Evaluating Gap Momentum in Futures Markets

Have you ever wondered if the seemingly random gaps that occur when the market opens could be the key to a powerful trading strategy? Imagine turning these unpredictable moments into a reliable edge in your futures trading. Today, we're diving deep into the gap momentum strategy to uncover its true potential and see if it can revolutionize your trading game.

Gap momentum trading is a fascinating strategy that taps into the often-overlooked phenomenon of market gaps. This technique draws its inspiration from the pioneering work of J. Welles Wilder and Joseph Granville's innovative On-Balance-Volume (OBV) method. Wilder, known for developing several technical analysis tools, was keenly interested in market gaps and their implications. Granville's OBV method, which accumulates volume based on price movements, laid the groundwork for analyzing momentum in a novel way.

By examining the difference between today's open and yesterday's close, traders can accumulate a series of positive and negative gaps to derive a gap ratio. When smoothed with a moving average, this ratio becomes a powerful indicator of market momentum.

In this article, inspired by Perry J. Kaufman's work in "Gap Momentum," published in Technical Analysis of Stocks & Commodities, we will delve into the mechanics of the gap momentum strategy, explore its application in futures markets, and present our findings from extensive testing.

I aim to determine whether this technique can offer a reliable edge in futures trading and provide actionable insights for algorithmic traders looking to enhance their strategies.

Understanding Gap Momentum

Historical Context

To fully appreciate the gap momentum strategy, it's essential to understand its roots. This approach draws heavily from the work of two notable figures in technical analysis: J. Welles Wilder and Joseph Granville. Wilder, a renowned technical analyst, developed several widely used indicators, including the Relative Strength Index (RSI) and the Average True Range (ATR). His fascination with market gaps—those sudden jumps in price that occur when the market opens—led to insights about their potential impact on future price movements.

Joseph Granville, another pioneer in technical analysis, introduced the On-Balance Volume (OBV) method. OBV accumulates volume based on whether prices close higher or lower than the previous day. This cumulative volume can reveal trends only after a period of time from price data alone. Granville's work showed that volume precedes price, meaning significant volume changes often lead to price movements.

Concept Explanation

Building on these foundations, the gap momentum strategy uses opening gaps to gauge market momentum. Here's a step-by-step breakdown of how this works:

1. Identify the Gap: The gap is simply the difference between today's opening price and yesterday's closing price. If today's open is higher than yesterday's close, it's a positive gap. If it's lower, it's a negative gap.
2. Calculate the Gap Ratio:
   • Positive Gaps: Sum all positive gaps over a set period (e.g., 20 days).
   • Negative Gaps: Sum all negative gaps (as positive numbers) over the same period.
   • Gap Ratio: Divide the sum of positive gaps by the sum of negative gaps. This ratio reflects the balance of upward versus downward momentum.
3. Create a Cumulative Series: Starting with zero, add the gap ratio to a cumulative total each day. This running total is the gap momentum indicator.
4. Smooth the Indicator: Apply a moving average to the gap momentum indicator to smooth out the noise and provide a clearer signal. The length of this moving average can vary, but a common choice is 20 days.

Example Calculation:

Let's walk through a simple example with a 5-day period:

• Day 1: Open = 102, Close = 100, Gap = 2 (positive)
• Day 2: Open = 101, Close = 103, Gap = -2 (negative)
• Day 3: Open = 104, Close = 101, Gap = 3 (positive)
• Day 4: Open = 102, Close = 104, Gap = -2 (negative)
• Day 5: Open = 105, Close = 103, Gap = 2 (positive)

Summing the gaps over 5 days:

• Positive Gaps: 2 + 3 + 2 = 7
• Negative Gaps: 2 + 2 = 4
• Gap Ratio: 7 / 4 = 1.75

Each day, we add this ratio to the cumulative series. If we start with zero, the cumulative series over five days will be:

• Day 1: 0 + 1.75 = 1.75
• Day 2: 1.75 + 1.75 = 3.5
• Day 3: 3.5 + 1.75 = 5.25
• Day 4: 5.25 + 1.75 = 7
• Day 5: 7 + 1.75 = 8.75

Trading Signals

Once we have our smoothed gap momentum indicator, we can use it to generate trading signals:

• Buy Signal: When the smoothed gap momentum indicator turns upward, indicating increasing positive gaps.
• Sell Signal: When the smoothed gap momentum indicator turns downward, indicating increasing negative gaps or decreasing positive gaps.

By following these signals, traders can capture the momentum market gaps indicate. 

Understanding the theory and calculation behind the gap momentum indicator is crucial. In the next section, we'll dive into how to implement this strategy in EasyLanguage and test it on various futures markets to see how it performs.

Implementing the Gap Momentum Strategy

Implementing the gap momentum strategy in EasyLanguage involves several steps, from setting up the inputs and variables to calculating the gap momentum indicator and generating trading signals. Here’s a detailed guide to help you understand and implement this strategy.

Step 1: Defining Inputs and Variables

First, we must define the inputs and variables used in our EasyLanguage script. These include the number of periods to evaluate gaps, the period for calculating the signal average, and the trading bias (long, short, or both).

Step 2: Calculating Gap Momentum

We then calculate the gap momentum using a custom function. This function evaluates the gaps over the specified period and computes the gap ratio.

Step 3: Implementing Trading Logic

After calculating the gap momentum, we implement the trading logic based on the signal generated. This involves buying or selling based on the direction of the gap momentum indicator.

Notice this is a stop-and-reverse strategy. Put another way, it's constantly switching between long and short.

Step 4: Testing and Optimization

Finally, we test the strategy in various future markets to evaluate its performance. This involves running the script on historical data, which we'll do in the next section.

Testing the Strategy on Futures Markets

I will test this concept on the @ES, @GC, and @CL markets. I like to trade these markets and should provide a decent representation of the Gap Momentum concept.

I'm going to create three charts for each of our three markets to test with the following settings:

• In-Sample History: 2006-2021
• Out-of-Sample History: 2022-June 25, 2024
• Timeframe: Daily Bars
• Slippage and Commissions: $0
• Maximum Bars: 200

I will then optimize the following Gap Momentum strategies across the following input ranges:

• iPeriod: 5 to 60 in steps of 5
• iSignalPeriod: 5 to 100 in steps of 5
• iSignalTrigger: 1 to 10 in steps of 1
• iLSB: -1 to 1 in steps of 1

After I optimize the in-sample historical data, I will apply the best settings to the out-of-sample data. This will give us a more realistic look at the performance.

Results of the Gap Momentum

@ES Market Results

It is not too surprising that the optimizer picked the long side as the best-performing. We see this constantly as the stock index markets have a historical long-side bias.  Below are the optimized results applied to the out-of-sample data segment.