05_CombinedBinHAndClucV6H_OPT_02

import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
import logging
import pandas as pd
import numpy as np
from pandas import DataFrame, Series, DatetimeIndex, merge
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.strategy import IntParameter, DecimalParameter, CategoricalParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from functools import reduce
































logger = logging.getLogger(__name__)


class 05_CombinedBinHAndClucV6H_OPT_02(IStrategy):

    minimal_roi = {
        "0": 0.0181
    }

    max_open_trades = 5

    stoploss = -0.99
    use_custom_stoploss = True

    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025

    timeframe = '5m'
    informative_timeframe = '1h'

    startup_candle_count: int = 200

    process_only_new_candles = False


    use_sell_signal = True
    sell_profit_only = True
    sell_profit_offset = 0.001
    ignore_roi_if_buy_signal = True

    buy_params = {
        "buy_bin_bbdelta_close": 0.01,
        "buy_bin_closedelta_close": 0.007,
        "buy_bin_tail_bbdelta": 0.242,
        "buy_cluc_close_bblowerband": 1.0,
        "buy_cluc_volume": 28,
        "buy_long_rsi_diff": 44.444,
        "buy_minimum_conditions": 2,
        "buy_bin_enable": True,
        "buy_bin_guard": True,
        "buy_cluc_enable": True,
        "buy_cluc_guard": True,
        "buy_long_enable": True,
    }

    sell_params = {
        "cstp_bail_time": 1,
        "cstp_loss_threshold": 0.0,
        "sell_roi_override_rsi_threshold": 64
    }



    cust_log_verbose = False











    cust_optimize = {
        'buy_bin_bbdelta_close': True,
        'buy_bin_closedelta_close': True,
        'buy_bin_tail_bbdelta': True,
        'buy_bin_guard': False,
        'buy_cluc_close_bblowerband': True,
        'buy_cluc_volume': True,
        'buy_cluc_guard': False,
        'buy_long_rsi_diff': True,
        'buy_bin_enable': False,
        'buy_cluc_enable': False,
        'buy_long_enable': False,
        'buy_minimum_conditions': True,
        'sell_roi_override_rsi_threshold': True,
        'cstp_bail_time': False,
        'cstp_loss_threshold': False
    }

    buy_bin_bbdelta_close =  DecimalParameter(0.0, 0.05, default=0.001, space='buy', optimize=cust_optimize['buy_bin_bbdelta_close'], load=True)
    buy_bin_closedelta_close = DecimalParameter(0.0, 0.03, default=0.007, decimals=4, space='buy', optimize=cust_optimize['buy_bin_closedelta_close'], load=True)
    buy_bin_tail_bbdelta = DecimalParameter(0.0, 1.0, default=0.242, decimals=3, space='buy', optimize=cust_optimize['buy_bin_tail_bbdelta'], load=True)
    buy_bin_guard = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_bin_guard'], load=True)

    buy_cluc_close_bblowerband = DecimalParameter(0.0, 1.5, default=1, decimals=3, space='buy', optimize=cust_optimize['buy_cluc_close_bblowerband'], load=True)
    buy_cluc_volume = IntParameter(10, 40, default=28, space='buy', optimize=cust_optimize['buy_cluc_volume'], load=True)
    buy_cluc_guard = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_cluc_guard'], load=True)

    buy_long_rsi_diff = DecimalParameter(40, 45, default=44.444, decimals=3, space='buy', optimize=cust_optimize['buy_long_rsi_diff'], load=True)

    buy_bin_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_bin_enable'], load=True)
    buy_cluc_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_cluc_enable'], load=True)
    buy_long_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_long_enable'], load=True)

    buy_minimum_conditions = IntParameter(1, 2, default=2, space='buy', optimize=cust_optimize['buy_minimum_conditions'], load=True)

    sell_roi_override_rsi_threshold = IntParameter(40, 70, default=64, space='sell', optimize=cust_optimize['sell_roi_override_rsi_threshold'], load=True)

    cstp_bail_time = IntParameter(1, 36, default=5, space='sell', optimize=cust_optimize['cstp_bail_time'])
    cstp_loss_threshold = DecimalParameter(-0.25, 0, default=0, decimals=2, space='sell', optimize=cust_optimize['cstp_loss_threshold'])


    """
    Informative Pairs
    """
    def informative_pairs(self):

        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]

        return informative_pairs


    """
    Informative Timeframe Indicators
    """
    def get_informative_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)

        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        ssldown, sslup = SSLChannels_ATR(dataframe, 20)
        dataframe['ssl-up'] = sslup
        dataframe['ssl-down'] = ssldown
        dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down')

        return dataframe


    """
    Main Timeframe Indicators
    """
    def get_main_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['lower'] = bb_40['lower']
        dataframe['mid'] = bb_40['mid']
        dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()

        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        if self.config['runmode'].value == 'hyperopt':
            dataframe = dataframe.dropna()

        return dataframe


    """
    Populate Informative and Main Timeframe Indicators
    """
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
   
        if self.config['runmode'].value in ('backtest', 'hyperopt'):
            assert (timeframe_to_minutes(self.timeframe) <= 5), "Backtest this strategy in a timeframe of 5m or less."

        assert self.dp, "DataProvider is required for multiple timeframes."

        informative = self.get_informative_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
        dataframe = self.get_main_indicators(dataframe, metadata)

        return dataframe


    """
    Buy Signal
    """
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []

        dataframe.loc[:, 'buy_cond_bin'] = False
        dataframe.loc[:, 'buy_cond_cluc'] = False
        dataframe.loc[:, 'buy_cond_long'] = False
        dataframe.loc[:, 'conditions_count'] = 0

        dataframe.loc[
            (

                (
                    (
                        (dataframe['close'] > dataframe['ema_200_1h']) &
                        (dataframe['ema_50'] > dataframe['ema_200']) &
                        (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                        (self.buy_bin_guard.value == True)
                    ) |
                    (self.buy_bin_guard.value == False)
                ) &

                dataframe['lower'].shift().gt(0) &

                dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bin_bbdelta_close.value) &
                dataframe['closedelta'].gt(dataframe['close'] * self.buy_bin_closedelta_close.value) &
                dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bin_tail_bbdelta.value) &

                dataframe['close'].lt(dataframe['lower'].shift()) &
                dataframe['close'].le(dataframe['close'].shift()) &
                (self.buy_bin_enable.value == True)
            ),
            'buy_cond_bin'
        ] = 1

        dataframe.loc[
            (   

                (
                    (
                        (dataframe['close'] > dataframe['ema_200']) &
                        (dataframe['close'] > dataframe['ema_200_1h']) &
                        (self.buy_cluc_guard.value == True)
                    ) |
                    (self.buy_cluc_guard.value == False)
                ) &

                (dataframe['close'] < dataframe['ema_50']) &
                (dataframe['close'] < self.buy_cluc_close_bblowerband.value * dataframe['bb_lowerband']) &
                (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * self.buy_cluc_volume.value)) &
                (self.buy_cluc_enable.value == True)
            ),
            'buy_cond_cluc'
        ] = 1

        dataframe.loc[
            (
                (dataframe['close'] < dataframe['sma_5']) &
                (dataframe['ssl-dir_1h'] == 'up') &
                (dataframe['ema_50'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_long_rsi_diff.value) &
                (self.buy_long_enable.value == True)
            ),
            'buy_cond_long'
        ] = 1

        dataframe.loc[:, 'conditions_count'] = dataframe['buy_cond_bin'].astype(int) + dataframe['buy_cond_cluc'].astype(int) + dataframe['buy_cond_long'].astype(int)

        conditions.append(dataframe['conditions_count'] >= self.buy_minimum_conditions.value)
        conditions.append(dataframe['volume'].gt(0))

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'buy'
            ] = 1

        if self.cust_log_verbose == True:
            for index, row in dataframe.iterrows():
                if row['buy'] == 1:               
                    buy_cond_details = f"count={int(row['conditions_count'])}/bin={int(row['buy_cond_bin'])}/cluc={int(row['buy_cond_cluc'])}/long={int(row['buy_cond_long'])}"
                    logger.info(f"{metadata['pair']} - candle: {row['date']} - buy condition - details: {buy_cond_details}")

        return dataframe


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

        conditions = []

        conditions.append(
            (dataframe['close'] > dataframe['bb_upperband']) &
            (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
            (dataframe['volume'] > 0) # Make sure Volume is not 0
        )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'sell'
            ] = 1

        return dataframe


    """
    Custom Stop Loss
    """
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        
        if (current_profit < self.cstp_loss_threshold.value) & (current_time - timedelta(hours=int(self.cstp_bail_time.value)) > trade.open_date_utc):
            return 0.01

        return self.stoploss


    """
    Trade Exit Confirmation
    """
    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        if self.cust_log_verbose == True:
            logger.info(f"{pair} - candle: {last_candle['date']} - exit trade {sell_reason} with profit {trade.calc_profit_ratio(rate)}")

        if sell_reason == 'force_sell':
            return True

        if last_candle is not None and ((sell_reason == 'roi') ):
            rsi = 0
            if 'rsi' in last_candle.index:
                rsi = last_candle['rsi']
           
            if ( (rsi >= self.sell_roi_override_rsi_threshold.value) ):
                if self.cust_log_verbose == True:
                    logger.info(f"{pair} - candle: {last_candle['date']} - not exiting trade with current profit {trade.calc_profit_ratio(rate)}, rsi = {rsi} which is > than {self.sell_roi_override_rsi_threshold.value}")
                return False
            else:
                return True

        return True


def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    df = dataframe.copy()

    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])

    return df['sslDown'], df['sslUp']