# source: https://raw.githubusercontent.com/eZ3KIEL29640/FT_strat/96ce227518eb9d0c29f3c5f163bd18cc10fbe48d/ARIMA_5.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List, Optional, Tuple, Union from functools import reduce from pandas import DataFrame, Series import warnings import pandas as pd # -------------------------------- 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, informative from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter, BooleanParameter import technical.indicators as ftt import math import logging from scipy.signal import find_peaks, find_peaks_cwt import warnings from math import ceil from datetime import datetime, timezone, timedelta from typing import Dict, List, Optional, Tuple, Union from pmdarima import auto_arima from pmdarima import model_selection from sklearn.metrics import mean_squared_error import time warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) '\n\n\n\n________/\\\\\\\\\\__/\\\\______________/\\\\________________/\\\\________/\\\\_____/\\\\\\\\\\___/\\\\\\_____/\\\\__________________________/\\\\______________________/\\\\\\\\_________________ \n _____/\\\\////////__\\/\\\\__________/\\\\\\\\_______________\\/\\\\_____/\\\\//____/\\\\///////\\\\_\\/\\\\\\___\\/\\\\________________________/\\\\\\____________________/\\\\/////\\\\_______________ \n ___/\\\\/___________\\/\\\\_________\\/////\\\\_______________\\/\\\\__/\\\\//______\\///______/\\\\__\\/\\\\/\\\\__\\/\\\\_____/\\\\_____________/\\\\/\\\\___________________/\\\\____\\//\\\\______________ \n __/\\\\_____________\\/\\\\_____________\\/\\\\_____/\\\\\\\\_\\/\\\\\\//\\\\_____________/\\\\//___\\/\\\\//\\\\_\\/\\\\__/\\\\\\\\\\\\______/\\\\/\\/\\\\________/\\\\\\\\_\\/\\\\_____\\/\\\\__/\\\\\\\\\\_ \n _\\/\\\\_____________\\/\\\\\\\\\\______\\/\\\\___/\\\\//////__\\/\\\\//_\\//\\\\___________\\////\\\\__\\/\\\\//\\\\/\\\\_\\////\\\\////_____/\\\\/__\\/\\\\______/\\\\//////__\\/\\\\_____\\/\\\\_\\/\\\\//////__ \n _\\//\\\\____________\\/\\\\/////\\\\_____\\/\\\\__/\\\\_________\\/\\\\____\\//\\\\_____________\\//\\\\_\\/\\\\_\\//\\\\/\\\\____\\/\\\\_______/\\\\\\\\\\\\\\\\__/\\\\_________\\/\\\\_____\\/\\\\_\\/\\\\\\\\\\_ \n __\\///\\\\__________\\/\\\\___\\/\\\\_____\\/\\\\_\\//\\\\________\\/\\\\_____\\//\\\\___/\\\\______/\\\\__\\/\\\\__\\//\\\\\\____\\/\\\\_/\\__\\///////////\\\\//__\\//\\\\________\\//\\\\____/\\\\__\\////////\\\\_ \n ____\\////\\\\\\\\\\_\\/\\\\___\\/\\\\_____\\/\\\\__\\///\\\\\\\\_\\/\\\\______\\//\\\\_\\///\\\\\\\\\\/___\\/\\\\___\\//\\\\\\____\\//\\\\\\_____________\\/\\\\_____\\///\\\\\\\\__\\///\\\\\\\\/____/\\\\\\\\\\_ \n _______\\/////////__\\///____\\///______\\///_____\\////////__\\///________\\///____\\/////////_____\\///_____\\/////______\\/////______________\\///________\\////////_____\\///////_____\\//////////__\n\n\n\n\n\n' pd.set_option('display.float_format', lambda x: '%.7f' % x) logger = logging.getLogger(__name__) class Github_eZ3KIEL29640_FT_strat__ARIMA_5__20240702_135028(IStrategy): INTERFACE_VERSION = 3 # Stoploss: stoploss = -0.03 # Trailing stop: use_custom_stoploss = True # Initialize dicts for arima storage last_run_time = {} arima_model = {} last_run_time_1h = {} arima_model_1h = {} last_run_time_4h = {} arima_model_4h = {} # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.05 ignore_roi_if_entry_signal = False ## Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} # Optimal timeframe for the strategy timeframe = '5m' startup_candle_count = 400 process_only_new_candles = True # Custom Entry last_entry_price = None # Hyper-opt parameters base_nb_candles_buy = IntParameter(150, 200, default=184, space='buy', optimize=True, load=True) up = DecimalParameter(low=1.02, high=1.025, default=1.02, decimals=3, space='buy', optimize=True, load=True) dn = DecimalParameter(low=0.983, high=0.987, default=0.984, decimals=3, space='buy', optimize=True, load=True) increment = DecimalParameter(low=1.0005, high=1.001, default=1.0007, decimals=4, space='buy', optimize=True, load=True) atr_length = IntParameter(5, 30, default=5, space='buy', optimize=True, load=True) window = IntParameter(10, 30, default=16, space='buy', optimize=True, load=True) window_1h = IntParameter(10, 30, default=8, space='buy', optimize=True, load=True) window_4h = IntParameter(2, 30, default=2, space='buy', optimize=True, load=True) x = DecimalParameter(low=1.2, high=1.75, default=1.6, decimals=2, space='buy', optimize=True, load=True) x_1h = DecimalParameter(low=1.2, high=1.75, default=1.5, decimals=2, space='buy', optimize=True, load=True) x_4h = DecimalParameter(low=1.2, high=1.75, default=1.3, decimals=2, space='buy', optimize=True, load=True) ### trailing stop loss optimiziation ### tsl_target3 = DecimalParameter(low=0.1, high=0.15, default=0.15, decimals=2, space='sell', optimize=True, load=True) ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, decimals=3, space='sell', optimize=True, load=True) tsl_target2 = DecimalParameter(low=0.06, high=0.1, default=0.1, decimals=3, space='sell', optimize=True, load=True) ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, decimals=3, space='sell', optimize=True, load=True) tsl_target1 = DecimalParameter(low=0.04, high=0.08, default=0.06, decimals=3, space='sell', optimize=True, load=True) ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, decimals=3, space='sell', optimize=True, load=True) tsl_target0 = DecimalParameter(low=0.03, high=0.06, default=0.04, decimals=3, space='sell', optimize=True, load=True) ts0 = DecimalParameter(low=0.005, high=0.012, default=0.01, decimals=3, space='sell', optimize=True, load=True) moon = IntParameter(80, 90, default=85, space='sell', optimize=True) @property def protections(self): return [{'method': 'CooldownPeriod', 'stop_duration_candles': 5}, {'method': 'MaxDrawdown', 'lookback_period_candles': 48, 'trade_limit': 20, 'stop_duration_candles': 4, 'max_allowed_drawdown': 0.05}, {'method': 'StoplossGuard', 'lookback_period_candles': 24, 'trade_limit': 4, 'stop_duration_candles': 12, 'only_per_pair': False}, {'method': 'LowProfitPairs', 'lookback_period_candles': 6, 'trade_limit': 2, 'stop_duration_candles': 60, 'required_profit': 0.02}, {'method': 'LowProfitPairs', 'lookback_period_candles': 24, 'trade_limit': 4, 'stop_duration_candles': 2, 'required_profit': 0.01}] ### Trailing Stop ### def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_candle['max_l'] > 0.0035: if current_profit > self.tsl_target3.value: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl3 {self.tsl_target3.value}/{self.ts3.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl3 {self.tsl_target3.value}/{self.ts3.value} activated') return self.ts3.value if current_profit > self.tsl_target2.value: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl2 {self.tsl_target2.value}/{self.ts2.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl2 {self.tsl_target2.value}/{self.ts2.value} activated') return self.ts2.value if current_profit > self.tsl_target1.value: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl1 {self.tsl_target1.value}/{self.ts1.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl1 {self.tsl_target1.value}/{self.ts1.value} activated') return self.ts1.value if current_profit > self.tsl_target0.value: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl0 {self.tsl_target0.value}/{self.ts0.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl0 {self.tsl_target0.value}/{self.ts0.value} activated') return self.ts0.value elif current_profit > self.tsl_target0.value: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} SWINGING FOR THE MOON!!!') return 0.99 return self.stoploss def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) entry_price = (dataframe['close'].iat[-1] + dataframe['open'].iat[-1] + proposed_rate + proposed_rate) / 4 logger.info(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}") # Check if there is a stored last entry price and if it matches the proposed entry price if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0001: # Tolerance for floating-point comparison entry_price *= self.increment.value # Increment by 0.2% logger.info(f'{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}.') # Update the last entry price self.last_entry_price = entry_price return entry_price def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if exit_reason == 'roi' and last_candle['min_l'] > last_candle['max_l'] * 3: return False # Handle freak events if exit_reason == 'roi' and trade.calc_profit_ratio(rate) < 0.003: logger.info(f'{trade.pair} ROI is below 0') self.dp.send_msg(f'{trade.pair} ROI is below 0') return False if exit_reason == 'partial_exit' and trade.calc_profit_ratio(rate) < 0: logger.info(f'{trade.pair} partial exit is below 0') self.dp.send_msg(f'{trade.pair} partial exit is below 0') return False return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['decision'] = 0 pair = metadata['pair'] current_time = time.time() dataframe['OHLC4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 size = len(dataframe) - 288 train, test = model_selection.train_test_split(dataframe['OHLC4'], train_size=size) # Initialize values for the current pair if not already done if pair not in self.last_run_time: self.last_run_time[pair] = current_time logger.info(f'Initial ARIMA {self.timeframe} Model Training for {pair}') # Fit ARIMA model start_time = time.time() self.arima_model[pair] = auto_arima(train, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') fitting_time = time.time() - start_time logger.info(f'{pair} - ARIMA {self.timeframe} Model fitted in {fitting_time:.2f} seconds') # Check if it's time to retrain for the current pair if current_time - self.last_run_time[pair] >= 3600: # Check if an hour has passed logger.info(f'Auto Fitting ARIMA {self.timeframe} Model for {pair}') self.last_run_time[pair] = current_time # Fit ARIMA model start_time = time.time() self.arima_model[pair] = auto_arima(train, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') fitting_time = time.time() - start_time logger.info(f'{pair} - ARIMA {self.timeframe} Model fitted in {fitting_time:.2f} seconds') # Use the previously fitted ARIMA model for forecasting if self.arima_model[pair] is not None: start_time = time.time() future_forecast, conf_int = self.arima_model[pair].predict(n_periods=test.shape[0], return_conf_int=True) inference_time = time.time() - start_time timeleft = current_time - self.last_run_time[pair] if timeleft <= 3600 and timeleft != 0: logger.info(f'{pair} - ARIMA {self.timeframe} Model re-optimized in {timeleft:.2f} seconds') # Extract upper and lower confidence intervals lower_confidence, upper_confidence = (conf_int[:, 0], conf_int[:, 1]) logger.info(f"{pair} - Inference time: {inference_time:.2f} seconds | Current Price: {dataframe['OHLC4'].iloc[-1]:.7f} | {self.timeframe} Future Forecast: {future_forecast.iloc[-1]:.7f}") dataframe['rmse'] = 0 dataframe['accuracy_perc'] = 0 dataframe['reward'] = 0 dataframe['rmse'] = np.sqrt(mean_squared_error(test, future_forecast)) dataframe['accuracy_perc'] = 100 * (1 - dataframe['rmse'].iloc[-1] / dataframe['OHLC4'].iloc[-1]) dataframe['reward'] = (future_forecast.iloc[-1] / dataframe['OHLC4'].iloc[-1] - 1) * 100 rmse = dataframe['rmse'].iloc[-1] accuracy_perc = dataframe['accuracy_perc'].iloc[-1] reward = dataframe['reward'].iloc[-1] # Apply rolling window operation to the 'OHLC4' column rolling_window = dataframe['OHLC4'].rolling(self.window.value) # 5.25 hrs # Calculate the peak-to-peak value on the resulting rolling window data ptp_value = rolling_window.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['move'] = ptp_value / dataframe['OHLC4'] dataframe['move_mean'] = dataframe['move'].mean() dataframe['move_mean_x'] = dataframe['move'].mean() * self.x.value move = '{:.2f}'.format(dataframe['move'].iloc[-1] * 100) move_mean = '{:.2f}'.format(dataframe['move_mean'].iloc[-1] * 100) if future_forecast.iloc[-1] > dataframe['OHLC4'].iloc[-1]: direction = 'Up' dataframe['decision'] = 1 else: direction = 'Down' dataframe['decision'] = -1 logger.info(f'{pair} - Test RMSE: {rmse:.3f} | Accuracy: {accuracy_perc:.2f}% | Potential Profit: {move}% | Avg. Profit: {move_mean}% | {self.timeframe} Trend: {direction}') dataframe['arima_predictions'] = pd.Series(future_forecast) dataframe['lower_confidence'] = pd.Series(lower_confidence) dataframe['upper_confidence'] = pd.Series(upper_confidence) dataframe['atr_pcnt'] = ta.ATR(dataframe, timeperiod=self.atr_length.value) / dataframe['OHLC4'] dataframe['vol_z_score'] = (dataframe['volume'] - dataframe['volume'].rolling(window=30).mean()) / dataframe['volume'].rolling(window=30).std() dataframe['vol_anomaly'] = np.where(dataframe['vol_z_score'] > 3, 1, 0) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['sma'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] dataframe['sma_up'] = dataframe['sma'] * self.up.value dataframe['sma_dn'] = dataframe['sma'] * self.dn.value dataframe['max_l'] = dataframe['OHLC4'].rolling(120).max() / dataframe['OHLC4'] - 1 dataframe['min_l'] = abs(dataframe['OHLC4'].rolling(120).min() / dataframe['OHLC4'] - 1) dataframe['max'] = dataframe['OHLC4'].rolling(4).max() / dataframe['OHLC4'] - 1 dataframe['min'] = abs(dataframe['OHLC4'].rolling(4).min() / dataframe['OHLC4'] - 1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condition1 = (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(6) < dataframe['move_mean'].shift(6)) & (dataframe['min'] < dataframe['max']) & (dataframe['min_l'] < dataframe['max_l']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['OHLC4'] < dataframe['sma_dn']) & (dataframe['sma_dn'].shift() > dataframe['sma_dn']) & (dataframe['volume'] > 0) dataframe.loc[condition1, 'enter_long'] = 1 dataframe.loc[condition1, 'enter_tag'] = 'Up Trend Soon below sma_dn' condition2 = (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean_x']) & (dataframe['min'] < dataframe['max']) & (dataframe['min_l'] < dataframe['max_l']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['OHLC4'] < dataframe['sma_dn']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['volume'] > 0) dataframe.loc[condition2, 'enter_long'] = 1 dataframe.loc[condition2, 'enter_tag'] = 'Move Mean Fib below sma_dn' condition3 = (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(6) < dataframe['move_mean'].shift(6)) & (dataframe['min'] < dataframe['max']) & (dataframe['min_l'] < dataframe['max_l']) & (dataframe['OHLC4'] < dataframe['sma']) & (dataframe['volume'] > 0) dataframe.loc[condition3, 'enter_long'] = 1 dataframe.loc[condition3, 'enter_tag'] = 'Up Trend Soon below sma' condition4 = (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean_x']) & (dataframe['min'] < dataframe['max']) & (dataframe['min_l'] < dataframe['max_l']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['OHLC4'] < dataframe['sma']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['volume'] > 0) dataframe.loc[condition4, 'enter_long'] = 1 dataframe.loc[condition4, 'enter_tag'] = 'Move Mean Fib below sma' condition5150 = (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['min'] < dataframe['max']) & (dataframe['OHLC4'] > dataframe['sma']) & (dataframe['sma_up'].shift() < dataframe['sma']) & (dataframe['volume'] > 0) dataframe.loc[condition5150, 'enter_long'] = 1 dataframe.loc[condition5150, 'enter_tag'] = 'Hope this works...' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condition5 = (dataframe['decision'] == -1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(6) < dataframe['move_mean'].shift(6)) & (dataframe['min'] > dataframe['max']) & (dataframe['min_l'] > dataframe['max_l']) & (dataframe['volume'] > 0) dataframe.loc[condition5, 'exit_long'] = 1 dataframe.loc[condition5, 'exit_tag'] = 'Down Trend Soon' condition6 = (dataframe['decision'] == -1) & (dataframe['move'] >= dataframe['move_mean_x']) & (dataframe['move'].shift(3) >= dataframe['move_mean_x'].shift(3)) & (dataframe['min'] > dataframe['max']) & (dataframe['min_l'] > dataframe['max_l']) & (dataframe['volume'] > 0) dataframe.loc[condition6, 'exit_long'] = 1 dataframe.loc[condition6, 'exit_tag'] = 'Move Mean Fib' return dataframe class ARIMA_5(Github_eZ3KIEL29640_FT_strat__ARIMA_5__20240702_135028): INTERFACE_VERSION = 3 # Original idea by @MukavaValkku, code by @tirail and @stash86 # # This class is designed to inherit from yours and starts trailing buy with your buy signals # Trailing buy starts at any buy signal and will move to next candles if the trailing still active # Trailing buy stops with BUY if : price decreases and rises again more than trailing_buy_offset # Trailing buy stops with NO BUY : current price is > initial price * (1 + trailing_buy_max) OR custom_sell tag # IT IS NOT COMPATIBLE WITH BACKTEST/HYPEROPT # process_only_new_candles = True custom_info_trail_buy = dict() # Trailing buy parameters trailing_buy_order_enabled = True trailing_expire_seconds = 1800 # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, buy the coin trailing_buy_uptrend_enabled = True trailing_expire_seconds_uptrend = 1800 min_uptrend_trailing_profit = 0.005 debug_mode = True trailing_buy_max_stop = 0.008 # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop) trailing_buy_max_buy = 0.01 # buy if price between uplimit (=min of serie (current_price * (1 + trailing_buy_offset())) and (start_price * 1+trailing_buy_max_buy)) init_trailing_dict = {'trailing_buy_order_started': False, 'trailing_buy_order_uplimit': 0, 'start_trailing_price': 0, 'enter_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': False} def trailing_buy(self, pair, reinit=False): # returns trailing buy info for pair (init if necessary) if not pair in self.custom_info_trail_buy: self.custom_info_trail_buy[pair] = dict() if reinit or not 'trailing_buy' in self.custom_info_trail_buy[pair]: self.custom_info_trail_buy[pair]['trailing_buy'] = self.init_trailing_dict.copy() return self.custom_info_trail_buy[pair]['trailing_buy'] def trailing_buy_info(self, pair: str, current_price: float): # current_time live, dry run current_time = datetime.now(timezone.utc) if not self.debug_mode: return trailing_buy = self.trailing_buy(pair) duration = 0 try: duration = current_time - trailing_buy['start_trailing_time'] except TypeError: duration = 0 finally: logger.info(f"pair: {pair} : start: {trailing_buy['start_trailing_price']:.4f}, duration: {duration}, current: {current_price:.4f}, uplimit: {trailing_buy['trailing_buy_order_uplimit']:.4f}, profit: {self.current_trailing_profit_ratio(pair, current_price) * 100:.2f}%, offset: {trailing_buy['offset']}") def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float: trailing_buy = self.trailing_buy(pair) if trailing_buy['trailing_buy_order_started']: return (trailing_buy['start_trailing_price'] - current_price) / trailing_buy['start_trailing_price'] else: return 0 def trailing_buy_offset(self, dataframe, pair: str, current_price: float): # return rebound limit before a buy in % of initial price, function of current price # return None to stop trailing buy (will start again at next buy signal) # return 'forcebuy' to force immediate buy # (example with 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no buy, uplimit updated to 99.5), 3price 98 (no buy uplimit updated to 98.5), 4th price 99 -> BUY current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price) default_offset = 0.005 trailing_buy = self.trailing_buy(pair) if not trailing_buy['trailing_buy_order_started']: return default_offset # example with duration and indicators # dry run, live only last_candle = dataframe.iloc[-1] current_time = datetime.now(timezone.utc) trailing_duration = current_time - trailing_buy['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if current_trailing_profit_ratio > 0 and last_candle['enter_long'] == 1: # more than 1h, price under first signal, buy signal still active -> buy return 'forcebuy' else: # wait for next signal return None elif self.trailing_buy_uptrend_enabled and trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend and (current_trailing_profit_ratio < -1 * self.min_uptrend_trailing_profit): # less than 90s and price is rising, buy return 'forcebuy' if current_trailing_profit_ratio < 0: # current price is higher than initial price return default_offset trailing_buy_offset = {0.06: 0.02, 0.03: 0.01, 0: default_offset} for key in trailing_buy_offset: if current_trailing_profit_ratio > key: return trailing_buy_offset[key] return default_offset # end of trailing buy parameters # ----------------------------------------------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) self.trailing_buy(metadata['pair']) return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs) if val: if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): val = False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) >= 1: last_candle = dataframe.iloc[-1].squeeze() current_price = rate trailing_buy = self.trailing_buy(pair) trailing_buy_offset = self.trailing_buy_offset(dataframe, pair, current_price) if trailing_buy['allow_trailing']: if not trailing_buy['trailing_buy_order_started'] and last_candle['enter_long'] == 1: # start trailing buy # self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_started'] = True # self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = last_candle['close'] # self.custom_info_trail_buy[pair]['trailing_buy']['start_trailing_price'] = last_candle['close'] # self.custom_info_trail_buy[pair]['trailing_buy']['buy_tag'] = f"initial_buy_tag (strat trail price {last_candle['close']})" # self.custom_info_trail_buy[pair]['trailing_buy']['start_trailing_time'] = datetime.now(timezone.utc) # self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = 0 trailing_buy['trailing_buy_order_started'] = True trailing_buy['trailing_buy_order_uplimit'] = last_candle['close'] trailing_buy['start_trailing_price'] = last_candle['close'] trailing_buy['enter_tag'] = last_candle['enter_tag'] trailing_buy['start_trailing_time'] = datetime.now(timezone.utc) trailing_buy['offset'] = 0 self.trailing_buy_info(pair, current_price) logger.info(f"start trailing buy for {pair} at {last_candle['close']}") elif trailing_buy['trailing_buy_order_started']: if trailing_buy_offset == 'forcebuy': # buy in custom conditions val = True ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100) self.trailing_buy_info(pair, current_price) logger.info(f'price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full') elif trailing_buy_offset is None: # stop trailing buy custom conditions self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because "trailing buy offset" returned None') elif current_price < trailing_buy['trailing_buy_order_uplimit']: # update uplimit old_uplimit = trailing_buy['trailing_buy_order_uplimit'] self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = min(current_price * (1 + trailing_buy_offset), self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit']) self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = trailing_buy_offset self.trailing_buy_info(pair, current_price) logger.info(f"update trailing buy for {pair} at {old_uplimit} -> {self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit']}") elif current_price < trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy): # buy ! current price > uplimit && lower thant starting price val = True ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100) self.trailing_buy_info(pair, current_price) logger.info(f"current price ({current_price}) > uplimit ({trailing_buy['trailing_buy_order_uplimit']}) and lower than starting price price ({trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy)}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full") elif current_price > trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_stop): # stop trailing buy because price is too high self.trailing_buy(pair, reinit=True) self.trailing_buy_info(pair, current_price) logger.info(f'STOP trailing buy for {pair} because of the price is higher than starting price * {1 + self.trailing_buy_max_stop}') else: # uplimit > current_price > max_price, continue trailing and wait for the price to go down self.trailing_buy_info(pair, current_price) logger.info(f'price too high for {pair} !') else: logger.info(f'Wait for next buy signal for {pair}') if val == True: self.trailing_buy_info(pair, rate) self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because I buy it') return val def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_entry_trend(dataframe, metadata) if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): last_candle = dataframe.iloc[-1].squeeze() trailing_buy = self.trailing_buy(metadata['pair']) if last_candle['enter_long'] == 1: if not trailing_buy['trailing_buy_order_started']: open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all() if not open_trades: logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!") # self.custom_info_trail_buy[metadata['pair']]['trailing_buy']['allow_trailing'] = True trailing_buy['allow_trailing'] = True initial_buy_tag = last_candle['enter_tag'] if 'enter_tag' in last_candle else 'buy signal' dataframe.loc[:, 'enter_tag'] = f"{initial_buy_tag} (start trail price {last_candle['close']})" elif trailing_buy['trailing_buy_order_started'] == True: logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!") dataframe.loc[:, 'enter_long'] = 1 dataframe.loc[:, 'enter_tag'] = trailing_buy['enter_tag'] # dataframe['buy'] = 1 return dataframe