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BollingerBandDipStrategy

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jimmson/freqtrader/user_data/strategies/BollingerBandDipStrategy.py · ★1 · first seen 2026-07-19 · repo updated 2025-10-13 · ⬇ 1 download

Basics mode: spot timeframe: 3m interface version: 3
Settings stoploss: -0.99 has minimal roi trailing protections hyperopt hyperopt params: 4
Indicators Bollinger_Bands DEMA EMA MACD RSI talib
Concepts risk_management trailing
Methods leverage protections
15 related strategies (⧉ identical code, ≈ similar name)

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from datetime import datetime
from typing import Optional
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import IStrategy, merge_informative_pair, IntParameter
from pandas import DataFrame
import talib.abstract as ta

bb_color = 'rgba(255,76,46,0.2)'


class BollingerBandDipStrategy(IStrategy):
    INTERFACE_VERSION = 3

    # Strategy parameters
    timeframe = '3m'
    informative_timeframe = '4h'

    buy_params = {
        "leverage_amount": 10,
        "percent_below_lowerband": 22,
        "rsi_down": 20,
    }

    sell_params = {
        "rsi_up": 70,
    }

    minimal_roi = {
        "0": 1.5,
    }

    # Stoploss:
    stoploss = -0.99 # value loaded from strategy
    use_custom_stoploss = False

    # Trailing stop:
    trailing_stop = True  # Enable trailing stop
    trailing_stop_positive = 0.99
    trailing_stop_positive_offset = 1.0
    trailing_only_offset_is_reached = True  # Ensure trailing stop only activates after offset is reached

    # Percentage below lowerband
    percent_below_lowerband = IntParameter(10, 20, default=16, space="buy")
    leverage_amount = IntParameter(1, 15, default=5, space="buy")
    rsi_down = IntParameter(15, 45, default=30, space="buy")
    rsi_up = IntParameter(65, 95, default=70, space="sell")

    # Bollinger Bands parameters
    bb_length = 10
    bb_stddev = 1.8

    plot_config = {
        'main_plot': {
            'bb_threshold': {'color': 'orange'},
            'bb_lowerband_4h': {'color': bb_color},
            'bb_upperband_4h': {
                'color': bb_color,
                'fill_to': 'bb_lowerband_4h',
                'fill_label': 'Bollinger Band 4h',
                'fill_color': bb_color,
            },
        },
        'subplots': {
            'RSI': {
                'rsi_mid': {'color': 'purple'},
                'rsi_up': {'color': 'green'},
                'rsi_down': {'color': 'red'},
                'rsi': {'color': 'blue'},
                'rsi_ema': {'color': 'grey'},
            },
        },
    }

    @property
    def protections(self):
        return [
            {
                "method": "StoplossGuard",
                "lookback_period_candles": 500,
                "trade_limit": 1,
                "stop_duration_candles": 1000,
                "only_per_pair": True,
                "only_per_side": False
            }
        ]

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate indicators for Bollinger Bands strategy
        """
        if not self.dp:
            # Don't do anything if DataProvider is not available.
            return dataframe

        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)

        bollinger_4h = ta.BBANDS(
            informative,
            timeperiod=self.bb_length,
            nbdevup=self.bb_stddev,
            nbdevdn=self.bb_stddev
        )

        # Add Bollinger Bands to the 4h informative dataframe
        informative['bb_lowerband'] = bollinger_4h['lowerband']
        informative['bb_middleband'] = bollinger_4h['middleband']
        informative['bb_upperband'] = bollinger_4h['upperband']

        for val in self.percent_below_lowerband.range:
            informative[f'bb_threshold_{val}'] = bollinger_4h['lowerband'].mul(
                (100 - self.percent_below_lowerband.value) / 100)

        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe,
                                           ffill=True)

        dataframe['dema'] = ta.DEMA(dataframe, timeperiod=3, price='low')

        dataframe['bb_threshold'] = dataframe[f'bb_threshold_{self.percent_below_lowerband.value}_4h']

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_mid'] = 50
        dataframe['rsi_up'] = self.rsi_up.value
        dataframe['rsi_down'] = self.rsi_down.value
        dataframe['rsi_ema'] = ta.EMA(dataframe['rsi'], timeperiod=3)

            # Calculate MACD
        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        dataframe['dema_cross_above_bb_threshold'] = qtpylib.crossed_above(dataframe['dema'], dataframe['bb_threshold'])
        dataframe['rsi_ema_cross_above_down'] = qtpylib.crossed_above(dataframe['rsi_ema'], dataframe['rsi_down'])
        dataframe['rsi_ema_cross_above_up'] = qtpylib.crossed_above(dataframe['rsi_ema'], dataframe['rsi_up'])
        dataframe['rsi_cross_above_down'] = qtpylib.crossed_above(dataframe['rsi'], dataframe['rsi_down'])
        dataframe['rsi_cross_below_down'] = qtpylib.crossed_below(dataframe['rsi'], dataframe['rsi_down'])
        dataframe['rsi_cross_above_up'] = qtpylib.crossed_above(dataframe['rsi'], dataframe['rsi_up'])
        # dataframe['rsi_cross_below_up'] = qtpylib.crossed_below(dataframe['rsi'], dataframe['rsi_up'])

        return dataframe


    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Define entry conditions for long positions
        """
        dataframe.loc[
            (dataframe['rsi_cross_above_down'] == True) &
            (dataframe['dema'] < dataframe['bb_threshold']),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Define exit conditions for long positions
        """
        dataframe.loc[
            dataframe['rsi_cross_above_up'] == True,
            'exit_long'] = 1

        return dataframe

    def leverage(
        self,
        pair: str,
        current_time: datetime,
        current_rate: float,
        proposed_leverage: float,
        max_leverage: float,
        entry_tag: Optional[str],
        side: str,
        **kwargs,
    ) -> float:
        """
        Calculate leverage based on the provided settings
        """
        return self.leverage_amount.value