# --- Do not remove these libs --- import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import pandas_ta as pta from typing import Dict, List from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series, DatetimeIndex, merge from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open from functools import reduce from technical.indicators import RMI, zema # -------------------------------- 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 # Williams %R def williams_r(dataframe: DataFrame, period: int=14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe['high'].rolling(center=False, window=period).max() lowest_low = dataframe['low'].rolling(center=False, window=period).min() WR = Series((highest_high - dataframe['close']) / (highest_high - lowest_low), name=f'{period} Williams %R') return WR * -100 class BB_RPB_TSL_RNG_TBS_GOLD(IStrategy): INTERFACE_VERSION = 3 '\n BB_RPB_TSL\n @author jilv220\n Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html )\n RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py )\n The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )\n I modified it to better suit my taste and added Hyperopt for this strategy.\n ' ########################################################################## # Hyperopt result area # entry space ## ## ## ## ## ## ## entry_params = {'entry_btc_safe': -289, 'entry_btc_safe_1d': -0.05, 'entry_threshold': 0.003, 'entry_bb_factor': 0.999, 'entry_bb_delta': 0.025, 'entry_bb_width': 0.095, 'entry_cci': -116, 'entry_cci_length': 25, 'entry_rmi': 49, 'entry_rmi_length': 17, 'entry_srsi_fk': 32, 'entry_closedelta': 12.148, 'entry_ema_diff': 0.022, 'entry_adx': 20, 'entry_fastd': 20, 'entry_fastk': 22, 'entry_ema_cofi': 0.98, 'entry_ewo_high': 4.179, 'entry_ema_high_2': 1.087, 'entry_ema_low_2': 0.97} # exit space exit_params = {'pHSL': -0.178, 'pPF_1': 0.019, 'pPF_2': 0.065, 'pSL_1': 0.019, 'pSL_2': 0.062, 'exit_btc_safe': -389, 'base_nb_candles_exit': 24, 'high_offset': 0.991, 'high_offset_2': 0.997} # really hard to use this minimal_roi = {'0': 0.1} # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' # Disabled stoploss = -0.049 # Custom stoploss use_custom_stoploss = True use_exit_signal = True process_only_new_candles = True ############################################################################ ## Buy params is_optimize_dip = False entry_rmi = IntParameter(30, 50, default=35, optimize=is_optimize_dip) entry_cci = IntParameter(-135, -90, default=-133, optimize=is_optimize_dip) entry_srsi_fk = IntParameter(30, 50, default=25, optimize=is_optimize_dip) entry_cci_length = IntParameter(25, 45, default=25, optimize=is_optimize_dip) entry_rmi_length = IntParameter(8, 20, default=8, optimize=is_optimize_dip) is_optimize_break = False entry_bb_width = DecimalParameter(0.05, 0.2, default=0.15, optimize=is_optimize_break) entry_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize=is_optimize_break) is_optimize_local_dip = False entry_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize=is_optimize_local_dip) entry_bb_factor = DecimalParameter(0.99, 0.999, default=0.995, optimize=False) entry_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize=is_optimize_local_dip) is_optimize_ewo = False entry_rsi_fast = IntParameter(35, 50, default=45, optimize=False) entry_rsi = IntParameter(15, 30, default=35, optimize=False) entry_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize=is_optimize_ewo) entry_ema_low = DecimalParameter(0.9, 0.99, default=0.942, optimize=is_optimize_ewo) entry_ema_high = DecimalParameter(0.95, 1.2, default=1.084, optimize=is_optimize_ewo) is_optimize_ewo_2 = False entry_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96, optimize=is_optimize_ewo_2) entry_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09, optimize=is_optimize_ewo_2) is_optimize_cofi = False entry_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97, optimize=is_optimize_cofi) entry_fastk = IntParameter(20, 30, default=20, optimize=is_optimize_cofi) entry_fastd = IntParameter(20, 30, default=20, optimize=is_optimize_cofi) entry_adx = IntParameter(20, 30, default=30, optimize=is_optimize_cofi) entry_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi) is_optimize_btc_safe = False entry_btc_safe = IntParameter(-300, 50, default=-200, optimize=is_optimize_btc_safe) entry_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize=is_optimize_btc_safe) entry_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize=is_optimize_btc_safe) # Buy params toggle entry_is_dip_enabled = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_is_break_enabled = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) ## Sell params exit_btc_safe = IntParameter(-400, -300, default=-365, optimize=True) base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=True) ## Trailing params # hard stoploss profit pHSL = DecimalParameter(-0.2, -0.04, default=-0.08, decimals=3, space='exit', load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='exit', load=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='exit', load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='exit', load=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='exit', load=True) ############################################################################ def informative_pairs(self): pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, self.timeframe) for pair in pairs] if self.config['stake_currency'] in ['USDT', 'BUSD', 'USDC', 'DAI', 'TUSD', 'PAX', 'USD', 'EUR', 'GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = 'BTC/USDT' informative_pairs.append((btc_info_pair, self.timeframe)) #informative_pairs = [("BTC/BUSD", "5m")] return informative_pairs ############################################################################ ## Custom Trailing stoploss ( credit to Perkmeister for this custom stoploss to help the strategy ride a green candle ) 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: assert self.dp, 'DataProvider is required for multiple timeframes.' # Bollinger bands (hyperopt hard to implement) 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'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] ### BTC protection # BTC info if self.config['stake_currency'] in ['USDT', 'BUSD', 'USDC', 'DAI', 'TUSD', 'PAX', 'USD', 'EUR', 'GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = 'BTC/USDT' inf_tf = '5m' informative = self.dp.get_pair_dataframe(btc_info_pair, timeframe=inf_tf) informative_past = informative.copy().shift(1) # Get recent BTC info # BTC 5m dump protection informative_past_source = (informative_past['open'] + informative_past['close'] + informative_past['high'] + informative_past['low']) / 4 # Get BTC price informative_threshold = informative_past_source * self.entry_threshold.value # BTC dump n% in 5 min informative_past_delta = informative_past['close'].shift(1) - informative_past['close'] # should be positive if dump informative_diff = informative_threshold - informative_past_delta # Need be larger than 0 dataframe['btc_threshold'] = informative_threshold dataframe['btc_diff'] = informative_diff # BTC 1d dump protection informative_past_1d = informative.copy().shift(288) informative_past_source_1d = (informative_past_1d['open'] + informative_past_1d['close'] + informative_past_1d['high'] + informative_past_1d['low']) / 4 dataframe['btc_5m'] = informative_past_source dataframe['btc_1d'] = informative_past_source_1d ### Other checks dataframe['bb_width'] = (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'] dataframe['bb_delta'] = (dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'] dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int') # CCI hyperopt for val in self.entry_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) dataframe['cci'] = ta.CCI(dataframe, 26) dataframe['cci_long'] = ta.CCI(dataframe, 170) # RMI hyperopt for val in self.entry_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) #dataframe['rmi'] = RMI(dataframe, length=8, mom=4) # SRSI hyperopt ? stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] # BinH dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # SMA dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) # CTI dataframe['cti'] = pta.cti(dataframe['close'], length=20) # EMA dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # Elliot dataframe['EWO'] = EWO(dataframe, 50, 200) # 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) # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) # Calculate all ma_exit values for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'entry_tag'] = '' if self.entry_is_dip_enabled.value: is_dip = (dataframe[f'rmi_length_{self.entry_rmi_length.value}'] < self.entry_rmi.value) & (dataframe[f'cci_length_{self.entry_cci_length.value}'] <= self.entry_cci.value) & (dataframe['srsi_fk'] < self.entry_srsi_fk.value) #conditions.append(is_dip) if self.entry_is_break_enabled.value: #"entry_bb_delta": 0.025 0.036 #"entry_bb_width": 0.095 0.133 # from BinH is_break = (dataframe['bb_delta'] > self.entry_bb_delta.value) & (dataframe['bb_width'] > self.entry_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_closedelta.value / 1000) & (dataframe['close'] < dataframe['bb_lowerband3'] * self.entry_bb_factor.value) #conditions.append(is_break) # from NFI next gen is_local_uptrend = (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.entry_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_closedelta.value / 1000) # from SMA offset is_ewo = (dataframe['rsi_fast'] < self.entry_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.entry_ema_low.value) & (dataframe['EWO'] > self.entry_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.entry_ema_high.value) & (dataframe['rsi'] < self.entry_rsi.value) is_ewo_2 = (dataframe['rsi_fast'] < self.entry_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.entry_ema_low_2.value) & (dataframe['EWO'] > self.entry_ewo_high.value) & (dataframe['close'] < dataframe['ema_16'] * self.entry_ema_high_2.value) & (dataframe['rsi'] < self.entry_rsi.value) is_cofi = (dataframe['open'] < dataframe['ema_8'] * self.entry_ema_cofi.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']) & (dataframe['fastk'] < self.entry_fastk.value) & (dataframe['fastd'] < self.entry_fastd.value) & (dataframe['adx'] > self.entry_adx.value) & (dataframe['EWO'] > self.entry_ewo_high.value) # NFI quick mode is_nfi_32 = (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 19) & (dataframe['close'] < dataframe['sma_15'] * 0.942) & (dataframe['cti'] < -0.86) is_nfi_33 = (dataframe['close'] < dataframe['ema_13'] * 0.978) & (dataframe['EWO'] > 8) & (dataframe['cti'] < -0.88) & (dataframe['rsi'] < 32) & (dataframe['r_14'] < -98.0) & (dataframe['volume'] < dataframe['volume_mean_4'] * 2.5) # is_btc_safe = ( # (dataframe['btc_diff'] > self.entry_btc_safe.value) # &(dataframe['btc_5m'] - dataframe['btc_1d'] > dataframe['btc_1d'] * self.entry_btc_safe_1d.value) # &(dataframe['volume'] > 0) # Make sure Volume is not 0 # ) is_BB_checked = is_dip & is_break #print(dataframe['btc_5m']) #print(dataframe['btc_1d']) #print(dataframe['btc_5m'] - dataframe['btc_1d']) #print(dataframe['btc_1d'] * -0.025) #print(dataframe['btc_5m'] - dataframe['btc_1d'] > dataframe['btc_1d'] * -0.025) ## condition append conditions.append(is_BB_checked) # ~1.7 89% dataframe.loc[is_BB_checked, 'entry_tag'] += 'bb ' conditions.append(is_local_uptrend) # ~3.84 90.2% dataframe.loc[is_local_uptrend, 'entry_tag'] += 'local uptrend ' conditions.append(is_ewo) # ~2.26 93.5% dataframe.loc[is_ewo, 'entry_tag'] += 'ewo ' conditions.append(is_ewo_2) # ~3.68 90.3% dataframe.loc[is_ewo_2, 'entry_tag'] += 'ewo2 ' conditions.append(is_cofi) # ~3.21 90.8% dataframe.loc[is_cofi, 'entry_tag'] += 'cofi ' conditions.append(is_nfi_32) # ~2.43 91.3% dataframe.loc[is_nfi_32, 'entry_tag'] += 'nfi 32 ' conditions.append(is_nfi_33) # ~0.11 100% dataframe.loc[is_nfi_33, 'entry_tag'] += 'nfi 33 ' if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'entry'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (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[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow'])) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit'] = 1 return dataframe class TrailingBuyStrat2(BB_RPB_TSL_RNG_TBS_GOLD): # 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, 'entry_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['entry'] == 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['entry'] == 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['entry_tag'] = last_candle['entry_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['entry'] == 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['entry_tag'] if 'entry_tag' in last_candle else 'entry signal' dataframe.loc[:, 'entry_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[:, 'entry'] = 1 dataframe.loc[:, 'entry_tag'] = trailing_entry['entry_tag'] # dataframe['entry'] = 1 return dataframe