Bollinger Band Squeeze Breakout Trading Strategy with Trailing Stops

This article describes a trading strategy implemented in Backtrader that capitalizes on periods of low volatility followed by price breakouts. The strategy uses Bollinger Bands to identify a “squeeze” (low volatility) and enters trades when the price breaks out of the bands, employing trailing stops to manage risk and lock in profits.

Strategy Overview

The Bollinger Band Squeeze Breakout Trading Strategy integrates the following components:

• Bollinger Bands: Uses a 7-day period and 1.0 standard deviation factor to define upper and lower bands around a moving average, identifying price boundaries.
• Squeeze Detection: Measures Bollinger Bandwidth (distance between bands relative to the middle band) and identifies a squeeze when the current bandwidth is at its lowest over a 30-day period.
• Breakout Entry: Enters long positions when the price closes above the upper band or short positions when it closes below the lower band during a squeeze.
• Trailing Stops: Applies a 2% trailing stop-loss upon entry to protect against adverse price movements while allowing profits to run.
• Position Management: Ensures no new trades are placed while an order is pending or a position is active, maintaining disciplined trade execution.

Code Implementation

Below is the complete Backtrader code for the strategy:

import backtrader as bt

class BollingerBandSqueezeStrategy(bt.Strategy):
    """
    A strategy that enters on a breakout after a period of low volatility
    and uses a trailing stop for risk management.
    1. Identify Squeeze: Bollinger Bandwidth is at a multi-period low.
    2. Enter on Breakout: Price closes outside the bands.
    3. Exit: A trailing stop-loss order is placed upon entry.
    """
    params = (
        ('bband_period', 7),
        ('bband_devfactor', 1.0),
        ('squeeze_period', 30),
        ('trail_percent', 0.02),  # Trailing stop loss percentage (e.g., 2%)
    )

    def __init__(self):
        self.order = None
        self.dataclose = self.datas[0].close

        # Add Bollinger Bands indicator
        self.bband = bt.indicators.BollingerBands(
            self.datas[0],
            period=self.p.bband_period,
            devfactor=self.p.bband_devfactor
        )

        # Calculate Bollinger Bandwidth
        bb_bandwidth = (self.bband.lines.top - self.bband.lines.bot) / self.bband.lines.mid
        
        # Find the lowest bandwidth over the squeeze_period
        self.lowest_bb_width = bt.indicators.Lowest(
            bb_bandwidth, period=self.p.squeeze_period
        )

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # An order has been submitted/accepted - nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if order.status in [order.Completed]:
            if order.isbuy():
                # --- Trailing Stop for LONG position ---
                # The stop price will be calculated based on the trail_percent
                self.sell(exectype=bt.Order.StopTrail, trailpercent=self.p.trail_percent)

            elif order.issell():
                # --- Trailing Stop for SHORT position ---
                self.buy(exectype=bt.Order.StopTrail, trailpercent=self.p.trail_percent)

        self.order = None

    def next(self):
        # Check for pending orders and sufficient data
        if self.order or len(self) < self.p.squeeze_period:
            return

        # Check if a squeeze is happening by comparing the current bandwidth to its historic low
        is_squeeze = self.lowest_bb_width[-1] == ((self.bband.top[-1] - self.bband.bot[-1]) / self.bband.mid[-1])

        # Only enter if not already in a position
        if not self.position:
            if is_squeeze:
                # Breakout to the upside
                if self.dataclose[0] > self.bband.top[0]:
                    self.order = self.buy()
                # Breakout to the downside
                elif self.dataclose[0] < self.bband.bot[0]:
                    self.order = self.sell()

Strategy Explanation

1. BollingerBandSqueezeStrategy

The strategy identifies low-volatility periods and trades breakouts with risk management:

• Indicators:

  • Bollinger Bands: Calculated with a 7-day period and 1.0 standard deviation factor, creating upper, middle, and lower bands based on the closing price.
  • Bollinger Bandwidth: Measures the distance between the upper and lower bands divided by the middle band to gauge volatility.
  • Squeeze Detection: Identifies a squeeze when the current bandwidth equals the lowest bandwidth over the past 30 days, indicating a period of low volatility.

• Trading Logic (next):

  • Data Check: Requires at least 30 days of data to compute the lowest bandwidth and ensure indicator reliability.
  • Pending Order Check: Skips trading if an order is pending to avoid overlapping trades.
  • Squeeze Confirmation: Checks if the current Bollinger Bandwidth is at its 30-day low, signaling a squeeze.
  • Entry Conditions:
    • Long: Enters a buy order if the current price closes above the upper Bollinger Band during a squeeze.
    • Short: Enters a sell order if the current price closes below the lower Bollinger Band during a squeeze.
  • Exit Mechanism: Relies on trailing stops set upon entry (handled in notify_order).

• Order Management (notify_order):

  • Order Status: Ignores submitted or accepted orders, resetting the order tracker when completed.
  • Trailing Stops:
    • For long positions: Places a trailing stop sell order with a 2% trail below the current price.
    • For short positions: Places a trailing stop buy order with a 2% trail above the current price.
  • Ensures only completed orders trigger stop placement, avoiding premature actions.

Key Features

• Volatility Squeeze Detection: Identifies low-volatility periods using Bollinger Bandwidth, targeting breakouts with high potential for significant moves.
• Breakout Trading: Enters trades when the price breaks outside the Bollinger Bands, capitalizing on momentum following a squeeze.
• Trailing Stop Risk Management: Uses a 2% trailing stop to protect profits while allowing the trade to follow the trend.
• Simplicity: Relies on a single indicator (Bollinger Bands) and straightforward logic, making it easy to implement and understand.
• Position Control: Prevents multiple simultaneous positions or overlapping orders, ensuring disciplined execution.

Potential Improvements

• Parameter Tuning: Adjust bband_period, bband_devfactor, squeeze_period, or trail_percent to optimize for specific assets or market conditions.
• Additional Filters: Incorporate volume or momentum indicators (e.g., ADX) to confirm breakouts and reduce false signals.
• Fixed Stop-Loss: Add a fixed stop-loss alongside trailing stops for additional risk control.
• Performance Metrics: Include analyzers for returns, Sharpe ratio, and trade statistics to evaluate strategy effectiveness.
• Multi-Asset Testing: Apply the strategy to multiple assets to assess performance across different markets.

This strategy is designed for markets with periodic low-volatility periods followed by strong breakouts, such as stocks, forex, or cryptocurrencies, and can be backtested to evaluate its effectiveness across various timeframes and assets.