AverageStrategy

# --- Do not remove these libs ---
import talib.abstract as ta
from pandas import DataFrame

import coingro.vendor.qtpylib.indicators as qtpylib
from coingro.strategy import IntParameter, IStrategy

# --------------------------------


class AverageStrategy(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 timeframe for the strategy
    timeframe = "4h"

    buy_range_short = IntParameter(5, 20, default=8)
    buy_range_long = IntParameter(20, 120, default=21)

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

        # Combine all ranges ... to avoid duplicate calculation
        for val in list(set(list(self.buy_range_short.range) + list(self.buy_range_long.range))):
            dataframe[f"ema{val}"] = ta.EMA(dataframe, timeperiod=val)

        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[f"ema{self.buy_range_short.value}"],
                    dataframe[f"ema{self.buy_range_long.value}"],
                )
                & (dataframe["volume"] > 0)
            ),
            "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_above(
                    dataframe[f"ema{self.buy_range_long.value}"],
                    dataframe[f"ema{self.buy_range_short.value}"],
                )
                & (dataframe["volume"] > 0)
            ),
            "sell",
        ] = 1
        return dataframe