COACHING CUE
I have found that if I start my day with physical exercise and biofeedback, I can sustain calm concentration as an effective strategy for maximizing my energy and focus. If you start your day run down and distracted, you’re likely to become even more fatigued and scattered during the trading day. Part of preparation is to study the market; part is also to keep yourself in a physical and cognitive mode that maximizes performance.
LESSON 90: USE DATA TO IMPROVE TRADING PERFORMANCE
Rainsford “Rennie” Yang is the author of the Market Tells web site and newsletter (www.markettells.com), which generates trade ideas through historical analyses of stock market behavior. His service is unusually helpful in finding trading edges, particularly with respect to generating trend-catcher alerts during the day. The ideas can either be traded outright or can be used to inform discretionary decisions from favored setups. For traders who don’t have the time, skills, or inclination to conduct their own historical research (see Chapter 10), a service such as Market Tells is invaluable.
David Adler is the Director of Trader DNA (www.traderdna.com), which markets a program for tracking trading performance over time. The software captures information about futures trades and generates a series of metrics that reveal areas of trading strength and weakness. This information is especially helpful for high-frequency traders, who would find it impossible to manually enter trades into a log for analysis. Results are charted as well as summarized in print, providing easy-to-understand reports.
When I asked Rennie to summarize his most useful self-coaching practices, he most generously shared some of the patterns from his historical research. He included daily/weekly analysis and intraday analysis in his response, which I quote extensively in the following pages.
Daily/Weekly Analysis
“When advancers recently outnumbered decliners by more than a 3:1 margin on the NYSE and the market continued to push higher over the next few sessions,” Rennie recounts, “I brought up the Master Spreadsheet where I conduct all of my testing and research. It contains the daily data back to 1980 and weekly data back to 1950 on all of the major averages and all of the market internals (breadth, volume, new highs/lows, etc.) to make testing quick and easy. In this case, I searched for instances when the S&P was higher three days after a 3:1 positive breadth session and examined the market’s performance over the next two weeks. Such lopsided breadth days can mark buying climaxes, in which buying power is exhausted and the market trades lower short-term. But when the market remains on firm ground in the days following such a lopsided positive breadth session, I would expect the S&P to continue moving higher over the next two weeks.”
Rennie Yang explains how to conduct this analysis: “To keep things simple, let’s assume I have a spreadsheet containing daily S&P 500 and NYSE advance/decline data. Column A has the date, while columns B and C contain the daily S&P 500 closing price and NYSE advance/decline ratio, respectively. Starting at the fifth row in column D, enter the following formula:
= if(and(c2 > 3, b5>b2), (b15-b5)/b5,””)
“This states: if the advance/decline ratio from three days ago was over 3.0 and the S&P closed above its three-day ago close, show the percentage gain for the S&P over the next ten trading days. Fill this column down to the point where the data ends and quickly scan the results. You can immediately see that the hypothesis seems correct. Over the last 30 examples, the S&P has been higher 10 trading days later in 25 out of 30 cases, or 83 percent of the time.”
To get a sense for whether an edge is present, it is important to compare a historical pattern over X days with the market’s general tendency over X days.
“That may look like a bullish edge,” Rennie points out, “but first you need to check the S&Ps at-any-time odds of posting a higher close 10 trading days later. Here’s a quick and easy method. Go back to the fifth row in Column D of the sample spreadsheet above and change the formula to read “=if(b15>b5,1,””).” This means that if the S&Ps close two weeks later is greater than today’s closing S&P, print a one, otherwise print nothing . Then fill this column down to the point where the data ends. In most spreadsheet applications, such as Excel, you’ll see in the lower right corner the summation of all those 1s. Dividing that result by the number of days in the sample reveals the at-any-time odds—57 percent. In other words, on any given day, the chances that the S&P will be higher two weeks later have been 57 percent. That is far less than the 83 percent odds when the S&P is higher three days after a 3:1 breadth session. This confirms the original hypothesis that the chances for a market rally over the intermediate-term are far better than average, meaning there’s a clearly bullish edge.
“Instead of relying on traditional indicators, most of which merely manipulate and regurgitate price action, look beneath the surface of the major averages at the market internals such as breadth, up/down volume, new highs/lows, NYSE TICK action, etc,” Rennie advises. “This is the area in which I’ve found the majority of trading setups that stand up to historical testing. Does a surge in new 52-week lows portend an intermediate-term bottom? Is a 90 percent up volume day bullish? How about a cluster of 80 percent up volume days in a short time frame? Just about any concept you can imagine can be quickly researched and tested with the proper preparation. By maintaining your own version of a master spreadsheet and conducting your own testing and research, you’ll know when a concept truly provides a bullish or bearish edge. Consistently exploit that edge, and you’ll have a leg up on the competition.”
It is powerful when you find a pattern with an edge, but even more powerful when your edge is the ability to find and trade many such patterns.
Intraday Analysis
“The NYSE TICK is probably the single most helpful intraday indicator,” Rennie Yang asserts. “It tells you, at a glance, how many issues last traded on an uptick versus a downtick. A reading of +500, for instance, means that, at that moment, 500 more issues last traded on an uptick. When you first view a chart of the NYSE TICK, it will look as if it’s too noisy to be of any use . . .But change your viewpoint and you’ll see an entirely different picture . . .You can actually hide the NYSE TICK itself and just plot the 20-period moving average of the TICK to gain considerable insight into the supply/demand equation. Is the average holding above zero, meaning generally more buying power, or is the average holding below zero, reflecting better selling pressure? That’s something every day trader should know.
“Here’s another technique for utilizing intraday TICK readings,” Rennie offers. “Many data feeds such as e-Signal allow you to export data in real time to a spreadsheet. Through a technology known as DDE (dynamic data exchange), it’s a relatively simple process to have one-minute NYSE TICK data updating constantly in your spreadsheet. Once this has been accomplished, you can easily create your own cumulative TICK. Here’s how: Set up a spreadsheet with columns A, B, and C containing the date, time, and close of the NYSE TICK ($TICK in e-Signal). It should start at row 2 and contain the last 390 one-minute bars, the equivalent of one full trading day. In the first row of column D, enter a zero. In the first row of column E, enter a space, followed by today’s date (the space is due to a quirk on e-Signal’s part). Then jump down to the second row of column D and enter the following formula: “and fill it down through all 390 rows. This states that, if the date matches today, then take the most recent closing one-minute TICK and add it to the running total for the session. As the data comes in, this will automatically build a cumulative TICK in column D, which can then be charted to provide a real-time intraday chart of the cumulative TICK. Draw a line at the zero mark and watch the cumulative TICK reveal the underlying buying and selling pressure that is hidden in the noise of the NYSE TICK.”
= if(a2 = $E$1,c2+d1,d1)
The cumulative TICK reveals the trend of daily sentiment.
David Adler approaches the use of data for self-coaching in a different manner, focusing on the assessment of trading performance itself. “The philoso
phy behind TraderDNA, which I firmly believe,” he explains, “is the idea of being cognizant of what happened (in terms of your performance) within a given session, week, month, etc. of your trading, so that, going forward, the negative aspects can be minimized and the positive can be maximized. The fundamental idea is that, if the trader is able to look back on a certain time period of his trading and understand more about the overall result, then he can be proactive . . . in preventing the same mistakes going forward. Likewise, he can identify strengths and focus on situations that are likely to result in a profit based upon what his past trading has shown.
“Our users extract their order data from their front-end software,” David Adler notes, “and import the data into TraderDNA. This affords them the opportunity to thoroughly analyze their data in order to understand more about the strengths and weaknesses of their trading: specifically, their performance trends, where their losses came from, characteristics of their trades, the differences in their winners and losers, amongst other things.” Here are some of the analytics provided by the software, along with David’s commentary: 1. Hour of day analysis. “Because markets trade differently throughout the day,” David explains, “many of our users measure their performance (in terms of average P/L, risk taken, profit opportunity, number of winners/losers, size of winners/losers) by the time of day the trade occurred. This helps them to use their past performance to determine the most ideal times for them to trade a given market.”
2. Winning trades versus losing trades. “In looking for differences in winning and losing trades, it’s helpful—and necessary—to group all winners together and group all losers together and then apply metrics to each category,” David points out. “The value in doing so is the opportunity for you to discover the differences in your winning trades and your losing trades.” Among the metrics applied to both the winning and losing trades are the number of winners and losers; the average win and average loss size; the number of times a trader added to winning and losing positions; the average amount of heat taken in a trade before it was covered for a profit or loss; and the average time it has taken to hit the point of maximum heat. The latter is an especially interesting metric in that, by comparing winning and losing trades, it can help guide traders to formulate rules for the proper amount of time to be holding positions.If you know how much heat you take on winners versus losers and how long it takes you to reach that point of maximum heat, you can set guidelines for when and where it might be prudent to cut your losers.
3. Comparing results among market/product traded. “Traders that trade more than one market/product sometimes have difficulty interpreting how their performance compares in their trading of each market,” David Adler observes. “Oftentimes the trader will be very profitable in one market but have consistently less profit or even losses in other markets. If you trade more than one market, it’s important to split up any analysis or performance reporting that you do by the markets you trade.” Among the metrics he applies to different markets are: the total amount earned/lost per market; the average win and average loss for each market; the number of consecutive winning and losing trades for each market; the average risk incurred among trades for each market; the average lost profit opportunity for each market; the number of times you added to losing and winning positions per market; the average amount of time spent in losing trades per market; the maximum losing and winning trades per market; and breakdowns by hour of the day for each market. My experience with metrics is that these breakdowns by market will often shift over time, as certain markets yield greater opportunities and others go dry. Tracking results over time can be a great way of seeing, in real time, when and how markets are changing.
“I’ve seen numerous traders increase their P/L by minimizing trading losses and increasing the frequency and/or size of their winners after applying one or more of the techniques above,” David concludes. “From what I’ve seen, it’s most effective to conduct your analysis or review of your trading no more than once a week, and ideally once every two weeks or even once a month.”
Intuition—the result of implicit learning that occurs after long periods of observing market patterns—may play an important role in getting traders into and out of positions. Even the most intuitive and discretionary trading, however, can benefit from analytics: knowing which markets and time frames offer opportunity and measuring how well you’re taking advantage of that opportunity. Ultimately, you are your own trading system . Your task, as your own performance coach, is to know how your system operates, avoid its shortcomings, and maximize its strengths. The insights and tools provided by Rennie and Dave are excellent guides in the quest to become more scientific in the management of our trading business.
COACHING CUE
The contributors to this chapter have provided a wealth of insights, derived from firsthand experience, as to the principles and practices that can improve your trading. A worthwhile exercise is to review each of the contributor’s ideas and identify the overlap: the points emphasized by more than one contributor. These points of overlap represent important best practices that can guide your efforts going forward.
RESOURCES
The Become Your Own Trading Coach blog is the primary supplemental resource for this book. You can find links and additional posts on the topic of coaching processes at the home page on the blog for Chapter 9: http://becomeyourowntradingcoach.blogspot.com/2008/08/daily-trading-coach-chapter-nine-links.html
The contributors to this chapter maintain their own web sites, which offer a wealth of resources to developing traders. Here are links to the contributors to this chapter and their web sites: http://becomeyourowntradingcoach.blogspot.com/2008/08/contributors-to-daily-trading-coach.html
For background on technical analysis, Brian Shannon’s book is a useful resource for mentorship: www.technicalanalysisbook.com/
Ray Barros’s book The Nature of Trends details his approach to trading and trading psychology; see also the seminars he offers on these topics: www.tradingsuccess.com/
John Forman’s book The Essentials of Trading is an excellent introduction to the practice and business of trading: www.theessentialsoftrading.com/Blog/index.php/the-essentials-of-trading/
The NewsFlashr site is a great way of staying on top of many popular trading-related blogs, as well as news: www.newsflashr.com/feeds/business_blogs.html
CHAPTER 10
Looking for
the Edge
Finding Historical Patterns
in Markets
Science is the great antidote to the poison of enthusiasm and superstition.
—Adam Smith
Traders commonly refer to having an edge in markets. What this means is that they have a positive expectancy regarding the returns from their trades. Card counting can provide an edge to a poker player, but how can traders count the cards of their markets and put probabilities on their side? One way of accomplishing this is historical investigation. While history may not repeat exactly in markets, we can identify patterns that have been associated with a directional edge in the past and hypothesize that these will yield similar tendencies in the immediate future. By knowing market history, we identify patterns to guide trade ideas.
So how can we investigate market history to uncover such patterns? This has been a recurring topic of reader interest on the TraderFeed blog. If you’re going to mentor yourself as a trader, your efforts will be greatly aided by your ability to test the patterns you trade. After all, if you know the edge associated with what you’re trading, you’re most likely to sustain the confidence needed to see those trades through.
A thorough presentation of testing market ideas would take a book in itself, but this chapter should get you started. Armed with a historical database and Excel, you can greatly improve your ability to find worthy market hypotheses to guide your trading. Let’s get started . . .
LESSON 91: USE HISTORICAL PATTERNS IN TRADING
A trading guru declares that he has turned bearish be
cause the S&P 500 Index has fallen below its 200-day average. Is this a reasonable basis for setting your trading or investing strategy? Is there truly an edge to selling the market when it moves below its moving averages?
The only way we can determine the answer is through investigation. Otherwise, investing and trading become little more than exercises in faith and superstition. Because markets have behaved in a particular way in the past does not guarantee that they will act that way now. Still, history provides the best guide we have. Markets, like people, will never be perfectly predictable. But if we’ve observed people over time in different conditions, we can arrive at some generalizations about their tendencies. Similarly, a careful exploration of markets under different historical conditions can help us find regularities worth exploiting.
As it turns out, moving average strategies—so often touted in the popular trading press—are not so robust. As of my writing this, since 1980, the average 200-day gain in the S&P 500 Index following occasions when we’ve traded above the 200-day moving average has been 8.68 percent. When we’ve been below the 200-day moving average, the next 200 days in the S&P 500 Index have returned 7.32 percent. That’s not a huge difference, and it’s hardly grounds to turn bearish on a market. When David Aronson tested more than 6,000 technical indicators for his excellent book, Evidence-Based Technical Analysis, he found a similar lack of robustness: not a single indicator emerged as a significant predictor of future market returns.
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