Judgmental “Heuristics” Or Biases and Developing Your Trading System, Part Three.

So far in this series, we have looked at biases regarding randomness, which is the tendency people have to seek patterns where none exist and to invent the existence of unjustified causal relationships. Because people attempt to understand and make order out of the market, they assume that the longer a trend continues, the more likely it will suddenly turn around. This manifests into the "gamblers fallacy," which is a very common trap that traders fall in to and lose money when they do. And, last week we examined data reliability as it relates to the degree to which information reflects what is really happening. We focused on the representation bias, availability bias, anchoring bias, and hindsight bias.

Today we continue with four common misuses of sampling variability in relation to system development, and finish with some tips to help overcome all of these biases.

Sampling Variability. Most people misuse the basic concepts of sampling theory in making predictions and designing trading systems. The first principle, which is highly abused, is that you can make more accurate estimates of the true population probability from larger samples than from smaller samples. In other words, you can get a much more accurate estimate of the reliability of a trading signal from a large sample than from a small sample. In our earlier example, Jack said that his pattern predicted a higher market price 35% of the time. The accuracy of his estimate would be much better if it were based on 100 measures of the pattern than if it were based on 20 measures. Unfortunately, most people follow a bias called the law of small numbers. Once they observe a phenomenon occurring a few times, they believe they understand it and know its likelihood.

People tend to form their opinions based on a few cases, and fail to revise their opinions upon the receipt of new data to the extent that they should, based on probability theory. Traders tend to stick to their old opinions rather than updating them as new information becomes available.
We call this the conservatism bias. This points out the importance of doing a thorough, objective testing of your market observations on a set of data that is different from the data in which you made the observation.

Traders want consistent information from various sources, such as three oscillators based upon the same data (which of course are likely to show similar results). However, this consistent information will lead to increased confidence, but not to increased accuracy of prediction.
We call this the consistency of information bias. What it means is that traders are likely to add more indicators in order to get more consistent information so they can feel confident about it. But adding more indicators is not likely to give one more accurate information. This points out the importance of developing a simple, robust trading system.

A fourth major misuse of sampling variability is that people fail to understand that the amount of variability in a sample is positively related to the degree of randomness in the sample. Once you have observed a relationship in a set of data, it is no longer random with respect to that relationship. The more relationships you observe with respect to various parameters in the data, the less random the data is with respect to those parameters. Unfortunately, system developers frequently make this mistake when they use a sample of data to optimize a system and then test the system on the same data. Once a set of data has been used to optimize a system’s parameters, then it is not random with respect to those parameters. As a result, when you use the same sample of data to test the system, you can expect it to do well in the test, but this has nothing to do with how it will work as a system trading real money. Data must be tested on a sample that is independent from the sample used to observe the original relationship.

How to overcome judgmental biases

You probably cannot totally overcome the effect of the various judgmental biases. One reason is that one of the most prevalent biases is the ego bias in which people decide, “Yes, I understand all of this, but it applies to other people, not to me. I’m a very special person and it doesn’t apply in my case.” Nevertheless, if you are willing to assume you are human and that these biases do apply to you, then you can take steps to minimize their impact.

Remember, your job as a trader is to find an edge in the markets. You must capitalize on that edge, so you will make money in the long run, while doing everything possible to preserve your capital in the short run. As a result, I strongly recommend that you spend a lot of time writing down your objectives and designing something to meet those objectives.

What is an objective?

Your objective is your goal, your target. It is the thing that you want to attain or accomplish.
Objectives set the roadmap for the entire system development process. How would one know how to get someplace if they didn’t know where they were going first? It is easy enough to see that if one trader had an objective such as “I want a system that trades long-term stocks, that requires my attention only once each week and makes 20% per year” compared to a trader’s objective that was “I want to actively trade my mother’s retirement account for four hours each day, without holding overnight positions” then two completely different systems would be required. The objectives or goals are very different. There are endless configurations of objectives. The point is you need to specifically know what it is that you are trying to attain; and only then can you develop a trading system that will help you attain it.

Observe the markets as an artist would. Be creative. Determine relationships in the market that occur over a wide variety of markets and market conditions. Remember, you are not trying to explain the markets, but just determine some market relationships you can capitalize upon. The more widespread the relationship—does it occur in different markets and different types of markets—the more likely you will be able to profit from it.

Be willing to be unique. Think about how you can best represent the price of the market. Notice relationships in the patterns of price movement that you can capitalize upon. Once you have observed some relationships, figure out how to measure them. If you can avoid common indicators, then you probably have a real edge.

Simple is probably better. Why? Because the more complex the relationship, the more likely it is to be unique to particular markets and the less likely it is to make you money.
Be sure you understand the edge the relationships you observe in your data give you. Do your observations make sense? How do they give you an edge? Also be sure that you can write down your observations in enough detail so you can recognize them as they occur and not just in hindsight.

Understand money management so you can capitalize on your observations. Trade according to a predetermined plan rather than your emotions.

Be sure to objectively test your observations on extensive market data that is different from the data you used to observe the relationships in the first place. Objective testing is very important because with subjective testing you will tend to see what you want to see. In other words, the market will confirm your expectations.

Many of the psychological issues described in this article are covered in my How to Develop a Winning Trading System that Fits You workshop and home study program. These programs will help you clarify your objectives, and then show you how to design a trading system to meet those objectives.

About Van Tharp: Trading coach, and author Dr. Van K. Tharp, is widely recognized for his best-selling book Trade Your Way to Financial Fre-edom 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.

Judgmental “Heuristics” Or Biases and Developing Your Trading System, Part Two.

Last week we looked at the randomness bias, which is the tendency people have to seek patterns where none exist and to invent the existence of unjustified causal relationships. Because people attempt to understand and make order out of the market, they assume that the longer a trend continues, the more likely it will suddenly turn around. This manifests into the "gambler's fallacy" which is a very common trap that traders fall in to and lose money when they do.

This week we will cover the topic of data reliability and biases that come up in this area.

Reliability. When people obtain information, they fail to assess how reliable their data is, where reliability refers to the degree to which information reflects what is really happening. What traders observe in the market, with the possible exception of floor traders and other market makers, is not the market, but some sort of visual representation of the market. Thus, you are responding to a bar chart or a candlestick chart, or a point and figure chart, or to a representation of the market profile, etc.—and not to the real market. Furthermore, few people make decisions from that information alone. Instead, they distort the information even more by using indicators. These indicators are essentially shortcuts or heuristics that people have thought up to condense, organize and make sense of the data. Interestingly, there are hundreds of possible indicators—in fact, hundreds of thousands if you count various permutations and combinations—but most traders use only about 20 of the most common ones in their decision making.

Market information is certainly distorted, and thus less reliable, when it is transformed into various indicators. The less reliable the information is, the less value it has for predicting. Using our example from last week when Jack observed patterns in the market, reliability is a measure of how accurately Jack’s pattern actually predicts a sharp move in the market. Many people might notice a pattern or relationship in the market and then use it in developing a system without ever determining how reliable the relationship is. Accurately knowing how well the pattern predicts the move is very important information for any person wanting to develop a trading system.

A lot of the biases people have in their decision making tend to distort reliability in some way. For example, we have many biases keeping us from knowing the true probability of an event happening. The true probability refers to the actual probability of the event occurring as opposed to a statistical estimate of the probability from a small sample.

One such bias that keeps people from developing a good trading system is called the representation bias. We tend to imagine that what we see or expect to see is typical of what can and/or will occur. Thus, if you observe a pattern in the market, you expect it to occur. If you develop some concept about the market, you will look for data to support that concept in the market, and you will probably find it whether it exists or not.

Once again, if you do not test objectively, and understand the results of the testing, you will probably find that your observations, in developing a trading system, tend to confirm what you expect to find. Thus, the representation bias is particularly important when it comes to assessing various trading signals. Are you considering the true probability rate in assessing your indicator? That is, are you considering the percentage of time a particular indicator is followed by the predicted outcome? Probably not!

I cannot overemphasize enough that trading indicators are merely ways of representing things of interest. Does a significant chart pattern actually mean that buyers are about to dominant sellers, or vice versa, and produce a significant price change? Of course not! It merely represents the possibility such an event might occur. Thus, any indicators you develop for buying or selling in markets are your way of representing potential trading opportunities. It is not the opportunity per se. Yet most traders, because of this particular bias, act as if the indicators are what they represent. It is like the indicators (be they stochastic, RSI, or moving averages) start becoming reality, instead of a representation of a concept or a belief in your head. When you realize this, you will become much more attuned to what trading is all about and less concerned about indicators and understanding the market.

Another bias that keeps people from understanding the true probability of an event happening, and thus distorts its reliability, is called the availability bias. We make predictions based upon how available the information is to us instead of the true probability rate in the population. Thus, when you first start looking at the market, the data sample you use will determine what you observe. In addition, strong emotional experiences, which affect how strongly information stands out in our minds, tend to strongly bias our decisions.

When people start to develop an estimate of how much a trading system can earn in a year or how many winning trades it will have, or any other estimate of its reliability, they tend to start with a set point. They then make adjustments to that figure according to anticipated changes in conditions. The initial set point is called an anchor. The dangers associated with using anchors in our decision making about trading systems (or anything else) is called the anchoring bias.

The first danger is that you assume there is some relationship between the anchor and what you are predicting. For example, in order to predict the price of the market a year from now, you would probably start making your estimate with the anchor of today’s price. Over a short period of time it may be an accurate basis for beginning to make an estimate (i.e., today’s price is a good starting price for forecasting the price in two or three days), but over a longer period of time the strategy does not allow for the unpredicted or the unexpected. That is why one of the most important parts of developing a trading system is extensive planning. And this extensive planning should include a careful consideration of everything that might go wrong.

The second danger in the anchoring bias is that people make an assumption that the initial set point or anchor itself is meaningful. For example, if you use the results of your testing to predict future results, you are assuming that those results are meaningful and will not change dramatically over time. This is probably true if your testing data is different from the data you used to develop the system and included enough samples to make future estimates reliable. But those are big “ifs.”

Another bias that tends to have a significant effect on trading decision-making is hindsight bias. People tend to see relationships in the market after they occur, and then assume they knew it all along. It’s very easy to point out such a relationship after it occurs. I’ve worked with a number of clients who claim that they cannot follow their signals. However, what tends to happen is that they do not recognize the signals while they occur. Instead, they see many possibilities in the data. But once the signal is complete, it is too late! They then criticize themselves for not taking it when it occurred. The typical response is, “I knew it all along. Why didn’t I take that signal?”

This problem will not occur if you write down your criteria for a signal in enough detail so that it could be entered into a computer. You can then make a checklist for your signal (or computerize it). Once you do, you will always see a signal when it occurs or the computer will see it for you. Thus, you really will know whether or not you actually knew it all along.

About Van Tharp: Trading coach, and author Dr. Van K. Tharp, is widely recognized for his best-selling book Trade Your Way to Financial Fre-edom 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.

Judgmental “Heuristics” Or Biases and Developing Your Trading System, Part One.

At any given instant, over two billion bits of information impinge upon your senses. Yet consciously, we can only process “7 +or - 2 chunks” of information. This tremendous reduction in information necessary to act upon external signals or make decisions is accomplished through various “heuristic” rules or shortcuts.

These rules, which are essential if you are to make any decisions at all, are both a strength and a limitation. They offer strength in that they provide tremendous shortcuts to making decisions. Decision-making would be practically impossible without them. However, they are a major weakness because people are unaware they are even occurring or how much they distort and delete information and bias our decision-making. For example, two such biases that make it difficult for most people even to make money in the markets are the gambler’s fallacy and the tendency to be risky in the realm of losses and conservative in the realm of profits—the opposite of what it takes to become a successful trader.

In this three-part article, we’ll explore several of these biases and how they might affect one’s trading and investing decisions. We will learn about randomness, sampling variability, and data reliability. Today let’s look at randomness and the gambler's fallacy.

The real “secret” to making money in the market has to do with developing an edge in the market by using probabilities and proper money management. Unfortunately, people have trouble distinguishing between luck and skill when it comes to market predictions. We are unable to comprehend the many factors influencing an event as complex as the movement of a market. For example, if we had access to the number of buyers and sellers in the market at a given time plus information about the conviction and capital behind each trade, we would probably find the markets to be very predictable. Thus, any uncertainty you may have about how the market is going to behave at any given time is in you, not in the market. When you accept the fact that uncertainty is in you, rather than in the market, you will suddenly find you have much greater control over your own behavior towards the market. More importantly, you will have much greater control over the process of designing a trading system and greater understanding of how that trading system works.

When you develop a trading system, you are essentially deciding upon a set of judgmental shortcuts to help you make a decision. Yet people are completely unaware of how we make most of our predictions and judgments, let alone any biases in the way we make them. Thus, the process of designing a trading system is replete with error and becomes a very difficult process. In order to simplify the process, traders need to understand the following major factors: randomness, sampling variability, and data reliability.

Randomness. People want to treat the world as if they could predict and understand everything. As a result, one of the most significant biases people have is to seek patterns where none exist and to invent the existence of unjustified causal relationships. Traders don’t want to trade probabilities. They want consistency. For example, people fail to understand that a random sequence can include a long string or what would be called a trend. Instead, they try to understand the “trend” as something that it isn’t, instead of accepting that such phenomena occur.

Understanding and trading well are not necessarily the same thing. People don’t understand randomness, yet they expect to be able to understand the market. They then build trading systems out of their attempts to understand the market by identifying unjustified causal relationships without ever realizing they are doing it. It is this expectation to understand markets that leads traders to search for “Holy Grail” trading systems that explain the “underlying order” of the markets. There is nothing wrong with building a trading system based on microcosmic glimpses into how the market might work; but you need to know what you’re doing when you’re doing it. You are not trying to understand some mysterious underlying order in the markets. You are developing a set of rules whose long term expectancy gives you an edge in the market, while allowing you to withstand the worst possible catastrophe that could occur in the short term.

For example, many people observe a relationship in the market and assume it explains how the market works.

Jack noticed when a particular pattern occurred in the market, it frequently moved 50 to 100 points higher. He assumed the pattern meant that strong hands were moving into the market. And, when the market didn’t follow the pattern, he became very confused. I said, “How often, when you observe this pattern, does the market move like that?” He responded, “About 35% of the time!” Thus, Jack had simply observed a pattern that was quite profitable 35% of the time. The rest of the time it had no meaning.

A relationship may occur only 35% of the time, and that may be something you can make money with, but it has nothing to do with being right or trying to explain something. What you must learn is that most trading systems come out of observations that have a certain probability of being correct. Those observations do not explain anything. Remember, a trading system is just a set of rules to guide behavior, nothing less or nothing more. Apparent random fluctuations in the market are caused by many more factors than you can possibly monitor in your system.

Develop the attitude of following rules
because they give you an edge in the market.
Avoid the need to understand or explain the market.

Because people attempt to understand and make order out of the market, they assume that the longer a trend continues, the more likely it will suddenly turn around. More importantly, traders are usually willing to bet larger amounts of money on that assumption. Thus, traders want to pick tops and bottoms in a trend—a behavior that tends to be as dangerous as stepping in front of a moving freight train, hoping it will stop and turn around just for you. These biases are usually referred to as the gambler’s fallacy. They have resulted in the ruin of millions of traders over the ages. The gambler’s fallacy is one of those biases, which make trading difficult without a system and proper money management. However, traders frequently develop counter-trend following systems because of this bias—usually with disastrous results.

About Van Tharp: Trading coach, and author Dr. Van K. Tharp, is widely recognized for his best-selling book Trade Your Way to Financial Fre-edom 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 http://www.iitm.com/.

Double the Risk Double the Reward?

If you have a profitable trading system or method that risks, say, 2% of capital per trade based on your entry price and stop loss and makes 30% per year, then why not double this risk level and make twice as much return per year? This seems like a good idea until you think about the fact that doubling the risk does not necessarily mean you will double the return. Why? Here are a couple of things to consider:

Increasing risk per trade increases the risk of total loss. Imagine if your risk was 50% per trade, you would only need to have 2 losing trades in a row to lose 75% of your capital. As your chances of total loss increase, this has the effect of reducing your average ending equity in a simulation of the possible returns from your system and means that doubling up on risk may not double the average return you get.

Winning and losing are not symmetrical. If you lose 50% of your capital you have to have a 100% gain to get back to where you started. If you lose 75% (as in the previous example) then you have to have a 400% gain just to get back to breakeven.

With these examples is obvious that simply increasing risk does not always have a corresponding increase in return, and at some point your risk of ruin goes up significantly – in same cases to almost a certainty. This means you are taking on extra risk to such an extent that you are almost guaranteeing you will lose your capital.

My advice would be to simulate your trading with gradually increasing position sizing and make sure that you are trading at a risk level that does have a corresponding increase in average return that you are comfortable with - otherwise you are simply taking on more risk with little to show for it.

Paul King
PMKing Trading LLC
www.pmkingtrading.com

Take Control

In my mind, control is an important issue that has a great deal to do with
understanding the process of trading and doing it successfully. There many
parts of the trading process where exercising control is relatively easy and
other parts of the process where control is much more difficult. For
example, the entry into a trade is a point where we are very much in
control. We set the conditions and the market must meet our conditions or
we will simply refuse to participate. This is clearly the point in the
trading process where we can exercise maximum control.

I can recall attending some lectures many years ago by George Lane (of
stochastic indicator fame) when he revealed to the audience a list of items
that he wanted to see before he entered a trade. His pre-entry checklist
had twenty-seven conditions on it. Being a skeptic of complex trading
strategies I don't recall what any of these twenty seven items were except
I'm sure at least one of them was the stochastic indicator. At the point of
carefully reviewing his checklist George was very much in control of the
situation and if the market didn't do exactly what he wanted he didn't
trade.

As I often point out in my lectures, entries are the easy part of trading.
This is because each of us has maximum control at this point. We can
exercise as much or as little control as we like. George Lane can require
every one of his twenty-seven criteria and I can require my usual two setups
and a trigger condition. However the control situation changes drastically
once we enter the trade. Our ability to control all the elements of the
trade now becomes much more difficult and far from absolute. Once we enter
a futures trade we know that we must exit that trade within a limited period
of time or we are going to be in trouble because the contract will expire.
Even stock traders who don't need to be concerned about expiring contracts
must exit their positions correctly if they wish to maximize their profits.

Exits are much more difficult than entries because we can not simply reverse
the entry process and require that the market do thus and such. Once we are
in the trade George Lane and I can both throw our lists out the window
because we can no longer dictate our terms to the market. The market is now
in control and we must be prepared to react to whatever the market does.
The market can do anything it wants once we have entered our trade and we
can be assured that the market doesn't care what conditions might be on our
list or what our preferences might be. Once we enter the trade we are at
the mercy of the market the market operates according to its own list and
that list of possibilities is much larger than George Lane's meager list of
twenty-seven items. The market's options are limitless. It can do anything
it wants whenever it wants and somehow we must be prepared to deal with it.
Where is our control now?

As we hold our trade we must be prepared for big moves against us and big
moves in our favor. (Surprisingly the big moves against us are much easier
to deal with than the moves in our favor. We will talk more about this in
just a minute.) Among the market's limitless possibilities are gaps,
reversals, limit moves, whipsaws, and perhaps worst of all, boring sideways
action that makes us wish we were trading something else. The market may
present us with inside days, outside days, reversal days, key reversal days,
high volume days, low volume days, expanding ranges, contracting ranges,
acceleration, and deceleration. We can be faced with days that are so big
that the chart looks like a propeller on the end of a stick or days that are
so small they just look like dots.

Because we have to be prepared for all this and more, it should be no wonder
that our exit strategies are often much more complex than our entry
strategies. We need to have solutions ready for any problem the market
might send our way. As I mentioned earlier, the losses are rarely the
problem because we can control those by simply setting a loss point and
closing out the trade if the loss point is hit. Here again we are facing an
issue of control and it is comforting to know that we do have a great deal
of control over our losses. If we want to design a system where the average
loss is $487.50 it wouldn't be difficult. We can absolutely control the
size of our losses and we must be certain that we do.

All of our exit strategies have to be carefully planned to be certain that
we control what can be controlled. First we must recognize and understand
what can be controlled and then we must make certain that we exercise
whatever control we have. It may be comforting to know that we can strictly
control losses but it is extremely discomforting to realize that we have
very little control of our profits. If we have a $500 profit, how do we
make it become a $1000 profit? Unfortunately holding on to the trade longer
gives us no assurance that we will eventually have a $1,000 profit.

In this instance we have very little control but let's see what we can do
with the control that we do have. Although the amount of profits can not be
controlled in the sense of our somehow forcing them to be larger, they can
be controlled in the sense that we don't have to let them become smaller or
turn into losses. Those of you who have purchased any of our systems will
appreciate that locking in open profits at various levels is important to
the success of our trading strategies. You will notice that in the "25 X
25" Bond System (free on the web site) we use a very tight channel to help
lock in profits after twenty-five days or after five Average True Ranges of
profit. We can't control the market and force it to give us five ATRs of
profit, but if it does we can make sure that we keep most of it. Protecting
our open profits is definitely within our control.

When conceptualizing a new trading system and when going through the design
and testing routine, be alert to issues of control. Look for what you can
control and make sure that you are controlling it to your benefit. Look at
what you can not control and as a minimum have some plan that will minimize
any possible damage. Thinking about control will make you a better trader
and implementing control will make your systems trade better.


Chuck Lebeau is the co-author of Computer Analysis of the Futures Market, and the former co-editor of Technical Traders Bulletin. Chuck is currently operates a website devoted to trading topics; www.traderclub.com.

Trading Jokes

How a deal is done, selling short.

Jack, a smart businessman, talks to his son.

Jack: I want you to marry a girl of my choice

Son : “I will choose my own bride!”

Jack: “But the girl is Bill Gates’s daughter.”

Son : “Well, in that case…”

Next Jack approaches Bill Gates.

Jack: “I have a husband for your daughter.”

Bill Gates: “But my daughter is too young to marry!”

Jack: “But this young man is a vice-president of the World Bank.”

Bill Gates: “Ah, in that case…”

Finally Jack goes to see the president of the World Bank.

Jack: “I have a young man to be recommended as a vice-president.”

President: “But I already have more vice-presidents than I need!”

Jack: “But this young man is Bill Gates’s son-in-law.”

President: “Ah, in that case…”

This is how business is done!!

The St. Petersburg Paradox

Imagine a simple coin-tossing game where you get paid $1 if you toss heads on the first toss, $2 if you get heads on the second toss, $4 on the third toss etc. The mathematical expectancy of this game is:

1/2 times $1 + 1/4 times $2 + 1/8 times $4 etc….

This means the game has a minimum payout of $1 and an infinite expectancy. An interesting paradox arises because if the game has infinite expectancy then it would seem to be reasonable to pay any amount to play the game. In reality, this is not a good idea so the question that requires an answer is:

How much should one pay to play each turn of the game?

This simple game is known as the St. Petersburg paradox as first devised by Nicholas Bernoulli. It is discussed in William Poundstone’s Fortune’s Formula (which is #12 on our ‘Trader Must Read Top List’ by the way) and addresses the question by assuming that the game is impractical because no-one can actually offer an infinite payout. If one changes the game to be capped at say, $1 billion, then the expectancy is reduced to just under $16. To me this seems like a poor answer to the dilemma that ‘cheats’ by adjusting the rules of the game, and gives an answer that is still too high a price to pay for playing (based on common sense and ‘gut feel’).

Note that none of this is directly related to position-sizing since the game has a minimum $1 payout and no maximum payout – there is no chance of loss and the payout does not increase with the amount wagered. This is a case where position-sizing algorithms do not have a direct bearing on the problem since the ‘correct’ amount to pay for a turn is fixed.

I have found that many times in trading (and other games of chance or gambling), there is a gulf between mathematical theory and practical reality that can be effectively bridged by simulation. In this case if one simulates the game as described a large number of turns (10,000 for example) and takes the total amount won divided by the number of turns, this average win per turn will be a good indication of the practical true value of a turn.

The results I got from my simulations were that a turn was worth between $5 and $9, so if a casino offered this game I would play if it cost less than 5 times the minimum win of $1. If you used a geometric mean instead of arithmetic mean you would get an even more conservative (lower) estimated value of a turn.

So, how does this relate to trading? Well, one can think of the future price of any equity in terms of mathematical expectancy. If you estimate that an equity has a 50% chance of increasing 100% in value, but only a 25% chance of decreasing 40% in value, then if it is currently selling for $100, the future expected price is:

$100 + (0.5 100) – (0.25 40) = $140

Therefore one could buy the equity at the current price of $100 and expect to make a profit of $40. Of course, these calculations are always completely dependent on accurate estimation of the probability of future price increases or decreases, which is not an exact science like the probability of a coin toss coming up heads or tails.

In my opinion, making money at trading partly relies on a more accurate estimation of these kinds of probabilities than other market participants in order to find ‘inefficiently-priced’ equities to trade.

Paul King
PMKing Trading LLC
www.pmkingtrading.com

What Is A Robust Trading System?

With computer’s as powerful as they are today its easy to optimize a trading system and make it look exceptional. However, as we’ve already discussed, an optimized system is not a good system. Just simply because you're able to train your computer to have 20/20 hindsight does not mean it will perform anything like that in the future (neural networks etc.).

The primary problem with optimizing past performance is that markets change in the future. A low volatility market suddenly becomes a high volatility market. A market prone to trends becomes a choppy directionless market. A market that had high leverage has it margin changed and now it has low leverage. A regulated market suddenly becomes unregulated. The list is endless.

What tends to happen is that market X will tend to start acting like market Y and market Y will tend to start acting like market Z etc. If you have perfectly optimized the system to trade market Z then you will be in trouble when it starts to trade like market X! This is a problem with many systems, especially stock index systems that tend to be optimized to a single market or sector. In spite of their occasional awesome looking results there's a drop of poison in their mix.

Contrast the previous scenario with one in which the system has been designed to work well most all markets A thru Z. Now, it does not matter if market Z starts to act like market Y or market A starts to act like market P etc.. They can change as many times as they want because the system has been designed to be universally robust with most ALL the various markets! Once again, the market characteristics can reshuffle themselves countless times and your system is like a Swiss Army knife that has proven in historical testing it can deal well with most all of those scenarios.

There are a few tip offs to an optimized system.

1. Unrealistically good looking performance
   
2. Only trades one market or sector well
   
3. Uses different rules (algorithms) for each market
   
4. Uses different inputs for each market even if the rules are the same
   
5. Uses different rules or inputs for initiating buys vs. sells
   
6. Does not factor in realistic transaction costs (slippage & commission)
   
7. Uses money management methods that don’t include market normalization (like single contract performance only)
   
8. Uses static numbers for all markets like a $2000 stop or $5000 profit target (some markets could hit those in an hour and others could take weeks). This may seem to contradict #3 but it does not. Its ok if markets have different stops and targets etc. as long as they were all dynamically computed from the same algorithm and inputs (as opposed to a static predetermined number across the board).
   

An important feature of a robust system is that it should weight every market equally. The testing should have been done in such a way to “normalize” the difference between the markets. For example, natural gas changes an average of a few thousand dollars per day per contract, however, Eurodollars change an average of a few hundred dollars a day per contract. You need a way to balance and normalize this difference in testing.

The reason you need to do this is because what if the system meets most of the above non-optimized rules. BUT, its trading one natural gas market contract for every one Eurodollar contract? The system would look great if in the past it had a lot of natural gas winners. However, what it in the future natural gas starts to have a lot of losing trades and the Eurodollar starts to have a lot of winning trades? Do you think a number of hundred dollar winning trades in a single Eurodollar contract are going to be enough to offset a number of THOUSAND dollar losing trades in a single natural gas contract?

If your trading 20 markets its because you want diversification. However, if your trading them all on a single contract basis then your not really diversified. You might have 50% of your portfolio accounting for 90% of the profits and losses! The problem is that moving forward, you will be dependant on specific markets instead of just a certain percentage of the markets (regardless of which ones). Its far more robust not to be dependant on certain markets within the portfolio. No one market should be any more meaningful than any other one.

In summary a robust system should do the following

1. Trade a large portfolio of markets successfully
   
2. Trade that large portfolio successfully over a very long test period
   
3. Use the exact same rules for every market
   
4. Use the exact same input values for every market even if the rules are the same
   
5. Have the exact same logic and input values for initiating both buys and sells
   
6. Factors in realistic transaction costs (slippage & commissions)
   
7. Be tested in a way where the markets have been normalized for risk (not single contract)
   
8. Doesn't use Static preset exits for all markets IE $2000 stop or $5000 profit target for all markets, but rather dynamically computed ones.
   

After you have done all of this, the final step would be to do some walk-forward testing. This means, test and develop your system on data up until year 2000 (for example). Then after all the testing is done see how it would have done from year 2000 until now etc. This helps avoid a lot of the benefit of hindsight. All of these are things we have done in the development our systems.

Feel free to email or contact us with any questions or comments on this subject. dhoffman@traderstech.net

Trading Jokes: Cow Economics

TRADITIONAL ECONOMICS
You have two cows. You sell one and buy a bull. Your herd multiplies and the economy grows. You retire on the income.

INDIAN ECONOMICS
You have two cows. You worship them.

PAKISTAN ECONOMICS
You don’t have any cows, for you ate all of them and you do not know how to produce cow; You claim that most of Indian cows belong to you. You ask the US for financial aid, China for military aid, British for Warplanes, Italy for machines, Germany for technology, French for submarines, Switzerland for loans, Russia for drugs and Japan for equipment. You buy the cows with all this and claim of exploitation by the world. Eat the cows, you will be left with no Cows. Start the cycle once again, claim…

AMERICAN ECONOMICS
You have two cows. You sell one and force the other to produce the milk of four cows. You profess surprise when the cow drops dead, to give a scientific explanation, allocate research funding out of public exchequer, to find out what really happened.

FRENCH ECONOMICS
You have two cows. You go on strike because you want three cows. May be world can face another cow revolution.

GERMAN ECONOMICS
You have two cows. You re engineer them so that they live for 100 years, eat once a month and milk themselves.

BRITISH ECONOMICS
You have two cows. Teach them meat eating. Feed them dead sheep, so they grow hefty. Now, they are both mad cows.

ITALIAN ECONOMICS
You have two cows. You don’t know where they are. You break for lunch, eat Italian Veg Pizza.

SWISS ECONOMICS
You have 5000 cows, none of which belong to you. You charge others for storing them. Distribute secret codes, to establish ownership.

JAPANESE ECONOMICS
You have two cows. You redesign them so that they are one-tenth the size of an ordinary cow and produce twenty times the milk. You then create cute cartoon cow images called Cowkimon and market them worldwide.

RUSSIAN ECONOMICS
You have two cows. You count them and learn you have five cows. You count them again and learn you have 42 cows. You count them again and learn you have 17 cows. You give up counting and open another bottle of vodka.

CHINESE ECONOMICS
You have two cows. You have 300 people milking them. You claim full employment, high bovine productivity and arrest anyone reporting the actual numbers.

What is a Mistake?

It is easy to beat yourself up for making that ‘stupid losing trade’, but is that really a mistake if you followed your trading rules, entered when you should have, used the correct position size, added to or reduced your position size when you needed to, and exited when you should? Losing trades, weeks, months, and even years are a normal part of trading, and it is much more useful to define a mistake in a way you can a) detect it, and b) fix it so it should not happen again.

My definition of a trading mistake that needs to be identified and fixed is one of the following circumstances:

• I did not stick to my pre-defined trading system, method, or rules
• I discovered my trading business plan was incomplete or inconsistent
• I chose (or was forced to) modify my trading rules intra-day
• I traded when I did not pass my daily ‘self test’
• I did not perform my daily, weekly, monthly, quarterly, or yearly review
• I tried to blame someone or something else for a trading mistake

Note that none of these circumstances has anything to do with making a losing trade, or going through a drawdown. They are all to do with trading discipline and the procedural aspects of implementing your trading system accurately. In fact, I actually track the impact of my trading mistakes by calculating how much I actually made trading versus what my systems should have made if I had performed with 100% accuracy.

If you calculate this ‘error’ value as a percentage of your trading profit, you will have a ‘discipline score’ which tells you how much of a (usually negative when you are inexperienced) effect you are having on your trading implementation.

Note that blaming other circumstances for errors is a mistake in itself. For example, if your connection to your broker goes down and you can’t exit a position in time, this is your mistake for a) choosing the broker, b) choosing to trade, and c) not having a contingency plan and alternative broker for exactly this (common) situation.

If you pre-define what a mistake looks like, and take personal responsibility for them, and also put procedures in place for preventing the same mistake happening again, you will be a long way towards being a disciplined and accurate trader with a significant edge

Paul King
PMKing Trading LLC
Source: http://www.pmkingtrading.com