Traders and Mistakes: Mechanical Traders

Excerpt by Van K. Tharp, Ph.D.

So let’s look at the psychology of trading from the angle of mistakes. When you don’t follow your rules, you make a mistake. It’s that simple. And making the same mistake repeatedly is called self-sabotage.

First, let me introduce one way to measure mistakes’ impact on your trading. Trader efficiency is a measure of how effective a trader is in making mistake free trades. So a person who makes five mistakes in 100 trades is 95% efficient. In the last five years I’ve requested that my Super Traders document their mistakes so that we can look at their efficiency levels. I have found that 95% is actually a very good trading efficiency level; many traders can’t even trade at 75% efficiency—which is terrible. That’s one mistake in about every four trades. This is most important for one category of traders: rule-based discretionary traders. In my opinion, when rule based discretionary traders become efficient, they are by far the best type of trader.

There are two other groups of traders I’d like to talk about: 1) mechanical traders and 2) no-rule discretionary traders.

With mechanical trading, I can be objective and not make mistakes (except the psychological mistake of overriding my system). Mechanical trading is objective. My system testing will allow me to determine my future results. Mechanical trading is accurate. If a system’s underlying logic cannot be turned into a mechanical trading system, it probably isn’t worth trading. Human judgment is too prone to errors. I can eliminate those through mechanical trading.

So then, is mechanical trading truly objective? I tend to think not because there are all sorts of errors that can creep into an automated trading system: data errors, errors in the software platform, errors in your own programming, and many more. (Interestingly, one of the main categories of errors that my Super Traders come up with consistently is programming errors.)
Let’s consider data errors. Is your data accurate or does it have bad ticks and other issues with it? Mechanical traders are always dealing with data errors of some sort. For example, price errors can show up in streaming data quite regularly. Sometimes those are resolved within seconds and the error “disappears” but, by that point, the bad data may have triggered a trade already. Additionally, historical stock data may or may not have dividend and split adjustments. And what happens when a company goes bankrupt? What if it goes private or is bought out by another company? Those companies’ data may simply disappear from your data set.

There are a lot more variations of an entry that a mechanical system would miss, but you get the point. As soon as you state your rules so precisely that a computer can execute the trades, you open yourself to errors of omission—good or outstanding trades that your automated system cannot take because of its precision. Those missed opportunities don’t qualify as mistakes but they severely limit the potential results of the underlying logic behind the system. The mechanical system results will look rather weak next to the results of a trader who used that same system and was allowed some discretion to take the all of those other trades that didn’t quite fit the precise mechanical system rules.