import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta 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, 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. return Series(index=bars.index, data=res) class ClucHAnix_hhll_Futures_2(IStrategy): """ Futures strategy based on the ClucHAnix_hhll strategy. """ can_short = True buy_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, "buy_hh_diff_48": 6.867, "buy_ll_diff_48": -12.884, } sell_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, "sell_bbmiddle_close": 0.97286, "sell_fisher": 0.48492, "leverage_num": 10, } minimal_roi = { "0": 0.103, "3": 0.05, "5": 0.033, "61": 0.027, "125": 0.011, "292": 0.005, } stoploss = -0.99 # use custom stoploss trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.012 trailing_only_offset_is_reached = False timeframe = '15m' startup_candle_count = 168 process_only_new_candles = True is_optimize_clucHA = False rocr_1h = RealParameter(0.5, 1.0, default=0.54904, space='buy', optimize = is_optimize_clucHA ) bbdelta_close = RealParameter(0.0005, 0.02, default=0.01965, space='buy', optimize = is_optimize_clucHA ) closedelta_close = RealParameter(0.0005, 0.02, default=0.00556, space='buy', optimize = is_optimize_clucHA ) bbdelta_tail = RealParameter(0.7, 1.0, default=0.95089, space='buy', optimize = is_optimize_clucHA ) close_bblower = RealParameter(0.0005, 0.02, default=0.00799, space='buy', optimize = is_optimize_clucHA ) is_optimize_hh_ll = False buy_hh_diff_48 = DecimalParameter(0.0, 15, default=1.087 , optimize = is_optimize_hh_ll ) buy_ll_diff_48 = DecimalParameter(-23, 40, default=1.087 , optimize = is_optimize_hh_ll ) is_optimize_slip = False max_slip = DecimalParameter(0.33, 0.80, default=0.33, decimals=3, optimize=is_optimize_slip , space='buy', load=True) is_optimize_sell = False sell_fisher = RealParameter(0.1, 0.5, default=0.38414, space='sell', optimize = is_optimize_sell) sell_bbmiddle_close = RealParameter(0.97, 1.1, default=1.07634, space='sell', optimize = is_optimize_sell) high_offset = DecimalParameter(0.90, 1.2, default=sell_params['high_offset'], space='sell', optimize = is_optimize_sell) high_offset_2 = DecimalParameter(0.90, 1.5, default=sell_params['high_offset_2'], space='sell', optimize = is_optimize_sell) is_optimize_trailing = False pPF_1 = DecimalParameter(0.011, 0.020, default=0.016, decimals=3, space='sell', load=True, optimize = is_optimize_trailing) pSL_1 = DecimalParameter(0.011, 0.020, default=0.011, decimals=3, space='sell', load=True, optimize = is_optimize_trailing) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True, optimize = is_optimize_trailing) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True, optimize = is_optimize_trailing) def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: return self.leverage_num.value def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: 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 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 if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) 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) 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 'sell_stoploss_deadfish' 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 'sell_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 'sell_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 'sell_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 'sell_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 'sell_stoploss_p_24_1_1' return None def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] 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'] 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) dataframe['sma_9'] = ta.SMA(dataframe['close'], timeperiod=9) dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) 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() dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) dataframe['hh_48'] = ta.MAX(dataframe['high'], 48) dataframe['hh_48_diff'] = (dataframe['hh_48'] - dataframe['close']) / dataframe['hh_48'] * 100 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) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['sma_200_dec_20'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) 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) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) 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) informative['cmf'] = chaikin_money_flow(informative, 20) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_buy_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.buy_hh_diff_48.value) & (dataframe['ll_48_diff'] > self.buy_ll_diff_48.value) ,'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( (dataframe['fisher'] > self.sell_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.sell_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) ,'sell'] = 1 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return self.populate_buy_trend(dataframe, metadata) def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return self.populate_sell_trend(dataframe, metadata) def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" 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 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!") 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 ClucHAnix_hhll_TB_Futures(ClucHAnix_hhll_Futures_2): process_only_new_candles = True custom_info_trail_buy = dict() trailing_buy_order_enabled = True trailing_expire_seconds = 1800 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): 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 = 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): 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 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)): return 'forcebuy' else: 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))): return 'forcebuy' if current_trailing_profit_ratio < 0: 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 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)): 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': 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 margin not sufficient") elif trailing_buy_offset is None: 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']: 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)): 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 ({(trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy))}). OK for {pair} ({ratio} %), order may not be triggered if margin not sufficient") elif current_price > (trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_stop)): 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: 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_buy_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!!!") 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'] return dataframe