Filtered Squeeze Breakout Trading Strategy with Long-Term Trend and Volume Filters

This article describes an advanced breakout trading strategy implemented in Backtrader that identifies low-volatility “squeeze” periods using Bollinger Bands and Keltner Channels, enhanced with a long-term trend filter and volume confirmation. The strategy trades breakouts with momentum confirmation and manages risk with ATR-based trailing stops.

Strategy Overview

The Filtered Squeeze Breakout Trading Strategy integrates the following components:

• Custom Keltner Channels: Defines a volatility channel using a 30-day Simple Moving Average (SMA) and 1.0 times the Average True Range (ATR) to set upper and lower bands.
• Bollinger Bands: Uses a 7-day period with a 1.0 standard deviation factor to detect tight price ranges indicative of low volatility.
• Squeeze Detection: Identifies a squeeze when Bollinger Bands are inside Keltner Channels, signaling low volatility and potential for a breakout.
• Long-Term Trend Filter: Uses a 30-day SMA to ensure breakouts align with the prevailing trend direction (price above SMA for buys, below for sells).
• Volume Confirmation: Requires the current volume to exceed the 7-day average volume by a factor of 1.2 to confirm breakout strength.
• MACD Confirmation: Uses a MACD with 7-day fast, 30-day slow, and 14-day signal periods to confirm bullish or bearish momentum.
• ATR Trailing Stops: Sets dynamic stop-loss levels using a 7-day ATR multiplied by 3.0, updated based on the highest/lowest price since entry for long/short positions.
• Position Management: Ensures no new trades are placed when an order is pending, maintaining disciplined execution.

Code Implementation

Below is the complete Backtrader code for the strategy, including the custom Keltner Channel indicator:

import backtrader as bt

class CustomKeltnerChannel(bt.Indicator):
    alias = ('KeltnerChannel',)
    lines = ('mid', 'top', 'bot',)
    plotinfo = dict(subplot=False)
    params = (('period', 20), ('devfactor', 1.5), ('movav', bt.indicators.SimpleMovingAverage),)
    def __init__(self):
        self.lines.mid = self.p.movav(self.data, period=self.p.period)
        atr = self.p.devfactor * bt.indicators.AverageTrueRange(self.data, period=self.p.period)
        self.lines.top = self.lines.mid + atr
        self.lines.bot = self.lines.mid - atr

class FilteredSqueezeStrategy(bt.Strategy):
    """
    An advanced breakout strategy using a single timeframe. It combines a
    Squeeze, a long-term trend filter, and a volume confirmation filter.
    """
    params = (
        # Squeeze detection
        ('bb_period', 7), ('bb_devfactor', 1.0),
        ('kc_period', 30), ('kc_devfactor', 1.0),
        # Momentum confirmation
        ('macd_fast', 7), ('macd_slow', 30), ('macd_signal', 14),
        # Volume Filter
        ('vol_ma_period', 7),
        ('vol_multiplier', 1.2),
        # Long-term trend filter
        ('long_term_ma_period', 30),
        # Risk Management
        ('atr_period', 7), ('atr_stop_multiplier', 3.0),
    )

    def __init__(self):
        self.order = None
        
        # --- All indicators now run on self.data (datas[0]) ---
        self.bband = bt.indicators.BollingerBands(self.data, period=self.p.bb_period, devfactor=self.p.bb_devfactor)
        self.keltner = CustomKeltnerChannel(self.data, period=self.p.kc_period, devfactor=self.p.kc_devfactor)
        self.macd = bt.indicators.MACD(self.data, period_me1=self.p.macd_fast, period_me2=self.p.macd_slow, period_signal=self.p.macd_signal)
        self.atr = bt.indicators.AverageTrueRange(self.data, period=self.p.atr_period)
        self.vol_ma = bt.indicators.SimpleMovingAverage(self.data.volume, period=self.p.vol_ma_period)
        
        # Long-term MA to replace the HTF filter
        self.long_term_ma = bt.indicators.SimpleMovingAverage(self.data, period=self.p.long_term_ma_period)

        # --- Trailing Stop State ---
        self.stop_price = None
        self.highest_price_since_entry = None
        self.lowest_price_since_entry = None

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]: 
            return
        if order.status in [order.Completed]:
            if self.position and self.stop_price is None:
                if order.isbuy():
                    self.highest_price_since_entry = self.data.high[0]
                    self.stop_price = self.highest_price_since_entry - (self.atr[0] * self.p.atr_stop_multiplier)
                elif order.issell():
                    self.lowest_price_since_entry = self.data.low[0]
                    self.stop_price = self.lowest_price_since_entry + (self.atr[0] * self.p.atr_stop_multiplier)
            elif not self.position:
                self.stop_price = None
                self.highest_price_since_entry = None
                self.lowest_price_since_entry = None
        self.order = None

    def next(self):
        if self.order: 
            return

        # --- Filter Conditions ---
        is_squeeze = (self.bband.top < self.keltner.top and self.bband.bot > self.keltner.bot)
        has_volume_spike = self.data.volume[0] > (self.vol_ma[0] * self.p.vol_multiplier)
        
        # Trend filter using long-term MA
        is_long_term_uptrend = self.data.close[0] > self.long_term_ma[0]
        is_long_term_downtrend = self.data.close[0] < self.long_term_ma[0]

        if not self.position and is_squeeze and has_volume_spike:
            price_breaks_up = self.data.close[0] > self.bband.top[0]
            price_breaks_down = self.data.close[0] < self.bband.bot[0]
            macd_is_bullish = self.macd.macd[0] > self.macd.signal[0]
            macd_is_bearish = self.macd.macd[0] < self.macd.signal[0]

            # Entry requires alignment with the long-term MA
            if price_breaks_up and macd_is_bullish and is_long_term_uptrend:
                self.order = self.buy()
            elif price_breaks_down and macd_is_bearish and is_long_term_downtrend:
                self.order = self.sell()
                
        elif self.position:
            # --- Manual ATR Trailing Stop Logic ---
            if self.position.size > 0: # Long
                self.highest_price_since_entry = max(self.highest_price_since_entry, self.data.high[0])
                new_stop = self.highest_price_since_entry - (self.atr[0] * self.p.atr_stop_multiplier)
                self.stop_price = max(self.stop_price, new_stop)
                if self.data.close[0] < self.stop_price: 
                    self.order = self.close()
            elif self.position.size < 0: # Short
                self.lowest_price_since_entry = min(self.lowest_price_since_entry, self.data.low[0])
                new_stop = self.lowest_price_since_entry + (self.atr[0] * self.p.atr_stop_multiplier)
                self.stop_price = min(self.stop_price, new_stop)
                if self.data.close[0] > self.stop_price: 
                    self.order = self.close()

Strategy Explanation

1. CustomKeltnerChannel Indicator

The custom Keltner Channel indicator defines a volatility-based channel:

• Components:
  • Mid Line: A 30-day Simple Moving Average (SMA) of the closing price.
  • Upper and Lower Bands: Calculated as the mid line plus/minus 1.0 times the 30-day ATR.
• Plotting: Displayed on the main price chart for visualization.

2. FilteredSqueezeStrategy

The strategy combines squeeze detection, trend alignment, volume confirmation, and momentum to trade breakouts:

• Indicators:

  • Bollinger Bands: Uses a 7-day period with a 1.0 standard deviation factor to detect tight price ranges.
  • Keltner Channels: Uses a 30-day period with a 1.0 ATR multiplier to define a broader volatility channel.
  • MACD: Combines 7-day fast, 30-day slow, and 14-day signal periods to confirm momentum (bullish when MACD line is above signal, bearish when below).
  • Volume MA: A 7-day SMA of volume to identify significant volume spikes (current volume > 1.2 times the average).
  • Long-Term MA: A 30-day SMA to filter trades in the direction of the prevailing trend.
  • ATR: A 7-day Average True Range for setting trailing stop levels.

• Trading Logic (next):

  • Pending Order Check: Skips trading if an order is pending to avoid overlapping trades.
  • Filter Conditions:
    • Squeeze: Bollinger Bands are inside Keltner Channels (upper Bollinger Band below upper Keltner, lower Bollinger Band above lower Keltner).
    • Volume Spike: Current volume exceeds 1.2 times the 7-day volume average.
    • Trend Alignment: Price is above the 30-day SMA for buy trades, below for sell trades.
  • Entry Conditions:
    • Bullish Entry: Enters a buy order if the price closes above the upper Bollinger Band, MACD is bullish, the price is above the long-term MA, and a volume spike is present during a squeeze.
    • Bearish Entry: Enters a sell order if the price closes below the lower Bollinger Band, MACD is bearish, the price is below the long-term MA, and a volume spike is present during a squeeze.
  • Trailing Stop Logic:
    • For long positions: Updates the stop to the highest price since entry minus 3 times the ATR, ensuring the stop moves up with favorable price action. Exits if the price falls below the stop.
    • For short positions: Updates the stop to the lowest price since entry plus 3 times the ATR, ensuring the stop moves down with favorable price action. Exits if the price rises above the stop.
  • Position Check: Only enters trades if no position is currently held, ensuring one trade at a time.

• Order Management (notify_order):

  • Ignores submitted or accepted orders, resetting the order tracker when completed.
  • Sets the initial trailing stop upon trade execution:
    • For long positions: Sets stop as the entry high minus 3 times ATR.
    • For short positions: Sets stop as the entry low plus 3 times ATR.
  • Resets stop and price tracking variables when the position is closed.

Key Features

• Squeeze Detection: Identifies low-volatility periods using Bollinger Bands inside Keltner Channels, targeting breakouts with high potential.
• Trend Alignment: Ensures trades align with the 30-day SMA trend, reducing trades against the prevailing market direction.
• Volume Confirmation: Requires a volume spike to confirm breakout strength, filtering out weak signals.
• Momentum Confirmation: Uses MACD to ensure breakout trades align with strong bullish or bearish momentum.
• ATR Trailing Stops: Dynamically adjusts stop-loss levels based on volatility, protecting profits while allowing trades to follow trends.
• Disciplined Execution: Prevents multiple simultaneous positions or overlapping orders, maintaining clear trade logic.

Potential Improvements

• Parameter Tuning: Adjust bb_period, kc_period, macd_fast, macd_slow, macd_signal, vol_ma_period, vol_multiplier, long_term_ma_period, or atr_stop_multiplier to optimize for specific assets or market conditions.
• Additional Filters: Incorporate other indicators like ADX or RSI to further confirm trend strength or avoid overbought/oversold conditions.
• Fixed Stop-Loss: Add a fixed stop-loss alongside trailing stops for additional risk control.
• Performance Metrics: Include analyzers for returns, Sharpe ratio, drawdown, 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 consolidations followed by strong, trend-aligned breakouts, suitable for assets like forex, stocks, or cryptocurrencies, and can be backtested to evaluate its effectiveness across various timeframes and assets.