FOttStrategy

from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib


class FOttStrategy(IStrategy):
    # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all'
    # It's encourage you find the values that better suites your needs and risk management strategies

    INTERFACE_VERSION: int = 3
    # ROI table:
    minimal_roi = {'0': 0.1, '30': 0.75, '60': 0.05, '120': 0.025}
    # minimal_roi = {"0": 1}

    # Stoploss:
    stoploss = -0.265

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.05
    trailing_stop_positive_offset = 0.1
    trailing_only_offset_is_reached = False

    timeframe = '1h'

    startup_candle_count = 18

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

        dataframe['ott'] = self.ott(dataframe)['OTT']
        dataframe['var'] = self.ott(dataframe)['VAR']
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)

        return dataframe

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

        dataframe.loc[
            (qtpylib.crossed_above(dataframe['var'], dataframe['ott'])),
            'enter_long',
        ] = 1

        dataframe.loc[
            (qtpylib.crossed_below(dataframe['var'], dataframe['ott'])),
            'enter_short',
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['adx'] > 60),
            'exit_long',
        ] = 1

        dataframe.loc[
            (dataframe['adx'] > 60),
            'exit_short',
        ] = 1

        return dataframe

    """
        Supertrend Indicator; adapted for freqtrade
        from: https://github.com/freqtrade/freqtrade-strategies/issues/30
    """

    def ott(self, dataframe: DataFrame):
        df = dataframe.copy()

        pds = 2
        percent = 1.4
        alpha = 2 / (pds + 1)

        df['ud1'] = np.where(
            df['close'] > df['close'].shift(1), (df['close'] - df['close'].shift()), 0
        )
        df['dd1'] = np.where(
            df['close'] < df['close'].shift(1), (df['close'].shift() - df['close']), 0
        )
        df['UD'] = df['ud1'].rolling(9).sum()
        df['DD'] = df['dd1'].rolling(9).sum()
        df['CMO'] = ((df['UD'] - df['DD']) / (df['UD'] + df['DD'])).fillna(0).abs()

        # df['Var'] = talib.EMA(df['close'], timeperiod=5)
        df['Var'] = 0.0
        for i in range(pds, len(df)):
            df['Var'].iat[i] = (alpha * df['CMO'].iat[i] * df['close'].iat[i]) + (
                1 - alpha * df['CMO'].iat[i]
            ) * df['Var'].iat[i - 1]

        df['fark'] = df['Var'] * percent * 0.01
        df['newlongstop'] = df['Var'] - df['fark']
        df['newshortstop'] = df['Var'] + df['fark']
        df['longstop'] = 0.0
        df['shortstop'] = 999999999999999999
        # df['dir'] = 1
        for i in df['UD']:

            def maxlongstop():
                df.loc[(df['newlongstop'] > df['longstop'].shift(1)), 'longstop'] = df[
                    'newlongstop'
                ]
                df.loc[(df['longstop'].shift(1) > df['newlongstop']), 'longstop'] = df[
                    'longstop'
                ].shift(1)

                return df['longstop']

            def minshortstop():
                df.loc[(df['newshortstop'] < df['shortstop'].shift(1)), 'shortstop'] = df[
                    'newshortstop'
                ]
                df.loc[(df['shortstop'].shift(1) < df['newshortstop']), 'shortstop'] = df[
                    'shortstop'
                ].shift(1)

                return df['shortstop']

            df['longstop'] = np.where(
                ((df['Var'] > df['longstop'].shift(1))),
                maxlongstop(),
                df['newlongstop'],
            )

            df['shortstop'] = np.where(
                ((df['Var'] < df['shortstop'].shift(1))),
                minshortstop(),
                df['newshortstop'],
            )

        # get xover

        df['xlongstop'] = np.where(
            (
                (df['Var'].shift(1) > df['longstop'].shift(1))
                & (df['Var'] < df['longstop'].shift(1))
            ),
            1,
            0,
        )

        df['xshortstop'] = np.where(
            (
                (df['Var'].shift(1) < df['shortstop'].shift(1))
                & (df['Var'] > df['shortstop'].shift(1))
            ),
            1,
            0,
        )

        df['trend'] = 0
        df['dir'] = 0
        for i in df['UD']:
            df['trend'] = np.where(
                ((df['xshortstop'] == 1)),
                1,
                (np.where((df['xlongstop'] == 1), -1, df['trend'].shift(1))),
            )

            df['dir'] = np.where(
                ((df['xshortstop'] == 1)),
                1,
                (np.where((df['xlongstop'] == 1), -1, df['dir'].shift(1).fillna(1))),
            )

        # get OTT

        df['MT'] = np.where(df['dir'] == 1, df['longstop'], df['shortstop'])
        df['OTT'] = np.where(
            df['Var'] > df['MT'],
            (df['MT'] * (200 + percent) / 200),
            (df['MT'] * (200 - percent) / 200),
        )
        df['OTT'] = df['OTT'].shift(2)

        return DataFrame(index=df.index, data={'OTT': df['OTT'], 'VAR': df['Var']})