from datetime import datetime, timedelta, timezone from functools import reduce from typing import List # --- Do not remove these libs --- 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 ClucHAnix5m(IStrategy): # Buy hyperspace params: 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 hyperspace params: sell_params = { # custom stoploss params, come from BB_RPB_TSL "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 } # 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 = 0.001 trailing_stop_positive_offset = 0.012 trailing_only_offset_is_reached = False """ END HYPEROPT """ 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', 'emergencysell': 'market', 'forcebuy': "market", 'forcesell': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # hard stoploss profit pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) # profit threshold 1, trigger point, SL_1 is used 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) # profit threshold 2, SL_2 is used 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) # buy param # ClucHA 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 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 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_uptrend 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_1 and ewo_low 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 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 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: # 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[:, '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[ # is_btc_safe & # broken? # is_pump_safe & reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_exit_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(ClucHAnix5m): process_only_new_candles = True custom_info_trail_buy = dict() # Trailing buy parameters trailing_buy_order_enabled = True trailing_expire_seconds = 300 # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, buy the coin 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): # returns trailing buy info for pair (init if necessary) 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 live, dry run 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): # return rebound limit before a buy in % of initial price, function of current price # return None to stop trailing buy (will start again at next buy signal) # return 'forcebuy' to force immediate buy # (example with 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no buy, uplimit updated to 99.5), 3price 98 (no buy 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_buy = self.trailing_buy(pair) if not trailing_buy['trailing_buy_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_buy['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if ((current_trailing_profit_ratio > 0) and (last_candle['buy'] == 1)): # more than 1h, price under first signal, buy signal still active -> buy return 'forcebuy' else: # wait for next signal 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))): # less than 90s and price is rising, buy return 'forcebuy' if current_trailing_profit_ratio < 0: # current price is higher than initial price 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 # end of trailing buy parameters # ----------------------------------------------------- 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)): # start trailing buy 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': # buy in custom conditions 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: # stop trailing buy custom conditions 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']: # update 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)): # buy ! current price > uplimit && lower thant starting price 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)): # stop trailing buy because price is too high 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: # uplimit > current_price > max_price, continue trailing and wait for the price to go down 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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_entry_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!!!") # self.custom_info_trail_buy[metadata['pair']]['trailing_buy']['allow_trailing'] = True 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'] # dataframe['buy'] = 1 return dataframe