EMA and Bollinger Band Pump Filter

Article Strategy library · Author: berlinguyinca

Summary

This short-term long-only strategy seeks entries after a pronounced decline, using several conditions together. On a five-minute timeframe, it requires closing price to be below the five- and twelve-period EMAs, at the twelve-period rolling low, and at or below the lower Bollinger Band. It also checks that current volume is below a threshold based on the prior 30 bars’ average volume. The stated intent is to avoid pump-and-dump conditions, although the entry rules themselves identify low-price, low-relative-volume conditions rather than defining a general pump detector.

An exit requires price to rise above both EMAs, reach the twelve-period rolling high, and meet or exceed the upper Bollinger Band. The code specifies a 5% stop loss and a 10% minimum ROI setting. No backtest window, market, performance results, or evidence that the filters avoid pump losses is provided. The source therefore communicates a specific indicator-based entry and exit recipe, but its effectiveness and applicability across assets remain unestablished.

Key ideas

The strategy looks for long entries when price is below short and medium EMAs, at a rolling low, and near or below the lower Bollinger Band.
The entry also requires volume below a threshold tied to the prior 30 bars’ average.
An exit combines price above both EMAs, a rolling high, and the upper Bollinger Band.
The code specifies a five-minute timeframe, a 5% stop loss, and a 10% minimum ROI setting.
No backtest results are supplied to establish the strategy’s performance or pump avoidance.

Tags

Crypto Technical indicators Mean reversion Risk management

Full text

# EMASkipPump


# EMASkipPump









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

## Source (GPL-3.0)

```python
from freqtrade.strategy 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
    """
    INTERFACE_VERSION: int = 3
    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_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

```

Shown in full with attribution under the source's licence. Licence: GPL-3.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.

EMA and Bollinger Band Filters for Avoiding Pump Conditions

Summary

Key ideas

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