Relative Momentum Acceleration Trading Strategy with ATR Trailing Stops

This article describes a trading strategy implemented in Backtrader that identifies significant price momentum shifts relative to an adaptive baseline trend. It uses a custom thrust oscillator to detect breakouts and employs ATR-based trailing stops to manage risk and capture profits during strong directional moves.

Strategy Overview

The Relative Momentum Acceleration Trading Strategy integrates the following components:

• Adaptive Baseline Trend: Uses a 30-day Kaufman’s Adaptive Moving Average (KAMA) to establish a dynamic trend baseline that adjusts to market volatility.
• Fast Momentum: Employs a 7-day Exponential Moving Average (EMA) to capture short-term price movements.
• Thrust Oscillator: Measures the relative difference between the fast EMA and KAMA, normalized by KAMA, to identify momentum acceleration.
• Breakout Detection: Uses Bollinger Bands on the thrust oscillator (30-day period, 1.0 standard deviation) to detect statistical breakouts.
• 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 RelativeMomentumAccel(bt.Strategy):
    """
    A strategy that enters on a statistical breakout of a custom oscillator
    measuring the acceleration of price away from its adaptive baseline trend.
    """
    params = (
        # Baseline Trend and Momentum
        ('kama_period', 30),
        ('fast_ema_period', 7),
        # Thrust Oscillator Breakout
        ('thrust_bb_period', 30),
        ('thrust_bb_devfactor', 1.0),
        # Risk Management
        ('atr_period', 7),
        ('atr_stop_multiplier', 3.0),
    )

    def __init__(self):
        self.order = None

        # --- Indicators ---
        self.kama = bt.indicators.KAMA(self.data.close, period=self.p.kama_period)
        self.fast_ema = bt.indicators.ExponentialMovingAverage(self.data.close, period=self.p.fast_ema_period)

        # --- Custom Thrust Oscillator ---
        # Note: A small number is added to self.kama to prevent division by zero in rare cases
        self.thrust_osc = (self.fast_ema - self.kama) / (self.kama + 1e-6)

        # --- Breakout Bands on the Oscillator ---
        self.thrust_bbands = bt.indicators.BollingerBands(
            self.thrust_osc,
            period=self.p.thrust_bb_period,
            devfactor=self.p.thrust_bb_devfactor
        )

        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:
            # --- Entry Logic ---
            # Buy when the thrust oscillator breaks above its upper band
            if self.thrust_osc[0] > self.thrust_bbands.top[0]:
                self.order = self.buy()
            # Sell when the thrust oscillator breaks below its lower band
            elif self.thrust_osc[0] < self.thrust_bbands.bot[0]:
                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. RelativeMomentumAccel Strategy

The strategy uses a custom thrust oscillator to detect significant momentum shifts and trades breakouts with ATR-based risk management:

• Indicators:

  • KAMA: A 30-day Kaufman’s Adaptive Moving Average, adapting to price volatility for a dynamic trend baseline.
  • Fast EMA: A 7-day Exponential Moving Average to capture short-term price momentum.
  • Thrust Oscillator: Calculates the difference between the fast EMA and KAMA, divided by KAMA (with a small constant to avoid division by zero), to measure relative momentum acceleration.
  • Bollinger Bands on Thrust Oscillator: Applies a 30-day period with 1.0 standard deviation to define breakout thresholds for the thrust oscillator.
  • 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:
    • Bullish Entry: Enters a buy order if the thrust oscillator exceeds its upper Bollinger Band, indicating strong upward momentum acceleration.
    • Bearish Entry: Enters a sell order if the thrust oscillator falls below its lower Bollinger Band, indicating strong downward momentum acceleration.
    • Only enters trades if no position is currently held, ensuring one trade at a time.
  • 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

• Custom Thrust Oscillator: Measures momentum acceleration relative to an adaptive trend (KAMA), capturing significant price shifts.
• Statistical Breakout Detection: Uses Bollinger Bands on the thrust oscillator to identify statistically significant momentum breakouts, reducing false signals.
• 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.
• Adaptive Trend Baseline: Leverages KAMA for a trend baseline that adjusts to market conditions, enhancing robustness.

Potential Improvements

• Parameter Tuning: Adjust kama_period, fast_ema_period, thrust_bb_period, thrust_bb_devfactor, or atr_stop_multiplier to optimize for specific assets or market conditions.
• Additional Filters: Incorporate volume or other trend indicators (e.g., ADX) to confirm momentum 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, 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 significant momentum shifts following periods of trend alignment, suitable for assets like forex, stocks, or cryptocurrencies, and can be backtested to evaluate its effectiveness across various timeframes and assets.