# source: https://raw.githubusercontent.com/guibvieira/freqtrade-crypto/6a35b174445193ad207ef8a562c10261315994cc/user_data/strategies/EMAVolume.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_guibvieira_freqtrade_crypto__EMAVolume__20220930_185924(IStrategy):
    """

    author@: Gert Wohlgemuth

    idea:
        buys and sells on crossovers - doesn't really perfom that well and its just a proof of concept
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "0": 0.5
    }

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

    # Optimal ticker interval for the strategy
    ticker_interval = '15m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema13']=ta.EMA(dataframe, timeperiod=13)
        dataframe['ema34']=ta.EMA(dataframe, timeperiod=34)
        dataframe['ema7']=ta.EMA(dataframe, timeperiod=7)
        dataframe['ema21']=ta.EMA(dataframe, timeperiod=21)
        dataframe['volume_mean'] = dataframe['volume'].rolling(window=10).mean()
        dataframe['ema50']=ta.EMA(dataframe, timeperiod=50)
        dataframe['ema200']=ta.EMA(dataframe, timeperiod=200)
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['ema13'], dataframe['ema34'])) &
                (dataframe['volume'] > dataframe['volume'].rolling(window=10).mean())
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (qtpylib.crossed_below(dataframe['ema13'], dataframe['ema34']))
            ),
            'sell'] = 1
        return dataframe