Quickie

# --- 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 Quickie(IStrategy):
    """

    author@: Gert Wohlgemuth

    idea:
        momentum based strategie. The main idea is that it closes trades very quickly,
        while avoiding excessive losses. Hence a rather moderate stop loss in this case
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "100": 0.01,
        "30": 0.03,
        "15": 0.06,
        "10": 0.15,
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.25

    # Optimal timeframe for the strategy
    timeframe = "5m"

    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["tema"] = ta.TEMA(dataframe, timeperiod=9)
        dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200)
        dataframe["sma_50"] = ta.SMA(dataframe, timeperiod=200)

        dataframe["adx"] = ta.ADX(dataframe)

        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe["close"], window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] > 30)
                & (dataframe["tema"] < dataframe["bb_middleband"])
                & (dataframe["tema"] > dataframe["tema"].shift(1))
                & (dataframe["sma_200"] > dataframe["close"])
            ),
            "buy",
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] > 70)
                & (dataframe["tema"] > dataframe["bb_middleband"])
                & (dataframe["tema"] < dataframe["tema"].shift(1))
            ),
            "sell",
        ] = 1
        return dataframe