from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame


import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import informative, stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt

def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['close'] * 100
    return emadif


class SMAOffsetProtectOptV1_3(IStrategy):
    INTERFACE_VERSION = 3

    buy_params = {
        "base_nb_candles_buy": 16,
        "ewo_high": 5.638,
        "ewo_low": -19.993,
        "low_offset": 0.978,
        "rsi_buy": 61,
    }

    sell_params = {
        "base_nb_candles_sell": 49,
        "high_offset": 1.006,
    }

    minimal_roi = {
        "0": 0.013
    }

    stoploss = -0.5

    base_nb_candles_buy = IntParameter(
        5, 80, default=6, space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(
        5, 80, default=6, space='sell', optimize=True)
    low_offset = DecimalParameter(
        0.9, 0.99, default=0.9, space='buy', optimize=True)
    high_offset = DecimalParameter(
        0.99, 1.1, default=1, space='sell', optimize=True)

    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0,
                               default=-12, space='buy', optimize=False)
    ewo_high = DecimalParameter(
        2.0, 12.0, default=4, space='buy', optimize=False)
    rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=False)

    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    timeframe = '5m'

    process_only_new_candles = True
    startup_candle_count = 30

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
        },
    }

    use_custom_stoploss = False

    @informative('1h')
    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe

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

        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

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

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        dataframe['ma_buy'] = ta.EMA(dataframe, timeperiod=int(self.base_nb_candles_buy.value))

        buy_ewo_high = (
            (dataframe['close'] < (dataframe['ma_buy'] * self.low_offset.value)) &
            (dataframe['EWO'] > self.ewo_high.value) &
            (dataframe['rsi'] < self.rsi_buy.value) &
            (dataframe['volume'] > 0)
        )
        dataframe.loc[buy_ewo_high, 'enter_tag'] += 'ewo_high '
        conditions.append(buy_ewo_high)

        buy_ewo_low = (
            (dataframe['close'] < (dataframe['ma_buy'] * self.low_offset.value)) &
            (dataframe['EWO'] < self.ewo_low.value) &
            (dataframe['volume'] > 0)
        )
        dataframe.loc[buy_ewo_low, 'enter_tag'] += 'ewo_low '
        conditions.append(buy_ewo_low)

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

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'exit_tag'] = ''

        dataframe['ma_sell'] = ta.EMA(dataframe, timeperiod=int(self.base_nb_candles_sell.value))

        sell_cond_1 = (                   
            (dataframe['close'] > (dataframe['ma_sell'] * self.high_offset.value)) &
            (dataframe['volume'] > 0)
        )
        conditions.append(sell_cond_1)
        dataframe.loc[sell_cond_1, 'exit_tag'] += 'ema sell '

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

        return dataframe