Feature

Understanding Market Type Part IV

by

Van K. Tharp, Ph.D.

When you have a trading system, you should always know how it performs under various market conditions. Previously, I’ve written about the direction of the market; however, in this article I’m going to focus on volatility as measured by the ATR.

I was recently asked, “Doesn’t the daily percent change work as measure of volatility?” No, it doesn’t because the market could go from 810 to 870 and close at 811. The change would be minimal compared to the overall daily range. That’s why we use the average true range, which also takes into account any opening gaps.

Again, my market consists of the S&P 500 index, and I have data for that index going back to 1950 (i.e., over 14,000 days). My overall measure of volatility is the 20 day ATR. However, the ATR depends on the price: The higher the price of the index, the bigger the ATR will be. Thus, we must use ATR as a percent of the close. This is illustrated in the table below.

This table shows the ranges of price, 20 day ATR, and ATR % of close over the last six decades. Notice that the mean ATR % does not change that much from decade to decade. The overall mean ATR % close is 1.3 with a standard deviation of 0.72.

After going through the extreme volatility of 2008-9, I’ve begun to suspect that such markets are extreme and generally only occur in extreme bearish conditions such as we’ve recently had and had in the 1930s. Data of the Dow Jones index, which goes back that far, suggest that this is indeed the case.

Anyway, I decided to sort the market types into four volatility groupings: 

• Normal: The average ATR% plus or minus 0.5 standard deviations. 
  

• Quiet: Anything less than a 0.5 standard deviation below the mean.
   

• Volatile: 0.5 standard deviations to three standard deviations above the mean.
   

• Very Volatile: Anything above three standard deviations from the mean.

The following table shows the distribution of our 14,755 days in these four categories.

Notice that very volatile markets are rare, occurring only about 1% of the time. Volatile markets occur about 25% of the time. Normal markets occur almost 40% of the time, while quiet markets occur about 35% of the time. 

The next table shows the average percent change in the S&P 500 during each of the four volatility-based market types.

Notice that the more volatile the market, the more likely the price is to go down. Similarly, the more quiet the market, the more likely the price is to go up (with both normal and quiet days being generally up days).

Let’s look at what happens to the market the next day after a given market classification. Here we are asking the question, “If the markets are highly volatile on March 3rd, what is the average percent gain on the following day (i.e., March 4th) regardless of the market type that day?” This data is shown in the next table.

Notice that as you move from volatile to quiet, the expected percent change the next day goes up. However, the largest expected percent change is in very volatile markets. And here the expected percent gain is not down (as it is during such markets), it is up. This leads me to two assumptions:

1. As markets get volatile, they are more likely to be down markets.
   

2. In the markets that follow very volatile markets the next day (even though much of the time the next day will still be the same market type), the average change is the largest percentage gain from any type of market.

Combining Volatility and Direction to Describe Market Type

Now let’s combine our five market types (defined last week) with our four volatility types and see what we can expect. In this case we are using the 100 day SQN™ on the daily percent change to determine market direction.

The data in this table confirm what we were suspecting from the percentage change data: Very volatile days only occur during bear and strong bear markets. Although the percent of days shows up as zero, there was one very volatile strong bull day. Thus every cell has at least one data point. On the other hand, bull and strong bull markets tend to be normal or quiet. 85.7% of the strong bull markets occur during normal or quiet conditions and 85.9% of bull markets occur during such conditions. 72.1% of neutral markets occur in normal and quiet conditions. However, when we move to bear and strong bear markets, the situation reverses. 54% of bear markets occur during normal and quiet conditions and only 30.5% of strong bear markets occur under such conditions, despite volatile and very volatile markets being somewhat infrequent.

Let’s look at our total market type and the average percentage gain/loss that is likely to occur under each market type condition. This is shown in the next table. There are no real surprises here.

Now let’s look at what happens the next day after a particular market type (the type might stay the same or it might not). In other words, if March 3rd is a volatile strong bull market, what is the likely percent change on March 4th (regardless of market type)? These data are shown in the next table.

When the market is bullish, we can expect the next day to be up. And when the market is strongly bearish, except for one condition, we can expect the market to be down. However, that condition (very volatile strong bear) is one in which the average gain the next day can average nearly 1%.

Some History

Finally, let’s look at some history with our new market type. The next table shows market type for 2009 based upon our new classification. It still isn’t perfect. June is showing up as bullish, despite the very toppy looking market. But remember, we are still using a long period in our definition of market type (i.e., 100 days). The 25 day SQN is also shown as a reference. I’ve highlighted the high and low points in the bear market in 2009 for both the 100 and 25 days’ SQNs. Our market type is determined by the 100 day, but we will be watching the 25 day closely.

 We will use this new market type classification in our monthly updates from here on. These articles on market type will only be available in our newsletter for the next two weeks. After that point they will be available only as a special report until they are reprinted in the 2nd edition of The Definitive Guide to Position Sizing™.

All of this work was done with the XLQ add on to Excel with the help of Leo van Rijswijk, the developer of XLQ. 

About Van Tharp: Trading coach, and author, Dr. Van K. Tharp is widely recognized for his best-selling books and his outstanding Peak Performance Home Study program - a highly regarded classic that is suitable for all levels of traders and investors. You can learn more about Van Tharp at www.iitm.com. 

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Trading Tip

Cutting Off the Left Side of the Bell Curve Part III

by
D.R. Barton, Jr.

“People who don't take risks generally make about 2 big mistakes a year. People who do take risks generally make about 2 big mistakes a year.”
-- Peter Drucker

Imagine working on a project where you’re building something—let’s say a storage shed in the backyard.

You pour the concrete foundation, and then you start framing in the structure. Walls go up, and then you put in the rafters to support the roof. After a couple of weeks of good work, it really looks like you’re building something special.

Then one day, you make a mistake. And then another. And the whole structure comes tumbling down. Weeks of work destroyed in just a couple of poor moves.

While this seems somewhat silly, almost absurd, it is exactly what happens to many traders’ accounts.

They spend weeks or even months building up the equity in their accounts, only to give it all back in one or two bad trades or one or two poorly managed trading days.

For the last two articles, we’ve been talking about managing the left side of the equity curve: finding a way to minimize individual and cumulative losses.

Last week we looked at a bell curve and talked about knocking out those trades that make up the “tail” of the loss side of the bell curve—the big losing trades that end up being more than twice as big as originally planned. Van rightfully classifies all of these trades as “mental errors” (all that are not caused by overnight gaps, etc.).

Today we’re going to talk about those days (for intraday trades) or weeks (for swing and longer term traders) that eat up weeks or months of profits.

These are the days where a trader enters into a downward spiral of losses that seem to compound and grow with every trade. Not only are mental mistakes made, but they are increased as the trading period continues. Mistakes like these come in bunches:

• Continually fighting a trend. When a trader tries to catch an exhaustion move or find a turning point in a strongly trending market. This can happen to day or swing traders. With each further extension of the trend, it seems like this has to be last gasp; surely this thing will turn around here! But losses mount, and without a way to break the cycle, they get worse and worse.
  
• Competitive or revenge trading. In our e-mini bootcamp that we’re holding this week, we have found this to be a mental mistake that rings true with so many participants! Having a trade go against you and following it up with a low-quality trade just to “have a winner” or to “show the market who’s boss” can lead to a series of trades that are pure equity killers.
  
• Any period when our trading strategy is not matched with the market (e.g., trend following in choppy markets, or counter-trend trading in trending markets). If our trading plan doesn’t have a good way to minimize trading in these periods (or stop trading all together), then trading losses can add up quickly.

Tools for Protecting and Managing the Left Side of the Equity Curve

Fortunately, we have some good tools at our disposal to manage these losing periods:

• Good old fashioned stop losses.  Most people reading this article are familiar with the use of stop losses. But knowing about them and using them in an unwavering manner are two different things! And until one uses stops with unswerving discipline, then nothing else will make any difference. This is the foundation to managing the left side of the equity bell curve.
  
• Understanding our trading systems and strategies.  The better we understand our strategies, the less likely we will be to trade or overtrade the strategy in a market where it performs poorly. I’ve often said in system design classes that I really like trading systems that have some self-regulating mechanisms in their designs that help them trade less often (or not at all) in unfavorable markets.
  
• Using a “circuit breaker”.  Almost all traders can benefit from using some sort of loss circuit breaker—a rule that makes the trader shut down after a certain loss for the day, week or month. This idea alone can help traders to manage those tails at the far left of the equity bell curve.

Thanks to all of you who have sent in your stories of mistakes and managing the left side of your bell curve! I’ll summarize some your great ideas next week. Anyone who would like to submit stories can send them to drbarton “at” iitm.com. 

Until next week…Great Trading!

D. R. 

About D.R. Barton, Jr.:  A passion for the systematic approach to the markets and lifelong love of teaching and learning have propelled D.R. Barton, Jr. to the top of the investment and trading arena.  He is a regularly featured guest on both Report on Business TV, and WTOP News Radio in Washington, D.C., and has been a guest on Bloomberg Radio. His articles have appeared on SmartMoney.com and Financial Advisor magazine. You may contact D.R. at  "drbarton" at "iitm.com".  

Q&A

Expectancy

Q: I've read both Trade Your Way and your Electronic Day Trading book. If I have an R-multiple derived after system testing a number of trades, once actual trading commences, do I enter all trades, regardless of  average R, and use the stop indicated by my system, or simply not exceed the average R derived from the expectancy study? — Lee Rousseau

A: Expectancy is the mean of the R-multiples. Half the samples you get in the future will be worse. Half will be better. You should always follow your system's stop. — Van

 

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