import logging
from numpy.lib import math
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):
    INTERFACE_VERSION = 3
    # 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
    '\n        Supertrend Indicator; adapted for freqtrade\n        from: https://github.com/freqtrade/freqtrade-strategies/issues/30\n    '

    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']})