Better Breakout Trading Model

In this article I will demonstrate a simple breakout strategy for the S&P that utilizes a breakout trading method that has produced consistent results on the S&P E-mini futures market since 1987.

In a recent article entitled, “Better Breakouts In The Electronic Age“, from the October issue of Futures Magazine, author Murray A. Ruggiero, Jr. describes how breakout strategies on the S&P market do not perform so well. It’s been my experience that breakout strategies, such as the common open-range breakout, on the E-mini futures market do not work that great. These types of strategies make use of the market’s 0830 Central open as a key price level to base long and short trades from. An offset value from the open price is calculated and a stop-buy order is placed above the open and a stop-sell order is placed below the open. The market is then free to move in either direction taking you into either a long trade or a short trade. The opposite resting order then acts as your initial stop loss value.

Ruggiero suggests that the electronic age has significantly reduced the open-range breakout strategy’s effectiveness. Why? The once important 0830 Central open of the cash session holds far less importance today as it did in the past because of the nearly 24hr access to the electronic market from any Internet enabled computer. Ruggiero puts it like this…

“The electronic markets have destroyed opening range breakouts for futures markets. Previously, markets were closed for 12 hours or more, whereas now they are closed only for a few hours, if that (30 minutes for E-minis). This has, in turn, made the open ineffective. Although the close is a valuable reference point, it never worked as well as the open, which was a powerful reflection of overnight sentiment shifts because of various reports, such as inflation and unemployment. While the open has lost its significance for commodity futures, stocks still are sensitive to this price point. Although most stocks trade overnight, volume is light and for N.Y. Stock Exchange stocks, we still have a specialist involved in opening the stocks each day.”

While I have no idea if this is the real culprit causing the loss of effectiveness in such trading strategies, breakout strategies on the S&P are difficult beasts to master. In my personal opinion, mean reverting strategies often perform better on the S&P, but let’s look at what Ruggiero has to say.

Ruggiero goes on to develop a test strategy designed to explore the market’s behavior in an attempt to help locate a potential “fix” to the problem of poor performance. I will not get into the details here simply because I would like to focus on Ruggiero’s solution and convert it into TradeStation’s EasyLanguage. However, I will say this: Ruggiero’s experiment was designed to find a better offset or stretch formula and to test what filter might help improve the performance. I found it interesting that attempting to filter trades by volatility did not help. Ruggiero tested taking trades only when the current volatility was rising above an average volatility. You would think this would be a perfect time to take trades, but it wasn’t. In the end, Ruggiero comes up with two very simple rules that produce very interesting results.

The New Offset Formula

The offset is based upon yesterday’s price action. More specifically yesterday’s close, yesterday’s high and yesterday’s low. The formula looks like this:

LowOffset = Absvalue( Close data2 - Low data2 );
HighOffset = Absvalue( Close data2 - High data2);
MaxOffset = Maxlist( LowOffset, HighOffset );
MaxOffsetAvg = Average( MaxOffset, 3 );

In the above code you can see we are using “data2” on many of the price references. In this case, this simply means we are looking at the daily bar chart to get these values. The breakout system we are creating will be trading on a 5-minute chart, but we also wish to reference the daily chart located on data2. We first take the difference between yesterday’s close and the two major price extremes. We then take the larger value of these two values and compute the three-day average. This final value is our offset and is called MaxOffsetAvg in our code.

This MaxOffsetAvg is then applied to the value of yesterday’s close to produce a breakout price where we will place our order to go-long. Below is a code example to place our stop-buy order.

Buy("LE") next bar at Close data2 + MaxOffsetAvg stop;

Momentum Filter

Ruggiero goes on to apply a simple momentum filter to his trades. The momentum calculation is nothing more than the difference between yesterday’s closing price and the average closing price over the past 40 days.

vMomentum  = Close data2 - Average( Close data2, Lookback );

You will notice when the momentum filter is applied, the system will only take long trades when the momentum is negative. In other words, we are looking for falling price action before we place our long orders.

If ( vMomentum < 0 ) And ( EntriesToday(Date) = 0 ) Then Buy("LE") next bar at Close data2 + MaxOffsetAvg stop;

Baseline System

With these simple rules Ruggiero has developed a breakout trading model for the S&P E-mini market. All the following examples are generated from historical price action of the S&P E-mini futures market from 1987 to October 5, 2012. A total of $30 was deducted from each trade to account for slippage and commissions.


The equity graph looks very good considering we are trading a breakout system across the entire lifespan of the S&P E-mini. There are some flat periods during the growth of the curve and there are a few sharp drawdown periods. But also remember we don’t have any stops applied to this system. The entry rules are dynamic, adjusting to the ever changing market volatility. The 40-day lookback period for the momentum filter is not optimized and other values around it also produce positive results. Overall, this baseline system looks very good in my opinion. It’s a promising start to a potential profitable trading model.

Baseline With Offset Stop Loss

The baseline system does not have a stop loss. Let’s add a dynamic stop loss to it. The most obvious stop level is to use our MaxOffsetAvg value. Once a trade is opened, simply place a stop order MaxOffsetAvg distance from our entry. Doing this produces the following results.

 The performance numbers certainly improved. We reduced our drawdown while increasing all other performance measures. Adding a stop clearly improved the system. This got me wondering what would taking half the MaxOffsetAvg value as a stop value look like. I like the idea that a breakout should not retrace much thus, we should only risk half the breakout range.  These results are below.

It looks like we have improved the system again. While the percentage of winning trades has fallen, we reduced our risk, thus improved most of the performance metrics. Overall, this looks promising. Ruggiero has developed a very interesting breakout concept and I thank him for sharing his ideas.

There is so much more that could be tested on this system. What about a regime filter? What about taking short trades by reversing our momentum filter? But this is a fantastic start for a profitable trading system. It looks like we just might have a winner here. I encourage you developers reading this to pursue this concept and share your ideas by leaving a comment below this article.


Better Breakout Strategy ( text file )
Better Breakout Strategy ( TradeStation ELD )
Better Breakout WorkSpace (TradeStation TWS)


10/10/2012 The Expectancy Score values were updated in the above tables due to an error in the calculation.

About the Author Jeff Swanson

Jeff is the founder of System Trader Success – a website and mission to empowering the retail trader with the proper knowledge and tools to become a profitable trader the world of quantitative/automated trading.

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