EMASkipPump

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 EMASkipPump(IStrategy):

    """
    basic strategy, which trys to avoid pump and dump market conditions.
    Shared from the tradingview
    slack
    """

    EMA_SHORT_TERM = 5
    EMA_MEDIUM_TERM = 12
    EMA_LONG_TERM = 21

    # Minimal ROI designed for the strategy.
    # we only sell after 100%, unless our sell points are found before
    minimal_roi = {"0": 0.1}

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    # should be converted to a trailing stop loss
    stoploss = -0.05

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Adds several different TA indicators to the given DataFrame"""

        dataframe["ema_{}".format(self.EMA_SHORT_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_SHORT_TERM
        )
        dataframe["ema_{}".format(self.EMA_MEDIUM_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_MEDIUM_TERM
        )
        dataframe["ema_{}".format(self.EMA_LONG_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_LONG_TERM
        )

        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["min"] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe["max"] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)

        return dataframe

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

        dataframe.loc[
            (dataframe["volume"] < (dataframe["volume"].rolling(window=30).mean().shift(1) * 20))
            & (dataframe["close"] < dataframe["ema_{}".format(self.EMA_SHORT_TERM)])
            & (dataframe["close"] < dataframe["ema_{}".format(self.EMA_MEDIUM_TERM)])
            & (dataframe["close"] == dataframe["min"])
            & (dataframe["close"] <= dataframe["bb_lowerband"]),
            "buy",
        ] = 1

        return dataframe

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

        dataframe.loc[
            (dataframe["close"] > dataframe["ema_{}".format(self.EMA_SHORT_TERM)])
            & (dataframe["close"] > dataframe["ema_{}".format(self.EMA_MEDIUM_TERM)])
            & (dataframe["close"] >= dataframe["max"])
            & (dataframe["close"] >= dataframe["bb_upperband"]),
            "sell",
        ] = 1

        return dataframe