EL

# --- 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

# @Rallipanos # changes by IcHiAT

# Buy hyperspace params:
buy_params = {
        "base_nb_candles_buy": 12,
        "ewo_high": 3.147,
        "ewo_low": -17.145,
        "low_offset": 0.987,
        "rsi_buy": 57,
    }

# Sell hyperspace params:
sell_params = {
        "base_nb_candles_sell": 22,
        "high_offset": 1.008,
        "high_offset_2": 1.016,
    }

def EWO(dataframe, ema_length=5, ema2_length=3):
    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 EL(IStrategy):
    INTERFACE_VERSION = 2
    """
    # ROI table:
    minimal_roi = {
        "0": 0.08,
        "20": 0.04,
        "40": 0.032,
        "87": 0.016,
        "201": 0,
        "202": -1
    }
    """
    @property
    def protections(self):
        return [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 5
            },
            {
                "method": "MaxDrawdown",
                "lookback_period_candles": 48,
                "trade_limit": 20,
                "stop_duration_candles": 4,
                "max_allowed_drawdown": 0.2
            },
            {
                "method": "StoplossGuard",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "only_per_pair": False
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 6,
                "trade_limit": 2,
                "stop_duration_candles": 60,
                "required_profit": 0.02
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "required_profit": 0.01
            }
        ]

    # ROI table:
    minimal_roi = {
        "0": 0.99,
        "200": -1
    }

    # Stoploss:
    stoploss = -0.20

    # 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(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], 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 = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

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

    ## Optional order time in force.
    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc'
    }

    # Optimal timeframe for the strategy
    timeframe = '5m'
    inf_1h = '1h'

    process_only_new_candles = True
    startup_candle_count = 400

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
            'hma_50': {'color': 'cyan'},
            'sma_9': {'color': 'yellow'},
        },
        "subplots": {
            "EWO": {
                "EWO": {"color": "#ba762b"},
            },
            "RSI": {
                "rsi": {'color': 'orange'},
                "rsi_fast": {'color': 'blue'},
                "rsi_slow": {'color': 'cyan'}
            },
            "cond": {
                "change": {}
            }
        }
    }



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

        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe['ma_buy'] = ta.EMA(dataframe, timeperiod=val)

        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe['ma_sell'] = ta.EMA(dataframe, timeperiod=val)

        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)


        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        # Elliot
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        dataframe['change'] = 100 / dataframe['open'] * dataframe['close'] - 100

        return dataframe

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

        buy_0 = (
            (dataframe['rsi_fast'] < 35) &
            (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['close'] < (dataframe['ma_sell'] * self.high_offset.value))
        )
        dataframe.loc[buy_0, 'enter_tag'] += 'e0'
        conditions.append(buy_0)

        buy_1 = (
            (dataframe['rsi_fast'] < 35) &
            (dataframe['close'] < (dataframe['ma_buy'] * self.low_offset.value)) &
            (dataframe['EWO'] < self.ewo_low.value) &
            (dataframe['volume'] > 0) &
            (dataframe['close'] < (dataframe['ma_sell'] * self.high_offset.value))
        )
        dataframe.loc[buy_1, 'enter_tag'] += 'e1'
        conditions.append(buy_1)

        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 = []

        conditions.append(
            (   (dataframe['close']>dataframe['hma_50'])&
                (dataframe['close'] > (dataframe['ma_sell'] * self.high_offset_2.value)) &
                (dataframe['rsi']>50)&
                (dataframe['volume'] > 0)&
                (dataframe['rsi_fast']>dataframe['rsi_slow'])

            )
            |
            (
                (dataframe['close']<dataframe['hma_50'])&
                (dataframe['close'] > (dataframe['ma_sell'] * self.high_offset.value)) &
                (dataframe['volume'] > 0)&
                (dataframe['rsi_fast']>dataframe['rsi_slow'])
            )

        )

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

        return dataframe