# -------------------------------- import logging import time from datetime import datetime, timedelta, timezone from functools import reduce from typing import Dict, List import numpy as np import pandas as pd import pandas_ta as pta import talib.abstract as ta import technical.indicators as ftt from pandas import DataFrame, Series from skopt.space import Dimension, Integer import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import PairLocks, Trade from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, merge_informative_pair, stoploss_from_open # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy logger = logging.getLogger(__name__) 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 ClucHAnix_BB_RPB_MOD_E0V1E_ROI(IStrategy): INTERFACE_VERSION = 3 '\n VERSION MODIFIED BY REUNIWARE (InvestDataSystems@Yahoo.Com / 2021)\n THIS VERSION CONTAINS HYPEROPT SETTINGS FROM E0V1E (cf. https://discord.gg/Ayvcvs6N )\n ' class HyperOpt: @staticmethod def generate_roi_table(params: Dict) -> Dict[int, float]: roi_table = {} roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] + params['roi_p4'] + params['roi_p5'] + params['roi_p6'] roi_table[params['roi_t6']] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] + params['roi_p4'] + params['roi_p5'] roi_table[params['roi_t6'] + params['roi_t5']] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] + params['roi_p4'] roi_table[params['roi_t6'] + params['roi_t5'] + params['roi_t4']] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] roi_table[params['roi_t6'] + params['roi_t5'] + params['roi_t4'] + params['roi_t3']] = params['roi_p1'] + params['roi_p2'] roi_table[params['roi_t6'] + params['roi_t5'] + params['roi_t4'] + params['roi_t3'] + params['roi_t2']] = params['roi_p1'] roi_table[params['roi_t6'] + params['roi_t5'] + params['roi_t4'] + params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0 return roi_table @staticmethod def roi_space() -> List[Dimension]: return [Integer(1, 15, name='roi_t6'), Integer(1, 45, name='roi_t5'), Integer(1, 90, name='roi_t4'), Integer(45, 120, name='roi_t3'), Integer(45, 180, name='roi_t2'), Integer(90, 300, name='roi_t1'), Real(0.005, 0.1, name='roi_p6'), Real(0.005, 0.07, name='roi_p5'), Real(0.005, 0.05, name='roi_p4'), Real(0.005, 0.025, name='roi_p3'), Real(0.005, 0.01, name='roi_p2'), Real(0.003, 0.007, name='roi_p1')] 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': -0.134, '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 = {'0': 0.10347601757573865, '3': 0.050495605759981035, '5': 0.03350898081823659, '61': 0.0275218557571848, '125': 0.011112591523667215, '292': 0.005185372158403069, '399': 0} # Stoploss: stoploss = -0.99 # use custom stoploss # Trailing stop: trailing_stop = False trailing_stop_positive = 0.3207 trailing_stop_positive_offset = 0.3849 trailing_only_offset_is_reached = False '\n END HYPEROPT\n ' timeframe = '5m' # 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(-0.2, -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'] #NOTE: dynamic offset dataframe['perc'] = (dataframe['high'] - dataframe['low']) / dataframe['low'] * 100 dataframe['avg3_perc'] = ta.EMA(dataframe['perc'], 3) dataframe['norm_perc'] = (dataframe['perc'] - dataframe['perc'].rolling(50).min()) / (dataframe['perc'].rolling(50).max() - dataframe['perc'].rolling(50).min()) 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 class ClucHAnix_BB_RPB_MOD_E0V1E_ROI_DYNAMIC_TB(ClucHAnix_BB_RPB_MOD_E0V1E_ROI): 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 = True trailing_expire_seconds_uptrend = 90 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_entry_max_stop = 0.01 # stop trailing entry if current_price > starting_price * (1+trailing_entry_max_stop) trailing_entry_max_entry = 0.002 # 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) last_candle = dataframe.iloc[-1] adapt = abs(last_candle['norm_perc']) default_offset = 0.003 * (1 + adapt) #NOTE: default_offset 0.003 <--> 0.006 #default_offset = adapt*0.01 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 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