from datetime import datetime, timedelta, timezone from functools import reduce from typing import List from freqtrade.strategy.interface import IStrategy from typing import Dict, List from pandas import DataFrame, Series import logging import pandas as pd import numpy as np from freqtrade.persistence import Trade import time 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 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. return Series(index=bars.index, data=res) class ClucHAnix_BB_RPB_MOD_E0V1E_DYNAMIC_TB1(IStrategy): buy_params = { "antipump_threshold": 0.133, "buy_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_buy": 13, "ewo_candles_sell": 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": False, "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_params = { "pHSL": -0.134, "pPF_1": 0.02, "pPF_2": 0.047, "pSL_1": 0.02, "pSL_2": 0.046, 'sell-fisher': 0.38414, 'sell-bbmiddle-close': 1.07634 } minimal_roi = { "0": 0.10347601757573865, "3": 0.050495605759981035, "5": 0.03350898081823659, "61": 0.0275218557571848, "125": 0.011112591523667215, "292": 0.005185372158403069, "399": 0 } stoploss = -0.99 # use custom stoploss trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.012 trailing_only_offset_is_reached = False """ END HYPEROPT """ timeframe = '5m' use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 200 order_types = { 'buy': 'market', 'sell': 'market', 'emergencysell': 'market', 'forcebuy': "market", 'forcesell': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=buy_params['clucha_bbdelta_close'], decimals=5, space='buy', optimize=True) clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=buy_params['clucha_bbdelta_tail'], decimals=5, space='buy', optimize=True) clucha_close_bblower = DecimalParameter(0.001, 0.05, default=buy_params['clucha_close_bblower'], decimals=5, space='buy', optimize=True) clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=buy_params['clucha_closedelta_close'], decimals=5, space='buy', optimize=True) clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=buy_params['clucha_rocr_1h'], decimals=5, space='buy', optimize=True) lambo1_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo1_ema_14_factor'], space='buy', optimize=True) lambo1_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_4_limit'], space='buy', optimize=True) lambo1_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_14_limit'], space='buy', optimize=True) lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) local_trend_ema_diff = DecimalParameter(0, 0.2, default=buy_params['local_trend_ema_diff'], space='buy', optimize=True) local_trend_bb_factor = DecimalParameter(0.8, 1.2, default=buy_params['local_trend_bb_factor'], space='buy', optimize=True) local_trend_closedelta = DecimalParameter(5.0, 30.0, default=buy_params['local_trend_closedelta'], space='buy', optimize=True) ewo_candles_buy = IntParameter(2, 30, default=buy_params['ewo_candles_buy'], space='buy', optimize=True) ewo_candles_sell = IntParameter(2, 35, default=buy_params['ewo_candles_sell'], space='buy', optimize=True) ewo_low_offset = DecimalParameter(0.7, 1.2, default=buy_params['ewo_low_offset'], decimals=5, space='buy', optimize=True) ewo_high_offset = DecimalParameter(0.75, 1.5, default=buy_params['ewo_high_offset'], decimals=5, space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 15.0, default=buy_params['ewo_high'], space='buy', optimize=True) ewo_1_rsi_14 = IntParameter(10, 100, default=buy_params['ewo_1_rsi_14'], space='buy', optimize=True) ewo_1_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_1_rsi_4'], space='buy', optimize=True) ewo_low_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_low_rsi_4'], space='buy', optimize=True) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) cofi_ema = DecimalParameter(0.6, 1.4, default=buy_params['cofi_ema'] , space='buy', optimize=True) cofi_fastk = IntParameter(1, 100, default=buy_params['cofi_fastk'], space='buy', optimize=True) cofi_fastd = IntParameter(1, 100, default=buy_params['cofi_fastd'], space='buy', optimize=True) cofi_adx = IntParameter(1, 100, default=buy_params['cofi_adx'], space='buy', optimize=True) cofi_ewo_high = DecimalParameter(1.0, 15.0, default=buy_params['cofi_ewo_high'], space='buy', optimize=True) nfi32_rsi_4 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True) nfi32_rsi_14 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True) nfi32_sma_factor = DecimalParameter(0.7, 1.2, default=buy_params['nfi32_sma_factor'], decimals=5, space='buy', optimize=True) nfi32_cti_limit = DecimalParameter(-1.2, 0, default=buy_params['nfi32_cti_limit'], decimals=5, space='buy', optimize=True) buy_btc_safe_1d = DecimalParameter(-0.5, -0.015, default=buy_params['buy_btc_safe_1d'], optimize=True) antipump_threshold = DecimalParameter(0, 0.4, default=buy_params['antipump_threshold'], space='buy', optimize=True) ewo_1_enabled = BooleanParameter(default=buy_params['ewo_1_enabled'], space='buy', optimize=True) ewo_low_enabled = BooleanParameter(default=buy_params['ewo_low_enabled'], space='buy', optimize=True) cofi_enabled = BooleanParameter(default=buy_params['cofi_enabled'], space='buy', optimize=True) lambo1_enabled = BooleanParameter(default=buy_params['lambo1_enabled'], space='buy', optimize=True) lambo2_enabled = BooleanParameter(default=buy_params['lambo2_enabled'], space='buy', optimize=True) local_trend_enabled = BooleanParameter(default=buy_params['local_trend_enabled'], space='buy', optimize=True) nfi32_enabled = BooleanParameter(default=buy_params['nfi32_enabled'], space='buy', optimize=True) clucha_enabled = BooleanParameter(default=buy_params['clucha_enabled'], space='buy', 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: 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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'] 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) dataframe['cti'] = pta.cti(dataframe["close"], length=20) 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) 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() 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) 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) 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) dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] dataframe['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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_buy.value)) dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_sell.value)) is_btc_safe = ( (pct_change(dataframe['btc_1d'], dataframe['btc_1m']).fillna(0) > self.buy_btc_safe_1d.value) & (dataframe['volume'] > 0) # Make sure Volume is not 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, 'buy_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, 'buy_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, 'buy_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, 'buy_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_buy_{self.ewo_candles_buy.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_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value)) ) dataframe.loc[ewo_1, 'buy_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_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value)) ) dataframe.loc[ewo_low, 'buy_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, 'buy_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, 'buy_tag'] += 'clucHA_' conditions.append(clucHA) dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.sell_params dataframe.loc[ (dataframe['fisher'] > params['sell-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['sell-bbmiddle-close']) > dataframe['bb_middleband']) & (dataframe['volume'] > 0) , 'sell' ] = 1 return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: trade.sell_reason = sell_reason + "_" + trade.buy_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_MOD2_ROI_DYNAMIC_TB(ClucHAnix_BB_RPB_MOD_E0V1E_DYNAMIC_TB1): process_only_new_candles = True custom_info_trail_buy = dict() trailing_buy_order_enabled = True trailing_expire_seconds = 300 trailing_buy_uptrend_enabled = True trailing_expire_seconds_uptrend = 90 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_buy_max_stop = 0.01 # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop) trailing_buy_max_buy = 0.002 # buy if price between uplimit (=min of serie (current_price * (1 + trailing_buy_offset())) and (start_price * 1+trailing_buy_max_buy)) init_trailing_dict = { 'trailing_buy_order_started': False, 'trailing_buy_order_uplimit': 0, 'start_trailing_price': 0, 'buy_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': True, } def trailing_buy(self, pair, reinit=False): if not pair in self.custom_info_trail_buy: self.custom_info_trail_buy[pair] = dict() if (reinit or not 'trailing_buy' in self.custom_info_trail_buy[pair]): self.custom_info_trail_buy[pair]['trailing_buy'] = self.init_trailing_dict.copy() return self.custom_info_trail_buy[pair]['trailing_buy'] def trailing_buy_info(self, pair: str, current_price: float): current_time = datetime.now(timezone.utc) if not self.debug_mode: return trailing_buy = self.trailing_buy(pair) duration = 0 try: duration = (current_time - trailing_buy['start_trailing_time']) except TypeError: duration = 0 finally: logger.info( f"pair: {pair} : " f"start: {trailing_buy['start_trailing_price']:.4f}, " f"duration: {duration}, " f"current: {current_price:.4f}, " f"uplimit: {trailing_buy['trailing_buy_order_uplimit']:.4f}, " f"profit: {self.current_trailing_profit_ratio(pair, current_price)*100:.2f}%, " f"offset: {trailing_buy['offset']}") def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float: trailing_buy = self.trailing_buy(pair) if trailing_buy['trailing_buy_order_started']: return (trailing_buy['start_trailing_price'] - current_price) / trailing_buy['start_trailing_price'] else: return 0 def trailing_buy_offset(self, dataframe, pair: str, current_price: float): 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 trailing_buy = self.trailing_buy(pair) if not trailing_buy['trailing_buy_order_started']: return default_offset last_candle = dataframe.iloc[-1] current_time = datetime.now(timezone.utc) trailing_duration = current_time - trailing_buy['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if ((current_trailing_profit_ratio > 0) and (last_candle['buy'] == 1)): return 'forcebuy' else: return None elif (self.trailing_buy_uptrend_enabled and (trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend) and (current_trailing_profit_ratio < (-1 * self.min_uptrend_trailing_profit))): return 'forcebuy' if current_trailing_profit_ratio < 0: return default_offset trailing_buy_offset = { 0.06: 0.02, 0.03: 0.01, 0: default_offset, } for key in trailing_buy_offset: if current_trailing_profit_ratio > key: return trailing_buy_offset[key] return default_offset def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) self.trailing_buy(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_buy_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_buy = self.trailing_buy(pair) trailing_buy_offset = self.trailing_buy_offset(dataframe, pair, current_price) if trailing_buy['allow_trailing']: if (not trailing_buy['trailing_buy_order_started'] and (last_candle['buy'] == 1)): trailing_buy['trailing_buy_order_started'] = True trailing_buy['trailing_buy_order_uplimit'] = last_candle['close'] trailing_buy['start_trailing_price'] = last_candle['close'] trailing_buy['buy_tag'] = last_candle['buy_tag'] trailing_buy['start_trailing_time'] = datetime.now(timezone.utc) trailing_buy['offset'] = 0 self.trailing_buy_info(pair, current_price) logger.info(f'start trailing buy for {pair} at {last_candle["close"]}') elif trailing_buy['trailing_buy_order_started']: if trailing_buy_offset == 'forcebuy': val = True ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100) self.trailing_buy_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_buy_offset is None: self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because "trailing buy offset" returned None') elif current_price < trailing_buy['trailing_buy_order_uplimit']: old_uplimit = trailing_buy["trailing_buy_order_uplimit"] self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = min(current_price * (1 + trailing_buy_offset), self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit']) self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = trailing_buy_offset self.trailing_buy_info(pair, current_price) logger.info(f'update trailing buy for {pair} at {old_uplimit} -> {self.custom_info_trail_buy[pair]["trailing_buy"]["trailing_buy_order_uplimit"]}') elif current_price < (trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy)): val = True ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100) self.trailing_buy_info(pair, current_price) logger.info(f"current price ({current_price}) > uplimit ({trailing_buy['trailing_buy_order_uplimit']}) and lower than starting price price ({(trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy))}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full") elif current_price > (trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_stop)): self.trailing_buy(pair, reinit=True) self.trailing_buy_info(pair, current_price) logger.info(f'STOP trailing buy for {pair} because of the price is higher than starting price * {1 + self.trailing_buy_max_stop}') else: self.trailing_buy_info(pair, current_price) logger.info(f'price too high for {pair} !') else: logger.info(f"Wait for next buy signal for {pair}") if (val == True): self.trailing_buy_info(pair, rate) self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because I buy it') return val def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_buy_trend(dataframe, metadata) if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): last_candle = dataframe.iloc[-1].squeeze() trailing_buy = self.trailing_buy(metadata['pair']) if (last_candle['buy'] == 1): if not trailing_buy['trailing_buy_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!!!") trailing_buy['allow_trailing'] = True initial_buy_tag = last_candle['buy_tag'] if 'buy_tag' in last_candle else 'buy signal' dataframe.loc[:, 'buy_tag'] = f"{initial_buy_tag} (start trail price {last_candle['close']})" else: if (trailing_buy['trailing_buy_order_started'] == True): logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!") dataframe.loc[:,'buy'] = 1 dataframe.loc[:, 'buy_tag'] = trailing_buy['buy_tag'] return dataframe