import logging
from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter

from numpy.lib import math
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame



import pandas as pd
import ta
from ta.utils import dropna
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce
import numpy as np


class Zeus_862(IStrategy):








    buy_params = {
        "buy_cat": "<R",
        "buy_real": 0.4967,
    }

    sell_params = {
        "sell_cat": "=R",
        "sell_real": 0.9366,
    }







    minimal_roi = {
        "0": 0.13,
        "33": 0.075,
        "50": 0.034,
        "1440": 0.013
    }

    stoploss = -0.99

    buy_real = DecimalParameter(
        0.001, 0.999, decimals=4, default=0.11908, space='buy')
    buy_cat = CategoricalParameter(
        [">R", "=R", "<R"], default='<R', space='buy')
    sell_real = DecimalParameter(
        0.001, 0.999, decimals=4, default=0.59608, space='sell')
    sell_cat = CategoricalParameter(
        [">R", "=R", "<R"], default='>R', space='sell')

    timeframe = '4h'

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


        dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(
            dataframe['high'],
            dataframe['low'],
            window1=9,
            window2=26,
            visual=False,
            fillna=False
        )
        KST = ta.trend.KSTIndicator(
            close=dataframe['close'],
            roc1=10,
            roc2=15,
            roc3=20,
            roc4=30,
            window1=10,
            window2=10,
            window3=10,
            window4=15,
            nsig=9,
            fillna=False
        )

        dataframe['trend_kst_diff'] = KST.kst_diff()

        tib = dataframe['trend_ichimoku_base']
        dataframe['trend_ichimoku_base'] = (
            tib-tib.min())/(tib.max()-tib.min())
        tkd = dataframe['trend_kst_diff']
        dataframe['trend_kst_diff'] = (tkd-tkd.min())/(tkd.max()-tkd.min())
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        IND = 'trend_ichimoku_base'
        REAL = self.buy_real.value
        OPR = self.buy_cat.value
        DFIND = dataframe[IND]

        if OPR == ">R":
            conditions.append(DFIND > REAL)
        elif OPR == "=R":
            conditions.append(np.isclose(DFIND, REAL))
        elif OPR == "<R":
            conditions.append(DFIND < REAL)

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

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        IND = 'trend_kst_diff'
        REAL = self.sell_real.value
        OPR = self.sell_cat.value
        DFIND = dataframe[IND]


        if OPR == ">R":
            conditions.append(DFIND > REAL)
        elif OPR == "=R":
            conditions.append(np.isclose(DFIND, REAL))
        elif OPR == "<R":
            conditions.append(DFIND < REAL)

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

        return dataframe