BinHV27_short

from typing import Dict, List
from datetime import datetime
from functools import reduce

from skopt.space import Dimension, Integer
import numpy as np

from freqtrade.persistence import Trade
from freqtrade.strategy import IntParameter, DecimalParameter, CategoricalParameter, informative
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series

import talib.abstract as ta
import numpy  # noqa


# custom indicators
# ##################################################################################################
def RMI(dataframe, *, length=20, mom=5):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
    """
    df = dataframe.copy()

    df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
    df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)

    df.fillna(0, inplace=True)

    df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
    df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)

    df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))

    return df["RMI"]


def zema(dataframe, period, field='close'):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/overlap_studies.py#L79
    Modified slightly to use ta.EMA instead of technical ema
    """
    df = dataframe.copy()

    df['ema1'] = ta.EMA(df[field], timeperiod=period)
    df['ema2'] = ta.EMA(df['ema1'], timeperiod=period)
    df['d'] = df['ema1'] - df['ema2']
    df['zema'] = df['ema1'] + df['d']

    return df['zema']


def same_length(bigger, shorter):
    return np.concatenate((np.full((bigger.shape[0] - shorter.shape[0]), np.nan), shorter))


def mastreak(dataframe: DataFrame, period: int = 4, field='close') -> Series:
    """
    MA Streak
    Port of: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/
    """
    df = dataframe.copy()

    avgval = zema(df, period, field)

    arr = np.diff(avgval)
    pos = np.clip(arr, 0, 1).astype(bool).cumsum()
    neg = np.clip(arr, -1, 0).astype(bool).cumsum()
    streak = np.where(arr >= 0, pos - np.maximum.accumulate(np.where(arr <= 0, pos, 0)),
                      -neg + np.maximum.accumulate(np.where(arr >= 0, neg, 0)))

    res = same_length(df['close'], streak)

    return res


def linear_growth(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
    """
    Simple linear growth function. Grows from start to end after end_time minutes (starts after start_time minutes)
    """
    time = max(0, trade_time - start_time)
    rate = (end - start) / (end_time - start_time)

    return min(end, start + (rate * time))


def pcc(dataframe: DataFrame, period: int = 20, mult: int = 2):
    """
    Percent Change Channel
    PCC is like KC unless it uses percentage changes in price to set channel distance.
    https://www.tradingview.com/script/6wwAWXA1-MA-Streak-Change-Channel/
    """
    df = dataframe.copy()

    df['previous_close'] = df['close'].shift()

    df['close_change'] = (df['close'] - df['previous_close']) / df['previous_close'] * 100
    df['high_change'] = (df['high'] - df['close']) / df['close'] * 100
    df['low_change'] = (df['low'] - df['close']) / df['close'] * 100

    df['delta'] = df['high_change'] - df['low_change']

    mid = zema(df, period, 'close_change')
    rangema = zema(df, period, 'delta')

    upper = mid + rangema * mult
    lower = mid - rangema * mult

    return upper, rangema, lower


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']


def linear_decay(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
    """
    Simple linear decay function. Decays from start to end after end_time minutes (starts after start_time minutes)
    """
    time = max(0, trade_time - start_time)
    rate = (start - end) / (end_time - start_time)

    return max(end, start - (rate * time))


# #####################################################################################################

class BinHV27_short(IStrategy):
    """

        strategy sponsored by user BinH from slack

    """

    minimal_roi = {
        "0": 100
    }

    stoploss = -0.1
    timeframe = '5m'

    process_only_new_candles = False
    startup_candle_count = 240

    # default False
    use_custom_stoploss = False

    custom_trade_info = {}

    can_short = True

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,

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

    # buy params
    buy_optimize = False
    buy_adx1 = IntParameter(low=10, high=100, default=25, space='buy', optimize=buy_optimize)
    buy_emarsi1 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx2 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
    buy_emarsi2 = IntParameter(low=20, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx3 = IntParameter(low=10, high=100, default=35, space='buy', optimize=buy_optimize)
    buy_emarsi3 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx4 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
    buy_emarsi4 = IntParameter(low=20, high=100, default=25, space='buy', optimize=buy_optimize)

    leverage_optimize = True
    leverage_num = IntParameter(low=1, high=10, default=1, space='buy', optimize=leverage_optimize)

    # custom exit
    ce_op = True
    csell_pullback_amount = DecimalParameter(0.005, 0.15, default=0.01, space='sell', load=True, optimize=ce_op)
    csell_roi_type = CategoricalParameter(['static', 'decay', 'step'], default='step', space='sell', load=True,
                                          optimize=ce_op)
    csell_roi_start = DecimalParameter(0.01, 0.15, default=0.01, space='sell', load=True, optimize=ce_op)
    csell_roi_end = DecimalParameter(0.0, 0.01, default=0, space='sell', load=True, optimize=ce_op)
    csell_roi_time = IntParameter(720, 1440, default=720, space='sell', load=True, optimize=ce_op)
    csell_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any', 'none'], default='any', space='sell',
                                            load=True, optimize=ce_op)
    csell_pullback = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=ce_op)
    csell_pullback_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=ce_op)
    csell_endtrend_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=ce_op)

    class HyperOpt:
        @staticmethod
        def generate_roi_table(params: Dict) -> Dict[int, float]:
            roi_table = {params['roi_t1']: 0}
            return roi_table

        @staticmethod
        def roi_space() -> List[Dimension]:
            roi_min_time = 10
            roi_max_time = 720

            roi_limits = {
                'roi_t1_min': int(roi_min_time),
                'roi_t1_max': int(roi_max_time)
            }

            return [
                Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1')
            ]

    @informative('1d', 'BTC/USDT')
    def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema90'] = ta.EMA(dataframe, timeperiod=90)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not metadata['pair'] in self.custom_trade_info:
            self.custom_trade_info[metadata['pair']] = {}
            if 'had-trend' not in self.custom_trade_info[metadata["pair"]]:
                self.custom_trade_info[metadata['pair']]['had-trend'] = False

        dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
        rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
        dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
        dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
        dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
        minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'})
        dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
        dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
        plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'})
        dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
        dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
        dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
        dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
        dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
        dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & (
                (dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300)
        dataframe['bigdown'] = ~dataframe['bigup']
        dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
        dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
        dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(
            dataframe['trend'].shift(2))
        dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe[
            'slowsma'].shift().gt(dataframe['slowsma'].shift(2))
        dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
        dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())

        dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
        dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
        dataframe['rmi-down-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() <= 2, 1, 0)

        # Indicators used only for ROI and Custom Stoploss
        ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
        dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down')

        # Trends, Peaks and Crosses
        dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['open'], 1, 0)
        dataframe['candle-down-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() <= 2, 1, 0)

        return dataframe

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

        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        buy_1 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                ~dataframe['preparechangetrend'] &
                ~dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx1.value) &
                dataframe['bigdown'] &
                dataframe['emarsi'].le(self.buy_emarsi1.value)
        )

        buy_2 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                ~dataframe['preparechangetrend'] &
                dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx2.value) &
                dataframe['bigdown'] &
                dataframe['emarsi'].le(self.buy_emarsi2.value)
        )

        buy_3 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                ~dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx3.value) &
                dataframe['bigup'] &
                dataframe['emarsi'].le(self.buy_emarsi3.value)
        )

        buy_4 = (
                dataframe['btc_usdt_close_1d'].lt(dataframe['btc_usdt_ema90_1d']) &
                dataframe['slowsma'].gt(0) &
                dataframe['close'].lt(dataframe['highsma']) &
                dataframe['close'].lt(dataframe['lowsma']) &
                dataframe['minusdi'].gt(dataframe['minusdiema']) &
                dataframe['rsi'].ge(dataframe['rsi'].shift()) &
                dataframe['continueup'] &
                dataframe['adx'].gt(self.buy_adx4.value) &
                dataframe['bigup'] &
                dataframe['emarsi'].le(self.buy_emarsi4.value)
        )

        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'

        conditions.append(buy_2)
        dataframe.loc[buy_2, 'enter_tag'] += 'buy_2'

        conditions.append(buy_3)
        dataframe.loc[buy_3, 'enter_tag'] += 'buy_3'

        conditions.append(buy_4)
        dataframe.loc[buy_4, 'enter_tag'] += 'buy_4'

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

        dataframe.loc[(), ['enter_long', 'enter_tag']] = (0, 'long_in')

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(), ['exit_short', 'exit_tag']] = (0, 'short_out')
        dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')
        return dataframe

    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 custom_exit(self, pair: str, trade: Trade, current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        max_profit = max(0, trade.calc_profit_ratio(trade.max_rate))
        pullback_value = max(0, (max_profit - self.csell_pullback_amount.value))
        in_trend = False

        # Determine our current ROI point based on the defined type
        if self.csell_roi_type.value == 'static':
            min_roi = self.csell_roi_start.value
        elif self.csell_roi_type.value == 'decay':
            min_roi = linear_decay(self.csell_roi_start.value, self.csell_roi_end.value, 0, self.csell_roi_time.value,
                                   trade_dur)
        elif self.csell_roi_type.value == 'step':
            if trade_dur < self.csell_roi_time.value:
                min_roi = self.csell_roi_start.value
            else:
                min_roi = self.csell_roi_end.value

        # Determine if there is a trend
        if self.csell_trend_type.value == 'rmi' or self.csell_trend_type.value == 'any':
            if last_candle['rmi-down-trend'] == 1:
                in_trend = True
        if self.csell_trend_type.value == 'ssl' or self.csell_trend_type.value == 'any':
            if last_candle['ssl-dir'] == 'down':
                in_trend = True
        if self.csell_trend_type.value == 'candle' or self.csell_trend_type.value == 'any':
            if last_candle['candle-down-trend'] == 1:
                in_trend = True

        # Don't sell if we are in a trend unless the pullback threshold is met
        if in_trend and current_profit > 0:
            # Record that we were in a trend for this trade/pair for a more useful sell message later
            self.custom_trade_info[trade.pair]['had-trend'] = True
            # If pullback is enabled and profit has pulled back allow a sell, maybe
            if self.csell_pullback.value and (current_profit <= pullback_value):
                if self.csell_pullback_respect_roi.value and current_profit > min_roi:
                    return 'intrend_pullback_roi'
                elif not self.csell_pullback_respect_roi.value:
                    if current_profit > min_roi:
                        return 'intrend_pullback_roi'
                    else:
                        return 'intrend_pullback_noroi'
            # We are in a trend and pullback is disabled or has not happened or various criteria were not met, hold
            return None
        # If we are not in a trend, just use the roi value
        elif not in_trend:
            if self.custom_trade_info[trade.pair]['had-trend']:
                if current_profit > min_roi:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_roi'
                elif not self.csell_endtrend_respect_roi.value:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_noroi'
            elif current_profit > min_roi:
                return 'notrend_roi'
        else:
            return None