import numpy as np import scipy.fft from scipy.fft import rfft, irfft import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import arrow from freqtrade.strategy import (IStrategy, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter) from typing import Dict, List, Optional, Tuple, Union from pandas import DataFrame, Series from functools import reduce from datetime import datetime, timedelta from freqtrade.persistence import Trade # Get rid of pandas warnings during backtesting import pandas as pd pd.options.mode.chained_assignment = None # default='warn' # Strategy specific imports, files must reside in same folder as strategy import sys from pathlib import Path sys.path.append(str(Path(__file__).parent)) import logging import warnings log = logging.getLogger(__name__) # log.setLevel(logging.DEBUG) warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) import custom_indicators as cta import pywt import scipy class FTT_DWT_FBB_FUTURES(IStrategy): INTERFACE_VERSION = 3 levarage_input = 3.0 # Do *not* hyperopt for the roi and stoploss spaces # ROI table: minimal_roi = {} # Stoploss: stoploss = -0.99 # Trailing stop: trailing_stop = False trailing_stop_positive = None trailing_stop_positive_offset = 0.0 trailing_only_offset_is_reached = False timeframe = '5m' inf_timeframe = '15m' use_custom_stoploss = True # Recommended use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Required startup_candle_count: int = 400 # must be power of 2 process_only_new_candles = True trading_mode = "futures" margin_mode = "isolated" can_short = True custom_trade_info = {} ################################### # Strategy Specific Variable Storage ## Hyperopt Variables # FBB_ hyperparams buy_bb_gain = DecimalParameter(0.01, 0.50, decimals=2, default=0.03, space='buy', load=True, optimize=True) buy_fisher_wr = DecimalParameter(-0.99, -0.75, decimals=2, default=-0.5, space='buy', load=True, optimize=True) buy_force_fisher_wr = DecimalParameter(-0.99, -0.85, decimals=2, default=-0.99, space='buy', load=True, optimize=True) sell_bb_gain = DecimalParameter(0.7, 1.5, decimals=2, default=0.8, space='sell', load=True, optimize=True) sell_fisher_wr = DecimalParameter(0.75, 0.99, decimals=2, default=0.75, space='sell', load=True, optimize=True) sell_force_fisher_wr = DecimalParameter(0.85, 0.99, decimals=2, default=0.99, space='sell', load=True, optimize=True) # FFT hyperparams entry_fft_diff = DecimalParameter(0.0, 5.0, decimals=1, default=2.0, space='buy', load=True, optimize=True) entry_fft_dev = DecimalParameter(-4.0, 0.00, decimals=1, default=-0.1, space='buy', load=True, optimize=True) # buy_fft_cutoff = DecimalParameter(1/16.0, 1/3.0, decimals=2, default=1/5.0, space='buy', load=True, optimize=True) exit_fft_diff = DecimalParameter(-5.0, 0.0, decimals=1, default=-0.01, space='buy', load=True, optimize=True) exit_fft_dev = DecimalParameter(0.00, 4.0, decimals=1, default=1.0, space='buy', load=True, optimize=True) fft_window = startup_candle_count fft_lookahead = 0 dwt_window = startup_candle_count # DWT hyperparams entry_long_dwt_diff = DecimalParameter(0.0, 5.0, decimals=1, default=2.0, space='buy', load=True, optimize=True) entry_short_dwt_diff = DecimalParameter(-5.0, 0.0, decimals=1, default=-2.0, space='buy', load=True, optimize=True) exit_long_dwt_diff = DecimalParameter(-5.0, 0.0, decimals=1, default=-2.0, space='sell', load=True, optimize=True) exit_short_dwt_diff = DecimalParameter(0.0, 5.0, decimals=1, default=-2.0, space='sell', load=True, optimize=True) entry_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'macd', 'adx'], default='rmi', space='buy', load=True, optimize=True) # Custom exit Profit (formerly Dynamic ROI) cexit_long_roi_type = CategoricalParameter(['static', 'decay', 'step'], default='step', space='sell', load=True, optimize=True) cexit_long_roi_time = IntParameter(720, 1440, default=720, space='sell', load=True, optimize=True) cexit_long_roi_start = DecimalParameter(0.01, 0.05, default=0.01, space='sell', load=True, optimize=True) cexit_long_roi_end = DecimalParameter(0.0, 0.01, default=0, space='sell', load=True, optimize=True) cexit_long_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any', 'none'], default='any', space='sell', load=True, optimize=True) cexit_long_pullback = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=True) cexit_long_pullback_amount = DecimalParameter(0.005, 0.03, default=0.01, space='sell', load=True, optimize=True) cexit_long_pullback_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True, optimize=True) cexit_long_endtrend_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True, optimize=True) cexit_short_roi_type = CategoricalParameter(['static', 'decay', 'step'], default='step', space='sell', load=True, optimize=True) cexit_short_roi_time = IntParameter(720, 1440, default=720, space='sell', load=True, optimize=True) cexit_short_roi_start = DecimalParameter(0.01, 0.05, default=0.01, space='sell', load=True, optimize=True) cexit_short_roi_end = DecimalParameter(0.0, 0.01, default=0, space='sell', load=True, optimize=True) cexit_short_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any', 'none'], default='any', space='sell', load=True, optimize=True) cexit_short_pullback = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=True) cexit_short_pullback_amount = DecimalParameter(0.005, 0.03, default=0.01, space='sell', load=True, optimize=True) cexit_short_pullback_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True, optimize=True) cexit_short_endtrend_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True, optimize=True) # Custom Stoploss cstop_loss_threshold = DecimalParameter(-0.05, -0.01, default=-0.03, space='sell', load=True, optimize=True) cstop_bail_how = CategoricalParameter(['roc', 'time', 'any', 'none'], default='none', space='sell', load=True, optimize=True) cstop_bail_roc = DecimalParameter(-5.0, -1.0, default=-3.0, space='sell', load=True, optimize=True) cstop_bail_time = IntParameter(60, 1440, default=720, space='sell', load=True, optimize=True) cstop_bail_time_trend = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=True) cstop_max_stoploss = DecimalParameter(-0.30, -0.01, default=-0.10, space='sell', load=True, optimize=True) ################################### """ Informative Pair Definitions """ def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_timeframe) for pair in pairs] return informative_pairs ################################### """ Indicator Definitions """ 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: if self.levarage_input > max_leverage: return max_leverage return self.levarage_input def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Base pair informative timeframe indicators curr_pair = metadata['pair'] informative = self.dp.get_pair_dataframe(pair=curr_pair, timeframe=self.inf_timeframe) # FFT informative['fft_dev'] = informative['close'].rolling(window=self.fft_window).apply(self.scaledModel) informative['fft_dev'] = informative['fft_dev'].fillna(0) # missing data can cause issue with ta functions informative['fft_slope'] = ta.LINEARREG_SLOPE(informative['fft_dev'], timeperiod=60) # merge into normal timeframe dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True) dataframe['scaled'] = dataframe['close'].rolling(window=self.fft_window).apply(self.scaledData) dataframe['fft_dev'] = dataframe[f"fft_dev_{self.inf_timeframe}"] dataframe['fft_slope'] = dataframe[f"fft_slope_{self.inf_timeframe}"] dataframe['fft_dev_diff'] = (dataframe['fft_dev'] - dataframe['scaled']) # DWT informative['dwt_model'] = informative['close'].rolling(window=self.dwt_window).apply(self.model) # merge into normal timeframe dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True) # calculate predictive indicators in shorter timeframe (not informative) dataframe['dwt_model'] = dataframe[f"dwt_model_{self.inf_timeframe}"] dataframe['dwt_model_diff'] = 100.0 * (dataframe['dwt_model'] - dataframe['close']) / dataframe['close'] # FisherBB dataframe['rsi'] = ta.RSI(dataframe, timeperiod=30) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) # Williams %R dataframe['wr'] = 0.02 * (self.williams_r(dataframe, period=30) + 50.0) # Combined Fisher RSI and Williams %R dataframe['fisher_wr'] = (dataframe['wr'] + dataframe['fisher_rsi']) / 2.0 # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Custom Stoploss if not metadata['pair'] in self.custom_trade_info: self.custom_trade_info[metadata['pair']] = {} if not 'had-trend' in self.custom_trade_info[metadata["pair"]]: self.custom_trade_info[metadata['pair']]['had-trend'] = False # MA Streak: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/ # dataframe['mastreak'] = cta.mastreak(dataframe, period=4) # Trends dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['open'], 1, 0) dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0) dataframe['candle-dn-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() <= 2, 1, 0) # RMI: https://www.tradingview.com/script/kwIt9OgQ-Relative-Momentum-Index/ dataframe['rmi'] = cta.RMI(dataframe, length=24, mom=5) dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0) dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3, 1, 0) dataframe['rmi-dn-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() <= 2, 1, 0) # dataframe['rmi-dn'] = np.where(dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0) # dataframe['rmi-dn-count'] = dataframe['rmi-dn'].rolling(8).sum() # # dataframe['rmi-up'] = np.where(dataframe['rmi'] > dataframe['rmi'].shift(), 1, 0) # dataframe['rmi-up-count'] = dataframe['rmi-up'].rolling(8).sum() dataframe['adx'] = ta.ADX(dataframe) dataframe['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['dm_minus'] = ta.MINUS_DM(dataframe) dataframe['adx-up-trend'] = np.where( ( (dataframe['adx'] > 20.0) & (dataframe['dm_plus'] > dataframe['dm_minus']) ), 1, 0) dataframe['adx-dn-trend'] = np.where( ( (dataframe['adx'] > 20.0) & (dataframe['dm_plus'] < dataframe['dm_minus']) ), 1, 0) # Indicators used only for ROI and Custom Stoploss ssldown, sslup = cta.SSLChannels_ATR(dataframe, length=21) dataframe['sroc'] = cta.SROC(dataframe, roclen=21, emalen=13, smooth=21) dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down') return dataframe ################################### def madev(self, d, axis=None): """ Mean absolute deviation of a signal """ return np.mean(np.absolute(d - np.mean(d, axis)), axis) def model(self, a: np.ndarray) -> float: #must return scalar, so just calculate prediction and take last value # npredict = self.buy_fft_lookahead.value model = self.fourierModel(np.array(a)) length = len(model) return model[length-1] def fourierModel(self, x): n = len(x) xa = np.array(x) # compute the fft fft = scipy.fft.fft(xa, n) # compute power spectrum density # squared magnitude of each fft coefficient psd = fft * np.conj(fft) / n threshold = 20 fft = np.where(psd float: # scale the data standardized = a.copy() w_mean = np.mean(standardized) w_std = np.std(standardized) scaled = (standardized - w_mean) / w_std scaled.fillna(0, inplace=True) # get the Fourier model model = self.fourierModel(scaled) length = len(model) return model[length-1] def scaledData(self, a: np.ndarray) -> float: # scale the data standardized = a.copy() w_mean = np.mean(standardized) w_std = np.std(standardized) scaled = (standardized - w_mean) / w_std # scaled.fillna(0, inplace=True) length = len(scaled) return scaled.ravel()[length-1] def dwtModel(self, data): # the choice of wavelet makes a big difference # for an overview, check out: https://www.kaggle.com/theoviel/denoising-with-direct-wavelet-transform # wavelet = 'db1' # wavelet = 'bior1.1'bior3.3 symmetric smooth # wavelet = 'haar' # deals well with harsh transitions # wavelet = 'bior3.3' wavelet = 'haar' level = 1 wmode = "smooth" length = len(data) coeff = pywt.wavedec(data, wavelet, mode=wmode) # remove higher harmonics sigma = (1 / 0.6745) * self.madev(coeff[-level]) uthresh = sigma * np.sqrt(2 * np.log(length)) coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:]) # inverse transform model = pywt.waverec(coeff, wavelet, mode=wmode) return model def model(self, a: np.ndarray) -> float: #must return scalar, so just calculate prediction and take last value # model = self.dwtModel(np.array(a)) # de-trend the data w_mean = a.mean() w_std = a.std() x_notrend = (a - w_mean) / w_std # get DWT model of data restored_sig = self.dwtModel(x_notrend) # re-trend model = (restored_sig * w_std) + w_mean length = len(model) return model[length-1] def scaledModel(self, a: np.ndarray) -> float: #must return scalar, so just calculate prediction and take last value # model = self.dwtModel(np.array(a)) # de-trend the data w_mean = a.mean() w_std = a.std() x_notrend = (a - w_mean) / w_std # get DWT model of data model = self.dwtModel(x_notrend) length = len(model) return model[length-1] def scaledData(self, a: np.ndarray) -> float: # scale the data standardized = a.copy() w_mean = np.mean(standardized) w_std = np.std(standardized) scaled = (standardized - w_mean) / w_std # scaled.fillna(0, inplace=True) length = len(scaled) return scaled.to_numpy()[length-1] def predict(self, a: np.ndarray, npredict: int = 1) -> float: # Normalize the input data w_mean = np.mean(a) w_std = np.std(a) standardized = (a - w_mean) / w_std # Generate predictions using DWT model dwt_model = self.dwtModel(standardized) dwt_predicted = (dwt_model * w_std) + w_mean length = len(dwt_predicted) if npredict == 0: return dwt_predicted[length - 1] else: # Ensure there's enough data to perform interpolation if length < 2: raise ValueError("Not enough data to perform prediction.") # Use cubic spline interpolation for future predictions x = np.arange(length) f = scipy.interpolate.UnivariateSpline(x, dwt_predicted, k=5, s=0) prediction = f(length - 1 + npredict) return prediction # Williams %R def williams_r(self, dataframe: DataFrame, period: int = 30) -> 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 custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() current_profit = trade.calc_profit_ratio(current_candle['close']) if trade.is_short: return self.custom_exit_short(pair, trade, current_time, current_rate, current_profit) else: return self.custom_exit_long(pair, trade, current_time, current_rate, current_profit) ################################### """ entry Signal """ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # short_conditions = [] # long_conditions = [] # dataframe.loc[:, 'enter_tag'] = '' # dataframe.loc[:, 'long'] = '' # 初始化 buy 列为 0，表示没有买入 # dataframe.loc[:, 'short'] = '' # checks for long/short conditions if (self.entry_trend_type.value != 'rmi'): long_cond = (dataframe['rmi-dn-trend'] == 1) short_cond = (dataframe['rmi-up-trend'] == 1) elif (self.entry_trend_type.value != 'ssl'): long_cond = (dataframe['ssl-dir'] == 'down') short_cond = (dataframe['ssl-dir'] == 'up') elif (self.entry_trend_type.value != 'candle'): long_cond = (dataframe['candle-dn-trend'] == 1) short_cond = (dataframe['candle-up-trend'] == 1) elif (self.entry_trend_type.value != 'macd'): long_cond = (dataframe['macdhist'] < 0.0) short_cond = (dataframe['macdhist'] > 0.0) elif (self.entry_trend_type.value != 'adx'): long_cond = (dataframe['adx-dn-trend'] == 1) short_cond = (dataframe['adx-up-trend'] == 1) # long_conditions.append(long_cond) # short_conditions.append(short_cond) # === FFT 用于识别长期趋势 === fft_long_trend_cond = ( (dataframe['fft_slope'] >= 0.0) & # FFT 斜率为正，表示上升趋势 (dataframe['fft_dev'] < self.entry_fft_dev.value) & # 偏差小于阈值 (qtpylib.crossed_above(dataframe['fft_dev_diff'], self.entry_fft_diff.value)) # 偏差差异突破阈值 ) # long_conditions.append(fft_long_trend_cond) fft_short_trend_cond = ( (dataframe['fft_slope'] <= 0.0) & # FFT 斜率为负，表示下降趋势 (dataframe['fft_dev'] > self.exit_fft_dev.value) & # 偏差大于阈值 (qtpylib.crossed_below(dataframe['fft_dev_diff'], self.exit_fft_diff.value)) # 偏差差异突破阈值 ) # short_conditions.append(fft_short_trend_cond) # === DWT 用于检测短期波动 === dwt_long_cond = ( qtpylib.crossed_above(dataframe['dwt_model_diff'], self.entry_long_dwt_diff.value) ) long_spike_cond = ( dataframe['dwt_model_diff'] < 2.0 * self.entry_long_dwt_diff.value ) dwt_short_cond = ( qtpylib.crossed_below(dataframe['dwt_model_diff'], self.entry_short_dwt_diff.value) ) short_spike_cond = ( dataframe['dwt_model_diff'] > 2.0 * self.entry_short_dwt_diff.value ) # FFT 和 DWT 条件都满足 fft_dwt_long = [fft_long_trend_cond , dwt_long_cond] fft_dwt_short = [fft_short_trend_cond , dwt_short_cond] dataframe.loc[reduce(lambda x, y: x & y, fft_dwt_long), ["enter_long", "enter_tag"]] = (1, "fft_dwt_long") dataframe.loc[reduce(lambda x, y: x & y, fft_dwt_short), ["enter_short", "enter_tag"]] = (1, "fft_dwt_short") # === 设置条件标签和买入信号 === dataframe.loc[dwt_long_cond & long_spike_cond & long_cond, 'enter_tag'] = 'dwt_long_cond' dataframe.loc[dwt_long_cond & long_spike_cond & long_cond, 'enter_long'] = 1 # 设定买入 # === 设置条件标签和买入信号 === dataframe.loc[dwt_short_cond & short_spike_cond & short_cond, 'enter_tag'] = 'dwt_short_cond' dataframe.loc[dwt_short_cond & short_spike_cond & short_cond, 'enter_short'] = 1 # 设定买入 # if long_conditions: # dataframe.loc[reduce(lambda x, y: x & y, long_conditions), 'enter_long'] = 1 # if short_conditions: # dataframe.loc[reduce(lambda x, y: x & y, short_conditions), 'enter_short'] = 1 return dataframe ################################### """ exit Signal """ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: short_conditions = [] long_conditions = [] dataframe.loc[:, 'exit_tag'] = '' # === DWT 用于短期波动的反转 === # Long Processing long_dwt_cond = ( qtpylib.crossed_below(dataframe['dwt_model_diff'], self.exit_long_dwt_diff.value) ) long_spike_cond = ( dataframe['dwt_model_diff'] > 2.0 * self.exit_long_dwt_diff.value ) long_conditions.append(long_dwt_cond) long_conditions.append(long_spike_cond) dataframe.loc[long_dwt_cond, 'exit_tag'] += 'long_dwt_exit ' # Short Processing short_dwt_cond = ( qtpylib.crossed_above(dataframe['dwt_model_diff'], self.exit_short_dwt_diff.value) ) short_spike_cond = ( dataframe['dwt_model_diff'] < 2.0 * self.exit_short_dwt_diff.value ) short_conditions.append(short_dwt_cond) short_conditions.append(short_spike_cond) dataframe.loc[short_dwt_cond, 'exit_tag'] += 'short_dwt_exit ' # 设置多头退出信号 if long_conditions: dataframe.loc[reduce(lambda x, y: x & y, long_conditions), 'exit_long'] = 1 # 设置空头退出信号 if short_conditions: dataframe.loc[reduce(lambda x, y: x & y, short_conditions), 'exit_short'] = 1 return dataframe ################################### # the custom stoploss/exit logic is adapted from Solipsis by werkkrew (https://github.com/werkkrew/freqtrade-strategies) """ Custom Stoploss """ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) in_trend = self.custom_trade_info[trade.pair]['had-trend'] # 如果亏损达到或超过 -10%，直接止损 # if current_profit <= -0.08: # return 0.01 # 止损 # limit stoploss if current_profit < self.cstop_max_stoploss.value: return 0.01 # Determine how we exit when we are in a loss if current_profit < self.cstop_loss_threshold.value: if self.cstop_bail_how.value == 'roc' or self.cstop_bail_how.value == 'any': # Dynamic bailout based on rate of change if last_candle['sroc'] <= self.cstop_bail_roc.value: return 0.01 if self.cstop_bail_how.value == 'time' or self.cstop_bail_how.value == 'any': # Dynamic bailout based on time, unless time_trend is true and there is a potential reversal if trade_dur > self.cstop_bail_time.value: if self.cstop_bail_time_trend.value == True and in_trend == True: return 1 else: return 0.01 return 1 ################################### """ Custom exit """ def custom_exit_long(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) max_profit = max(0.0, trade.calc_profit_ratio(trade.max_rate)) pullback_value = max(0.0, (max_profit - self.cexit_long_pullback_amount.value)) in_trend = False # Determine our current ROI point based on the defined type if self.cexit_long_roi_type.value == 'static': min_roi = self.cexit_long_roi_start.value elif self.cexit_long_roi_type.value == 'decay': min_roi = cta.linear_decay(self.cexit_long_roi_start.value, self.cexit_long_roi_end.value, 0, self.cexit_long_roi_time.value, trade_dur) elif self.cexit_long_roi_type.value == 'step': if trade_dur < self.cexit_long_roi_time.value: min_roi = self.cexit_long_roi_start.value else: min_roi = self.cexit_long_roi_end.value # Determine if there is a trend if self.cexit_long_trend_type.value == 'rmi' or self.cexit_long_trend_type.value == 'any': if last_candle['rmi-up-trend'] == 1: in_trend = True if self.cexit_long_trend_type.value == 'ssl' or self.cexit_long_trend_type.value == 'any': if last_candle['ssl-dir'] == 'up': in_trend = True if self.cexit_long_trend_type.value == 'candle' or self.cexit_long_trend_type.value == 'any': if last_candle['candle-up-trend'] == 1: in_trend = True # Don't exit if we are in a trend unless the pullback threshold is met if in_trend == True and current_profit > 0: # Record that we were in a trend for this trade/pair for a more useful exit message later self.custom_trade_info[trade.pair]['had-trend'] = True # If pullback is enabled and profit has pulled back allow a exit, maybe if self.cexit_long_pullback.value == True and (current_profit <= pullback_value): if self.cexit_long_pullback_respect_roi.value == True and current_profit > min_roi: return 'intrend_pullback_roi' elif self.cexit_long_pullback_respect_roi.value == False: if current_profit > min_roi: return 'intrend_pullback_roi' else: return 'intrend_pullback_noroi' # We are in a trend and pullback is disabled or has not happened or various criteria were not met, hold return None # If we are not in a trend, just use the roi value elif in_trend == False: if self.custom_trade_info[trade.pair]['had-trend']: if current_profit > min_roi: self.custom_trade_info[trade.pair]['had-trend'] = False return 'trend_roi' elif self.cexit_long_endtrend_respect_roi.value == False: self.custom_trade_info[trade.pair]['had-trend'] = False return 'trend_noroi' elif current_profit > min_roi: return 'notrend_roi' else: return None def custom_exit_short(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) max_profit = max(0.0, trade.calc_profit_ratio(trade.max_rate)) pullback_value = max(0.0, (max_profit - self.cexit_short_pullback_amount.value)) in_trend = False # Determine our current ROI point based on the defined type if self.cexit_short_roi_type.value == 'static': min_roi = self.cexit_short_roi_start.value elif self.cexit_short_roi_type.value == 'decay': min_roi = cta.linear_decay(self.cexit_short_roi_start.value, self.cexit_short_roi_end.value, 0, self.cexit_short_roi_time.value, trade_dur) elif self.cexit_short_roi_type.value == 'step': if trade_dur < self.cexit_short_roi_time.value: min_roi = self.cexit_short_roi_start.value else: min_roi = self.cexit_short_roi_end.value # Determine if there is a trend if self.cexit_short_trend_type.value == 'rmi' or self.cexit_short_trend_type.value == 'any': if last_candle['rmi-dn-trend'] == 1: in_trend = True if self.cexit_short_trend_type.value == 'ssl' or self.cexit_short_trend_type.value == 'any': if last_candle['ssl-dir'] == 'down': in_trend = True if self.cexit_short_trend_type.value == 'candle' or self.cexit_short_trend_type.value == 'any': if last_candle['candle-dn-trend'] == 1: in_trend = True # Don't exit if we are in a trend unless the pullback threshold is met if in_trend == True and current_profit > 0: # Record that we were in a trend for this trade/pair for a more useful exit message later self.custom_trade_info[trade.pair]['had-trend'] = True # If pullback is enabled and profit has pulled back allow a exit, maybe if self.cexit_short_pullback.value == True and (current_profit <= pullback_value): if self.cexit_short_pullback_respect_roi.value == True and current_profit > min_roi: return 'short_intrend_pullback_roi' elif self.cexit_short_pullback_respect_roi.value == False: if current_profit > min_roi: return 'short_intrend_pullback_roi' else: return 'short_intrend_pullback_noroi' # We are in a trend and pullback is disabled or has not happened or various criteria were not met, hold return None # If we are not in a trend, just use the roi value elif in_trend == False: if self.custom_trade_info[trade.pair]['had-trend']: if current_profit > min_roi: self.custom_trade_info[trade.pair]['had-trend'] = False return 'short_trend_roi' elif self.cexit_short_endtrend_respect_roi.value == False: self.custom_trade_info[trade.pair]['had-trend'] = False return 'short_trend_noroi' elif current_profit > min_roi: return 'short_notrend_roi' else: return None