# source: https://raw.githubusercontent.com/zxjason/Freqtrade_Strategies/6abb02b7bd39bc8f346e35c927aae14af7b4df1b/NASOSv5/NASOSv5_mod4.py ############################################################################################################# ## NASOSv5 Strategy by @zxjason ## ## https://github.com/zxjason/Freqtrade_Strategies ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ############################################################################################################# ## GENERAL RECOMMENDATIONS ## ## 在NASOSv5HO的基础上把SL设定在了0.0845 ## ############################################################################################################# ## DONATIONS ## ## usdc: 0xedB85Ce16ba8268F32925f6fF48190Cea97a9b81 ## ## REFERRAL LINKS ## ############################################################################################################# # --- Do not remove these libs --- # --- Do not remove these libs --- from logging import FATAL from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce 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 logging import pandas as pd logger = logging.getLogger(__name__) # @Rallipanos # @pluxury # with help from @stash86 and @Perkmeister def EWO(dataframe, ema_length=5, ema2_length=50): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 120 return emadif class Github_zxjason_Freqtrade_Strategies__NASOSv5_mod4__20250512_210217(IStrategy): INTERFACE_VERSION = 2 # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 20, "ewo_high": 4.299, "ewo_high_2": 8.492, "ewo_low": -8.476, "low_offset": 0.984, "low_offset_2": 0.901, "lookback_candles": 7, "profit_threshold": 1.036, "rsi_buy": 80, "rsi_fast_buy": 27, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 20, "high_offset": 1.01, "high_offset_2": 1.142, } # ROI table: # value loaded from strategy minimal_roi = { "0": 0.4 } # Stoploss: stoploss = -0.3 # value loaded from strategy # Trailing stop: trailing_stop = True # value loaded from strategy trailing_stop_positive = 0.001 # value loaded from strategy trailing_stop_positive_offset = 0.03 # value loaded from strategy trailing_only_offset_is_reached = True # value loaded from strategy # SMAOffset base_nb_candles_buy = IntParameter( 2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter( 2, 25, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) low_offset_2 = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=True) high_offset = DecimalParameter( 0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter( 0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 lookback_candles = IntParameter( 1, 36, default=buy_params['lookback_candles'], space='buy', optimize=True) profit_threshold = DecimalParameter(0.99, 1.05, default=buy_params['profit_threshold'], space='buy', optimize=True) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter( 2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) ewo_high_2 = DecimalParameter( -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True) rsi_buy = IntParameter(10, 80, default=buy_params['rsi_buy'], space='buy', optimize=True) rsi_fast_buy = IntParameter( 10, 50, default=buy_params['rsi_fast_buy'], space='buy', optimize=True) # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'ioc' } # Optimal timeframe for the strategy timeframe = '5m' inf_15m = '15m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = False plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, 'subplots': { 'rsi': { 'rsi': {'color': 'orange'}, 'rsi_fast': {'color': 'red'}, 'rsi_slow': {'color': 'green'}, }, 'ewo': { 'EWO': {'color': 'orange'} }, } } slippage_protection = { 'retries': 3, 'max_slippage': -0.02 } protections = [ # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 1, # "stop_duration_candles": 6, # "only_per_pair": True # }, # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 2, # "stop_duration_candles": 6, # "only_per_pair": False # }, { "method": "LowProfitPairs", "lookback_period_candles": 60, "trade_limit": 1, "stop_duration": 60, "required_profit": -0.05 }, { "method": "MaxDrawdown", "lookback_period_candles": 24, "trade_limit": 1, "stop_duration_candles": 12, "max_allowed_drawdown": 0.2 }, ] def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit > 0.3): return 0.05 elif (current_profit > 0.1): return 0.03 elif (current_profit > 0.06): return 0.02 elif (current_profit > 0.04): return 0.01 elif (current_profit > 0.025): return 0.005 elif (current_profit > 0.018): return 0.005 return 0.15 def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] if (last_candle is not None): if (sell_reason in ['sell_signal']): if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): # *1.2 return False # slippage try: state = self.slippage_protection['__pair_retries'] except KeyError: state = self.slippage_protection['__pair_retries'] = {} candle = dataframe.iloc[-1].squeeze() slippage = (rate / candle['close']) - 1 if slippage < self.slippage_protection['max_slippage']: pair_retries = state.get(pair, 0) if pair_retries < self.slippage_protection['retries']: state[pair] = pair_retries + 1 return False state[pair] = 0 return True def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '15m') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # # RSI # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # informative_1h['bb_lowerband'] = bollinger['lower'] # informative_1h['bb_middleband'] = bollinger['mid'] # informative_1h['bb_upperband'] = bollinger['upper'] return informative_1h def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_15m) # EMA # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # # RSI # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # informative_1h['bb_lowerband'] = bollinger['lower'] # informative_1h['bb_middleband'] = bollinger['mid'] # informative_1h['bb_upperband'] = bollinger['upper'] return informative_15m def normal_tf_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['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) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=5) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=25) """ ANTIPUMP THING TO TEST """ dataframe['pct_change'] = dataframe['close'].pct_change(periods=8) dataframe['pct_change_int'] = ((dataframe['pct_change'] > 0.15).astype(int) | (dataframe['pct_change'] < -0.15).astype(int)) dataframe['pct_change_short'] = dataframe['close'].pct_change(periods=8) dataframe['pct_change_int_short'] = ((dataframe['pct_change_short'] > 0.08).astype(int) | (dataframe['pct_change_short'] < -0.08).astype(int)) dataframe['ispumping'] = ( (dataframe['pct_change_int'].rolling(20).sum() >= 0.4) ).astype('int') dataframe['islongpumping'] = ( (dataframe['pct_change_int'].rolling(30).sum() >= 0.48) ).astype('int') dataframe['isshortpumping'] = ( (dataframe['pct_change_int_short'].rolling(10).sum() >= 0.10) ).astype('int') dataframe['recentispumping'] = (dataframe['ispumping'].rolling(300).max() > 0) | (dataframe['islongpumping'].rolling(300).max() > 0)# | (dataframe['isshortpumping'].rolling(300).max() > 0) """ END ANTIPUMP """ return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # informative_1h = self.informative_1h_indicators(dataframe, metadata) informative_15m = self.informative_15m_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dont_buy_conditions = [] dont_buy_conditions.append( ( # don't buy if there isn't 3% profit to be made (dataframe['close_15m'].rolling(self.lookback_candles.value).max() < (dataframe['close'] * self.profit_threshold.value)) ) ) dataframe.loc[ ( (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < ( dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))& (dataframe['rsi'] < 44) ), ['buy', 'buy_tag']] = (1, 'ewo1') dataframe.loc[ ( (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['rsi'] < 25) ), ['buy', 'buy_tag']] = (1, 'ewo2') dataframe.loc[ ( (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < ( dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ), ['buy', 'buy_tag']] = (1, 'ewolow') if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) | ( (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.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), 'sell' ]=1 return dataframe class NASOSv5HO(Github_zxjason_Freqtrade_Strategies__NASOSv5_mod4__20250512_210217): # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 8, "ewo_high": 4.13, "ewo_high_2": 4.477, "ewo_low": -19.076, "lookback_candles": 27, "low_offset": 0.988, "low_offset_2": 0.974, "profit_threshold": 1.049, "rsi_buy": 72, "rsi_fast_buy": 40, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 8, "high_offset": 1.012, "high_offset_2": 1.431, } # ROI table: # value loaded from strategy minimal_roi = { "0": 0.1 } # Stoploss: stoploss = -0.0845 # value loaded from strategy # Trailing stop: trailing_stop = True # value loaded from strategy trailing_stop_positive = 0.001 # value loaded from strategy trailing_stop_positive_offset = 0.03 # value loaded from strategy trailing_only_offset_is_reached = True # value loaded from strategy class NASOSv5PD(Github_zxjason_Freqtrade_Strategies__NASOSv5_mod4__20250512_210217): def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dont_buy_conditions = [] dont_buy_conditions.append( ( # don't buy if there isn't 3% profit to be made (dataframe['close_15m'].rolling(self.lookback_candles.value).max() < (dataframe['close'] * self.profit_threshold.value)) ) ) dont_buy_conditions.append( (dataframe['recentispumping'] == True) ) dataframe.loc[ ( (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < ( dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ), ['buy', 'buy_tag']] = (1, 'ewo1') dataframe.loc[ ( (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['rsi'] < 35) ), ['buy', 'buy_tag']] = (1, 'ewo2') dataframe.loc[ ( (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < ( dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ), ['buy', 'buy_tag']] = (1, 'ewolow') if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe class NASOSv5SL(Github_zxjason_Freqtrade_Strategies__NASOSv5_mod4__20250512_210217): sell_params = { "pHSL": -0.178, "pPF_1": 0.019, "pPF_2": 0.065, "pSL_1": 0.019, "pSL_2": 0.062, "base_nb_candles_sell": 12, "high_offset": 1.01, "high_offset_2": 1.142, } # hard stoploss profit pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) trailing_stop = False use_custom_stoploss = True ## 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) class TrailingBuyStrat(Github_zxjason_Freqtrade_Strategies__NASOSv5_mod4__20250512_210217): # if process_only_new_candles = True, then you need to use 1m timeframe (and normal strat timeframe as informative) trailing_buy_order_enabled = True trailing_buy_offset = 0.005 process_only_new_candles = True custom_info = dict() # custom_info should be a dict def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): tag = super(TrailingBuyStrat, self).custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs) if tag: self.custom_info[pair]['trailing_buy'] = { 'trailing_buy_order_started': False, 'trailing_buy_order_uplimit': 0, 'start_trailing_price': 0, 'buy_tag': None } logger.info(f'STOP trailing buy for {pair} because of {tag}') return tag def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super(TrailingBuyStrat, self).populate_indicators(dataframe, metadata) if not metadata["pair"] in self.custom_info: self.custom_info[metadata["pair"]] = dict() if not 'trailing_buy' in self.custom_info[metadata['pair']]: self.custom_info[metadata["pair"]]['trailing_buy'] = { 'trailing_buy_order_started': False, 'trailing_buy_order_uplimit': 0, 'start_trailing_price': 0, 'buy_tag': None } return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: val = super(TrailingBuyStrat, self).confirm_trade_exit(pair, trade, order_type, amount, rate, time_in_force, sell_reason, **kwargs) self.custom_info[pair]['trailing_buy']['trailing_buy_order_started'] = False self.custom_info[pair]['trailing_buy']['trailing_buy_order_uplimit'] = 0 self.custom_info[pair]['trailing_buy']['start_trailing_price'] = 0 self.custom_info[pair]['trailing_buy']['buy_tag'] = None return val def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def get_local_min(x): win = dataframe.loc[:, 'barssince_last_buy'].iloc[x.shape[0] - 1].astype('int') win = max(win, 0) return pd.Series(x).rolling(window=win).min().iloc[-1] dataframe = super(TrailingBuyStrat, self).populate_buy_trend(dataframe, metadata) dataframe = dataframe.rename(columns={"buy": "pre_buy"}) if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): # trailing live dry ticker, 1m last_candle = dataframe.iloc[-1].squeeze() if not self.process_only_new_candles: current_price = self.get_current_price(metadata["pair"]) else: current_price = last_candle['close'] dataframe['buy'] = 0 if not self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_started'] and last_candle['pre_buy'] == 1: self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_started'] = True self.custom_info[metadata["pair"]]['trailing_buy']['start_trailing_price'] = last_candle['close'] self.custom_info[metadata["pair"]]['trailing_buy']['buy_tag'] = last_candle['buy_tag'] self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_uplimit'] = last_candle[f'close'] logger.info(f'start trailing buy for {metadata["pair"]} at {last_candle["close"]}') elif self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_started']: if current_price < self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_uplimit']: self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_uplimit'] = min(current_price * (1 + self.trailing_buy_offset), self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_uplimit']) logger.info(f'update trailing buy for {metadata["pair"]} at {self.custom_info[metadata["pair"]]["trailing_buy"]["trailing_buy_order_uplimit"]}') elif current_price < self.custom_info[metadata["pair"]]['trailing_buy']['start_trailing_price']: dataframe.iloc[-1, dataframe.columns.get_loc('buy')] = 1 ratio = "%.2f" % ((current_price / self.custom_info[metadata['pair']]['trailing_buy']['start_trailing_price']) * 100) dataframe.iloc[-1, dataframe.columns.get_loc('buy_tag')] = f"{self.custom_info[metadata['pair']]['trailing_buy']['buy_tag']} ({ratio} %)" # stop trailing when buy signal ! prevent from buyin much higher price when slot is free self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_started'] = False self.custom_info[metadata["pair"]]['trailing_buy']['trailing_buy_order_uplimit'] = 0 self.custom_info[metadata["pair"]]['trailing_buy']['start_trailing_price'] = None self.custom_info[metadata["pair"]]['trailing_buy']['buy_tag'] = None else: logger.info(f'price to high for {metadata["pair"]} at {current_price} vs {self.custom_info[metadata["pair"]]["trailing_buy"]["trailing_buy_order_uplimit"]}') elif self.trailing_buy_order_enabled: # FOR BACKTEST # PROBABLY STILL NOT WORKING dataframe.loc[ (dataframe['pre_buy'] == 1) & (dataframe['pre_buy'].shift() == 0) , 'pre_buy_switch'] = 1 dataframe['pre_buy_switch'] = dataframe['pre_buy_switch'].fillna(0) dataframe['barssince_last_buy'] = dataframe['pre_buy_switch'].groupby(dataframe['pre_buy_switch'].cumsum()).cumcount() # Create integer positions of each row idx_positions = np.arange(len(dataframe)) # "shift" those integer positions by the amount in shift col shifted_idx_positions = idx_positions - dataframe["barssince_last_buy"] # get the label based index from our DatetimeIndex shifted_loc_index = dataframe.index[shifted_idx_positions] # Retrieve the "shifted" values and assign them as a new column dataframe["close_5m_last_buy"] = dataframe.loc[shifted_loc_index, "close_5m"].values dataframe.loc[:, 'close_lower'] = dataframe.loc[:, 'close'].expanding().apply(get_local_min) dataframe['close_lower'] = np.where(dataframe['close_lower'].isna() == True, dataframe['close'], dataframe['close_lower']) dataframe['close_lower_offset'] = dataframe['close_lower'] * (1 + self.trailing_buy_offset) dataframe['trailing_buy_order_uplimit'] = np.where(dataframe['barssince_last_buy'] < 20, pd.DataFrame([dataframe['close_5m_last_buy'], dataframe['close_lower_offset']]).min(), np.nan) dataframe.loc[ (dataframe['barssince_last_buy'] < 20) & # must buy within last 20 candles after signal (dataframe['close'] > dataframe['trailing_buy_order_uplimit']) , 'trailing_buy'] = 1 dataframe['trailing_buy_count'] = dataframe['trailing_buy'].rolling(20).sum() dataframe.log[ (dataframe['trailing_buy'] == 1) & (dataframe['trailing_buy_count'] == 1) , 'buy'] = 1 else: # No but trailing dataframe.loc[ (dataframe['pre_buy'] == 1) , 'buy'] = 1 return dataframe def get_current_price(self, pair: str) -> float: ticker = self.dp.ticker(pair) current_price = ticker['last'] return current_price