# source: https://raw.githubusercontent.com/mrgolfprat/ft_sample/d24c79b0d852e13757c6d523524b1660b19ec6e6/user_data/strategies/SMAOffsetProtectOptV1.py
# --- Do not remove these libs ---
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 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 github_mrgolfprat_ft_sample__SMAOffsetProtectOptV1__20211117_150019(IStrategy):
    INTERFACE_VERSION = 2

    # Buy hyperspace params:
    buy_params = {
        "base_nb_candles_buy": 13,
        "ewo_high": 5.835,
        "ewo_low": -19.909,
        "low_offset": 0.978,
        "rsi_buy": 55,
    }
    # Sell hyperspace params:
    sell_params = {
        "base_nb_candles_sell": 18,
        "high_offset": 1.012,
    }

    # and disable roi:
    # ROI table:
    minimal_roi = {
        "0": 100.0
    }

    # Stoploss:
    stoploss = -0.15

    protections = [
        # 	{
        # 		"method": "StoplossGuard",
        # 		"lookback_period_candles": 12,
        # 		"trade_limit": 1,
        # 		"stop_duration_candles": 6,
        # 		"only_per_pair": True
        # 	},
        # 	{
        # 		"method": "StoplossGuard",
        # 		"lookback_period_candles": 12,
        # 		"trade_limit": 2,
        # 		"stop_duration_candles": 6,
        # 		"only_per_pair": False
        # 	},
        {
            "method": "LowProfitPairs",
            "lookback_period_candles": 60,
            "trade_limit": 1,
            "stop_duration": 60,
            "required_profit": -0.05
        },
        {
            "method": "CooldownPeriod",
            "stop_duration_candles": 2
        }
    ]

    # SMAOffset
    base_nb_candles_buy = IntParameter(
        5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(
        5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
    low_offset = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
    high_offset = DecimalParameter(
        0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)

    # Protection
    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0,
                               default=buy_params['ewo_low'], space='buy', optimize=True)
    ewo_high = DecimalParameter(
        2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
    rsi_buy = IntParameter(
        30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True

    # Sell signal
    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.01
    ignore_roi_if_buy_signal = True

    # Optimal timeframe for the strategy
    timeframe = '5m'
    informative_timeframe = '1h'

    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

    def informative_pairs(self):

        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe)
                             for pair in pairs]

        return informative_pairs

    def get_informative_indicators(self, metadata: dict):

        dataframe = self.dp.get_pair_dataframe(
            pair=metadata['pair'], timeframe=self.informative_timeframe)

        return dataframe

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

        if self.config['runmode'].value == 'hyperopt':
            # Calculate all ma_buy values
            for val in self.base_nb_candles_buy.range:
                dataframe[f'ma_{val}'] = ta.EMA(dataframe, timeperiod=val)
        else:
            dataframe[f'ma_{self.base_nb_candles_buy.value}'] = ta.EMA(
                dataframe, timeperiod=self.base_nb_candles_buy.value)
            dataframe[f'ma_{self.base_nb_candles_sell.value}'] = ta.EMA(
                dataframe, timeperiod=self.base_nb_candles_sell.value)

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

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

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            (
                (dataframe['close'] < (dataframe[f'ma_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] > self.ewo_high.value) &
                (dataframe['rsi'] < self.rsi_buy.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                (dataframe['close'] < (dataframe[f'ma_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] < self.ewo_low.value) &
                (dataframe['volume'] > 0)
            )
        )

        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 = []

        conditions.append(
            (
                (dataframe['close'] > (dataframe[f'ma_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                (dataframe['volume'] > 0)
            )
        )

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

        return dataframe