ClucHAnix_hhll_Futures

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(IStrategy):
    """
    Futures strategy based on the ClucHAnix_hhll strategy.
    """
    can_short = True
    #hypered params
    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 hyperspace params:
    sell_params = {
        "pPF_1": 0.011,
        "pPF_2": 0.064,
        "pSL_1": 0.011,
        "pSL_2": 0.062,

        # sell signal params
        "high_offset": 0.907,
        "high_offset_2": 1.211,
        "sell_bbmiddle_close": 0.97286,
        "sell_fisher": 0.48492,
        # leverage set
        "leverage_num": 10,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.103,
        "3": 0.05,
        "5": 0.033,
        "61": 0.027,
        "125": 0.011,
        "292": 0.005,
    }

    # Futures stoploss:
    stoploss = -0.99  # use custom stoploss

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.012
    trailing_only_offset_is_reached = False

    # Timeframe and startup candle count
    timeframe = '15m'
    startup_candle_count = 168

    process_only_new_candles = True
    # order_types = {
    #     'buy': 'market',
    #     'sell': 'market',
    #     'stoploss': 'market',
    #     'stoploss_on_exchange': True,
    # }

    # buy params
    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 )

    ## Slippage params
    is_optimize_slip = False
    max_slip = DecimalParameter(0.33, 0.80, default=0.33, decimals=3, optimize=is_optimize_slip , space='buy', load=True)

    # sell params
    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

    # come from BB_RPB_TSL
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        # hard stoploss profit
        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

        # 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 = -0.99

        # Only for hyperopt invalid return
        if sl_profit >= current_profit:
            return -0.99

        return stoploss_from_open(sl_profit, current_profit)

    ## Confirm Entry
    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)

        # stoploss - deadfish
        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'

        # stoploss - pump
        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:
        # # Heikin Ashi Candles
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']

        # Set Up Bollinger Bands
        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']

        # BB 20
        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)

        # SMA
        dataframe['sma_9'] = ta.SMA(dataframe['close'], timeperiod=9)
        dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200)

        # HMA
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)

        # volume
        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()

        # ROCR
        dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)

        # hh48
        dataframe['hh_48'] = ta.MAX(dataframe['high'], 48)
        dataframe['hh_48_diff'] = (dataframe['hh_48'] - dataframe['close']) / dataframe['hh_48'] * 100

        # ll48
        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)

        # RSI
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        # sma dec 20
        dataframe['sma_200_dec_20'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)

        # EMA of VWMA Oscillator
        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)

        # CMF
        dataframe['cmf'] = chaikin_money_flow(dataframe, 20)

        # 1h tf
        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)

        # CMF
        informative['cmf'] = chaikin_money_flow(informative, 20)

        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    
        # Long entry conditions
        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)
            ),
            'enter_long'] = 1
    
        # Short entry conditions (inverse of the long entry)
        dataframe.loc[
            (
                (dataframe['rocr_1h'].lt(self.rocr_1h.value)) &
                (
                    (
                        (dataframe['lower'].shift().le(0)) &
                        (dataframe['bbdelta'].lt(dataframe['ha_close'] * self.bbdelta_close.value)) &
                        (dataframe['closedelta'].lt(dataframe['ha_close'] * self.closedelta_close.value)) &
                        (dataframe['tail'].gt(dataframe['bbdelta'] * self.bbdelta_tail.value)) &
                        (dataframe['ha_close'].gt(dataframe['lower'].shift())) &
                        (dataframe['ha_close'].ge(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)
            ),
            'enter_short'] = 1
    
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    
        # Long exit conditions
        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'])
            ),
            'exit_long'] = 1
    
        # Short exit conditions (inverse of the long exit)
        dataframe.loc[
            (
                (dataframe['fisher'] < self.sell_fisher.value) &
                (dataframe['ha_low'].ge(dataframe['ha_low'].shift(1))) &
                (dataframe['ha_low'].shift(1).ge(dataframe['ha_low'].shift(2))) &
                (dataframe['ha_close'].ge(dataframe['ha_close'].shift(1))) &
                (dataframe['ema_fast'] < dataframe['ha_close']) &
                ((dataframe['ha_close'] * self.sell_bbmiddle_close.value) < dataframe['bb_middleband'])
            ),
            'exit_short'] = 1
    
        return dataframe
        
        # Volume Weighted Moving Average
def vwma(dataframe: DataFrame, length: int = 10):
    """Indicator: Volume Weighted Moving Average (VWMA)"""
    # Calculate Result
    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

# Exponential moving average of a volume weighted simple moving average
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!")

# Chaikin Money Flow
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):

    process_only_new_candles = True

    # Trailing info dictionaries
    custom_info_trail_buy = dict()
    custom_info_trail_short = dict()

    # Trailing buy parameters
    trailing_buy_order_enabled = True
    trailing_expire_seconds = 1800

    # Trailing short parameters
    trailing_short_order_enabled = True
    trailing_expire_seconds_short = 1800

    # Additional parameters for buy and short trailing
    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
    trailing_buy_max_buy = 0.000

    # Initial structure for trailing dictionaries
    init_trailing_dict = {
        'trailing_order_started': False,
        'trailing_order_uplimit': 0,
        'start_trailing_price': 0,
        'tag': None,
        'start_trailing_time': None,
        'offset': 0,
        'allow_trailing': False,
    }

    def trailing_buy(self, pair, reinit=False):
        if pair not in self.custom_info_trail_buy:
            self.custom_info_trail_buy[pair] = dict()
        if reinit or 'trailing_buy' not 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_short(self, pair, reinit=False):
        if pair not in self.custom_info_trail_short:
            self.custom_info_trail_short[pair] = dict()
        if reinit or 'trailing_short' not in self.custom_info_trail_short[pair]:
            self.custom_info_trail_short[pair]['trailing_short'] = self.init_trailing_dict.copy()
        return self.custom_info_trail_short[pair]['trailing_short']

    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_order_uplimit']:.4f}, "
                f"profit: {self.current_trailing_profit_ratio(pair, current_price, 'buy')*100:.2f}%, "
                f"offset: {trailing_buy['offset']}")

    def trailing_short_info(self, pair: str, current_price: float):
        current_time = datetime.now(timezone.utc)
        if not self.debug_mode:
            return
        trailing_short = self.trailing_short(pair)

        duration = 0
        try:
            duration = (current_time - trailing_short['start_trailing_time'])
        except TypeError:
            duration = 0
        finally:
            logger.info(
                f"pair: {pair} : "
                f"start: {trailing_short['start_trailing_price']:.4f}, "
                f"duration: {duration}, "
                f"current: {current_price:.4f}, "
                f"uplimit: {trailing_short['trailing_order_uplimit']:.4f}, "
                f"profit: {self.current_trailing_profit_ratio(pair, current_price, 'short')*100:.2f}%, "
                f"offset: {trailing_short['offset']}")

    def current_trailing_profit_ratio(self, pair: str, current_price: float, trade_type: str = 'buy') -> float:
        if trade_type == 'buy':
            trailing_order = self.trailing_buy(pair)
            return (current_price - trailing_order['start_trailing_price']) / trailing_order['start_trailing_price'] if trailing_order['trailing_order_started'] else 0
        elif trade_type == 'short':
            trailing_order = self.trailing_short(pair)
            return (trailing_order['start_trailing_price'] - current_price) / trailing_order['start_trailing_price'] if trailing_order['trailing_order_started'] else 0

    def trailing_buy_offset(self, dataframe, pair: str, current_price: float):
        current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price, 'buy')
        default_offset = 0.005
        trailing_buy = self.trailing_buy(pair)

        if not trailing_buy['trailing_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['enter_long'] == 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 trailing_short_offset(self, dataframe, pair: str, current_price: float):
        current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price, 'short')
        default_offset = 0.005
        trailing_short = self.trailing_short(pair)

        if not trailing_short['trailing_order_started']:
            return default_offset

        last_candle = dataframe.iloc[-1]
        current_time = datetime.now(timezone.utc)
        trailing_duration = current_time - trailing_short['start_trailing_time']
        if trailing_duration.total_seconds() > self.trailing_expire_seconds_short:
            if (current_trailing_profit_ratio > 0) and (last_candle['enter_short'] == 1):
                return 'forceshort'
            else:
                return None

        if current_trailing_profit_ratio < 0:
            return default_offset

        trailing_short_offset = {
            0.06: 0.02,
            0.03: 0.01,
            0: default_offset,
        }

        for key in trailing_short_offset:
            if current_trailing_profit_ratio > key:
                return trailing_short_offset[key]

        return default_offset

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_indicators(dataframe, metadata)
        # Initialize trailing structures for both long and short positions
        self.trailing_buy(metadata['pair'])
        self.trailing_short(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:
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            if len(dataframe) >= 1:
                last_candle = dataframe.iloc[-1].squeeze()
                current_price = rate

                if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
                    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_order_started'] and last_candle['enter_long'] == 1:
                            # Start trailing buy
                            self.start_trailing_buy(trailing_buy, last_candle, pair, current_price)

                        elif trailing_buy['trailing_order_started']:
                            val = self.handle_trailing_buy(trailing_buy, trailing_buy_offset, current_price, pair)

                if self.trailing_short_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
                    trailing_short = self.trailing_short(pair)
                    trailing_short_offset = self.trailing_short_offset(dataframe, pair, current_price)

                    if trailing_short['allow_trailing']:
                        if not trailing_short['trailing_order_started'] and last_candle['enter_short'] == 1:
                            # Start trailing short
                            self.start_trailing_short(trailing_short, last_candle, pair, current_price)

                        elif trailing_short['trailing_order_started']:
                            val = self.handle_trailing_short(trailing_short, trailing_short_offset, current_price, pair)

                if val:
                    # Reset the trailing structures if a trade is confirmed
                    self.trailing_buy(pair, reinit=True)
                    self.trailing_short(pair, reinit=True)

        return val

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_entry_trend(dataframe, metadata)

        # Trailing logic for long positions
        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_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_order_started']:
                    logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!")
                    dataframe.loc[:,'enter_long'] = 1
                    dataframe.loc[:, 'buy_tag'] = trailing_buy['tag']

        # Trailing logic for short positions
        if self.trailing_short_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
            last_candle = dataframe.iloc[-1].squeeze()
            trailing_short = self.trailing_short(metadata['pair'])
            if last_candle['enter_short'] == 1:
                logger.info(f"Attempting to enter short for {metadata['pair']} at {last_candle['close']} with indicators: "
                            f"rocr_1h: {last_candle['rocr_1h']}, bbdelta: {last_candle['bbdelta']}, fisher: {last_candle['fisher']}")
                if not trailing_short['trailing_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_short' to True for {metadata['pair']} to start trailing short!!!")
                        trailing_short['allow_trailing'] = True
                        initial_short_tag = last_candle['short_tag'] if 'short_tag' in last_candle else 'short signal'
                        dataframe.loc[:, 'short_tag'] = f"{initial_short_tag} (start trail price {last_candle['close']})"
            else:
                if trailing_short['trailing_order_started']:
                    logger.info(f"Continue trailing short for {metadata['pair']}. Manually trigger short signal!!")
                    dataframe.loc[:, 'enter_short'] = 1
                    dataframe.loc[:, 'short_tag'] = trailing_short['tag']

        return dataframe