# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/TrailingBuyStratClucBBRPBMODE.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame, Series # -------------------------------- import logging import pandas as pd import numpy as np from datetime import datetime, timedelta, timezone import time # -------------------------------- strategy specific libs -------------------------------- import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas_ta as pta import talib.abstract as ta import technical.indicators as ftt from freqtrade.persistence import Trade, PairLocks from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, stoploss_from_open, merge_informative_pair from skopt.space import Dimension, Integer def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - rolling_std * num_of_std return (np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)) def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.0 return Series(index=bars.index, data=res) class Github_DerSalvador_freqtrade_helm_chart__TrailingBuyStratClucBBRPBMODE__20260416_224245(IStrategy): INTERFACE_VERSION = 3 # Buy hyperspace params: entry_params = {'antipump_threshold': 0.133, 'entry_btc_safe_1d': -0.311, 'clucha_bbdelta_close': 0.04796, 'clucha_bbdelta_tail': 0.93112, 'clucha_close_bblower': 0.01645, 'clucha_closedelta_close': 0.00931, 'clucha_enabled': False, 'clucha_rocr_1h': 0.41663, 'cofi_adx': 8, 'cofi_ema': 0.639, 'cofi_enabled': False, 'cofi_ewo_high': 5.6, 'cofi_fastd': 40, 'cofi_fastk': 13, 'ewo_1_enabled': False, 'ewo_1_rsi_14': 45, 'ewo_1_rsi_4': 7, 'ewo_candles_entry': 13, 'ewo_candles_exit': 19, 'ewo_high': 5.249, 'ewo_high_offset': 1.04116, 'ewo_low': -11.424, 'ewo_low_enabled': True, 'ewo_low_offset': 0.97463, 'ewo_low_rsi_4': 35, 'lambo1_ema_14_factor': 1.054, 'lambo1_enabled': False, 'lambo1_rsi_14_limit': 26, 'lambo1_rsi_4_limit': 18, 'lambo2_ema_14_factor': 0.981, 'lambo2_enabled': True, 'lambo2_rsi_14_limit': 39, 'lambo2_rsi_4_limit': 44, 'local_trend_bb_factor': 0.823, 'local_trend_closedelta': 19.253, 'local_trend_ema_diff': 0.125, 'local_trend_enabled': True, 'nfi32_cti_limit': -1.09639, 'nfi32_enabled': True, 'nfi32_rsi_14': 15, 'nfi32_rsi_4': 49, 'nfi32_sma_factor': 0.93391} # Sell hyperspace params: # custom stoploss params, come from BB_RPB_TSL exit_params = {'pHSL': -0.32, 'pPF_1': 0.02, 'pPF_2': 0.047, 'pSL_1': 0.02, 'pSL_2': 0.046, 'exit-fisher': 0.38414, 'exit-bbmiddle-close': 1.07634} # ROI table: minimal_roi = {'70': 0} # Stoploss: stoploss = -0.99 # use custom stoploss # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.012 trailing_only_offset_is_reached = False '\n END HYPEROPT\n ' timeframe = '1m' # Make sure these match or are not overridden in config use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Custom stoploss use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 200 order_types = {'entry': 'market', 'exit': 'market', 'emergencyexit': 'market', 'forceentry': 'market', 'forceexit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99} # hard stoploss profit pHSL = DecimalParameter(-0.5, -0.04, default=-0.08, decimals=3, space='exit', load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='exit', load=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='exit', load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='exit', load=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='exit', load=True) # entry param # ClucHA clucha_bbdelta_close = DecimalParameter(0.01, 0.05, default=entry_params['clucha_bbdelta_close'], decimals=5, space='entry', optimize=True) clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=entry_params['clucha_bbdelta_tail'], decimals=5, space='entry', optimize=True) clucha_close_bblower = DecimalParameter(0.001, 0.05, default=entry_params['clucha_close_bblower'], decimals=5, space='entry', optimize=True) clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=entry_params['clucha_closedelta_close'], decimals=5, space='entry', optimize=True) clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=entry_params['clucha_rocr_1h'], decimals=5, space='entry', optimize=True) # lambo1 lambo1_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=entry_params['lambo1_ema_14_factor'], space='entry', optimize=True) lambo1_rsi_4_limit = IntParameter(5, 60, default=entry_params['lambo1_rsi_4_limit'], space='entry', optimize=True) lambo1_rsi_14_limit = IntParameter(5, 60, default=entry_params['lambo1_rsi_14_limit'], space='entry', optimize=True) # lambo2 lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=entry_params['lambo2_ema_14_factor'], space='entry', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=entry_params['lambo2_rsi_4_limit'], space='entry', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=entry_params['lambo2_rsi_14_limit'], space='entry', optimize=True) # local_uptrend local_trend_ema_diff = DecimalParameter(0, 0.2, default=entry_params['local_trend_ema_diff'], space='entry', optimize=True) local_trend_bb_factor = DecimalParameter(0.8, 1.2, default=entry_params['local_trend_bb_factor'], space='entry', optimize=True) local_trend_closedelta = DecimalParameter(5.0, 30.0, default=entry_params['local_trend_closedelta'], space='entry', optimize=True) # ewo_1 and ewo_low ewo_candles_entry = IntParameter(2, 30, default=entry_params['ewo_candles_entry'], space='entry', optimize=True) ewo_candles_exit = IntParameter(2, 35, default=entry_params['ewo_candles_exit'], space='entry', optimize=True) ewo_low_offset = DecimalParameter(0.7, 1.2, default=entry_params['ewo_low_offset'], decimals=5, space='entry', optimize=True) ewo_high_offset = DecimalParameter(0.75, 1.5, default=entry_params['ewo_high_offset'], decimals=5, space='entry', optimize=True) ewo_high = DecimalParameter(2.0, 15.0, default=entry_params['ewo_high'], space='entry', optimize=True) ewo_1_rsi_14 = IntParameter(10, 100, default=entry_params['ewo_1_rsi_14'], space='entry', optimize=True) ewo_1_rsi_4 = IntParameter(1, 50, default=entry_params['ewo_1_rsi_4'], space='entry', optimize=True) ewo_low_rsi_4 = IntParameter(1, 50, default=entry_params['ewo_low_rsi_4'], space='entry', optimize=True) ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True) # cofi cofi_ema = DecimalParameter(0.6, 1.4, default=entry_params['cofi_ema'], space='entry', optimize=True) cofi_fastk = IntParameter(1, 100, default=entry_params['cofi_fastk'], space='entry', optimize=True) cofi_fastd = IntParameter(1, 100, default=entry_params['cofi_fastd'], space='entry', optimize=True) cofi_adx = IntParameter(1, 100, default=entry_params['cofi_adx'], space='entry', optimize=True) cofi_ewo_high = DecimalParameter(1.0, 15.0, default=entry_params['cofi_ewo_high'], space='entry', optimize=True) # nfi32 nfi32_rsi_4 = IntParameter(1, 100, default=entry_params['nfi32_rsi_4'], space='entry', optimize=True) nfi32_rsi_14 = IntParameter(1, 100, default=entry_params['nfi32_rsi_4'], space='entry', optimize=True) nfi32_sma_factor = DecimalParameter(0.7, 1.2, default=entry_params['nfi32_sma_factor'], decimals=5, space='entry', optimize=True) nfi32_cti_limit = DecimalParameter(-1.2, 0, default=entry_params['nfi32_cti_limit'], decimals=5, space='entry', optimize=True) entry_btc_safe_1d = DecimalParameter(-0.5, -0.015, default=entry_params['entry_btc_safe_1d'], optimize=True) antipump_threshold = DecimalParameter(0, 0.4, default=entry_params['antipump_threshold'], space='entry', optimize=True) ewo_1_enabled = BooleanParameter(default=entry_params['ewo_1_enabled'], space='entry', optimize=True) ewo_low_enabled = BooleanParameter(default=entry_params['ewo_low_enabled'], space='entry', optimize=True) cofi_enabled = BooleanParameter(default=entry_params['cofi_enabled'], space='entry', optimize=True) lambo1_enabled = BooleanParameter(default=entry_params['lambo1_enabled'], space='entry', optimize=True) lambo2_enabled = BooleanParameter(default=entry_params['lambo2_enabled'], space='entry', optimize=True) local_trend_enabled = BooleanParameter(default=entry_params['local_trend_enabled'], space='entry', optimize=True) nfi32_enabled = BooleanParameter(default=entry_params['nfi32_enabled'], space='entry', optimize=True) clucha_enabled = BooleanParameter(default=entry_params['clucha_enabled'], space='entry', optimize=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] informative_pairs += [('BTC/USDT', '1m')] informative_pairs += [('BTC/USDT', '1d')] return informative_pairs ############################################################################ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit 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 # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. 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 # Only for hyperopt invalid return if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) ############################################################################ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Heikin Ashi Candles heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) # CTI dataframe['cti'] = pta.cti(dataframe['close'], length=20) # Cofi 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) # Set Up Bollinger Bands mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid 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'] dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # # ClucHA dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # Elliot dataframe['EWO'] = EWO(dataframe, 50, 200) rsi = ta.RSI(dataframe) dataframe['rsi'] = rsi rsi = 0.1 * (rsi - 50) dataframe['fisher'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) 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) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) ### BTC protection dataframe['btc_1m'] = self.dp.get_pair_dataframe('BTC/USDT', timeframe='1m')['close'] btc_1d = self.dp.get_pair_dataframe('BTC/USDT', timeframe='1d')[['date', 'close']].rename(columns={'close': 'btc'}).shift(1) dataframe = merge_informative_pair(dataframe, btc_1d, '1m', '1d', ffill=True) # Pump strength 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'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' dataframe[f'ma_entry_{self.ewo_candles_entry.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_entry.value)) dataframe[f'ma_exit_{self.ewo_candles_exit.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_exit.value)) # Make sure Volume is not 0 is_btc_safe = (pct_change(dataframe['btc_1d'], dataframe['btc_1m']).fillna(0) > self.entry_btc_safe_1d.value) & (dataframe['volume'] > 0) is_pump_safe = dataframe['pump_strength'] < self.antipump_threshold.value lambo1 = bool(self.lambo1_enabled.value) & (dataframe['close'] < dataframe['ema_14'] * self.lambo1_ema_14_factor.value) & (dataframe['rsi_4'] < int(self.lambo1_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo1_rsi_14_limit.value)) dataframe.loc[lambo1, 'enter_tag'] += 'lambo1_' conditions.append(lambo1) lambo2 = bool(self.lambo2_enabled.value) & (dataframe['close'] < dataframe['ema_14'] * self.lambo2_ema_14_factor.value) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) dataframe.loc[lambo2, 'enter_tag'] += 'lambo2_' conditions.append(lambo2) local_uptrend = bool(self.local_trend_enabled.value) & (dataframe['ema_26'] > dataframe['ema_14']) & (dataframe['ema_26'] - dataframe['ema_14'] > dataframe['open'] * self.local_trend_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_14'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.local_trend_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.local_trend_closedelta.value / 1000) dataframe.loc[local_uptrend, 'enter_tag'] += 'local_uptrend_' conditions.append(local_uptrend) nfi_32 = bool(self.nfi32_enabled.value) & (dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) & (dataframe['rsi_4'] < self.nfi32_rsi_4.value) & (dataframe['rsi_14'] > self.nfi32_rsi_14.value) & (dataframe['close'] < dataframe['sma_15'] * self.nfi32_sma_factor.value) & (dataframe['cti'] < self.nfi32_cti_limit.value) dataframe.loc[nfi_32, 'enter_tag'] += 'nfi_32_' conditions.append(nfi_32) ewo_1 = bool(self.ewo_1_enabled.value) & (dataframe['rsi_4'] < self.ewo_1_rsi_4.value) & (dataframe['close'] < dataframe[f'ma_entry_{self.ewo_candles_entry.value}'] * self.ewo_low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi_14'] < self.ewo_1_rsi_14.value) & (dataframe['close'] < dataframe[f'ma_exit_{self.ewo_candles_exit.value}'] * self.ewo_high_offset.value) dataframe.loc[ewo_1, 'enter_tag'] += 'ewo1_' conditions.append(ewo_1) ewo_low = bool(self.ewo_low_enabled.value) & (dataframe['rsi_4'] < self.ewo_low_rsi_4.value) & (dataframe['close'] < dataframe[f'ma_entry_{self.ewo_candles_entry.value}'] * self.ewo_low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['close'] < dataframe[f'ma_exit_{self.ewo_candles_exit.value}'] * self.ewo_high_offset.value) dataframe.loc[ewo_low, 'enter_tag'] += 'ewo_low_' conditions.append(ewo_low) cofi = bool(self.cofi_enabled.value) & (dataframe['open'] < dataframe['ema_8'] * self.cofi_ema.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']) & (dataframe['fastk'] < self.cofi_fastk.value) & (dataframe['fastd'] < self.cofi_fastd.value) & (dataframe['adx'] > self.cofi_adx.value) & (dataframe['EWO'] > self.cofi_ewo_high.value) dataframe.loc[cofi, 'enter_tag'] += 'cofi_' conditions.append(cofi) clucHA = bool(self.clucha_enabled.value) & dataframe['rocr_1h'].gt(self.clucha_rocr_1h.value) & (dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['ha_close'] * self.clucha_bbdelta_close.value) & dataframe['ha_closedelta'].gt(dataframe['ha_close'] * self.clucha_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.clucha_bbdelta_tail.value) & dataframe['ha_close'].lt(dataframe['lower'].shift()) & dataframe['ha_close'].le(dataframe['ha_close'].shift()) | (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < self.clucha_close_bblower.value * dataframe['bb_lowerband'])) dataframe.loc[clucHA, 'enter_tag'] += 'clucHA_' conditions.append(clucHA) # is_btc_safe & # broken? # is_pump_safe & dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.exit_params dataframe.loc[(dataframe['fisher'] > params['exit-fisher']) & dataframe['ha_high'].le(dataframe['ha_high'].shift(1)) & dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2)) & dataframe['ha_close'].le(dataframe['ha_close'].shift(1)) & (dataframe['ema_fast'] > dataframe['ha_close']) & (dataframe['ha_close'] * params['exit-bbmiddle-close'] > dataframe['bb_middleband']) & (dataframe['volume'] > 0), 'exit_long'] = 1 return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: trade.exit_reason = exit_reason + '_' + trade.entry_tag return True def pct_change(a, b): return (b - a) / a 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 logger = logging.getLogger(__name__) class TrailingBuyStratClucBBRPBMODE(Github_DerSalvador_freqtrade_helm_chart__TrailingBuyStratClucBBRPBMODE__20260416_224245): # Original idea by @MukavaValkku, code by @tirail and @stash86 # # This class is designed to inherit from yours and starts trailing entry with your entry signals # Trailing entry starts at any entry signal and will move to next candles if the trailing still active # Trailing entry stops with BUY if : price decreases and rises again more than trailing_entry_offset # Trailing entry stops with NO BUY : current price is > initial price * (1 + trailing_entry_max) OR custom_exit tag # IT IS NOT COMPATIBLE WITH BACKTEST/HYPEROPT # process_only_new_candles = True custom_info_trail_entry = dict() # Trailing entry parameters trailing_entry_order_enabled = True trailing_expire_seconds = 1800 # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, entry the coin trailing_entry_uptrend_enabled = False trailing_expire_seconds_uptrend = 90 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_entry_max_stop = 0.02 # stop trailing entry if current_price > starting_price * (1+trailing_entry_max_stop) trailing_entry_max_entry = 0.0 # entry if price between uplimit (=min of serie (current_price * (1 + trailing_entry_offset())) and (start_price * 1+trailing_entry_max_entry)) init_trailing_dict = {'trailing_entry_order_started': False, 'trailing_entry_order_uplimit': 0, 'start_trailing_price': 0, 'enter_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': False} def trailing_entry(self, pair, reinit=False): # returns trailing entry info for pair (init if necessary) if not pair in self.custom_info_trail_entry: self.custom_info_trail_entry[pair] = dict() if reinit or not 'trailing_entry' in self.custom_info_trail_entry[pair]: self.custom_info_trail_entry[pair]['trailing_entry'] = self.init_trailing_dict.copy() return self.custom_info_trail_entry[pair]['trailing_entry'] def trailing_entry_info(self, pair: str, current_price: float): # current_time live, dry run current_time = datetime.now(timezone.utc) if not self.debug_mode: return trailing_entry = self.trailing_entry(pair) duration = 0 try: duration = current_time - trailing_entry['start_trailing_time'] except TypeError: duration = 0 finally: logger.info(f"pair: {pair} : start: {trailing_entry['start_trailing_price']:.4f}, duration: {duration}, current: {current_price:.4f}, uplimit: {trailing_entry['trailing_entry_order_uplimit']:.4f}, profit: {self.current_trailing_profit_ratio(pair, current_price) * 100:.2f}%, offset: {trailing_entry['offset']}") def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float: trailing_entry = self.trailing_entry(pair) if trailing_entry['trailing_entry_order_started']: return (trailing_entry['start_trailing_price'] - current_price) / trailing_entry['start_trailing_price'] else: return 0 def trailing_entry_offset(self, dataframe, pair: str, current_price: float): # return rebound limit before a entry in % of initial price, function of current price # return None to stop trailing entry (will start again at next entry signal) # return 'forceentry' to force immediate entry # (example with 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no entry, uplimit updated to 99.5), 3price 98 (no entry uplimit updated to 98.5), 4th price 99 -> BUY current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price) default_offset = 0.005 trailing_entry = self.trailing_entry(pair) if not trailing_entry['trailing_entry_order_started']: return default_offset # example with duration and indicators # dry run, live only last_candle = dataframe.iloc[-1] current_time = datetime.now(timezone.utc) trailing_duration = current_time - trailing_entry['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if current_trailing_profit_ratio > 0 and last_candle['enter_long'] == 1: # more than 1h, price under first signal, entry signal still active -> entry return 'forceentry' else: # wait for next signal return None elif self.trailing_entry_uptrend_enabled and trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend and (current_trailing_profit_ratio < -1 * self.min_uptrend_trailing_profit): # less than 90s and price is rising, entry return 'forceentry' if current_trailing_profit_ratio < 0: # current price is higher than initial price return default_offset trailing_entry_offset = {0.06: 0.02, 0.03: 0.01, 0: default_offset} for key in trailing_entry_offset: if current_trailing_profit_ratio > key: return trailing_entry_offset[key] return default_offset # end of trailing entry parameters # ----------------------------------------------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) self.trailing_entry(metadata['pair']) return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs) if val: if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): val = False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) >= 1: last_candle = dataframe.iloc[-1].squeeze() current_price = rate trailing_entry = self.trailing_entry(pair) trailing_entry_offset = self.trailing_entry_offset(dataframe, pair, current_price) if trailing_entry['allow_trailing']: if not trailing_entry['trailing_entry_order_started'] and last_candle['enter_long'] == 1: # start trailing entry # self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_started'] = True # self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'] = last_candle['close'] # self.custom_info_trail_entry[pair]['trailing_entry']['start_trailing_price'] = last_candle['close'] # self.custom_info_trail_entry[pair]['trailing_entry']['entry_tag'] = f"initial_entry_tag (strat trail price {last_candle['close']})" # self.custom_info_trail_entry[pair]['trailing_entry']['start_trailing_time'] = datetime.now(timezone.utc) # self.custom_info_trail_entry[pair]['trailing_entry']['offset'] = 0 trailing_entry['trailing_entry_order_started'] = True trailing_entry['trailing_entry_order_uplimit'] = last_candle['close'] trailing_entry['start_trailing_price'] = last_candle['close'] trailing_entry['enter_tag'] = last_candle['enter_tag'] trailing_entry['start_trailing_time'] = datetime.now(timezone.utc) trailing_entry['offset'] = 0 self.trailing_entry_info(pair, current_price) logger.info(f"start trailing entry for {pair} at {last_candle['close']}") elif trailing_entry['trailing_entry_order_started']: if trailing_entry_offset == 'forceentry': # entry in custom conditions val = True ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100) self.trailing_entry_info(pair, current_price) logger.info(f'price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full') elif trailing_entry_offset is None: # stop trailing entry custom conditions self.trailing_entry(pair, reinit=True) logger.info(f'STOP trailing entry for {pair} because "trailing entry offset" returned None') elif current_price < trailing_entry['trailing_entry_order_uplimit']: # update uplimit old_uplimit = trailing_entry['trailing_entry_order_uplimit'] self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'] = min(current_price * (1 + trailing_entry_offset), self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit']) self.custom_info_trail_entry[pair]['trailing_entry']['offset'] = trailing_entry_offset self.trailing_entry_info(pair, current_price) logger.info(f"update trailing entry for {pair} at {old_uplimit} -> {self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit']}") elif current_price < trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry): # entry ! current price > uplimit && lower thant starting price val = True ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100) self.trailing_entry_info(pair, current_price) logger.info(f"current price ({current_price}) > uplimit ({trailing_entry['trailing_entry_order_uplimit']}) and lower than starting price price ({trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry)}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full") elif current_price > trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_stop): # stop trailing entry because price is too high self.trailing_entry(pair, reinit=True) self.trailing_entry_info(pair, current_price) logger.info(f'STOP trailing entry for {pair} because of the price is higher than starting price * {1 + self.trailing_entry_max_stop}') else: # uplimit > current_price > max_price, continue trailing and wait for the price to go down self.trailing_entry_info(pair, current_price) logger.info(f'price too high for {pair} !') else: logger.info(f'Wait for next entry signal for {pair}') if val == True: self.trailing_entry_info(pair, rate) self.trailing_entry(pair, reinit=True) logger.info(f'STOP trailing entry for {pair} because I entry it') return val def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_entry_trend(dataframe, metadata) if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): last_candle = dataframe.iloc[-1].squeeze() trailing_entry = self.trailing_entry(metadata['pair']) if last_candle['enter_long'] == 1: if not trailing_entry['trailing_entry_order_started']: open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all() if not open_trades: logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!") # self.custom_info_trail_entry[metadata['pair']]['trailing_entry']['allow_trailing'] = True trailing_entry['allow_trailing'] = True initial_entry_tag = last_candle['enter_tag'] if 'enter_tag' in last_candle else 'entry signal' dataframe.loc[:, 'enter_tag'] = f"{initial_entry_tag} (start trail price {last_candle['close']})" elif trailing_entry['trailing_entry_order_started'] == True: logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger entry signal!!") dataframe.loc[:, 'enter_long'] = 1 dataframe.loc[:, 'enter_tag'] = trailing_entry['enter_tag'] # dataframe['entry'] = 1 return dataframe