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):
    INTERFACE_VERSION = 3
    '\n        basic strategy, which trys to avoid pump and dump market conditions. Shared from the tradingview\n        slack\n    '
    EMA_SHORT_TERM = 5
    EMA_MEDIUM_TERM = 12
    EMA_LONG_TERM = 21
    # Minimal ROI designed for the strategy.
    # we only exit after 100%, unless our exit 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_entry_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']), 'enter_long'] = 1
        return dataframe

    def populate_exit_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']), 'exit_long'] = 1
        return dataframe