# source: https://raw.githubusercontent.com/abozaralizadeh/FreqTrade_Warmup/4b079639b5172449c4f8dd7598088b34f0c3dde9/freqtrade_backtrace/user_data/strategies/MACDCrossoverWithTrend.py
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa


class github_abozaralizadeh_FreqTrade_Warmup__MACDCrossoverWithTrend__20220602_133955(IStrategy):

    """
    github_abozaralizadeh_FreqTrade_Warmup__MACDCrossoverWithTrend__20220602_133955
    author@: Paul Csapak
    github@: https://github.com/paulcpk/freqtrade-strategies-that-work

    How to use it?

    > freqtrade download-data --timeframes 1h --timerange=20180301-20200301
    > freqtrade backtesting --export trades -s github_abozaralizadeh_FreqTrade_Warmup__MACDCrossoverWithTrend__20220602_133955 --timeframe 1h --timerange=20180301-20200301
    > freqtrade plot-dataframe -s github_abozaralizadeh_FreqTrade_Warmup__MACDCrossoverWithTrend__20220602_133955 --indicators1 ema100 --timeframe 1h --timerange=20180301-20200301

    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "40": 0.0,
        "30": 0.01,
        "20": 0.02,
        "0": 0.04
    }

    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.2

    # Optimal timeframe for the strategy
    timeframe = '1h'

    # trailing stoploss
    trailing_stop = False
    trailing_stop_positive = 0.03
    trailing_stop_positive_offset = 0.04

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

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        return dataframe

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

        dataframe.loc[
            (
                (dataframe['macd'] < 0) &  # MACD is below zero
                # Signal crosses above MACD
                (qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) &
                (dataframe['low'] > dataframe['ema100']) &  # Candle low is above EMA
                # Ensure this candle had volume (important for backtesting)
                (dataframe['volume'] > 0)
            ),
            'buy'] = 1
        return dataframe

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

        dataframe.loc[
            (
                # MACD crosses above Signal
                (qtpylib.crossed_below(dataframe['macd'], 0)) | 
                (dataframe['low'] < dataframe['ema100'])  # OR price is below trend ema
            ),
            'sell'] = 1
        return dataframe