# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/ClucHAnix_hhll.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import time import logging from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, RealParameter from pandas import DataFrame, Series from datetime import datetime, timedelta, timezone from freqtrade.persistence import Trade logger = logging.getLogger(__name__) def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - rolling_std * num_of_std return (np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)) def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.0 return Series(index=bars.index, data=res) class Github_DerSalvador_freqtrade_helm_chart__ClucHAnix_hhll__20260115_122204(IStrategy): INTERFACE_VERSION = 3 '\n Please only use this with TrailingBuy\n ' #hypered params ## ## ## entry_params = {'max_slip': 0.73, 'bbdelta_close': 0.01846, 'bbdelta_tail': 0.98973, 'close_bblower': 0.00785, 'closedelta_close': 0.01009, 'rocr_1h': 0.5411, 'entry_hh_diff_48': 6.867, 'entry_ll_diff_48': -12.884} # Sell hyperspace params: # exit signal params exit_params = {'pPF_1': 0.011, 'pPF_2': 0.064, 'pSL_1': 0.011, 'pSL_2': 0.062, 'high_offset': 0.907, 'high_offset_2': 1.211, 'exit_bbmiddle_close': 0.97286, 'exit_fisher': 0.48492} # ROI table: minimal_roi = {'0': 0.103, '3': 0.05, '5': 0.033, '61': 0.027, '125': 0.011, '292': 0.005} # 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 '\n END HYPEROPT\n ' timeframe = '5m' # Make sure these match or are not overridden in config use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Custom stoploss use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 168 order_types = {'entry': 'market', 'exit': 'market', 'emergencyexit': 'market', 'forceentry': 'market', 'forceexit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99} # entry params is_optimize_clucHA = False rocr_1h = RealParameter(0.5, 1.0, default=0.54904, space='entry', optimize=is_optimize_clucHA) bbdelta_close = RealParameter(0.0005, 0.02, default=0.01965, space='entry', optimize=is_optimize_clucHA) closedelta_close = RealParameter(0.0005, 0.02, default=0.00556, space='entry', optimize=is_optimize_clucHA) bbdelta_tail = RealParameter(0.7, 1.0, default=0.95089, space='entry', optimize=is_optimize_clucHA) close_bblower = RealParameter(0.0005, 0.02, default=0.00799, space='entry', optimize=is_optimize_clucHA) is_optimize_hh_ll = False entry_hh_diff_48 = DecimalParameter(0.0, 15, default=1.087, optimize=is_optimize_hh_ll) entry_ll_diff_48 = DecimalParameter(-23, 40, default=1.087, optimize=is_optimize_hh_ll) ## Slippage params is_optimize_slip = False max_slip = DecimalParameter(0.33, 0.8, default=0.33, decimals=3, optimize=is_optimize_slip, space='entry', load=True) # exit params is_optimize_exit = False exit_fisher = RealParameter(0.1, 0.5, default=0.38414, space='exit', optimize=is_optimize_exit) exit_bbmiddle_close = RealParameter(0.97, 1.1, default=1.07634, space='exit', optimize=is_optimize_exit) high_offset = DecimalParameter(0.9, 1.2, default=exit_params['high_offset'], space='exit', optimize=is_optimize_exit) high_offset_2 = DecimalParameter(0.9, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=is_optimize_exit) is_optimize_trailing = False pPF_1 = DecimalParameter(0.011, 0.02, default=0.016, decimals=3, space='exit', load=True, optimize=is_optimize_trailing) pSL_1 = DecimalParameter(0.011, 0.02, default=0.011, decimals=3, space='exit', load=True, optimize=is_optimize_trailing) pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='exit', load=True, optimize=is_optimize_trailing) pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='exit', load=True, optimize=is_optimize_trailing) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs # come from BB_RPB_TSL def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value sl_profit = -0.99 # 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 = -0.99 # Only for hyperopt invalid return if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) ## Confirm Entry def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) max_slip = self.max_slip.value if len(dataframe) < 1: return False dataframe = dataframe.iloc[-1].squeeze() if rate > dataframe['close']: slippage = (rate / dataframe['close'] - 1) * 100 if slippage < max_slip: return True else: return False return True def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate # stoploss - deadfish if current_profit < -0.063 and last_candle['close'] < last_candle['ema_200'] and (last_candle['bb_width'] < 0.043) and (last_candle['close'] > last_candle['bb_middleband2'] * 0.954) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * 2.37): return 'exit_stoploss_deadfish' # stoploss - pump if last_candle['hl_pct_change_48_1h'] > 0.95: if -0.04 > current_profit > -0.08 and max_profit < 0.005 and (max_loss < 0.08) and (last_candle['close'] < last_candle['ema_200']) and last_candle['sma_200_dec_20'] and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (last_candle['ema_vwma_osc_96'] < 0.0) and (last_candle['cmf'] < -0.25) and (last_candle['cmf_1h'] < -0.0): return 'exit_stoploss_p_48_1_1' elif -0.04 > current_profit > -0.08 and max_profit < 0.01 and (max_loss < 0.08) and (last_candle['close'] < last_candle['ema_200']) and last_candle['sma_200_dec_20'] and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (last_candle['ema_vwma_osc_96'] < 0.0) and (last_candle['cmf'] < -0.25) and (last_candle['cmf_1h'] < -0.0): return 'exit_stoploss_p_48_1_2' if last_candle['hl_pct_change_36_1h'] > 0.7: if -0.04 > current_profit > -0.08 and max_loss < 0.08 and (max_profit > current_profit + 0.1) and (last_candle['close'] < last_candle['ema_200']) and last_candle['sma_200_dec_20'] and last_candle['sma_200_dec_20_1h'] and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (last_candle['ema_vwma_osc_96'] < 0.0) and (last_candle['cmf'] < -0.25) and (last_candle['cmf_1h'] < -0.0): return 'exit_stoploss_p_36_1_1' if last_candle['hl_pct_change_36_1h'] > 0.5: if -0.05 > current_profit > -0.08 and max_loss < 0.08 and (max_profit > current_profit + 0.1) and (last_candle['close'] < last_candle['ema_200']) and last_candle['sma_200_dec_20'] and last_candle['sma_200_dec_20_1h'] and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (last_candle['ema_vwma_osc_96'] < 0.0) and (last_candle['cmf'] < -0.25) and (last_candle['cmf_1h'] < -0.0) and (last_candle['rsi'] < 40.0): return 'exit_stoploss_p_36_2_1' if last_candle['hl_pct_change_24_1h'] > 0.6: if -0.04 > current_profit > -0.08 and max_loss < 0.08 and (last_candle['close'] < last_candle['ema_200']) and last_candle['sma_200_dec_20'] and last_candle['sma_200_dec_20_1h'] and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (last_candle['ema_vwma_osc_96'] < 0.0) and (last_candle['cmf'] < -0.25) and (last_candle['cmf_1h'] < -0.0): return 'exit_stoploss_p_24_1_1' return None 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'] # 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 dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['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'] # BB 20 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['bb_width'] = (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'] dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['ema_24'] = ta.EMA(dataframe['close'], timeperiod=24) dataframe['ema_200'] = ta.EMA(dataframe['close'], timeperiod=200) # SMA dataframe['sma_9'] = ta.SMA(dataframe['close'], timeperiod=9) dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200) # HMA dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) # volume dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() # ROCR dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # hh48 dataframe['hh_48'] = ta.MAX(dataframe['high'], 48) dataframe['hh_48_diff'] = (dataframe['hh_48'] - dataframe['close']) / dataframe['hh_48'] * 100 # ll48 dataframe['ll_48'] = ta.MIN(dataframe['low'], 48) dataframe['ll_48_diff'] = (dataframe['close'] - dataframe['ll_48']) / dataframe['ll_48'] * 100 rsi = ta.RSI(dataframe) dataframe['rsi'] = rsi rsi = 0.1 * (rsi - 50) dataframe['fisher'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # RSI dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # sma dec 20 dataframe['sma_200_dec_20'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # EMA of VWMA Oscillator dataframe['ema_vwma_osc_32'] = ema_vwma_osc(dataframe, 32) dataframe['ema_vwma_osc_64'] = ema_vwma_osc(dataframe, 64) dataframe['ema_vwma_osc_96'] = ema_vwma_osc(dataframe, 96) # CMF dataframe['cmf'] = chaikin_money_flow(dataframe, 20) # 1h tf 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) informative['sma_200'] = ta.SMA(informative['close'], timeperiod=200) informative['hl_pct_change_48'] = range_percent_change(informative, 'HL', 48) informative['hl_pct_change_36'] = range_percent_change(informative, 'HL', 36) informative['hl_pct_change_24'] = range_percent_change(informative, 'HL', 24) informative['sma_200_dec_20'] = informative['sma_200'] < informative['sma_200'].shift(20) # CMF informative['cmf'] = chaikin_money_flow(informative, 20) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[dataframe['rocr_1h'].gt(self.rocr_1h.value) & (dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['ha_close'] * self.bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['ha_close'] * self.closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.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.close_bblower.value * dataframe['bb_lowerband'])) & (dataframe['hh_48_diff'] > self.entry_hh_diff_48.value) & (dataframe['ll_48_diff'] > self.entry_ll_diff_48.value), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[((dataframe['fisher'] > self.exit_fisher.value) & 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'] * self.exit_bbmiddle_close.value > dataframe['bb_middleband']) | (dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe['ema_24'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['sma_9'] > dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1) * 0.005) & (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe['ema_24'] * self.high_offset.value) & (dataframe['rsi_fast'] > dataframe['rsi_slow'])) & (dataframe['volume'] > 0), 'exit_long'] = 1 return dataframe # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int=10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) vwma = vwma.fillna(0, inplace=True) return vwma # Exponential moving average of a volume weighted simple moving average def ema_vwma_osc(dataframe, len_slow_ma): slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma)) return (slow_ema - slow_ema.shift(1)) / slow_ema.shift(1) * 100 def range_percent_change(dataframe: DataFrame, method, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param method: High to Low / Open to Close :param length: int The length to look back """ if method == 'HL': return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min() elif method == 'OC': return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min() else: raise ValueError(f'Method {method} not defined!') # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = (dataframe['close'] - dataframe['low'] - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum() if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') class Github_DerSalvador_freqtrade_helm_chart__ClucHAnix_hhll__20260115_122204_TB(Github_DerSalvador_freqtrade_helm_chart__ClucHAnix_hhll__20260115_122204): # Original idea by @MukavaValkku, code by @tirail and @stash86 # # This class is designed to inherit from yours and starts trailing entry with your entry signals # Trailing entry starts at any entry signal and will move to next candles if the trailing still active # Trailing entry stops with BUY if : price decreases and rises again more than trailing_entry_offset # Trailing entry stops with NO BUY : current price is > initial price * (1 + trailing_entry_max) OR custom_exit tag # IT IS NOT COMPATIBLE WITH BACKTEST/HYPEROPT # process_only_new_candles = True custom_info_trail_entry = dict() # Trailing entry parameters trailing_entry_order_enabled = True trailing_expire_seconds = 1800 # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, entry the coin trailing_entry_uptrend_enabled = False trailing_expire_seconds_uptrend = 90 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_entry_max_stop = 0.02 # stop trailing entry if current_price > starting_price * (1+trailing_entry_max_stop) trailing_entry_max_entry = 0.0 # entry if price between uplimit (=min of serie (current_price * (1 + trailing_entry_offset())) and (start_price * 1+trailing_entry_max_entry)) init_trailing_dict = {'trailing_entry_order_started': False, 'trailing_entry_order_uplimit': 0, 'start_trailing_price': 0, 'enter_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': False} def trailing_entry(self, pair, reinit=False): # returns trailing entry info for pair (init if necessary) if not pair in self.custom_info_trail_entry: self.custom_info_trail_entry[pair] = dict() if reinit or not 'trailing_entry' in self.custom_info_trail_entry[pair]: self.custom_info_trail_entry[pair]['trailing_entry'] = self.init_trailing_dict.copy() return self.custom_info_trail_entry[pair]['trailing_entry'] def trailing_entry_info(self, pair: str, current_price: float): # current_time live, dry run current_time = datetime.now(timezone.utc) if not self.debug_mode: return trailing_entry = self.trailing_entry(pair) duration = 0 try: duration = current_time - trailing_entry['start_trailing_time'] except TypeError: duration = 0 finally: logger.info(f"pair: {pair} : start: {trailing_entry['start_trailing_price']:.4f}, duration: {duration}, current: {current_price:.4f}, uplimit: {trailing_entry['trailing_entry_order_uplimit']:.4f}, profit: {self.current_trailing_profit_ratio(pair, current_price) * 100:.2f}%, offset: {trailing_entry['offset']}") def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float: trailing_entry = self.trailing_entry(pair) if trailing_entry['trailing_entry_order_started']: return (trailing_entry['start_trailing_price'] - current_price) / trailing_entry['start_trailing_price'] else: return 0 def trailing_entry_offset(self, dataframe, pair: str, current_price: float): # return rebound limit before a entry in % of initial price, function of current price # return None to stop trailing entry (will start again at next entry signal) # return 'forceentry' to force immediate entry # (example with 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no entry, uplimit updated to 99.5), 3price 98 (no entry uplimit updated to 98.5), 4th price 99 -> BUY current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price) default_offset = 0.005 trailing_entry = self.trailing_entry(pair) if not trailing_entry['trailing_entry_order_started']: return default_offset # example with duration and indicators # dry run, live only last_candle = dataframe.iloc[-1] current_time = datetime.now(timezone.utc) trailing_duration = current_time - trailing_entry['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if current_trailing_profit_ratio > 0 and last_candle['enter_long'] == 1: # more than 1h, price under first signal, entry signal still active -> entry return 'forceentry' else: # wait for next signal return None elif self.trailing_entry_uptrend_enabled and trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend and (current_trailing_profit_ratio < -1 * self.min_uptrend_trailing_profit): # less than 90s and price is rising, entry return 'forceentry' if current_trailing_profit_ratio < 0: # current price is higher than initial price return default_offset trailing_entry_offset = {0.06: 0.02, 0.03: 0.01, 0: default_offset} for key in trailing_entry_offset: if current_trailing_profit_ratio > key: return trailing_entry_offset[key] return default_offset # end of trailing entry parameters # ----------------------------------------------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) self.trailing_entry(metadata['pair']) return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs) if val: if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): val = False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) >= 1: last_candle = dataframe.iloc[-1].squeeze() current_price = rate trailing_entry = self.trailing_entry(pair) trailing_entry_offset = self.trailing_entry_offset(dataframe, pair, current_price) if trailing_entry['allow_trailing']: if not trailing_entry['trailing_entry_order_started'] and last_candle['enter_long'] == 1: # start trailing entry # self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_started'] = True # self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'] = last_candle['close'] # self.custom_info_trail_entry[pair]['trailing_entry']['start_trailing_price'] = last_candle['close'] # self.custom_info_trail_entry[pair]['trailing_entry']['entry_tag'] = f"initial_entry_tag (strat trail price {last_candle['close']})" # self.custom_info_trail_entry[pair]['trailing_entry']['start_trailing_time'] = datetime.now(timezone.utc) # self.custom_info_trail_entry[pair]['trailing_entry']['offset'] = 0 trailing_entry['trailing_entry_order_started'] = True trailing_entry['trailing_entry_order_uplimit'] = last_candle['close'] trailing_entry['start_trailing_price'] = last_candle['close'] trailing_entry['enter_tag'] = last_candle['enter_tag'] trailing_entry['start_trailing_time'] = datetime.now(timezone.utc) trailing_entry['offset'] = 0 self.trailing_entry_info(pair, current_price) logger.info(f"start trailing entry for {pair} at {last_candle['close']}") elif trailing_entry['trailing_entry_order_started']: if trailing_entry_offset == 'forceentry': # entry in custom conditions val = True ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100) self.trailing_entry_info(pair, current_price) logger.info(f'price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full') elif trailing_entry_offset is None: # stop trailing entry custom conditions self.trailing_entry(pair, reinit=True) logger.info(f'STOP trailing entry for {pair} because "trailing entry offset" returned None') elif current_price < trailing_entry['trailing_entry_order_uplimit']: # update uplimit old_uplimit = trailing_entry['trailing_entry_order_uplimit'] self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'] = min(current_price * (1 + trailing_entry_offset), self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit']) self.custom_info_trail_entry[pair]['trailing_entry']['offset'] = trailing_entry_offset self.trailing_entry_info(pair, current_price) logger.info(f"update trailing entry for {pair} at {old_uplimit} -> {self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit']}") elif current_price < trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry): # entry ! current price > uplimit && lower thant starting price val = True ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100) self.trailing_entry_info(pair, current_price) logger.info(f"current price ({current_price}) > uplimit ({trailing_entry['trailing_entry_order_uplimit']}) and lower than starting price price ({trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry)}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full") elif current_price > trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_stop): # stop trailing entry because price is too high self.trailing_entry(pair, reinit=True) self.trailing_entry_info(pair, current_price) logger.info(f'STOP trailing entry for {pair} because of the price is higher than starting price * {1 + self.trailing_entry_max_stop}') else: # uplimit > current_price > max_price, continue trailing and wait for the price to go down self.trailing_entry_info(pair, current_price) logger.info(f'price too high for {pair} !') else: logger.info(f'Wait for next entry signal for {pair}') if val == True: self.trailing_entry_info(pair, rate) self.trailing_entry(pair, reinit=True) logger.info(f'STOP trailing entry for {pair} because I entry it') return val def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_entry_trend(dataframe, metadata) if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): last_candle = dataframe.iloc[-1].squeeze() trailing_entry = self.trailing_entry(metadata['pair']) if last_candle['enter_long'] == 1: if not trailing_entry['trailing_entry_order_started']: open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all() if not open_trades: logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!") # self.custom_info_trail_entry[metadata['pair']]['trailing_entry']['allow_trailing'] = True trailing_entry['allow_trailing'] = True initial_entry_tag = last_candle['enter_tag'] if 'enter_tag' in last_candle else 'entry signal' dataframe.loc[:, 'enter_tag'] = f"{initial_entry_tag} (start trail price {last_candle['close']})" elif trailing_entry['trailing_entry_order_started'] == True: logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger entry signal!!") dataframe.loc[:, 'enter_long'] = 1 dataframe.loc[:, 'enter_tag'] = trailing_entry['enter_tag'] # dataframe['entry'] = 1 return dataframe