Momentum Ignition Trading Strategy with Volatility and Trend Filters

This article describes a trading strategy implemented in Backtrader that identifies low-volatility consolidation periods and trades breakouts in the Rate of Change (ROC) momentum indicator, aligned with the long-term trend. The strategy uses ATR-based trailing stops to manage risk and capture profits during strong directional moves.

Strategy Overview

The Momentum Ignition Breakout Trading Strategy integrates the following components:

• Consolidation Detection: Identifies low-volatility periods when the 30-day standard deviation of price is less than 5% of the current price.
• Momentum Breakout: Uses a 7-day ROC indicator to detect significant momentum shifts, triggering trades when ROC exceeds its 30-day moving average by 1.0 standard deviation.
• Trend Alignment: Ensures trades align with the long-term trend using a 30-day Simple Moving Average (SMA) of the closing price.
• ATR Trailing Stops: Sets dynamic stop-loss levels using a 7-day Average True Range (ATR) multiplied by 3.0, updated based on the highest/lowest price since entry.
• Position Management: Prevents new trades when an order is pending, ensuring disciplined execution.

Code Implementation

Below is the complete Backtrader code for the strategy:

import backtrader as bt

class MomentumIgnitionStrategy(bt.Strategy):
    """
    Identifies periods of low price volatility and enters on a statistical
    breakout in the Rate of Change (ROC) momentum indicator, aligned
    with the long-term trend.
    """
    params = (
        # Volatility Filter
        ('consolidation_period', 30),
        ('consolidation_threshold', 0.05), # Max StdDev as % of price
        # Momentum Breakout
        ('roc_period', 7),
        ('roc_ma_period', 30),
        ('roc_breakout_std', 1.0), # ROC must exceed N StdDevs of its MA
        # Trend Filter
        ('trend_period', 30),
        # Risk Management
        ('atr_period', 7),
        ('atr_stop_multiplier', 3.0),
    )

    def __init__(self):
        self.order = None

        # --- Indicators ---
        self.price_stddev = bt.indicators.StandardDeviation(self.data.close, period=self.p.consolidation_period)
        self.roc = bt.indicators.RateOfChange(self.data.close, period=self.p.roc_period)
        self.roc_ma = bt.indicators.SimpleMovingAverage(self.roc, period=self.p.roc_ma_period)
        self.roc_stddev = bt.indicators.StandardDeviation(self.roc, period=self.p.roc_ma_period)
        self.trend_sma = bt.indicators.SimpleMovingAverage(self.data.close, period=self.p.trend_period)
        self.atr = bt.indicators.AverageTrueRange(self.data, period=self.p.atr_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

        if not self.position:
            # --- Filter Conditions ---
            # 1. Is the market consolidating (low price volatility)?
            is_consolidating = (self.price_stddev[0] / self.data.close[0]) < self.p.consolidation_threshold
            
            # 2. Is the macro trend aligned?
            is_macro_uptrend = self.data.close[0] > self.trend_sma[0]
            is_macro_downtrend = self.data.close[0] < self.trend_sma[0]

            if is_consolidating:
                # 3. Has momentum "ignited" with a statistical breakout?
                roc_upper_band = self.roc_ma[0] + (self.roc_stddev[0] * self.p.roc_breakout_std)
                roc_lower_band = self.roc_ma[0] - (self.roc_stddev[0] * self.p.roc_breakout_std)
                
                is_mom_breakout_up = self.roc[0] > roc_upper_band
                is_mom_breakout_down = self.roc[0] < roc_lower_band
                
                # --- Entry Logic ---
                if is_macro_uptrend and is_mom_breakout_up:
                    self.order = self.buy()
                elif is_macro_downtrend and is_mom_breakout_down:
                    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. MomentumIgnitionStrategy

The strategy identifies low-volatility periods and trades momentum breakouts aligned with the long-term trend:

• Indicators:

  • Price Volatility: Measures the 30-day standard deviation of closing prices to detect consolidation (low volatility when StdDev is less than 5% of the current price).
  • Rate of Change (ROC): Calculates the 7-day percentage change in closing price to gauge momentum.
  • ROC Moving Average and StdDev: Uses a 30-day SMA of ROC and its standard deviation to define statistical breakout thresholds.
  • Trend SMA: A 30-day Simple Moving Average to determine the long-term trend direction.
  • 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.
  • Entry Conditions:
    • Consolidation: Price standard deviation divided by the current price must be below 0.05 (5%).
    • Trend Alignment: Price must be above the 30-day SMA for bullish trades or below it for bearish trades.
    • Momentum Breakout:
      • Bullish: ROC exceeds its 30-day moving average plus 1.0 times its standard deviation.
      • Bearish: ROC falls below its 30-day moving average minus 1.0 times its standard deviation.
    • Enters a buy order for bullish conditions or a sell order for bearish conditions, only if no position is held.
  • 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.

• 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

• Consolidation Detection: Identifies low-volatility periods using price standard deviation, targeting breakouts with high potential for significant moves.
• Momentum Breakout: Uses a statistical threshold (ROC exceeding its moving average by 1.0 standard deviation) to confirm strong momentum.
• Trend Alignment: Ensures trades align with the long-term trend via a 30-day SMA, reducing false signals in choppy markets.
• 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 consolidation_period, consolidation_threshold, roc_period, roc_breakout_std, or atr_stop_multiplier to optimize for specific assets or market conditions.
• Additional Filters: Incorporate volume or other momentum indicators (e.g., ADX) to further confirm breakout signals.
• 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 momentum-driven breakouts, suitable for assets like forex, stocks, or cryptocurrencies, and can be backtested to evaluate its effectiveness across various timeframes and assets.