EMASkipPump

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa


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