ClucMay72018

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

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

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

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


class ClucMay72018(IStrategy):
    """

    author@: Gert Wohlgemuth

    works on new objectify branch!

    """

    # Minimal ROI designed for the strategy.
    # 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 = -0.05

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=5)
        rsiframe = DataFrame(dataframe["rsi"]).rename(columns={"rsi": "close"})
        dataframe["emarsi"] = ta.EMA(rsiframe, timeperiod=5)
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["adx"] = ta.ADX(dataframe)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["ema100"] = ta.EMA(dataframe, timeperiod=50)
        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[
            (
                (dataframe["close"] < dataframe["ema100"])
                & (dataframe["close"] < 0.985 * dataframe["bb_lowerband"])
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].rolling(window=30).mean().shift(1) * 20)
                )
            ),
            "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[((dataframe["close"] > dataframe["bb_middleband"])), "sell"] = 1
        return dataframe