ARIMASTR

# --- 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)
pd.set_option('display.float_format', lambda x: '%.7f' % x)
logger = logging.getLogger(__name__)

class ARIMASTR(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(ARIMASTR):
    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