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 ClucHAnix_BB_RPB_MOD(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": 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: sell_params = { # custom stoploss params, come from BB_RPB_TSL "pHSL": -0.32, "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": 0.05, "15": 0.04, "51": 0.03, "81": 0.02, "112": 0.01, "154": 0.0001, "240": -10 } # 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 = '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', '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'] 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 TrailingBuyStrat2(ClucHAnix_BB_RPB_MOD): # Original idea by @MukavaValkku, code by @tirail and @stash86 # # This class is designed to inherit from yours and starts trailing buy with your buy signals # Trailing buy starts at any buy signal and will move to next candles if the trailing still active # Trailing buy stops with BUY if : price decreases and rises again more than trailing_buy_offset # Trailing buy stops with NO BUY : current price is > initial price * (1 + trailing_buy_max) OR custom_exit tag # IT IS NOT COMPATIBLE WITH BACKTEST/HYPEROPT # process_only_new_candles = True custom_info_trail_buy = dict() # Trailing buy parameters trailing_buy_order_enabled = True trailing_expire_seconds = 1800 # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, buy the coin trailing_buy_uptrend_enabled = False trailing_expire_seconds_uptrend = 90 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_buy_max_stop = 0.02 # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop) trailing_buy_max_buy = 0.000 # 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': False, } 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) default_offset = 0.005 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 # self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_started'] = True # self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = last_candle['close'] # self.custom_info_trail_buy[pair]['trailing_buy']['start_trailing_price'] = last_candle['close'] # self.custom_info_trail_buy[pair]['trailing_buy']['buy_tag'] = f"initial_buy_tag (strat trail price {last_candle['close']})" # self.custom_info_trail_buy[pair]['trailing_buy']['start_trailing_time'] = datetime.now(timezone.utc) # self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = 0 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