ClucHAnix_hhll.py

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import time
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, timedelta, 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(IStrategy):
    """
    Please only use this with TrailingBuy
    """
    
    #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,
    }

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

    # 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

    """
    END HYPEROPT
    """

    timeframe = '5m'

    # Make sure these match or are not overridden in config
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Custom stoploss
    use_custom_stoploss = True

    process_only_new_candles = True
    startup_candle_count = 168

    order_types = {
        'buy': 'market',
        'sell': 'market',
        'emergencysell': 'market',
        'forcebuy': "market",
        'forcesell': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }

    # 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 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_sell(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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            ( dataframe['rocr_1h'].gt(self.rocr_1h.value) )
            &
            (   (
                    (dataframe['lower'].shift().gt(0)) &
                    (dataframe['bbdelta'].gt(dataframe['ha_close'] * self.bbdelta_close.value)) &
                    (dataframe['closedelta'].gt(dataframe['ha_close'] * self.closedelta_close.value)) &
                    (dataframe['tail'].lt(dataframe['bbdelta'] * self.bbdelta_tail.value)) &
                    (dataframe['ha_close'].lt(dataframe['lower'].shift())) &
                    (dataframe['ha_close'].le(dataframe['ha_close'].shift()))
                )
                |
                (
                    (dataframe['ha_close'] < dataframe['ema_slow']) &
                    (dataframe['ha_close'] < self.close_bblower.value * dataframe['bb_lowerband'])
                )
            )
            &
            (dataframe['hh_48_diff'] > self.buy_hh_diff_48.value)
            &
            (dataframe['ll_48_diff'] > self.buy_ll_diff_48.value)
        ,'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (   (
                    (dataframe['fisher'] > self.sell_fisher.value) &
                    (dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) &
                    (dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) &
                    (dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) &
                    (dataframe['ema_fast'] > dataframe['ha_close']) &
                    ((dataframe['ha_close'] * self.sell_bbmiddle_close.value) > dataframe['bb_middleband'])
                )
                |
                (
                    (dataframe['close'] > dataframe['sma_9']) &
                    (dataframe['close'] > (dataframe['ema_24'] * self.high_offset_2.value)) &
                    (dataframe['rsi'] > 50) &
                    (dataframe['rsi_fast'] > dataframe['rsi_slow'])
                )
                |
                (
                    (dataframe['sma_9'] > (dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1) * 0.005 )) &
                    (dataframe['close'] < dataframe['hma_50']) &
                    (dataframe['close'] > (dataframe['ema_24'] * self.high_offset.value)) &
                    (dataframe['rsi_fast'] > dataframe['rsi_slow'])
                )
            )
            &
            (dataframe['volume'] > 0)

        ,'sell'] = 1

        return dataframe

# 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(ClucHAnix_hhll):
    # 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 = False
    trailing_expire_seconds_uptrend = 90
    min_uptrend_trailing_profit = 0.02

    debug_mode = True
    trailing_buy_max_stop = 0.02  # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop)
    trailing_buy_max_buy = 0.000  # buy if price between uplimit (=min of serie (current_price * (1 + trailing_buy_offset())) and (start_price * 1+trailing_buy_max_buy))

    init_trailing_dict = {
        'trailing_buy_order_started': False,
        'trailing_buy_order_uplimit': 0,
        'start_trailing_price': 0,
        'buy_tag': None,
        'start_trailing_time': None,
        'offset': 0,
        'allow_trailing': False,
    }

    def trailing_buy(self, pair, reinit=False):
        # 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} : "
                f"start: {trailing_buy['start_trailing_price']:.4f}, "
                f"duration: {duration}, "
                f"current: {current_price:.4f}, "
                f"uplimit: {trailing_buy['trailing_buy_order_uplimit']:.4f}, "
                f"profit: {self.current_trailing_profit_ratio(pair, current_price)*100:.2f}%, "
                f"offset: {trailing_buy['offset']}")

    def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float:
        trailing_buy = self.trailing_buy(pair)
        if trailing_buy['trailing_buy_order_started']:
            return (trailing_buy['start_trailing_price'] - current_price) / trailing_buy['start_trailing_price']
        else:
            return 0

    def trailing_buy_offset(self, dataframe, pair: str, current_price: float):
        # 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['buy'] == 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['buy'] == 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['buy_tag'] = last_candle['buy_tag']
                            trailing_buy['start_trailing_time'] = datetime.now(timezone.utc)
                            trailing_buy['offset'] = 0

                            self.trailing_buy_info(pair, current_price)
                            logger.info(f'start trailing buy for {pair} at {last_candle["close"]}')

                        elif trailing_buy['trailing_buy_order_started']:
                            if trailing_buy_offset == 'forcebuy':
                                # 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_buy_trend(dataframe, metadata)

        if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
            last_candle = dataframe.iloc[-1].squeeze()
            trailing_buy = self.trailing_buy(metadata['pair'])
            if (last_candle['buy'] == 1):
                if not trailing_buy['trailing_buy_order_started']:
                    open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True), ]).all()
                    if not open_trades:
                        logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!")
                        # self.custom_info_trail_buy[metadata['pair']]['trailing_buy']['allow_trailing'] = True
                        trailing_buy['allow_trailing'] = True
                        initial_buy_tag = last_candle['buy_tag'] if 'buy_tag' in last_candle else 'buy signal'
                        dataframe.loc[:, 'buy_tag'] = f"{initial_buy_tag} (start trail price {last_candle['close']})"
            else:
                if (trailing_buy['trailing_buy_order_started'] == True):
                    logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!")
                    dataframe.loc[:,'buy'] = 1
                    dataframe.loc[:, 'buy_tag'] = trailing_buy['buy_tag']
                    # dataframe['buy'] = 1

        return dataframe