Cell phones are already more powerful than the personal computers of the not too distant past. The barrier will not be the compute power that you can carry in your pocket. If you want to talk about the needed processing power or storage, any level you want to talk about will be in your computer someday.

There are three other hurdles that the cell phone needs to overcome to deliver all of the utility of the modern personal computer. The PC wins today for screen size, keyboard, and relative security, but cell phones already have options that show that these advantages are on the way out. The personal phone in a cell phone is almost on your doorstep in a package that will be hard to resist.

Projector Screens

You can already get a cell phone with a projector good enough to put on a quality presentation. It won’t be too long before we see a cell phone that can project a screen of almost any size that creates a clear, sharp image in full daylight. At this point, the cell phone will have display capabilities that at least rival the capabilities of a desktop PC.

Keyboards

Already lots of phones provide the capability to plug in a standard keyboard. The only barriers to be hurdled here are design problems with the keyboards themselves. So far, none of the keyboards that you can carry in your pocket can really deliver the feel of a quality keyboard. That will happen soon for those of us that cling to the need for a keyboard.

For those who are looking for another means of input, voice is coming of age. It works somewhat on Windows mobile phones, and it seems to work very well on Android phones. I haven’t seen this on iPhones, but I suspect they are better at voice recognition just like I assume that they are better at everything else. They all seem to be better at voice commands than voice text entry.

Security

The worst security feature of cell phones is how easy they are to lose. I don’t think we can fix that problem, but we can change the impact of a loss. First, we can have an automatic backup system where all of your data is kept on a secure server somewhere. I believe that Apple’s Mobile Me does that, and there have to be many competitors. These services will get better and better.

It seems like you also need the other side of the coin. That is, you need to be able to erase your phone remotely when it is lost. Some phones may have this already. It certain

We believe that the most important output from these efforts is the discovery of relationships between decision factors. It’s not the price you pay when you buy or the price you get when you sell that determines profit, it’s the difference between the two.

That means that the most important relationships we are looking for are the relationships between selection factors and exit factors. We like using response surface graphs to show these because they show the relationships clearly, and the general picture they show differs little when we try it on different data sets. This leads us to believe that the relationships are robust even though the optima are often simply coincidental.

Response surface graphs also show which settings are clearly dysfunctional. It becomes pretty clear after looking at just a few sets of results that there is a range of settings that might work, and a range that is clearly disastrous. This is true for both the individual factor settings and for the relationships between them. Note that findings of dysfunctional settings tend to be far more robust than findings of optima. Knowing what to avoid doesn’t tell you how to do the job right, but there is clearly value in avoiding known disaster area.

In addition to interactions between selection factors and exit factors, there are also clearly interactions between various selection factors and interactions between various exit factors. For example, an either OR rule for selection factors means that you get into more trades while an AND rule reduces the number of trades. We know that without performing any tests, but more trades or less trades is not the critical answer we need. We need to know whether an OR rule gives us a higher winning percentage and greater profit, and whether an AND rule gets us into more trades that lead to more profits.

The same is true with exit rules. Do they work well together or do they get in each other’s way? Generally speaking, there is a desired exit process and a set of fall-back exit processes. You don’t want the fall-back processes to unduly interfere with the desired process, but you can’t let your primary exit process to be so dominant that the backup plans don’t go into effect until it is too late. Response surface graphs show these relationships very clearly.

Not all of the critical interactions are two-factor. There can be three, four, or even more factors that are working together. The more complex an interaction, the less likely it is to be robust, but many three-factor reactions are robust. Showing three-factor interactions is a minor problems that we address currently with animations. Here is an example.

We show the most powerful factors on the axes. Here this is the critical selection factor and the primary exit factor, and that is generally the case. The third factor, the backup exit strategy, we show with the animation. The animation shows that the third factor changes the degree but not the nature of the reaction between the first two factors. This presents a fairly clear picture of what is going on in the trading system, and we can see that these pictures will look very similar if we apply them to a variety of data sets.

It is important to see whether the charts you get out of the data support or contradict your basic theories. The basic theory here is that a move in one direction is most likely to be reverse if it swings far enough, and most likely to make a solid profit when you are looking for the swing back to be half as large as the initial move. The size of the swings varies considerably, but the relationship between size of the initial swing and size of the secondary swing is pretty consistent.

The fallback exit factor is of less importance. By itself, it never determines whether the system will win or lose, but does affect how much it will win or lose.

What settings should we choose for our working system? It’s still something of a crap shoot, but we have a reasonable idea of the direction to shoot in. The chances of picking the best values is only slightly more than zero, but the chances of picking pretty good values is very high. There may be other response surfaces that you can look at like days in position or winning percentage that will push you in one direction or another, but you still are moving from analysis of the past to prediction of the future. The stock market will never be a chemistry problem.

External risk management is strictly about the relative size of your bet. Strictly, it is about the degree of affordability of the worst possible loss. A prudent external risk management plan ensures that each individual bet and each collection of correlated bets risks less than half of your available bankroll. The external risk management process should be the same for all bets. The primary question you should always be asking yourself is how much you can lose and how much you can afford to lose.

External risk management is far more important than internal risk management. Good external risk management keeps you in the game when things go wrong. Never forget that eventually things will go wrong.

Internal risk management is disaster identification and mitigation. It’s all about what can go wrong, and what can be done to prevent it. Internal risk management starts with a list a list of the possible disasters. A prevention and mitigation strategy is then developed for each risk. The process is simple and straight-forward. Just going through the process gets many of the risks under control, and it is essential to getting the risks solidly under control.

The quality of internal risk management is a factor in the execution of external risk management. The more the internal risks are kept under control, the less risky the overall bet is. Always consider the internal risk management when evaluating the probability of loss.

In an ordinary research environment, the time to design an experiment uses a tiny fraction of the time. Analysis takes slightly longer. Time to set up and execute the experiment takes far more than ninety percent of the total time in the process. When a problem requires an answer in significantly shorter amount of time, the only hope is to reduce the time it takes to execute the tests. If you need an answer within two days, you need to reduce the execution time for each trial to about a minute or less.

You can get incremental improvements in the speed of trading system tests by getting faster and faster computers, but the real secret of speeding up trials is to consider less data. Each piece of data requires processing, even if the process is simply to ignore that piece of information. Do you need or even want ten years of data if you are testing a system that will trade a thousand times a day? Do you need six months of test data? Can you find what you are looking for in the last week’s data?

A week is the shortest time frame that really makes sense in the context of the problems we have looked at, but your problem might allow even shorter time frames. If you are talking about tick data, even a common PC can process a week of tick data is less than a minute. While you might be able to find your answers with even less data than this, it is hard to see why you might want to.

Once you get the total execution time of an experiment down in the neighborhood of a single day, the dynamics change. If you are taking a couple of days to design the experiment and a week to analyze it, that becomes the place to save time. An experiment that takes six months to run demands two days of design and at least a week of analysis.

Any environment like high frequency trading that have severe enough time demands to drive the setup and execution time for experiments down to a scale of hours will also demand rapid design and analysis.

More rapid design is fairly easy to achieve. The painful and tedious cost of most experiment design is deciding on the compromises. You need to compromise because of the cost of executing each trial of an experiment. For a trading system, the cost of executing these trials is strictly the cost of the time. Once that problem is solved, additional trials do not impose additional costs. Actually generating a design once you have decided on the goals of the experiment only takes a few minutes with today’s tools.

Some research organizations devote weeks or months to analysis. This is entirely justified when an experiment takes six months to run because you are obligated to wring every possible bit of information from each experiment. Experiments using trials that have little or no costs to them generate far more data than experiment

However, everyone who has ever analyzed an experiment will tell you that some results are obvious at a glance and other results are only obvious after a particular perspective has been imposed on the data. Finding an answer to a particular question is usually not that time consuming. Primary analysis does not have to take days or weeks. Even the mountain of data generated by a high speed experiment can be parsed rapidly if that is done with a narrow focus.

Secondary analysis still can and should be done. Unexpected results are often only found in the secondary analysis, and discovery is all about unexpected results.

I am constantly trying to figure out why the best players are that good. It’s easy enough to see why the bad players are so bad. They bluff too often, call too often, don’t raise to protect their hands, don’t control their emotions, and have no understanding of position. You can become a mediocre to good player simply by addressing these problems, but great players have something else. As a matter of fact, even the great players still have some of these problems on an occasional basis.

Something else makes them great. I don’t think it is that the best players have more heart, more brains, or more luck, though everything does go easier when you have more advantages on your side. I think what separates the great players is their awareness that poker is a relative game. The great players spend most of their attention focusing on what their opponent has. Bad players often don’t think at all about the opponent’s cards, good players think about them at the time of decision, and great players think about them even when they are not in the hand. Every time you show a hand down, you are giving a great player another weapon to beat you with.

The great player knows that there is no greater advantage in poker than knowing your opponent’s cards. We could all be poker millionaires if we knew what our opponents have and they don’t know what we have. One of the best examples of this was a couple of years ago in the main event at the World Series of Poker. Sammy Farhar called a massive pre-flop raise early on the first day with only a pair of deuces. He knew that the nervous amateur making the bet would not have made that bet with anything other than a pair of aces. When a deuce came on the flop, Sammy got the rest of the amateur’s chips. I’m don’t think that the implied odds that he got really justified Sammy’s call, but know just exactly where he stood was a powerful incentive that he could not resist. He made it pay off.

Daniel Negraneau is always talking about what his opponents have when he is heads-up in a hand. I am sure that he only refrains from discussing what other people have in their hands in other situations because that would be outside the etiquette of the game, but I am sure he is thinking about it all of the time. I find Daniel the most instructive of the leading pros to watch for just this reason. Listen to the questions that he asks himself.

Why did he bet that much? Why did he check? What did that card do for him? How likely is it that my opponent is bluffing? Is this a guy who only bets the nuts? How nervous is this person about this bet? How many chips does this opponent have left? What is this opponent likely to do if I raise?

Daniel doesn’t always come up with the right answers and he’s quick to notice when he is wrong, but he is always asking and answering the questions. That gives him many chances to be right than he would have if he never thought of those questions.

It’s just like that in business and investing. Asking all of the right questions doesn’t guarantee that you will win, but it does increase your chances dramatically. There are never any guarantees, but there are lots of edges if you look for them.

Just spend a few minutes thinking about the last few years at some companies disappeared.  They disappeared not because they came up with the wrong answers; many disappeared because they simply didn’t ask the right questions.

It would be really nice if Linked-In Updates would report when I post another entry, but they don’t. You actually have to go to my profile page to see that I even have a blog, and that doesn’t show to people outside your network unless you have an upgraded (not free) account. Upgrade to the business account, and people will see your blog on your profile if you so desire.

Always remember that even if no one reads your blog for months at a time, somebody will when you go to look for a job or attempt to sign up a new client.

That means that you don’t write about politics unless you want to make about half of the people out there want to avoid you for your political leanings, you don’t want to talk about religion unless you are in the religion business, and you don’t want to talk about your partying life unless you are selling your partying life. You wouldn’t talk about your bad habits in an interview. A blog will get to your interview before you do so keep that in mind.

In fact, one of the nice things about having a blog is that it is like an interview where you are allowed to ask the questions and give the answers, but someone who did not participate in that process gets to judge it. Make sure that you ask questions that are interesting to your perspective interviewers or customers, and make sure that you provide thoughtful answers. They can’t always be the right answers, but they can always be thoughtful. And because it is in writing, you don’t have to show your thought until it is complete.

If you can write, you should. Everybody needs people who can write, and the more technical your field is, the more your ability to write will be appreciated. However, you do need to remember that your Linked-In persona is the one you are taking to work. Your Linked related blog is not where you want to rant and rave.

You should only write about things that relate to the way you earn your living or want to earn your living. That gives lots of us a lot of latitude as long as we are creative. For example, I am a process professional who builds software, directs research, and markets that research. I’ve been an entrepreneur for forty years. I can write about anything from at least one of those perspectives, but I do have to couch it in that perspective.

For example, I love to play golf, but I’m not in the golf business. That means I can talk about the marketing of Tiger Woods (a topic I find fascinating), but my Linked-In related blog is not a place to discuss potential cures for my hook. If I was in the golf business, I’d have a little more latitude. If I was a golf architect, I could do course reviews or blogs on hole design. If I was a club maker, I could write about swing dynamics. The point is that you should only write about things that have a chance to help your business.

So how does this and other articles on Linked-In fit in for me? Linked-In is a tool that I am trying to exploit to its fullest extent. Part of my role and part of my interest is always on marketing, and Linked-In is a marketing tool. You have to understand marketing to some extent no matter what your job is because unless someone is buying, you may not be getting paid for long.

We started applying Design of Experiments to automated trading systems about a year ago, but the tests were taking a long time. We were working with intraday data, and that is really dense. It took many months to complete a study that used just two years of market data. Even at that cost, we were able to run a couple of studies and became convinced that the approach was valid. Unfortunately, the systems were proprietary and secret so we were having a little trouble talking about our approach in any detail without betraying a confidence.

A couple of months ago, Ernest Chan suggested that we test something from the book Short Term Trading Strategies That Work. We needed to talk about something other than our own systems. There were two major surprises.

The first was the speed of the tests. Our tests for the options trading systems that we built took six hours for each trial for each expiry (month). An eighteen month study takes about three months running ten of these tests at a time in parallel. The strategies in the book used only end-of-day data. As a result, a test on a twenty-year set of data takes one quarter of a second! We had the study completed, and article on it accepted by a publisher in less than a week.

This is mind-boggling because in most experimentation the cost of the trials is the overwhelming majority of the cost of the experiment. The challenge of most of Design of Experiments is to get as much information out with as few trials as possible. One of the problems with our business model when we built Strategy was that there very few people who needed enough experiment designs to keep us in business because they would get a design, take months or even years to run the experiment, then need our help or software for about two hours to interpret the results. When the trials are free and run this fast, the design of the experiment and the analysis become significantly more difficult and time-consuming. That’s fine with us because we have some unique advantages there.

The response to our efforts was the second surprise. We had never heard of this book, but it turns out to be very popular in the trading community. Many people have read, and those that haven’t, had at least heard of it. Many people had a common basis for understanding our work, and we are starting to have real conversations about applying this technology. We are moving on to models that we couldn’t even consider when the cost of trials was an issue.

I’m no fan of complexity myself. I want everything not only simple enough so that I can understand it, but also so simple I can’t misunderstand it. I want it simple enough so that I can learn everything about it. Yet it is complexity that makes our modern life modern. Cars are more complex than walking, computers are more complex than pencil and paper, and a steel driving machine is more complex than a hammer.

A monkey understands a hammer. You could teach a monkey to use a steel-driving machine, but not to build or maintain one. Most of us are like that monkey. That is, we can only make use of a vast amount of the modern world because someone has found a way to package complexity in an easy-to-use wrapper. We can exploit complexity because someone else has taken care of all of the details. In fact, complexity beats simplicity on a wide variety of measures.

Once complexity gets out of control, it can be very dangerous. A drunk behind the wheel of a car and a madman with a nuclear weapon are just two such examples. Complexity is powerful, and control is easily lost. Simplicity may win in the long run, but complexity wins time after time in the short run. Are you going to bet on the man with the hammer or on the steel-driving machine?

I think we are all going to bet on the machine. It has too many advantages, each of which is an encapsulation of complexity. We want complexity on our side.

I think this is true when you are following or building an investment management system or a trading system as well. If you want to beat the other systems that are out there, you have to have more complexity on your side. You need to add the right kind of complexity, you need to keep it under control, and you need to deliver in an easy to use package even if you are the only user. The more details you need to keep in mind to use your system, the more you are likely to misuse it.

Let’s look at a simple investment plan. Buy real estate. Lots of people have made lots of money with a plan no more complex than this, but it is easy to see how a little complexity could make it better. For example, it gets considerably more complex if the plan is simply expanded to add one condition: “Buy real estate if you can afford it.” The added condition encapsulates a significant amount of complexity, and that complexity should make the plan work better.

If you actually want to make sure that you are getting a good deal, that complicates the plan even more. “Buy real estate when you can get it for less than it is worth for a price you can afford.” It still looks like a pretty simple plan, but we can all attest that our ability to state simply and plainly what needs to be done doesn’t really mean that it is simple or easy.

There isn’t really much simplicity around until we impose it by not thinking about most of what is going on. That’s not a problem for us. People are very good at ignoring most of what is going on. It’s how we get through every day.

We should have because the most complex experiments were always run by people who had the least expensive trials, but even testing print cartridges makes a noticeable dent in a budget when you want to run a hundred trials. At the very least, the tests will still take hours, perhaps days, to run.

When we started applying Design of Experiments to financial engineering, we were strictly interested in intraday trading systems. Testing these systems had a moderate cost. For example, if we wanted to run sixteen trials on three years of data, it did not cost us much money, but it did take several months. That has a significant cost when you want to know the answers right now.

We wanted to talk about those experiments, but a number of factors prevented that We looked around for another system that we could experiment on. Ernest Chan recommended that we look at systems in Short Term Trading Strategies That Work and High Probability ETF Trading, two books by Larry Connors and Cesar Alvarez. As Ernie correctly perceived, Connors and Alvarez described systems that lent themselves very well to a Design of Experiments. We came up with lots of ideas for experiments right away, and we picked one out to try it out.

My partner Ron is very fast at this kind of thing, and he had a simulator ready to run the test after about a day of work. He had to organize the data because we had never worked with an end-of-day system before. Then he started running the test. We knew they would go quickly, but they only took about a quarter of a second to run a trial on fifteen years of data. It took far less than half a minute to run 56 trials.

This is as close to instant gratification as an experimenter will ever get! It took a few hours to analyze the results, but it was essentially “same day service”. You usually have months separating the planning of the experiment and the analysis.

Immediately, we began to think of more complex experiments looking at more factors and using higher order models. Since the higher order models required more trials, we rarely even thought in that direction. The cubic model was the highest order model supported by the software we were using, and we very rarely had the opportunity to use that. Most people are looking for experiment designs with less trials, not more. In fact, the point of most experiment design software on the market today is to get the most information out of the fewest number of trials.

Notice how the goal changes when the cost of the trials approaches zero. The goal in this environment is to get the best possible predictions without regard to the number of trials it requires. Another fifty trials? Another hundred trials? How about another thousand trials? We don’t care when the trials are free.

Each quantified trading system starts with a Profit Plan. A profit plan is a theory that describes the actions that will be followed under the plan and explains how those actions will be converted into profit. Never forget that your profit plan is a theory. If it works, it is a good theory; if it doesn’t, it’s not. Theories of profit rarely become laws.

All theories start with observations. For example, you might first observe that stocks tend to bounce back after a sell-off. That makes you look closer, and you find this behavior seems to be a lot more prevalent in stocks that are doing well than in stocks in the general process of tanking. Your conceptual theory might then become “If a stock is doing well overall, you can make money buying on the dips Dump them as soon as the price bounces back.”

That’s a theory, but not a quantified theory. You can make it a quantified theory, as Connors and Alvarez did, by adopting numbers that define “doing well overall”, “the dips”, and “bounces back”. I am sure there are a lot of traders out there that did well enough with this rule with no quantification, but quantification brings two specific advantages. Once a theory becomes quantified, it can be executed by a computer, and once it can be executed by a computed, it can be tested exhaustively.

The process that Connors and Alvarez define on page 62 of Short Term Trading Strategies That Work is a good example of what is needed.

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1. The S&P 500 Index is above the 200-day moving average

2. The 2-period RSI of the S&P 500 Index closes below 5.

3. Buy the S&P on the close.

4. Exit when the S&P closes above its 5-period moving average.

Step number one checks to see if the S&P is “doing well overall”. Is the 200-day moving average the best test of this? You can test for this. (We did, and found that every long-term average was almost equally effective so 200 is as good a number as any.)

Step number two is a quantified definition of how to spot a dip. You can now test whether this works, Is this the best indicator? You don’t know, but you can now test it against other alternatives. Quantification does not start with the assumption that you know the right numbers; it starts with the assumption that you can test and discover the right numbers.

Finally, step four quantifies what a bounce back is. Is the 5 period average the right indicator? Again, you can test for that.

A quantified system has rules that can be automated and tested. If you are automating a quantified trading system, keep in mind that the ability to run tests is likely to be the most important contribution of such a system.