# source: https://raw.githubusercontent.com/eZ3KIEL29640/FQ_strat/9d14d615d982407149534d620b860ef3453520f2/EI4_t4c0s_V2_15.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from typing import Optional from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt import math import logging logger = logging.getLogger(__name__) # @Rallipanos # changes by IcHiAT def EWO(dataframe, ema_length=5, ema2_length=3): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif class Github_eZ3KIEL29640_FQ_strat__EI4_t4c0s_V2_15__20240627_174354(IStrategy): # ROI table: # minimal_roi = { # "0": 0.08, # "20": 0.04, # "40": 0.032, # "87": 0.016, # "201": 0 # } # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 12, "rsi_buy": 58, "ewo_high": 3.001, "ewo_low": -10.289, "low_offset": 0.987, "lambo2_ema_14_factor": 0.981, "lambo2_enabled": True, "lambo2_rsi_14_limit": 39, "lambo2_rsi_4_limit": 44, "buy_adx": 20, "buy_fastd": 20, "buy_fastk": 22, "buy_ema_cofi": 0.98, "buy_ewo_high": 4.179 } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 22, "high_offset": 1.014, "high_offset_2": 1.01 } @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02 }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01 } ] # ROI table: minimal_roi = { "0": 0.99, } # Stoploss: stoploss = -0.99 sl1 = DecimalParameter(-0.013, -0.005, default=-0.013, space='sell', optimize=True) # SMAOffset base_nb_candles_buy = IntParameter(8, 30, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(8, 30, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) # lambo2 lambo2_ema_14_factor = DecimalParameter(0.975, 0.995, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(30, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(30, 55, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 rsi_buy = IntParameter(35, 60, default=buy_params['rsi_buy'], space='buy', optimize=True) move = IntParameter(35, 60, default=48, space='buy', optimize=True) mms = IntParameter(6, 20, default=12, space='buy', optimize=True) mml = IntParameter(300, 400, default=360, space='buy', optimize=True) #cofi is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi) increment = DecimalParameter(low=1.0005, high=1.001, default=1.0007, decimals=4 ,space='buy', optimize=True, load=True) use_custom_stoploss = True process_only_new_candles = True # Custom Entry last_entry_price = None # Unclog unclog_days = IntParameter(1, 5, default=4, space='sell', optimize=True) unclog = DecimalParameter(0.01, 0.08, default=0.04, decimals=2, space='sell', optimize=True) ### Trailing Stop ### def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() SLT1 = current_candle['move_mean'] SL1 = self.sl1.value SLT2 = current_candle['move_mean_x'] SL2 = current_candle['move_mean_x'] - current_candle['move_mean'] display_profit = current_profit * 100 slt1 = SLT1 * 100 sl1 = SL1 * 100 slt2 = SLT2 * 100 sl2 = SL2 * 100 if current_candle['max_l'] > .003: #ignore stoploss if setting new highs if SLT2 is not None and current_profit > SLT2: self.dp.send_msg(f'*** {pair} *** Profit {display_profit:.2f}% - {slt2:.2f}/{sl2:.2f} activated') logger.info(f'*** {pair} *** Profit {display_profit:.2f}% - {slt2:.2f}/{sl2:.2f} activated') return SL2 if SLT1 is not None and current_profit > SLT1: self.dp.send_msg(f'*** {pair} *** Profit {display_profit:.2f} - {SLT1:.2f}/{SL1:.2f} activated') logger.info(f'*** {pair} *** Profit {display_profit:.2f}% - {slt1:.2f}/{sl1:.2f} activated') return SL1 else: if SLT1 is not None and current_profit > SL1: self.dp.send_msg(f'*** {pair} *** Profit {display_profit:.2f}% SWINGING FOR THE MOON!!!') logger.info(f'*** {pair} *** Profit {display_profit:.2f}% SWINGING FOR THE MOON!!!') return 0.99 return self.stoploss def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) entry_price = (dataframe['close'].iat[-1] + dataframe['open'].iat[-1] + proposed_rate + proposed_rate) / 4 logger.info(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}") # Check if there is a stored last entry price and if it matches the proposed entry price if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0001: # Tolerance for floating-point comparison entry_price *= self.increment.value # Increment by 0.2% logger.info(f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}.") # Update the last entry price self.last_entry_price = entry_price return entry_price 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: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if exit_reason == 'roi' and (last_candle['max_l'] < 0.003): return False # Handle freak events if exit_reason == 'Down Trend Soon' and trade.calc_profit_ratio(rate) < 0.003: logger.info(f"{trade.pair} Waiting for Profit") self.dp.send_msg(f'{trade.pair} Waiting for Profit') return False if exit_reason == 'roi' and trade.calc_profit_ratio(rate) < 0.003: logger.info(f"{trade.pair} ROI is below 0") self.dp.send_msg(f'{trade.pair} ROI is below 0') return False if exit_reason == 'partial_exit' and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} partial exit is below 0") self.dp.send_msg(f'{trade.pair} partial exit is below 0') return False if exit_reason == 'trailing_stop_loss' and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} trailing stop price is below 0") self.dp.send_msg(f'{trade.pair} trailing stop price is below 0') return False return True def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): # Sell any positions at a loss if they are held for more than 7 days. if current_profit < -self.unclog.value and (current_time - trade.open_date_utc).days >= self.unclog_days.value: return 'unclog' # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False ## Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } # Optimal timeframe for the strategy timeframe = '15m' position_adjustment_enable = False process_only_new_candles = True startup_candle_count = 400 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate all ma_buy values for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['ma_lo'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * (self.low_offset.value) dataframe['ma_hi'] = dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * (self.high_offset.value) dataframe['ma_hi_2'] = dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * (self.high_offset_2.value) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) # HMA-BUY SQUEEZE dataframe['HMA_SQZ'] = (((dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] - dataframe['hma_50']) / dataframe[f'ma_buy_{self.base_nb_candles_buy.value}']) * 100) dataframe['zero'] = 0 # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe.loc[dataframe['EWO'] > 0, "EWO_UP"] = dataframe['EWO'] dataframe.loc[dataframe['EWO'] < 0, "EWO_DN"] = dataframe['EWO'] dataframe['EWO_UP'].ffill() dataframe['EWO_DN'].ffill() dataframe['EWO_MEAN_UP'] = dataframe['EWO_UP'].mean() dataframe['EWO_MEAN_DN'] = dataframe['EWO_DN'].mean() dataframe['EWO_UP_FIB'] = dataframe['EWO_MEAN_UP'] * 1.618 dataframe['EWO_DN_FIB'] = dataframe['EWO_MEAN_DN'] * 1.618 # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) #lambo2 dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) # # Pump strength # dataframe['zema_30'] = ftt.dema(dataframe, period=30) # dataframe['zema_200'] = ftt.dema(dataframe, period=200) # dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] # 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) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['OHLC4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 # Check how far we are from min and max dataframe['max'] = dataframe['OHLC4'].rolling(self.mms.value).max() / dataframe['OHLC4'] - 1 dataframe['min'] = abs(dataframe['OHLC4'].rolling(self.mms.value).min() / dataframe['OHLC4'] - 1) dataframe['max_l'] = dataframe['OHLC4'].rolling(self.mml.value).max() / dataframe['OHLC4'] - 1 dataframe['min_l'] = abs(dataframe['OHLC4'].rolling(self.mml.value).min() / dataframe['OHLC4'] - 1) # Apply rolling window operation to the 'OHLC4'column rolling_window = dataframe['OHLC4'].rolling(self.move.value) rolling_max = rolling_window.max() rolling_min = rolling_window.min() # Calculate the peak-to-peak value on the resulting rolling window data ptp_value = rolling_window.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['move'] = ptp_value / dataframe['OHLC4'] dataframe['move_mean'] = dataframe['move'].mean() dataframe['move_mean_x'] = dataframe['move'].mean() * 1.6 dataframe['exit_mean'] = rolling_min * (1 + dataframe['move_mean']) dataframe['exit_mean_x'] = rolling_min * (1 + dataframe['move_mean_x']) dataframe['enter_mean'] = rolling_max * (1 - dataframe['move_mean']) dataframe['enter_mean_x'] = rolling_max * (1 - dataframe['move_mean_x']) dataframe['atr_pcnt'] = (ta.ATR(dataframe, timeperiod=5) / dataframe['OHLC4']) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: lambo2 = ( (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['atr_pcnt'] > dataframe['min_l']) & (dataframe['volume'] > 0) ) dataframe.loc[lambo2, 'enter_long'] = 1 dataframe.loc[lambo2, 'enter_tag'] = 'lambo ' buy1ewo = ( (dataframe['rsi_fast'] < 35 ) & (dataframe['close'] < dataframe['ma_lo']) & (dataframe['EWO'] > dataframe['EWO_MEAN_UP']) & (dataframe['close'] < dataframe['enter_mean_x']) & (dataframe['close'].shift() < dataframe['enter_mean_x'].shift()) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['atr_pcnt'] > dataframe['min']) & (dataframe['volume'] > 0) ) dataframe.loc[buy1ewo, 'enter_long'] = 1 dataframe.loc[buy1ewo, 'enter_tag'] = 'buy1ewo' buy2ewo = ( (dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe['ma_lo']) & (dataframe['EWO'] < dataframe['EWO_DN_FIB']) & (dataframe['atr_pcnt'] > dataframe['min']) & (dataframe['volume'] > 0) ) dataframe.loc[buy2ewo, 'enter_long'] = 1 dataframe.loc[buy2ewo, 'enter_tag'] = 'buy2ewo' is_cofi = ( (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > dataframe['EWO_MEAN_UP']) & (dataframe['atr_pcnt'] > dataframe['min']) & (dataframe['volume'] > 0) ) dataframe.loc[is_cofi, 'enter_long'] = 1 dataframe.loc[is_cofi, 'enter_tag'] = 'cofi' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condition5 = ( (dataframe['close'] > dataframe['hma_50']) & (dataframe['close'] > dataframe['ma_hi_2']) & (dataframe['close'] > dataframe['exit_mean_x']) & (dataframe['rsi'] > 50 ) & (dataframe['volume'] > 0 ) & (dataframe['rsi_fast']>dataframe['rsi_slow']) ) dataframe.loc[condition5, 'exit_long'] = 1 dataframe.loc[condition5, 'exit_tag'] = 'Close > Offset Hi 2' condition6 = ( (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe['ma_hi']) & (dataframe['volume'] > 0) & (dataframe['rsi_fast']>dataframe['rsi_slow']) ) dataframe.loc[condition6, 'exit_long'] = 1 dataframe.loc[condition6, 'exit_tag'] = 'Close > Offset Hi 1' return dataframe def pct_change(a, b): return (b - a) / a class Github_eZ3KIEL29640_FQ_strat__EI4_t4c0s_V2_15__20240627_174354(Github_eZ3KIEL29640_FQ_strat__EI4_t4c0s_V2_15__20240627_174354st): INTERFACE_VERSION = 3 # 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_sell 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 = True trailing_expire_seconds_uptrend = 1800 min_uptrend_trailing_profit = 0.005 debug_mode = True trailing_buy_max_stop = 0.008 # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop) trailing_buy_max_buy = 0.01 # 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, 'enter_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} : start: {trailing_buy['start_trailing_price']:.4f}, duration: {duration}, current: {current_price:.4f}, uplimit: {trailing_buy['trailing_buy_order_uplimit']:.4f}, profit: {self.current_trailing_profit_ratio(pair, current_price) * 100:.2f}%, 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['enter_long'] == 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['enter_long'] == 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['enter_tag'] = last_candle['enter_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['enter_long'] == 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['enter_tag'] if 'enter_tag' in last_candle else 'buy signal' dataframe.loc[:, 'enter_tag'] = f"{initial_buy_tag} (start trail price {last_candle['close']})" elif trailing_buy['trailing_buy_order_started'] == True: logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!") dataframe.loc[:, 'enter_long'] = 1 dataframe.loc[:, 'enter_tag'] = trailing_buy['enter_tag'] # dataframe['buy'] = 1 return dataframe