Fundamentals of Short-Term Trading: Part I
By Brett N. Steenbarger, Ph.D.

In this article, I will describe patterns of price behavior on an intraday basis and their implications for trading.  I believe that an adequate consideration of how price changes actually occur during the day will challenge traditional methods of trading and open the door to new ways of viewing and analyzing the markets. 

The Challenge of Stationarity
I’d like to begin this article with a set of descriptive data on the ES market, the main market that I trade.  For purposes of convenience, I looked at the market between October 9^th, 2003 and January 16^th, 2004, which gave me 68 full days of data.  I broke down each trading morning (9:30 ET – 12:00 ET) into half-hour segments to see how each segment compares to the ones around it. 

Below is a table of the average range and standard deviation (in ES points) for each 30 minute period in the morning. 

Now let’s look at the average number of trades placed per minute during each half-hour period from 10/9/03 to 1/16/04:

 Here’s the average volume of trading in contracts per minute during each 30 minute morning period: 

Finally, let’s look at the average one minute level of the NYSE Composite TICK over each half-hour period in the morning from 10/9/03 through 1/16/04: 

What do these numbers tell us?  Most traders are aware that there is more volatility and volume in morning trading versus the early afternoon, and more volume and volatility late in the day than in the middle.  These half-hour figures, however, drawn solely from early day trading, suggest that even the morning hours are not uniform.  Volume and volatility is highest in the first half hour and tends to wane through the morning, with particularly notable drops from 10:30 ET on.

This suggests that even the very short-term trader is going to run into problems of stationarity.  When analyzing a market from hour to hour, we are—to a large extent—comparing apples and oranges.  The time series of price changes from one period may not be drawn from the same distribution as the time series of price changes from the next or the one before it.  This seriously compromises any technical analysis strategy (moving averages, oscillators, chart pattern analysis) that involves blending one period’s trading with adjacent ones.

The lack of intraday stationarity also compromises quantitative efforts to model the markets, because we cannot use period one’s data to predict period two if we have reason to believe that the two periods were not drawn from the same distribution of price changes.  To use the analogy from my previous article on stationarity, if we count cards in blackjack while the dealer is drawing from a two deck shoe, our count will be invalid once the dealer switches to a four deck shoe.  The market, as dealer, is changing shoes every hour of the trading day.  And this is a very big challenge to short-term trading.

Re-Visioning Market Analysis
Most traders, myself included, tend to view the market vertically.  That is, if we build a spreadsheet, we array the recent data on top of the prior data and create all sorts of statistical manipulations that aggregate the data from bottom up.  Vertical market analysis is problematic, however, in that it runs into the aforementioned challenge of stationarity.

When I created the tables above, I was looking at the market horizontally.  Instead of putting each day’s data on top of the previous values, I placed it to the right.  That means that the rows of the spreadsheets represent common time periods—in the case of the data above where we looked at ranges, these were thirty-minute periods.  Viewing data horizontally tells us some interesting things, in part because there is greater likelihood of stationarity across sixty common time periods than across sixty adjacent, different periods. 

Let me give a concrete example.  Suppose during a given five minute period of the day we see 800 ES trades being placed.  Is that a meaningful volume or not?  If the 800 trades occur during the opening half hour of trading, the volume is not significant.  On the other hand, 800 trades in a five minute period that occurs between 11:30 – 12:00 ET would be close to the top 5% of all values for that period.  The average volume in early morning is actually a mini buying or selling climax around noon.  And, as we shall see later, this is an important piece of information.

Here’s another example:  Suppose we break out of a hour-long range and make a new high or new low on the ES.  What are the odds of the move continuing in its breakout direction?  If you aggregate all similar breakout moves through the day, you’ll get a very fuzzy reading.  About half the breakout moves will continue; half will reverse.  But if you analyze the market horizontally, you’ll find that breakouts behave differently early in the trading day than later on.  There are many more false breakouts as you move on through the day.  Why?  On average, the reduced volume/volatility of those later hours makes it more difficult to power new market trends.

But wait!  If the odds and extent of breakout moves is different from one hour to the next, then that means that chart patterns will vary from one period to the next.  That also means that oscillator readings—what constitutes overbought and oversold—will similarly vary. 

Here’s something to try: If you want to analyze the market by chart patterns or indicator readings, switch your analysis from vertical to horizontal.  Look only at similar time segments from a stationary lookback period in the market and see what the market has done when the patterns or readings have been similar to those observed currently.  If you see a breakout from a two-hour range that occurs at 9:45 ET, look at all similar breakouts that have occurred in the first half-hour of trading.  The chances are good that your findings will be less fuzzy—and may even reveal a tradable edge.

Equivalent Bars: Another Approach to Slaying the Stationarity Beast
Richard Arms once came up with an intriguing idea: He drew charts where the bars were defined by volume rather than time.  Tick charts accomplish something similar.  Each bar represents X number of trades, not X units of time.  The reason this is a promising concept is that volume and volatility are very highly correlated.  If we draw our bars on a chart in such a way where they have equal volume, the odds are improved that we will have a stationary intraday distribution as we move from one bar to the next.  This would improve our vertical analyses of the markets.  For instance, if we wanted to use a 14 period RSI to define overbought and oversold levels, we would be on firmer ground if each of the fourteen periods were relatively uniform and drawn from the same distribution of values.

If we take the data from the tables above, we might think about making each bar equal approximately 2000 contracts of volume.  That would, on average, give us one bar for each of the first two half-hours for the day; then one bar for each 45-minute period later in the morning; and one bar for each hour around midday.  Making this segmentation of the day standard (where we always equate, say, the first half-hour of trading with the full noon hour) is a quicker and dirtier solution than Arms’, but it does have advantages as well.  When you draw bars that are supposed to be equivalent in volume and volatility and then you see an unusually large or small bar, it is much easier to visually identify the significance of the breakout or consolidation.

Making the bars equivalent also affects the holding period of a trade.  Instead of holding a trade for X hours—where morning hours will expose you to much more volatility than midday hours—you would hold the trade for X bars.  Each trade would be more similar to others, which is helpful for risk control. 

Most important of all, however, is that you could have greater confidence that the chart patterns and indicator readings that emerge on a uniform bar chart will be more reliable than those that show up on a standard chart.  A breakout of certain size from bar 1 to bar 2 will be more likely to have the same meaning early in the day as later, since you are adjusting the time value of the bars.

My basic trading is intraday, but when I hold a position for swing periods, I use the equivalent bars to help me time the trade.  A future article will detail this swing trading and how it addresses stationarity concerns.

Scalping: Still Another Response to Nonstationarity
In many ways, scalping is the opposite of creating equivalent bars.  The scalper holds a trade for a very short period of time—so short that the next bars are likely to be drawn from the same distribution as the previous ones.  Scalping reduces the average size of gains and losses per trade and runs the very significant risk of overtrading and allowing commissions and slippage to eat away at equity.  If, however, the scalper can find reliable patterns for trading, this can be the tortoise’s response to the swing-trader’s hare.

Scalping can be anything as short as trading the next tick if you’re on the floor to holding a trade for multiple minutes.  I define scalping pragmatically as exiting a position within a time frame after which you normally expect the distribution of price changes to shift.  Thus, a scalp might be held for under 30 minutes early in the day, but could be held for over an hour around midday.  To use the above idea of equivalent bars, a scalp is a position held within one of those bars.

Given this definition, most of my trading is scalping.  Here’s an example:  A market drops on high volume at 11:00 ET, with the NYSE Composite TICK hitting –750.  Despite this drop, the market makes only a marginal new low for the day before rebounding smartly as the TICK moves to zero.  As the market pulls back lazily on only modestly negative TICK, I might enter that trade on the long side to take advantage of the failed downside breakout.  The recent low—and the –750 TICK level—serve as logical stops.  On the first surge in upside volume and NYSE TICK, suggesting that the shorts are panicking to cover their positions, I might exit the position and take a few quick points of profit—particularly if it appears the larger time frame trend is down. 

Note that a key to this trading is the horizontal analysis of the market.  I know that the volume is high on the downside breakout attempt, because I know the exact distribution of volume for the 11:00 hour.  I also know that the TICK reading is extreme for that hour based on an analysis of distribution.  The horizontal analyses allow me to objectively define a buying or selling panic.  I am buying a panic where the market shows underlying strength; selling a panic where there is weakness.  Because the trade takes place within a half hour period, I need not be overly concerned about shifting distributions of price changes.  I can use standard one-minute charts and indicators without the need for equivalence adjustment.

Summary
In a future article, I will elaborate both the scalping and swing trading strategies that I am developing.  I will also be following the results of trading on my site’s weblog.  My hope is that this article stimulates your thinking about markets and market analyses, making you question off-the-shelf modes of analysis and encouraging you to create your own.  Designing the methods of trading that best fit your lifestyle and personality is half the trading psychology battle.  I will have more on that topic in the next article in this series.

Brett N. Steenbarger, Ph.D. is a clinical psychologist and active trader, writer, and researcher for the past 20 years, Brett is the author of The Psychology of Trading (Wiley; 2003) and numerous articles on trading psychology for print and online financial publications.  Click here for full bio >>