Simple

# --- 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 Simple(IStrategy):
    """

    author@: Gert Wohlgemuth

    idea:
        this strategy is based on the book, 'The Simple Strategy' and can be found in detail here:

        https://www.amazon.com/Simple-Strategy-Powerful-Trading-Futures-ebook/dp/B00E66QPCG/ref=sr_1_1?ie=UTF8&qid=1525202675&sr=8-1&keywords=the+simple+strategy
    """

    # 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": 0.01}

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

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=7)

        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe["close"], window=12, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_middleband"] = bollinger["mid"]

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    (dataframe["macd"] > 0)  # over 0
                    & (dataframe["macd"] > dataframe["macdsignal"])  # over signal
                    & (dataframe["bb_upperband"] > dataframe["bb_upperband"].shift(1))
                    # pointed up
                    & (dataframe["rsi"] > 70)  # optional filter, need to investigate
                )
            ),
            "buy",
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # different strategy used for sell points, due to be able to duplicate it to 100%
        dataframe.loc[((dataframe["rsi"] > 80)), "sell"] = 1
        return dataframe