The Anatomy of a Quantified Trading Process

A quantified trading system is a system that can be automated, but it is not necessarily an automated system. A quantified process is precisely defined, uses numeric inputs, and produces measureable outputs.

Each quantified trading system starts with a Profit Plan. A profit plan is a theory that describes the actions that will be followed under the plan and explains how those actions will be converted into profit. Never forget that your profit plan is a theory. If it works, it is a good theory; if it doesn’t, it’s not. Theories of profit rarely become laws.

All theories start with observations. For example, you might first observe that stocks tend to bounce back after a sell-off. That makes you look closer, and you find this behavior seems to be a lot more prevalent in stocks that are doing well than in stocks in the general process of tanking. Your conceptual theory might then become “If a stock is doing well overall, you can make money buying on the dips Dump them as soon as the price bounces back.”

That’s a theory, but not a quantified theory. You can make it a quantified theory, as Connors and Alvarez did, by adopting numbers that define “doing well overall”, “the dips”, and “bounces back”. I am sure there are a lot of traders out there that did well enough with this rule with no quantification, but quantification brings two specific advantages. Once a theory becomes quantified, it can be executed by a computer, and once it can be executed by a computed, it can be tested exhaustively.

The process that Connors and Alvarez define on page 62 of Short Term Trading Strategies That Work is a good example of what is needed.


1. The S&P 500 Index is above the 200-day moving average

2. The 2-period RSI of the S&P 500 Index closes below 5.

3. Buy the S&P on the close.

4. Exit when the S&P closes above its 5-period moving average.

Step number one checks to see if the S&P is “doing well overall”. Is the 200-day moving average the best test of this? You can test for this. (We did, and found that every long-term average was almost equally effective so 200 is as good a number as any.)

Step number two is a quantified definition of how to spot a dip. You can now test whether this works, Is this the best indicator? You don’t know, but you can now test it against other alternatives. Quantification does not start with the assumption that you know the right numbers; it starts with the assumption that you can test and discover the right numbers.

Finally, step four quantifies what a bounce back is. Is the 5 period average the right indicator? Again, you can test for that.

A quantified system has rules that can be automated and tested. If you are automating a quantified trading system, keep in mind that the ability to run tests is likely to be the most important contribution of such a system.


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