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Trading Model Construction: Tracking the S&P 500

Here's a look at the 8-step approach to building your own trading model -- in action.

As a follow-up to "How to Build Your Own Trading Model in 8 Steps," I will now walk you through an actual example of the construction of a trading model.

Step 1. Develop a Hypothesis

I want to test whether we can observe and quantify momentum-based movements in the market. Thus, if the markets are moving up, will they continue to move up? Conversely, if the markets are moving down, will they continue to move down?

Momentum can indicate buying or selling by large

institutions that are investors that can essentially



If the answers to my questions are yes and yes, can we profit from such moves?

Step 2. Build a Database

When building a database, the most important question to ask yourself is what data do I need to accumulate?

The "market" is a very general term, which also can also be quite subjective when attempting to define it. Given that the

S&P 500

is the broadest basket of

large capitalizationstocks; is the most widely used

index as an investment

benchmark; and, has the most active and

TheStreet Recommends

liquid exchange-traded funds (


) associated with that index, I believe that the SPX is most representative of the overall stock market.

Over many years, I have developed and maintained a database of the SPX. My data goes back to 1950 and lists by date, the S&P 500's opening price, high price, low price and closing price. I have spent many hours on data services such as





Yahoo Finance

and other databases accumulating this information and insuring that the data was "clean" and researched any "issues" when they may have occurred.

Many of the database issues took place in earlier years. For example, prior to 1962, only closing data is available and I have had to live with such limitations in my model-building endeavors. However, for the purposes of


model, only closing data is necessary, so we're not constrained by the lack of detail before 1962.

Step 3. Make Observations

When scrolling through my database I have noticed that the S&P 500 tends to run in streaks. That's to say a positive day is likely to be followed by another positive day until that streak ends. Similarly, down days tend to be followed by down days. Furthermore, since the S&P 500 is up over time, I have to conclude that two things:

1. There are more up days than down days and/or...

2. The magnitude of up days is greater than the magnitude of down days.

Step 4. Develop Calculations

This is my favorite step: torture the data.

The hypothesis (step one) and observations (step three) present many questions which we will need to look into. This can be an iterative process, whereby questions beget new observations, which then result in further questions.

Question 1: What is the distribution of up days and down days?

Result: For the period under review (1950 - 2007), there were a total of 14,592 trading days. Of those days, 7,817 (53.57%) were up days and 6,775 (46.43%) we down days

Question 2: What is the magnitude of up and down days?

Result: On average, when advancing, the S&P will be up 0.61%. On average, when declining the S&P will drop 0.63%.

Step 5. Define the Trading Rule

With the magnitude of up and down days roughly equal


the quantity of up days far exceeding that of down days, let's revise the hypothesis (step one) in order to define a trading rule.

Here it is: If the S&P advances for the first time in a row, hold it until the S&P declines.

Step 6. Back-Test


Step 7. Determine Whether the Model Makes Money

I went back to my database and created this table:

By reading the results of the back-tested data, I can now determine if the model makes money.

This table is a back-testing of returns if you bought the S&P at the market close on a day in which it went up for the first time in a row and held it until the close of the day in which the index declined.

Here is how to read this table:

Days in streak. The amount of consecutive days that the S&P rose.

Rate of continuation. The frequency by which a winning streak was extended. For example, 54.84% of the time an eight-day winning streak extended to at least a ninth day.

Average return the next day. One-day streaks will return on average 0.25% on the following day (whether or not day two is up or down), two-day streaks will return -- on average -- 0.20% on the following day (whether or not day three is up or down) and so on. As you can see from the data, if you catch a streak, you can pile up some nice gains. (If you are curious, the 14-day streak took place in April 1971. Had you traded this model then, the return for that streak would have been 3.54%).

Some observations from the table:

Up until a 12-day streak, the rate of continuation is greater than 53.57%. Thus, it appears more likely for the S&P to rise on subsequent days of a streak than on any random day.

If the average up day minus the average down day has yielded a -0.02% return, then waiting until a new winning streak begins will likely generate higher returns.

On average, we get at least one winning streak per year that lasts eight or more days. (The last streak of at least eight days was in 2004 and it lasted nine days).

Step 8. Determine Whether the Model Can Be Traded?

Clearly, as individual investors, it is not feasible to trade the entire S&P 500. Thus we have to look for a close substitute. The best proxy for the S&P would be one of its ETFs, such as the


(SPY) - Get SPDR S&P 500 ETF Trust Report

or the

Ultra S&P 500

(SSO) - Get ProShares Ultra S&P 500 Report


Homework Time

As you build your own trading model, your homework is to "paper-trade" the model above via the SPY or SSO.

Remember, before you put any real

capital to work, test how well (or poorly) your trades would have performed.

At the time of publication, Rothbort was long SSO, although positions can change at any time.

Scott Rothbort has over 20 years of experience in the financial services industry. In 2002, Rothbort founded LakeView Asset Management, LLC, a registered investment advisor based in Millburn, N.J., which offers customized individually managed separate accounts, including proprietary long/short strategies to its high net worth clientele.

Immediately prior to that, Rothbort worked at Merrill Lynch for 10 years, where he was instrumental in building the global equity derivative business and managed the global equity swap business from its inception. Rothbort previously held international assignments in Tokyo, Hong Kong and London while working for Morgan Stanley and County NatWest Securities.

Rothbort holds an MBA in finance and international business from the Stern School of Business of New York University and a BS in economics and accounting from the Wharton School of Business of the University of Pennsylvania. He is a Term Professor of Finance and the Chief Market Strategist for the Stillman School of Business of Seton Hall University.

For more information about Scott Rothbort and LakeView Asset Management, LLC, visit the company's Web site at

. Scott appreciates your feedback;

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