In the first post in this series, we took a look at how traders often lose their ideas when their stop levels are hit. In this post, we’ll examine a different, but related, cognitive mistake. Many traders will place trades based upon price patterns and “setups” without truly understanding how their market is behaving. This is a particular problem when market regimes change and markets change their behavior. Knowledge is necessary, but not sufficient, in trading success. We also need to understand what is happening in our markets so that we can profit from the behavior of other market participants.
One variable important for understanding is volume and especially changes in trading volume. If volume is increasing in a stock, index, or other instrument, it means that new participants have entered the market. We want to examine how our market responds to this expansion of participation, because that will provide us with important clues as to who is in the market and how they are leaning. For instance, if we’re trading a small cap stock with a relatively small float, a meaningful expansion of volume almost certainly indicates speculative interest among small traders. These traders are active as daytraders and often pile into momentum when a stock moves. Knowing this, we can get ahead of their activity. A large cap stock, on the other hand, is dominated by institutional traders who will wait for good prices and execute their orders over a period of time. If we can study the stock and see how it has moved on high volume in the past, we can reverse-engineer the execution algorithms used by the large traders and front-run their accumulation of shares. Stocks index volume is often significant as a function of time of day, as different participants are active at different time zones and times within each zone. When we see volume expanding and a breakout early in the U.S. session, this often has implications for trending through the day.
Another variable important for understanding is the correlation among related market instruments. If an auto stock is making a move, it pays to check out other auto stocks and the broader list of industrial shares. We want to determine if this is an idiosyncratic move, specific to the company, or whether institutions are accumulating shares in particular industries and sectors. Seeing how sectors behave before we trade can help us distinguish between rotational environments, which are often rangebound, and trending environments.
A football team would never call a play without checking out the defense of the opponent. Similarly, we want to understand the market environment before we call a play with our capital. When we act before we understand, we implicitly assume that all price patterns are equal in their meaning and significance. If that were true, wouldn’t sophisticated algorithmic participants already have mined such simple “setups”? It is precisely the complexity of movement at different levels of participation, different times of day, and different co-movements of instruments that makes trading challenging, even for the algos. Great traders don’t have a passion for trading; they have a passion for understanding markets. That’s what makes professional trading different from gambling.
Learning to Trade: Market Cycles
Learning to Trade: Building Our Understanding