self.buy_ada_r_14.value) & (dataframe['mama_diff'] < self.buy_ada_mama_diff.value) & (dataframe['cti'] < self.buy_ada_cti.value) & (pump_protection_strict) & (rsi_check) ) is_nfi_32 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 19) & (dataframe['close'] < dataframe['sma_15'] * 0.942) & (dataframe['cti'] < -0.86) ) is_nfi_33 = ( (dataframe['close'] < (dataframe['ema_13'] * 0.978)) & (dataframe['EWO'] > 8) & (dataframe['cti'] < -0.88) & (dataframe['rsi'] < 32) & (dataframe['r_14'] < -98.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.5)) ) is_nfix_39 = ( (dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * 1.01)) & (dataframe['ema_200'] > (dataframe['ema_200'].shift(48) * 1.07)) & (dataframe['bb_lowerband2_40'].shift().gt(0)) & (dataframe['bb_delta_cluc'].gt(dataframe['close'] * 0.056)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.01)) & (dataframe['tail'].lt(dataframe['bb_delta_cluc'] * 0.5)) & (dataframe['close'].lt(dataframe['bb_lowerband2_40'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['close'] > dataframe['ema_50'] * 0.912) ) is_nfix_201 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift()) & (dataframe['rsi_fast'] < 30.0) & (dataframe['ema_20_1h'] > dataframe['ema_26_1h']) & (dataframe['close'] < dataframe['sma_15'] * 0.953) & (dataframe['cti'] < -0.82) & (dataframe['cci'] < -210.0) & (is_pump_1) & (rsi_check) ) is_nfix_1 = ( (((dataframe['close'] - dataframe['open'].rolling(12).min()) / dataframe['open'].rolling(12).min()) > 0.027) & (dataframe['rsi'] < 35.0) & (dataframe['r_32'] < -80.0) & (dataframe['mfi'] < 31.0) & (dataframe['rsi_1h'] > 30.0) & (dataframe['rsi_1h'] < 84.0) & (dataframe['r_480_1h'] > -99.0) & (rsi_check) ) is_nfix_6 = ( (dataframe['close'] < dataframe['sma_15'] * 0.937) & (dataframe['crsi'] < 30.0) & (dataframe['rsi'] < dataframe['rsi'].shift(1)) & (dataframe['rsi'] < 28.0) & (dataframe['cti'] < -0.78) & (dataframe['cci'] < -200.0) & (dataframe['r_480_1h'] < -12.0) & (rsi_check) ) is_nfix_7 = ( (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.94) & (dataframe['close'] < dataframe['bb_lowerband2'] * 0.995) & (dataframe['cti'] < -0.9) & (dataframe['r_14'] < -95.0) & (rsi_check) ) is_nfix_8 = ( (dataframe['close'] < dataframe['sma_30'] * 0.927) & (dataframe['EWO'] > 3.2) & (dataframe['rsi'] < 33.0) & (dataframe['cti'] < -0.9) & (dataframe['r_14'] < -97.0) & (rsi_check) ) is_nfix_12 = ( (dataframe['close'] < dataframe['ema_20'] * 0.938) & (dataframe['EWO'] > 0.1) & (dataframe['rsi'] < 40.0) & (dataframe['cti'] < -0.9) & (dataframe['r_480_1h'] < -20.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.8)) & (dataframe['close'] > (dataframe['sup_level_1h'] * 0.9)) & (rsi_check) & (max_EWO_check) ) is_nfi7_33 = ( (dataframe['moderi_96']) & (dataframe['cti'] < -0.88) & (dataframe['close'] < (dataframe['ema_13'] * 0.988)) & (dataframe['EWO'] > 6.4) & (dataframe['rsi'] < 32.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.0)) & (pump_protection_loose) & (rsi_check) ) is_nfi_sma_3 = ( (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['close'] * 0.059) & (dataframe['ha_closedelta'] > dataframe['close'] * 0.023) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * 0.24) & (dataframe['close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['close'] < dataframe['close'].shift()) & (btc_dump == 0) ) is_BB_checked = is_dip & is_break conditions.append(is_BB_checked) # P dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb ' conditions.append(is_local_uptrend) dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local_uptrend ' conditions.append(is_ewo) dataframe.loc[is_ewo, 'buy_tag'] += 'ewo ' conditions.append(is_ewo_2) dataframe.loc[is_ewo_2, 'buy_tag'] += 'ewo2 ' conditions.append(is_no_trend_4) dataframe.loc[is_no_trend_4, 'buy_tag'] += 'no_trend_4 ' conditions.append(is_vwap) dataframe.loc[is_vwap, 'buy_tag'] += 'vwap ' conditions.append(is_vwap_2) dataframe.loc[is_vwap_2, 'buy_tag'] += 'vwap_2 ' conditions.append(is_vwap_3) dataframe.loc[is_vwap_3, 'buy_tag'] += 'vwap_3 ' conditions.append(is_VWAP) dataframe.loc[is_VWAP, 'buy_tag'] += 'VWAP ' conditions.append(is_insta) dataframe.loc[is_insta, 'buy_tag'] += 'insta ' conditions.append(is_adaptive) dataframe.loc[is_adaptive, 'buy_tag'] += 'adaptive ' conditions.append(is_nfi_32) dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi_32 ' conditions.append(is_nfi_33) dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi_33 ' conditions.append(is_nfi7_33) dataframe.loc[is_nfi7_33, 'buy_tag'] += '7_33 ' conditions.append(is_nfi_sma_3) dataframe.loc[is_nfi_sma_3, 'buy_tag'] += 'sma_3 ' conditions.append(is_nfix_1) dataframe.loc[is_nfix_1, 'buy_tag'] += 'x_1 ' conditions.append(is_nfix_6) dataframe.loc[is_nfix_6, 'buy_tag'] += 'x_6 ' conditions.append(is_nfix_7) dataframe.loc[is_nfix_7, 'buy_tag'] += 'x_7 ' conditions.append(is_nfix_8) dataframe.loc[is_nfix_8, 'buy_tag'] += 'x_8 ' conditions.append(is_nfix_12) dataframe.loc[is_nfix_12, 'buy_tag'] += 'x_12 ' conditions.append(is_nfix_39) dataframe.loc[is_nfix_39, 'buy_tag'] += 'x_39 ' conditions.append(is_nfix_201) dataframe.loc[is_nfix_201, 'buy_tag'] += 'x_201 ' conditions.append(is_V_5) dataframe.loc[is_V_5, 'buy_tag'] += 'V_5 ' if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['volume'] > 0), 'sell' ] = 0 return dataframe def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') def momdiv(dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30) -> DataFrame: mom: Series = ta.MOM(dataframe, timeperiod=mom_length) upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0) buy = qtpylib.crossed_below(mom, lowerband) sell = qtpylib.crossed_above(mom, upperband) hh = dataframe['high'].rolling(lookback).max() ll = dataframe['low'].rolling(lookback).min() coh = dataframe['high'] >= hh col = dataframe['low'] <= ll df = DataFrame({ "momdiv_mom": mom, "momdiv_upperb": upperband, "momdiv_lowerb": lowerband, "momdiv_buy": buy, "momdiv_sell": sell, "momdiv_coh": coh, "momdiv_col": col, }, index=dataframe['close'].index) return df class BB_RTR_dca (Github_remiotore_freqtrade__BB_RTR_dca_1__20260111_210550): position_adjustment_enable = True initial_safety_order_trigger = -0.08 max_safety_orders = 2 safety_order_step_scale = 0.5 #SS safety_order_volume_scale = 1.6 #OS max_dca_multiplier = (1 + max_safety_orders) if (max_safety_orders > 0): if (safety_order_volume_scale > 1): max_dca_multiplier = (2 + (safety_order_volume_scale * (math.pow(safety_order_volume_scale, (max_safety_orders - 1)) - 1) / (safety_order_volume_scale - 1))) elif (safety_order_volume_scale < 1): max_dca_multiplier = (2 + (safety_order_volume_scale * (1 - math.pow(safety_order_volume_scale, (max_safety_orders - 1))) / (1 - safety_order_volume_scale))) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs) -> float: if self.config['stake_amount'] == 'unlimited': return proposed_stake / self.max_dca_multiplier return proposed_stake def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if current_profit > self.initial_safety_order_trigger: return None count_of_buys = trade.nr_of_successful_buys if 1 <= count_of_buys <= self.max_safety_orders: safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys) if (self.safety_order_step_scale > 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1)) elif (self.safety_order_step_scale < 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale,(count_of_buys - 1))) / (1 - self.safety_order_step_scale)) if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None) stake_amount = stake_amount / self.max_dca_multiplier stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, (count_of_buys - 1)) amount = stake_amount / current_rate logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}") return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') return None return None