AdxSmas

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

import coingro.vendor.qtpylib.indicators as qtpylib
from coingro.strategy.interface import IStrategy

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


class AdxSmas(IStrategy):
    """

    author@: Gert Wohlgemuth

    converted from:

    https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AdxSmas.cs

    """

    # Minimal ROI designed for the strategy.
    # adjust based on market conditions. We would recommend to keep it low for quick turn arounds
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {"0": 0.1}

    # Optimal stoploss designed for the strategy
    stoploss = -0.25

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        dataframe["short"] = ta.SMA(dataframe, timeperiod=3)
        dataframe["long"] = ta.SMA(dataframe, timeperiod=6)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] > 25)
                & (qtpylib.crossed_above(dataframe["short"], dataframe["long"]))
            ),
            "buy",
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] < 25)
                & (qtpylib.crossed_above(dataframe["long"], dataframe["short"]))
            ),
            "sell",
        ] = 1
        return dataframe