Innumeracy

John Allen Paulos is his book Innumeracy writes:

Some would-be advisor puts a logo on some fancy stationery and sends out 32,000 letters to potential investors in a stock letter. The letters tell of his company's elaborate computer model, his financial expertise and inside contacts. In 16,000 of these letters he predicts the index will rise, and in the other 16,000 he predicts a decline. No matter whether the index rises or falls, a follow-up letter is sent, but only to the 16,000 people who initially received the correct "prediction." To 8,000 of them, a rise is predicted for the next week; to the other 8,000, a decline. Whatever happens now, 8,000 people will have received two correct predictions. Again, to those 8,000 people only, letters are sent concerning the index's performance the following week: 4,000 predicting a rise; 4,000 a decline. Whatever the outcome, 4,000 people have now received three straight correct predictions. This is iterated a few more times, until 500 people have received six straight correct "predictions." These 500 people are now reminded of this and told that in order to continue to receive this valuable information for the seventh week they must each contribute $500. If they all pay, that's $250,000 for our advisor. If this is done knowingly and with intent to defraud, this is an illegal con game. Yet it's considered acceptable if it's done unknowingly by earnest but ignorant publishers of stock newsletters, or by practitioners of quack medicine, or by television evangelists. There's always enough random success to justify almost anything to someone who wants to believe.

This is a great example showing how unsuspecting (& hopeful) people can be swayed into believing that a guru has magical predictive powers. It happens all the time.

Read Bestselling Book Trend Following and visit the Trend Following Blog at Michael Covel

Is Trading Style Hard-Wired?

Perhaps the most common assumption made by traders and trading psychologists alike is that people are capable of adapting to any market or trading methodology.

An increasing body of knowledge, however, suggests that many of the personality factors that affect decision-making under conditions of risk and uncertainty are hard-wired. Brain scans, for instance, predict who will be risk takers in gambling situations and who will be risk-averse. As one researcher bluntly puts it, "Brain activity predicts behavior."

A key element in decision-making is the brain's reactivity to loss vs. gain in decision-making. When the brain responds much more to loss than gain, individuals are far more likely to be averse to gambling. When the brain's "reward centers" respond to winning about as much as losing, the result is risk-seeking behavior. Interestingly, the brain areas that respond to winning money are the same as those that respond to cocaine or chocolate. This helps to account for the frequency of addictive behavior among gamblers--and traders.

Such individuals are not only risk-seeking, but stimulation-seeking. They have a low tolerance for boredom and use risky behavior to generate interest in their environment. Research suggests that easily bored individuals are more prone to depression and addiction than other people. Easily bored people also tend to have lower attention spans than others, increasing the odds of impulsive behavior. Such individuals would have particular difficulty adhering to trading plans and discipline.

Different brain mechanisms have been found to mediate risk-taking and risk-aversion in financial situations. A particularly interesting finding is that the dopamine reward center kicks in about two seconds prior to making a risky decision. Conversely, a brain center that is responsible for emotions of anxiety and repulsion is activated prior to suboptimal, risk-averse decisions.

We have no problem with the notion that some individuals are born with physiques that enable them to be successful in athletic contests. It is more controversial, however, to assert that some of us may be born with greater ability to make rational financial decisions than others. And there may be people with brain processes and psychological makeups who should avoid trading as assiduously as they might avoid cocaine.

It's nice to think that any trader can trade any market or methodology. The reality, however, is that we seem to be hard-wired to prefer certain levels of risk and stimulation. It may be more effective to fit trading methods to individuals than to hope to overcome hard wiring and teach a particular style to all traders.

Mechanical Trading Systems

Before we define a mechanical trading system it's best to understand fully the concept of a trading system:

What is a Trading System?

Trading systems are defined by a common set of rules which encapsulate all buy and sell (entry and exit) trading decisions. All trades executed following these common rules belong to the trading system.

This is easy to follow with the help of an example.

John has a share portfolio which consists of some Blue Chip shares and some smaller Speculative Shares. Even with this classification John has two distinct Trading Systems,“Blue Chip Shares” and “Speculative Shares”. Imagine the flexibility and analysis capabilities obtained by managing each trading system independent of the other!

In the example above, John could split his share portfolio into as many trading systems as he likes. Remember, you can create as many trading systems as you like, defined by an unlimited number of rules.

For example, John uses a rule that he will buy shares in all companies which have a PE ratio below 10 and volume turnover of more than $15,000,000 per day. John creates a trading system called “LowPEHighTurnover” to manage all his investments.

All future trades that John makes based upon his rules will belong to the "LowPEHighTurnover" Trading System. To extend this concept even further you can get creative and set up numerous trading systems which are independent of each another.

The benefit of creating trading systems is the high level of control and analysis capabilities which it provides. Having numerous Trading Systems is only beneficial when you have the right tools to help you with the analysis process.

What is a Mechanical Trading System?

A more advanced concept of trading systems is that of mechanical trading systems. Mechanical trading systems are designed to be totally mechanical in nature. A mechanical trading system executed correctly will exclude the undue influences of emotion, which can hinder the performance of many traders.

Human emotion is one of the most complex and hard to control areas of trading. No trader or investor has been able to conquer the market without first controlling their emotions.

Mechanical trading systems are defined by a distinct set of rules which instruct the trader what should be done and when to do it (entry timing and exit timing). It provides a signal when the trader should enter a trade and when the trader should exit a trade.

As the rules of a mechanical trading system are clearly defined, we can backtest the mechanical trading system over historical data. Backtesting is important to provide confidence that your mechanical trading system will be profitable. Backtesting is required before you undertake to commit any capital to a mechanical trading system. The results obtained from backtesting will provide an indication of the system's profitability, sustainability and highlight the characteristics of the system to illustrate how it will behave in a real life trading situation.

When performed correctly, backtesing is the closest we can get to determining the sustainability of a mechanical trading system without actually risking any money. Backtesting provides an easy way to gain confidence regarding the profitability of the system before you make the decision to commit your hard earned money to trading it.

Mechanical trading systems can be defined as a method of generating trading signals and quantifying risk independent of a trader's discretion. Although the advantages of utilising a mechanical trading system are many, most market participants agree that their greatest benefit is the tempering of destructive emotions, considered the enemy of all successful traders, from the decision making process.

Types of Mechanical Trading Systems

Mechanical trading systems which primarily use price data (high, open, low, close and volume) to derive trading signals can be classed as being either trend following or reversal based.

Trend following and reversal based trading systems have distinctly different characteristics.

Their characteristics will influence which type of trading system you decide to trade or design. The frequency and duration of trades signaled by either system are amongst the common differences between these two trading system types.

Trend following trading systems:

Trend following trading systems try to capitalise upon an established trend in the price of the security. For this reason, trend following systems tend to be traded over a longer time horizon than a reversal system.

When compared directly to a reversal system, the duration of the trades in a trend following system are distinctly longer and the number of trades taken by the system is less.

Trend following systems are most suited to be traded for long term gains as the capital and time requirement for this type of system is less than a reversal system.

Reversal systems:

Reversal systems on the other hand try to identify a change in the direction of a security and capitalise upon this change in direction.

Reversal systems anticipate a change in direction of a security and as a result will signal more trades on average than a trend following system. The increased number of trades is compensated by a shorter trade duration.

Reversal systems are most suited to traders who wish to be more active in the market. The potential for short term gains is high however reversal systems require more discipline and time to trade correctly.

Backtesting your trading system

What is going to give you the confidence to start trading your hard earned money on your trading system idea?

Any wise investor will do as much testing as possible before starting to trade a system with real money.The best option is to backtest the idea over historical data to determine how well it would have performed over that set of data.

Interpreting these results will provide you with sufficient information to assess the potential of the trading system.

When backtesting, your aim should be to replicate real life as close as possible. This means a proper backtest will need to effectively go back in time and start trading your mechanical trading system moving forward to the end of the data set.

So, how do we go about doing this?

In today's technological world, you can use the power of computers to complete the backtesting for you, to do this you will need:

1. A computer.
2. Backtesting software.
3. Historical data.

The only other alternative is to perform a manual backtest. This is not only time consuming but very hard to replicate and test variations of your mechanical system.

In other words, you should automate the backtesting process.

So long as your mechanical trading system works with just price data (open, high, low, close, volume) you will be able to utilise backtesting software to perform the test.

It will be no use backtesting, if your historical data does not enable you to test your idea. For example, you create a mechanical trading system with the following buy rule:

Purchase the share when the 10 day moving average of the close crosses above the 50 day moving average of the close.

The rule above can be tested quite comfortably over historical data which contains only price data. If, on the other hand your buy signal rule was a little more complex as detailed below:

Purchase the share when the 10 day moving average of the close crosses above the 50 day moving average of the close and the price to earnings ratio was 80% or lower than its value 4 months prior.

This rule is very complex as it introduces data which is not often supplied or maintained in a database of price information.

To successfully backtest this would involve obtaining historical data of the close as well as the price to earnings ratio (PE ratio). Obtaining historical information on a group of equities would normally consist of only the open, high, low, close and volume for each period.

Because of this limitation many mechanical trading systems are designed around purely technical indicators and rules based around only the open, high, low, close and volume information (price information).

Any mechanical trading system designed around fundamental data is really beyond the scope of retail investors due to the lack of historical data available to conduct a complete backtest.

With this in mind, remember that backtesting is not compulsory. You can start trading your own mechanical trading system without completing backtesting, it is a high risk strategy which is not advised.

Read on to learn more about the backtesting process.

With advances in technology, a wide range of trading system backtesting software is available. The benefits obtained from backtesting software cannot be underestimated. It will save you time and provide an endless opportunity to fine tune and test variations of your ideas.

A small outlay in capital to purchase good backtesting software will potentially save you thousands in the market if you have not backtested your ideas properly, it's a very wise investment if you are considering designing a mechanical trading system.

This is a quick guide to highlight some of the items which you should keep in mind when searching for backtesting software.

What backtesting methodology does it employ?

Sounds like a really complicated question, but it's not. Always remember that backtesting software should represent real life as closely as possible when it performs the test.

This way, you will have more confidence in the results. Never forget it will be your hard earned money on the line when you start trading for real!

“Real life” simply means that as we trade in our everyday lives we can make trades as much as our capital will allow.

For example, suppose you have two backtesting software applications, one of them performed the backtest by going back in time and starting from the start of the data moving forward one day at a time. The other started from today and worked its way backwards.

Which one of these represents real life the closest? Of course the first one is the obvious choice.

You should always ask the following questions about the backtesting methodology which the software employs:

1. Does the test move forward from the start of the data and step through each period of that data?
2. Does the backtest software monitor my money available for each trade and not trade if there is no money?
3. Does the software stop the test when I run out of money? In other terms does the test continue after you experienced negative equity. Remember in real life you cannot continue to trade if you have no money left.

Is the backtest software dependant upon any other software?

You need to find out if the backtesting software is dependent upon you owning another piece of software in order for it to run.

This is not necessarily a bad thing as the software may be utilising functionality from another piece of software which is superior in the industry. It is not necessarily a bad thing for the backtesting software to rely upon another piece of software, in fact, it may make the backtesting software better as the programmers and designers have had more time to concentrate on the backtesting functionality of their program with minimal time needed to be spent on the functions which the other software performs.

If you are required to own another piece of software you will be best advised to investigate that software application first.

What kind of data does it accept?

All backtesting software will require the use of data in order for it to perform its backtest.

Even when the data only consists of open, high, low, close and volume information, the software application may rely upon a certain format. Make sure the data you have or are wishing to obtain is fully compatible with the backtesting software.

Will it backtest leveraged instruments?

People will design and create mechanical trading systems for all types of instruments. The most common will be a normal share.

What if you designed a mechanical trading system for a leveraged instrument? You will need to backtest your idea using the correct leverage settings for the instrument.

If you think you will be in a position to design a trading system for leveraged instruments then you need to make sure the backtesting software caters for those instruments.

Is there a limit to the number of instruments that can be backtested?

If you design a trading system for a universe of 1,000 stocks you will need to make sure the backtesting software can cater for the large number of stocks (items) required for the backtest.

We have known some backtesting software to be limited to a maximum number of items, this is an area for concern as it puts a caveat on the backtest results and on the design of the mechanical trading system.

Make sure you can run the backtest on a limitless number of items.

Backtest results, what should I expect?

Once you have performed a backtest you will begin to analyse the results. The results you obtain from the backtesting software should be comprehensive enough to give you a clear indication of the potential of your system.

Expect nothing more than an absolute abundance of statistics and graphs to illustrate the results of your trading system.

When it comes to trading system performance data, the more the better. The more statistics and graphs you can analyse will enable you to tune and tweak you trading system enabling you to create a more powerful system which you can have confidence trading in real life.

In addition to backtesting results you will need to have the same level of detail and sophistication when you start trading your system in real life, this is where Stator - Advanced Finance Management takes over.

Remember, the more statistics the better.

Make sure the backtest recognises your account capital as it moves forward in time.

In real life you start trading with a set amount of money (capital).

If you trade this amount of capital and run out of money after one year, you can't continue. It's a simple equation, running out of money means you can't continue trading.

Make sure the backtest follows this same real life scenario, as it will be no use having ten years worth of results when your account capital reached negative figures after only one year.

In real life this event would have forced you to stop trading.

Make sure the backtest allows the use of money management.

Make sure you can play around with various money management (position sizing) models when performing the backtest.

Money management can dramatically change the results of any trading system. Having the ability to adjust your position size based upon risk is a very powerful concept which can dramatically improve the results of any trading system.

We highly recommend being able to adjust position sizing when performing a backtest.

For more information about various money management models and position sizing click here.

Understand what language the backtesting software speaks.

Computers do only what humans instruct them to do.

When you design a trading system, you want to have no limitations on the ideas which you can test. Any good backtesting software will be controlled by some programming language, make sure this language is as flexible as possible.

You will find some backtesting software uses established programming languages and others have their own programming language, either way make sure this language offers a wide range of options and flexibility.

The more flexible the programming language, the more creative your trading system can be.

Some points to bear in mind when backtesting

We previously made mention that backtesting should be designed to mimic real life as close as possible.

The following points are issues which you need to be aware of when performing any trading system backtest.

Ensure you have clean historical data.

When preparing to perform a backtest you need to ensure you have clean data. Make sure the data is correct (adjusted for splits etc) and contains the exact universe of stocks you wish to backtest.

For example, if you have designed a mechanical trading system which you wish to backtest on the Australian S&P ASX 200, your data will have to comprise of stocks which make up the S&P ASX 200 index.

Good, clean data is crucial to a proper backtest. Make sure you obtain good quality data for your backtest.

Setup is everything.

These days, running a backtest simulation will not take long at all. Be careful however that you check all the settings before you run the backtest.

It's also advisable that when the backtest is complete you re-check the settings.

If the results are too good to be true then they probably are.

You won't be the first person to backtest various trading system ideas. Many beginners fall into the trap of running a backtest and getting carried away with results which are too good to be true.

Whenever you get results which just seem too good, take the time to check and re-check your code.

Make sure you have programmed the system to represent real life and some common areas to check may be:

1 . Your entry is looking into the future. (can't do this in real life)
2 . You enter a position on the open however part of your entry criteria is the close of this same day. (can't do this in real life)
3 . You use an indicator which uses future periods to determine its value (zig zag indicator is a perfect example)

The real motto with this tip is to check and re-check your code to validate that it's representative of real life.

Searching for the Holy Grail is pointless.

Sooner or later you will find yourself testing ideas in the hope of stumbling across that magic secret which will unlock the market and all its profits.

This won't be the case, you will never create a trading system which has a 100% success rate. Many have tried and many have failed.

The Holy Grail does not exist.

You should be looking for a good trading system with minimal draw-down (the maximum equity during the trading systems life) and a good reward risk ratio.

Many trading systems have more losing trades than they do winning and the system still makes money. A perfect example are trend-trading systems made famous by the Turtle Traders, this system only had ~40% winning trades.

Successful Traders are Goal-Oriented, Disciplined, and Ambitious People

by Adrienne Toghraie, Trader’s Coach

“What brings you joy?" The answer should just pop out. After all, successful investors and traders are highly goal-oriented, disciplined, and ambitious people. How can you have such qualities in abundance and not know what makes you happy? Yet, strangely enough, a fair number of people cannot answer.

“Okay Adrienne" you say. “Suppose I’m one of those people who doesn’t know what brings them joy…What does that have to do with making money in the markets?" If you do not know what brings you joy, you are very likely not going to be able to sustain a long and successful career.

#The things that bring you joy can sustain you when things go badly.

When you are having a bad day, when you have experienced a serious trading loss, when you are feeling sad because a close friend has died, when your son wrecks your car, or when you begin to question what life is all about, you need to have things in your life that bring you joy. And you need to know immediately what they are so that you can call upon them to remind you that life is still good even when some things about it are going badly.

#The things that bring you joy can give you a reason to succeed.

I know a number of people who gave up successful careers in the markets because they had no reason to be successful. There was nothing in their lives that they wanted to support, to nurture, and to see to completion.

#The things that bring you joy give you the energy, enthusiasm and perseverance to keep going.

Joy is the juice in your veins, the lift in your step, and the air under your wings. It’s what keeps you working on that system and finding the answer to that nagging problem.

#The things that bring you joy combat depression and pessimism.

Negative emotional states can cause a trader to miss trading signals and lose opportunities. Pessimism can result in depression, or deepen and extend a depression. Depression can put a rapid end to a trading career.

#The things that bring you joy make you a joy to be around.

A spouse who sees you only when you are feeling joyless can begin to feel that you are a liability in life. He or she may need to fill life with the company of those who make life happy and pleasant. After all, don’t you want to be around people who are happy and can make you smile and laugh? A good and supportive marriage is one of the most important assets a trader can possibly have.

#The things that bring you joy help you to think more creatively and more clearly.

Imagination works much better when the mind is at peace than it does when it is filled with miserable and obsessive thoughts. Great ideas and insights are more likely to come in moments of joy than when the mind is in turmoil. Opportunities seem to abound when you are happy and positive. The same opportunities will be difficult to see when you are mired in pessimism. The most successful investors and traders are able to use their intuition in making decisions. Intuition is available only when your mind is at peace.

#The things that bring you joy allow you to feel more joy in the things that normally do not bring you joy.

When you are able to feel joy in one area, there is a spillover effect into other areas of your life, just as there is a spillover effect when you are feeling angry, pessimistic and upset.

Recently I worked with a client who began our session together with a long series of sad stories and laments. Clearly, Charles’ life had not been going well for a long time. His wife had left him, children avoided him and he had given up his trading career as a successful money manager. Charles was living with close friends who tried to encourage him to get on with his life and who recommended he call me.

When I talked with him, Charles sounded hopeless. I asked him what brought him joy in his life; he hesitated and barked at me, “What relevance does joy have in this conversation?" Did I not understand that there was no joy left in his life? But I was undeterred. Yes, joy was the central issue for Charles. If he had nothing that brought him joy, it was unlikely that he would have an anchor to keep him from drifting further away from a successful life. Without something that brought him joy, I would have a hard time giving him a reason to succeed. Without a font of joy, I could not squeeze out any excitement, energy or enthusiasm for the rigours of putting his life back together.

When Charles and I got together for private work I pressed him to go back to times in his life when he was doing things that made him happy. It turned out that he had loved to play the saxophone when he was at school. He had also loved to read historical novels, especially ones about submariners in World War II. In his childhood he had lived in Connecticut and had loved camping in the woods. As we progressed, he began to discover that there were many, many things that had once brought him joy that he had slowly abandoned or forgotten. I convinced Charles to spend time walking through the beautiful North Carolina forests near his home, dusting off his old saxophone and starting to play it again, and going to the library to find some of the newer historical novels and accounts from World War II.

Without any conscious thought, Charles began to find a new energy and passion for getting back into the trading game. When they saw his new sense of his own worthiness, associates who knew his ability as a money manager were eager to invest their capital with him. He is now earning money for his clients and has made positive steps to change his whole life. When I saw him several days ago, he had a bounce in his step, he was dressed like a winner, and he was filled with a sense of optimism. Charles is well on the way to recovering success and happiness. The lesson for him was that if he had allowed himself to do the things that created joy in his life, he would not have reached the bottom.

If, like Charles, you cannot say what brings you joy in life, then you too can look back to the days when you were carefree and spent time doing the things that made you happy. As you begin to list them, you will discover that the list will expand rapidly. Choose three simple things you would like to add back into your life, and then go for it. You will be amazed at the spillover effect on your work in the markets.

If you can figure out what brings you joy, then you can focus your thoughts and energy on those things in your life. The resulting positive energy will, in turn, seep into the rest of your life, including your investing and trading, and will open up new opportunities for success.

The mine field

The second set of circumstances that creates an environment for ‘heart and mastery’ comes from those who overcome tremendous adversity. Having what it takes to conquer exceptionally difficult circumstances gives you the endurance to conquer yourself and to achieve mastery.

Enter Sea Biscuit

The movie, Sea Biscuit, is a true story about how several people and one horse combined their efforts to create a champion. To make this happen they had to be in the right place at the right time, experience wonderful triumphs, face great difficulties and make unusually risky choices. All of them faced great adversity and each of them developed the ‘heart’ of a winner.

They also had the gift of being able to recognise the ‘heart’ in each other. The horse’s owner lost his son in an accident that he blamed himself for. He also lost his wife because she blamed him, too. He needed an opportunity to overcome his sadness and guilt. The trainer, who was living from hand to mouth, needed someone to recognise his special talent for connecting with animals. He was presented with an opportunity to train a horse of his choice.

As a teen, the jockey had to leave his family because they could no longer afford to support him. He longed for the opportunity to prove that he was the special person that his father wanted him to be. Of course, there was Sea Biscuit. The horse needed an environment of nurturing to show his breeding, talent and ability to be a winner. Sea Biscuit was the ultimate of what ‘heart’ can do for the soul of man. His triumph brought a nation together after the bitter days of the Great Depression by giving people hope for the future.

There are many people throughout history who have overcome great odds and adversity in their quest for greatness. Some people with ‘heart’ are:

• Dr. Milton Erickson, who in spite of polio became the greatest hypnotherapist of all time.
• Tiger Woods, who in spite of the race barrier became one of the greatest golfers of all time.
• Lance Armstrong, who in spite of cancer in his lungs and brain won the Tour de France five times in a row Thomas Edison, who was sent home from school because the teachers said he was too stupid to learn anything, but became one of the greatest inventors in history.
• Benjamin Franklin was the fifteenth of seventeen children of a poor candle maker. His first obstacle was that he had only one year of schooling. He went on to teach himself philosophy, four languages, the classics, writing for publication, science, finance, politics, diplomacy – to become one of the best educated and greatest Americans.
• Annette Kellerman was a sickly and lame woman who became the World Diving Champion and was judged the world’s most perfectly formed woman.

In the ‘rags to riches’ stories that I have collected over the years, there are a few that I think about when I go through a challenging time in my own life. One that stands out is the story of a trader named Roger. He was born into a family that had a difficult time keeping food on the table and a roof over their heads. His father was in a wheelchair and took care of the three children at home. His mother, who had very little education, worked in a candy factory production line. Roger did what he could as the oldest son to help support the family by becoming a paperboy and doing other odd jobs. Since he was unable to finish high school, he thought that he was destined to do blue collar work for the rest of his days. Until the day that changed his life.

Roger was in the habit of doing good deeds for people because he saw his father and mother help others despite their difficulties. He learned from a very young age that good deeds gave you good feelings. With all of the sad moments in his life, he longed for good feelings. So he looked for opportunities to help others.

Roger was working as a maintenance man in an office building in Chicago. One day he found a briefcase filled with important and valuable items, such as contracts and certificates, in the restroom. An executive at the Chicago Board Options Exchange had hurriedly left it there. Roger returned it intact and the man wanted to reward him for his honesty. The man was impressed with Roger and asked about his life. He wanted to know how a good-looking, honourable young man was cleaning buildings for a living.

After Roger told him about his life the man offered him a job as a runner at the CBOE. Runner led to trader, trader led to super trader and super trader was able to send his brothers and sister to school and give his parents a more comfortable life.

While Roger seemed to have been given his break when he became a runner, his training for becoming a trader started from overcoming his difficult life and recognising the value of good deeds.

True grit

Yes, there is opportunity for creating ‘heart’ beyond adversity and being born into it. This third set of circumstances is for those who:

• set their sights on a goal for mastery with tasks that are developed out of a good plan
• seek out good models and mentors.
• develop the discipline necessary to follow good rules consistently.
• create a nurturing supportive environment.
• become a constant student of the markets.
• balance all areas of life to support mastery.
• handle psychological issues that create sabotage.
• work with a success coach.
• become a mentor to others.

Suzy was a woman who sought out what she wanted and did whatever it took to be the best she could be. She was a high achiever in school and in the corporate world. Then she became a top multi-level marketing consultant while creating a nurturing home for her family. Suzy’s husband was looking for a new career after retirement and in the process, he and Suzy both became interested in trading. She transferred the experience of her multi-marketing skills into becoming successful as a trader. Working side by side with her husband and using the same formula for success, Suzy blossomed into becoming a master trader while her husband developed into a good trader.

The fact is that with the right formula those with the ‘heart of a trader’ will blossom while others will only be ‘good’.

Stray bullet of greatness

There is another form of greatness. There are those people who, in the process of seeking out one form of success, achieve another.

One of the most talented people that I know in the financial field has written several best selling financial books. He is an exceptional trading teacher and has developed several profitable trading systems. He is one of the best in the world at analysing markets. The only problem is that his target was to become a great trader and that success has eluded him. He has solved the problem by having someone else pull the trigger on trades for him. While he has experienced the rewards of greatness, there is a part of him that would give it all up to become a great trader.

Conclusion

I believe that the ‘Heart of a Trader’ can be developed. Yes, it is easier for some because of their experiences and resources, but even the best of traders may not reach their top performance if they do not follow a good model. The fortunate thing about working on becoming a great trader is that there is always more than one winner. Even if you only become a ‘good’ trader, in trading “Good ain’t bad."

Adrienne Toghraie, Trader’s Coach, has lectured and coached some of the most successful people around the world, and has published eight books on the psychology of trading

Back testing

From Traderpedia

Definition: Testing a strategy on historical data in order to see if it would have been profitable.

Contents [hide] 1 What is a backtest? 2 The data mining problem 2.1 How to combat the data mining problem 3 Results of a backtest

What is a backtest?

A backtest is a simulation of how a trader with a mechanical trading system would have responded to market data, i.e by buying, selling, or forgoing any transaction at all. Backtesting is a way of testing the signals given by a trading system in order to see whether it would have been profitable in the past.

As with any simulation, the more realistic the backtest, the more useful it is. Obviously, the goal of any trader is to maximize return after slippage and transaction costs (while avoiding unnecessary risk) and these costs should of course be included in the test.

The data mining problem

Just because a strategy would have worked in the past had a traderstumbled upon it does not necessarily mean it is reasonable to think it will continue to work in the future. The Super Bowl Indicator (NFC team wins, market goes up) and the hemline indicator (skirts long, sell short) are obvious examples, but don't be lulled into thinking that any strategy based on more sensible criteria is sure to work. Backtesters must be careful to avoid hyping results based on a limited set of data. There may be something special about the data the backtester used (e.g., it's all January data) that causes anomalous results. Or the results may simply have been caused by chance.

Data mining is even more of a hazard as backtesters use new programming techniques and greater computer horsepower. "Genetic algorithms" sometimes spit out eye-popping numbers when using historical data. It remains to be seen whether these algorithms actually help predict anything. As O'Shaugnessy said in What Works on Wall Street, "Torture the data enough and it will confess to anything."

How to combat the data mining problem

There are three primary ways to combat data mining.

The most obvious is to view with suspicion results that contradict common sense or other well-designed studies.

The second way to avoid data mining is to divide the available historical data into a "play" set and a "confirmation" set. Use the play set to experiment with, and then see if the confirmation set confirms your hypothesis. For example, if you had 5 years of data at your disposal to test an intra-day system, you might have a play set of 24 randomly selected months and a confirmation set of 24 different months. This method is almost sure to tell you whether you are really eliminating problem children or just trying too hard. If you do not have fairly large set of data to start with, you cannot use this technique. Be very cautious in interpreting your results.

The third way is to avoid over-optimising or "curve fitting" your system. By this I mean continually tweaking and adding parameters in ordre to increase the overall profitability. Do this to excess and you are likely to end up with a brilliant system for the period of time under scrutiny, but one that fails miserably as market conditions change. The best systems tend to be fairly simple.

Results of a backtest

Here are the questions most traders want a backtest to answer before putting money into a trading system:

• Does the system provide returns that are significantly better than an appropriate benchmark?
• What is the maximum drawdown of the system?
• Is the system realistic in terms of slippage costs?
• How many trades per year are necessary for the system?
• How long is the typical position kept open in the system?
• What is the ratio of the average winner to the average loser (the profit/loss ratio)?
• What is the ratio of winning to losing trades (the win/loss ratio)?
• What is the maximum adverse excursion?
• What is the system's expected value?
• What are the characteristics of the system's equity curve?
• Is it realistic to assume an entry and exit could always be made when indicated?

How To Make A Million In 40 Trades

This article is a true story of how a friend of mine made a million dollars in 40 trades during a three month period. I should mention first that he did start with $100,000. I could have called this my Jerry Maguire moment. You know the movie with Tom Cruise where he decides to write a mission statement.

Think of this - double a dollar 20 time and you have over one million dollars.

$ Dollars $	Doubled
$1.00	$2.00
$2.00	$4.00
$4.00	$8.00
$8.00	$16.00
$16.00	$32.00
$32.00	$64.00
$64.00	$128.00
$128.00	$256.00
$256.00	$512.00
$512.00	$1,024.00
$1,024.00	$2,048.00
$2,048.00	$4,096.00
$4,096.00	$8,192.00
$8,192.00	$16,384.00
$16,384.00	$32,768.00
$32,768.00	$65,536.00
$65,536.00	$131,072.00
$131,072.00	$262,144.00
$262,144.00	$524,288.00
$524,288.00	$1,048,576.00

Before I start with this story I have to give you some background so that you can really appreciate the whole episode.

It all happened in the 90's. I don't even think the Euro Dollar had been introduced for trading at the time.

Anyway, it was fairly early in my trading career and a few years earlier I had taken a course on Forex trading in London. You know, one of those "I'm a guru and this is the Holy Grail courses".

I distinctly remember that the course cost me £8,500, which was a lot of money in those days, hell, its still a lot of money for a course today.

At the time, I remember coming out from the course thinking that I had cracked it. I was already planning on the car I was going to buy and what sort of massive house I was going to live in.

The course finished on a Friday and by Tuesday I was set up with a broker and ready to make my fortune.

By the following Tuesday I had blown $10,000. I couldn't believe it. I had diligently applied everything I had learned and still lost money. I was thoroughly depressed. At the time I knew very little about money management but I knew enough to know that I wasn't going to make any money trading the way I had been.

I spent the next six months reading everything I could about the Forex market. I became totally obsessed with the thing. I would sometimes work 18 hours straight, studying and testing different ideas.

During all of this I kept in touch with the guy that originally taught me the course (Lets call him Peter as he is still in business as far as I know). I realized months later that the course was useless but by this time I had got to know Peter and he was a very likeable guy, it was hard not to like him even though I knew more than he did six months after I took the course.

At the time, I lived in a beautiful village in the heart of Perthshire called Blairgowrie. Just as a side note here. If you ever go to Scotland, make a point of heading up to Perthshire. Everyone goes to Edinburgh or Glasgow but trust me, the farther North you go in Scotland the more beautiful it gets and the people are much friendlier too.

So, picture the scene. I had eventually got my act together. I was making money trading, not a lot but enough to cover my living expenses and it was in the heady days before I had children so there always seemed to be time for things.

I would get up at around 5 am, make myself a big cup of black coffee, put on some Beethoven or Enya and settle in for the morning. My favorite technique was to try to catch a move on the London opening and be finished by midday.

It's funny you know but even I can see how the action in the market has change over the years. The 5 minute charts just seemed easier to trade in those days.

This left me time for my second passion of going to the movies. Both my wife and I used to be devoted moviegoers. I mean, we would watch every single new release and even the arty foreign ones too. Nowadays, with kids, all I get to watch is Toy Story, The Lion King Or Shrek over and over again.

Back to the story. About a week before this story starts I was speaking with Peter and asked him if he knew where I could get a copy of a manuscript by WD Gann that I was after.

Anyway, about a week later Peter gives me a call and tells me that he has this guy called Fred who has just taken the course and is struggling a bit. He asks me if I would spend the day with him and just try to help him.

I knew of course that the reason he wanted me to help him was because he didn't want the guy to ask for a refund but whatever the reason was, I wasn't interested. I was in my own little groove and life was good. I was doing OK in the markets, getting to see all the movies I could watch, in short I was happy.

This is where he tempted me with something he knew I would be interested in. Somehow he had managed to get his hands on the manuscript I was after. He wanted to make a deal. He would FedEx it down to me the same day if I would spend some time with Fred. He got me with the one thing he knew I would bite at.

Arrangement were made that I would collect Fred from Edinburgh airport on Monday morning.

About two days before I was due to collect Fred, he calls me. "Hi Mark this is Fred, Peter said that we are going to meet on Monday and I just wanted to touch base with you. So how much money are you making?"

Wow, this guy was to the point. I wondered if I had made a good decision agreeing to spend the day with him.

Monday morning comes and into the arrival lounge steps Fred. Big tall guy, over six foot tall. His hair was just starting to turn grey and he was dressed in baggy jeans and a T-shirt. I placed him about 36-40 years old.

"I thought I might see some sheep running around the airport". What do you mean, I said. "You know, highlands of Scotland, William Wallace and all that stuff." We both started laughing. I knew I was going to like this guy but he had a wicked sense of humor.

We made some general chit chat on the way back to Blairgowrie and eventually we got in front of the screen where I started to explain how I trade.

Around this time I was really into Fibonacci and the approach I used at that time was the forerunner to www.surefire-forex-trading.com.

This is where the real story starts.

Fred just sat there looking at me. He had his face resting on his hand with his elbow on the table, which made his face all scrunched up like a cabbage patch doll. I went on for about half a hour. Then suddenly, Fred pretended to let his elbow fall off the table. "Oh, sorry Mark, I was falling asleep. You could stun a pig with this stuff".

"What", I said, but I knew exactly what he meant.

"Well, I'm not interested in all this crap. Just show me the good stuff, you know, the thing that makes the money."

"This is the thing that makes the money Fred."

"I'm not going to do all this mathematical stuff, there's got to be an easier way to make money than doing all this stuff. Plus, at the rate you make money, I might be 60 before I make any decent money."

I had to laugh, Fred was an entirely different animal from me. He wanted to trade and make it big but he wasn't prepared to do the work.

We spent the rest of the day talking about trading and life in general. I laughed the whole day. This guy only knew how to do things one way and that was with both barrels blazing.

Fred eventually went home and things returned to normal. A few days later I get one of many calls that were to come from Fred.

"Hi buddy, I set up my account last week and it's live today."

Great I said. "Remember to take it easy."

"Its a bit late for that me old matey, I'm short the Swiss for a million."

I just listened dumb stuck. You could and still do get incredible leverage with Forex. In those days there were no such things as mini contracts. I had just started trading with two contracts and here was Fred on his first trade, jumping right in there with ten contracts.

How big is your stop I asked him.

"Stops are for wimps buddy. When I make a couple of grand I'll close the position."

"Listen Fred, that's dangerous."

"Don't worry me old matey. You can sit up there in the Highlands and watch the grass grow while I make the real money down here."

About three hours later he calls again. "Just made $5000 bucko. Put that in your pipe and smoke it." I laughed but I was worried about him.

A few days later Fred calls again. "You wont believe this. I was going to short the Pound so I went short 30 contracts and went out for a coffee. Anyway, when I get back you will never guess what happened. I screwed up. I pressed the buy button instead of the sell button and now I'm up $15,000."

I had also been trading the Pound and there had just been a nice move but I had made about $1000.

So what are you going to do now I said. Are you going to close the position? "Hell no. Push it until it hurts me old matey".

He eventually closed the position later in the week and was up about $45,000. Over the course of the next few weeks Fred made about six trades and was increasing his leverage as he went. He was now regularly trading 30 contracts plus. After about a month and a half his account was standing at $500,000.

Quick Explanation
The pip value varies depending on which currency pair you trade but lets say that a pip is worth $10 with one contract to make this easy. Fred was trading 30 contracts or about $300 a pip. If the pair moved 100 pips that would be $30,000. Contracts in Forex are also commonly known as "lots".

Back to our story. It didn't matter how much he made he wanted to use the maximum leverage he could and push his leverage to the limit. It was madness but no amount of reason was going to stop him.

He had also had a remarkable run. I don't remember the exact number but he had very few losing trades.

I was getting more worked up about his trading than he was. I eventually couldn't take it any more and told him I was flying down to see him. I was also curious to
see how he was doing this. What mad method was he using.

As it turned out, his method was remarkably simple.

Look at this chart

Basically at around midday he would just draw a straight line across the top and bottom of any consolidation he could see on a 5 minute chart. If he had a couple of closes above the consolidation he went long. If he had a couple of closes below the consolidation he went short. There was either no stop or one so far away that it didn't matter much. He just closed the position when he felt he had made enough or judged the market to be turning on him. It was a sort of breakout technique.

Things came to a head when Fred went on holiday. He didn't particularly want to go on holiday but he had arranged this months before him started to trade.

He had arranged to take his family to Disney Land and off he went. Finally I thought, some peace and quite. But not quite.

He could only have been on the ground for a few hours when I got the call. "What's the Yen doing." Forget it I said. You need to take a break and spend some time with the family. Silence on the other end of the phone.

A few hours later he calls again. "Right me old matey, I've just bought a fax machine, fax me over a chart of the Yen." I couldn't believe what I was hearing. He wanted to trade without a dealing station and no access to charts.

"No way Fred."

"Listen up buddy, I am going to take it easy, I just want to be in the market. Send me a 5 minute of the Yen and I will keep it to ten contracts." Reluctantly I agreed but made it clear I thought he was off his head. I knew that regardless of what I said he would find a way to trade.

As it turned out, even on his two week holiday he made over $100,000. Obviously going over his 10 contract limit he promised me.

I could go on here about his trades but the incredible run finally ended one Sunday night after about three months and around 40 trades, Fred had managed to parlay his initial starting capital up to one million dollars.

Now if you trade currencies, you know that nothing much happens on a Sunday night. Asia opens but generally there are no big moves.

The phone rings about 1 am and wakes me from my sleep. "What the F%$* is happening to the Swiss." He didn't even wait for an answer, he just hung up. I lay in bed for about ten minutes thinking about what Fred had said and then curiosity got the better of me, I had to go see for myself.

I knew as soon as I saw the chart what was worrying Fred. For some reason the Swiss had gone up over 100 pips on a Sunday night. I had never seen such a big move on a Sunday and I couldn't find any news as to why this might be happening. Fred must be short the Swiss I reasoned.

I decided to call him. "Your short the Swiss right?" yes, he replied. "I just don't understand it. I thought I would place my positions ahead of Mondays opening and then this Sh*% happened. What do you think I should do?"

I didn't know. "Look, you really only have two options, close the position now or wait for the London open and see what happens. Whatever you decide put a stop in to be on the safe side."

I remember watching that 5 minute chart of the Swiss all night long and about eight am London time the Swiss began to rise again. It had moved another 80 odd pips up. I called Fred. "What did you do." Silence on the other end of the phone. "Fred, what did you do."

"I shorted it again. I thought that as it had already moved so much it must be ready for a pullback so I shorted it again. There is something else Mark but I am too embarrassed to tell you."

"What is it Fred?"

"I've been adding contracts and now its looking real shaky."

I never did find out exactly how bad his situation was that day but I could guess. Not only had he shorted the pair again he had added contracts.

After that trade, nothing seemed to go right for Fred. He had some wins but in a period of about a month he lost everything. Even his starting capital. He was the
first trader I knew who actually had a margin call. That's when the broker calls you to tell you that there is either not enough money in the account to cover the position or it is getting dangerously close to that level.

I still consider Fred a close personal friend and we have remained friends throughout all the years. It took some time but Fred to recover but he did eventual make quite a bit of money in the property game.

Here's the moral of the story. I have met some incredible traders over the years. I even know one trader who makes millions of dollars a year and before you ask, no, he doesn't share his method with me.

Of all the hundreds of traders I have met over the years I only know a handful that still trade and make money year after year. All those traders without exception have strict money management principles and a simple method or system.

Don't be in a rush to make it in trading. You need to learn this profession. You need to have money management principles in place that allows you to stay in the game even when you go through a bad patch and trust me they will come.

I asked Fred one day why he never stopped or drastically reduced the amount he was trading when he had a million dollars. This is what he said.

"I have a glandular problem, I have this huge greedy gland that just wont let me stop. When I got to a million I immediately thought, why not ten million me old matey."

Here's a scary thought. There was a time during all this when I would have believed he could have done it.

Good Trading
Mark McRae

On Randomness and Streaks

One of my favorite books is Fooled by Randomness by Nicholas Taleb

http://www.amazon.com/gp/product/158...books&v=glance

I’d suggest that you purchase it and read it cover to cover several times. The major theme of the book is that people totally fail to understand randomness – even logical, educated people. In fact, sometimes logical, educated people can be fooled even more than the non-logical, and uneducated.

For example, last week in an article by Chuck Branscomb, you learned that good traders learn to tolerate long losing streaks, including making 12 losses in a row. We got several comments on that one.

Here is one of them:

You say that a winner still knows that he's a winner even after an expected 12 losses in a row. Who in his/her right mind would "expect" 12 losses in a row?

If a system is designed to win 50% of the time, the chances are only 2 in 10,000 of getting such a result if the system is performing as expected.

Even if the system is designed to win only one out of ten (and presumably make more than enough on the one win to make up for the nine losses), the probability of 12 losses in a row is still less than 50/50 at only 28%. Getting a result like 12 losses in a row would more than likely mean that the system is not working as intended.

I understand the intent of your statement, but show me a person who "knows" that he/she is a winner with a system that loses 12 in a row, and I would like to see that system ... so that I can take the other side of the trades.

My guess is that the author of this letter is very intelligent and understands quite a bit about probability. For example, he was able to illustrate that the odds of 12 losses in a row with a 50% system are only 2 in 10,000. He’s a little off — it’s actually 0.000244.

However, his calculations were based upon making 12 losses in a row in 12 trades with a 50% system. What if you make 100 trades each year? Or what if you are a short-term trader and make 1000 trades each year? Then the chances of a long losing streak are quite large.

For example, I did 20,000 Monte Carlo simulations of 100 trades with systems that are 25% correct; one that is 50% correct, and one that is 75% correct just to see what the losing streaks might be. What I found is shown in the table below:

1.)The first column is the win percentage of the system.
2.)The second column shows the length of the losing streak that will occur100%of the time in this system. That is, you are guaranteed of having a losing streak that long.

3,)The third column shows the average losing streak for that winning percentage. You have a 50% probability of getting a losing streak this long.

4).The fourth column shows a 10% probability losing streak. Losing streaks will happen this long 10% of the time if you make 100 trades each year.

5.)The next to last column shows a 1% probability losing steak. In other words losing streaks this long will happen 1% of the time if you make 100 trades each year.
6.)And the last column shows the maximum losing streak in 20,000 simulations of 100 trades.
Losing Streaks As A Function of Winning Percentage of Your System

See Chart Attachment

Notice that with a 50% system, you are almost guaranteed to have five losses in a row in 100 trades and you’ll probably get six losses in a row. If you simply calculated the probability of getting six losses in a row, you’d say it’s unlikely because the probability is 0.0156. You’d conclude that its nearly impossible.

But it’s not impossible in 100 trades. In fact, it’s almost certain.

In our simulation, we also found that you have a 10% chance of nine losses in a row and a 1% chance of 12 losses in a row. One percent is unlikely, but not impossible. And just when you decide it’s impossible, it would probably happen for you.

I don’t have a simulator that will easily do 20,000 simulations of 1,000 trades, which is still quite likely for a short term trader to make in a year. I will easily have over 1,000 trades this year and I’m not a day trader. Many of my clients will easily have 1,000 trades each year. I’m right on about 45% of my trades and I probably have a losing streak as big as 15-20 losses in a row this year. Some of my students (who are superb traders) frequently report losing streaks as long as 20 in a row.

Most long-term trend following systems are not right 50% of the time. They are more likely to be right 30-45% of the time. Thus, the probability of having 12 losses is a row in 100 trades is no longer just a possibility – it’s a distinct probability.

So what does this all mean for you?

It simply illustrates the point that the average person does not understand randomness, even the average highly intelligent person.

Second, it says that long losing streaks are quite possible. Most people who insist on being right will typically give up their system, thinking it is no good. In reality, the system is doing what you probably should expect.

And, lastly it shows the critical importance of position sizing. If you risk 1% on each trade, then after 12 straight losses you’d probably be down about 10% (i.e., you’d be down less than 12% because you’d only be risking 1% of what’s left after each loss).

Your next trade might be a 20R winner and you’d be up — even after 20 losses in a row.

Don't worry, only 2 out of every 100 traders will pick up on this information, the other 98 will be paying for your winning trades the rest of the year

A look At Optimization

To the new systems developer one of the most exciting things to play with is optimization. Optimization is using the power of the computer to examine every possible sequence of parameters and rules to find those that have worked out best in the past. With enough computer crunching power its possible to find systems that perfectly “predicted” the past. We can run number crunching PC's on automated routines and have them analyze billions of bits of data while we are sleeping! Many traders do this long enough and eventually "discoverer" the holy grail of trading systems. They jump into the markets with their new super predictive algorithms only to find they fall apart in real trading!

“What happened?” they ask themselves. The answer is that what they created was likely a system that was a statistical coincidence (known as a "curve fit"). Curve fitting is where a system has been optimized to a unique set of historical data. The problem is that the markets will behave much differently in the future than the past, therefore, a “perfect” trading system in the past could be useless in the future. For example, your computer finds the perfect dates in the past to have bought and then sold the market. Obviously this data does not mean anything in the future. . This is a simple example but most curve fits are some complex form of this basic concept.

Lets look at another flawed example. Assume we wanted to optimize a set of nickels that were most likely to land on heads. What we could do is flip a million nickels and only select those that landed on heads. Then, we can take those remaining nickels and flip them again, once again only choosing those that land on heads. We could repeat this process over and over again each time only choosing those nickels that land on heads. At this point we might conclude that we had narrowed down our nickels to only a small handful that were “optimized” to land on heads. We could then go out and make large bets with those nickels putting all our money on heads. We would quickly make a fortune right? WRONG!

Unfortunately we would very quickly lose our money. These nickels were not optimized for heads; they always did and always will have 50/50 odds. What might have confused some is that they thought they had found a predictable set of nickels when in fact they had just found a statistical coincidence!

Because there is so much data and so much computing power available, these kinds of errors find there way into trading systems all of the time. One of the worst offenders of such flawed optimized systems can be neural networks. When developing a system its imperative that optimizing is avoided as much as possible. You need to find NON curve-fit robust systems. There can be a place for certain types of optimizing, but it must be handled correctly.

Learning to Trade: The Psychology of Expertise

by Brett N. Steenbarger, Ph.D.

When people hear that I am an active trader and a professional psychologist, they naturally want to hear about techniques for mastering emotions in trading. That is an important topic to be sure, and later in this article I will even have a few things to say about it. But there is much more to psychology and trading than “trading psychology”, and that is the ground I hope to cover here. Specifically, I would like to address a surprisingly neglected question: How does one gain expertise as a trader?

It turns out that there are two broad answers to this question, focusing upon quantitative and qualitative insights into the markets. We can dub these research expertise and pattern-recognition expertise, respectively. These perspectives are much more than academic, theoretical issues. How we view knowledge and learning in the markets will shape the strategies we employ and—quite likely—the results we will obtain. In this article, I will summarize these two positions and then offer a third, unique perspective that draws upon recent research in the psychology of learning. I believe this third perspective, based on implicit learning, has important, practical implications for our development as traders.

Developing Expertise Through Research

The research answer to our question says that we gain trading expertise by performing superior research. We collect a database of market behavior and then we research variables (or combinations of variables) that are significantly associated with future price trends. This is the way of mechanical trading systems, as in the trading strategies developed with TradeStation and the systems featured on the www.futurestruth.com site. We become expert, the mechanical system trader would argue, by building a better mousetrap: finding the system with the lowest drawdown, least risk, greatest profit, etc.

A variation of the research answer can be seen in traders who rely on data-mining strategies. The data-miner questions whether there can be a single system appropriate for all markets or for all time frames. To use a phrase popularized by Victor Niederhoffer, the market embodies “ever-changing cycles”. The combination of predictors that worked in the bull market of 2000 may be disastrous a year later. The data-miner, therefore, engages in continuous research: modeling and remodeling the markets to capture the changing cycles. Tools for data mining can be found at www.kdnuggets.com.

There are hybrid strategies of research, in which an array of prefabricated mechanical systems are defined and then applied, data-mining style, to individual stocks to see which ones have predictive value at present. This is the approach of “scanning” software, such as Nirvana Systems’ OmniTrader. By scanning a universe of stocks and indices across an array of systems, it is possible to determine which systems are working best for particular trading vehicles.

As most traders are aware, the risk of research-based strategies is that of overfitting. If you define enough parameters and time periods, eventually you’ll find a combination that predicts the past very well—by complete chance. It is not at all unusual to find an optimized research strategy that performs poorly going forward. Reputable researchers develop and test their systems on independent data sets, so as to demonstrate the reliability of their findings.

Can quantitative, research-based strategies capture market expertise? I believe the answer is an unequivocal “Yes!” A perusal of the most successful hedge funds reveals a predominance of “quant shops”. Several research-based stock selection strategies, such as Jon Markman’s seasonal patterns (www.moneycentral.com) and the Value Line system (www.valueline.com), exhibit long-term track records that defy mere chance occurrence.

And yet it is also true that many successful traders neither rely upon mechanical systems nor data-mining. Indeed, one of Jack Schwager’s most interesting findings in his Market Wizards interviews was that the expert traders employed a wide range of strategies. Some were highly quantitative; others relied solely upon discretionary judgment. Several of the most legendary market participants—Warren Buffet and Peter Lynch, for example—employed research in their work, but ultimately based their decisions upon their personal synthesis of this research. Quantitative strategies can capture market expertise, but it would appear that all market expertise cannot be reduced to numbers.

Developing Expertise Through Pattern Recognition

The second major answer to the question of trading expertise is that of pattern recognition. The markets display patterns that repeat over time, across various time-scales. Traders gain expertise by acquiring information about these patterns and then learning to recognize the patterns for themselves. An analogy would be a medical student learning to diagnose a disease, such as pneumonia. Each disease is defined by a discrete set of signs and symptoms. By running appropriate tests and making proper observations of the patient, the medical student can gather the information needed to recognize pneumonia. Becoming an expert doctor requires seeing many patients and gaining practice in putting the pieces of information together rapidly and accurately.

The clearest example of gaining trading expertise through pattern recognition is the large literature on technical analysis. Most technical analysis books are like the books carried by medical students. They attempt to group market “signs” and “symptoms” into identifiable patterns that help the trader “diagnose” the market. Some of the patterns may be chart patterns; others may be based upon the identification of cycles, configurations of oscillators, etc. Like the doctor, the technical analyst cultivates expertise by seeing many markets and learning to identify the patterns in real time.

Note how the pattern recognition and research answers to the question of expertise lead to very different approaches to the training of traders. In the research perspective, traders learn to improve their trading by conducting better research. This means learning to use more sophisticated tools, gather more data, uncover better predictors, etc. From a pattern recognition vantage point, however, trading success will not come from performing more research. Rather, direct instruction from experts and massed practice leads to the development of competence (again like medical school, where the dictum is “See one, do one, teach one”).

Another way of stating this is that the research viewpoint treats trading as a science. We gain knowledge by uncovering new observations and patterns. The pattern recognition perspective treats trading as a performance activity. We gain proficiency through mentoring and constant practice. This is the way of the athlete, the musician, and the craftsperson.

Can expertise be acquired by learning patterns from others and then gaining experience identifying them on one’s own? It would seem so: this is traditionally how chess champions and Olympic athletes develop. There are also examples of such expertise development in trading: Linda Raschke’s chatroom (www.mrci.com/lbr) is an excellent example of a learning device that takes the pattern recognition approach. Users of the site can “listen in” as Linda—a Market Wizard trader herself—identifies market patterns in real time. My conversations with traders who have enrolled in this service leave me with little doubt that they have acquired profitable skills, eventually moving on to becoming successful independent traders. Richard Dennis’ experiment with the “Turtles” is perhaps the most famous example of how expertise (in this case, a pattern-based trading system) can be successfully modeled for people with little market background.

And yet there are nagging doubts about the actual value of the patterns typically described in market books and tapes. A comprehensive investigation of technical analysis strategies by Bauer and Dahlquist found very little evidence for their effectiveness. An attempt to quantify technical analysis patterns by Andrew Lo at MIT found that they did, indeed, contain information about future market moves, but hardly as much as isportrayed in the popular literature. Because pattern recognition entails a healthy measure of judgment, it is very difficult to demonstrate its efficacy outside of the expert’s hands. In other words, the expert trader may be utilizing more information in trading than he or she can verbalize. This is certainly the case for chess experts and athletes. While they can describe what they are doing, it is clear that their proficiency extends well beyond the application of a limited set of rules or patterns.

This phenomenon has been the subject of extensive study in psychotherapy research. It turns out that there really is a difference in results between expert therapists and novices. But it also turns out that there is a difference between what expert therapists say they do and what they actually do in their sessions. This was noted as far back as the days of Freud. While he advocated a set of strict therapeutic procedures to be followed, Freud’s own published cases deviated from these significantly. What appears to work in therapy is not what the therapists focus on—their behavioral techniques, psychoanalytic methods, etc.—but the ways in which these are employed. Using techniques in a sensitive way that gains the client’s trust and fits with the client’s understandings is more important than the procedures specific to those techniques.

So it may be with trading. Expert traders describe their work in terms of price-volatility patterns, momentum divergences, or a nesting of cycles, but it might be the ways in which these patterns are employed thatmakes for the expertise. Great traders may be able to identify patterns in their work, but it is not clear that their greatness lies in these patterns.

Implicit Learning: A New Perspective

The term implicit learning began with the research of Brooklyn College’s Arthur Reber in the mid 1960s. Since that time, it has been an active area of investigation, producing numerous journal articles and books.

Implicit learning can be contrasted with the research and pattern recognition perspectives described above, in that the latter are examples of explicit learning. By conducting research or by receiving instruction inmarket patterns, we are learning in a conscious, intentional fashion. The implicit learning research suggests that much of the expertise we acquire is the result of processes that are neither conscious norintentional.

A simple example drawn from Reber’s work will illustrate the idea. Suppose I invent an artificial “grammar”. In this grammar, there are rules that determine which letters can follow given letters and which cannot. If Iuse a very simple grammar such as MQTXG, then every time I show a subject the letter M, it should be followed by a Q; every time I flash a T, it should be followed by an X, etc.

The key in the research is that subjects are not told the rules behind the grammar in advance. They are simply shown a letter string (QT, for example) and asked whether it is “grammatical” or not. If they get theanswer wrong, they are given the correct answer and then shown another string. This continues for many trials, generally in the thousands.

Interestingly, the subjects eventually become quite proficient at distinguishing the grammatical strings from the ungrammatical ones. If they are shown a TX, they know this is right, but that TG is not. Nevertheless,if you ask the subjects to describe how they know the string is grammatical or not, they cannot verbalize any set of cogent rules. Indeed, many subjects insist that the letter arrangements are random—even asthey sort out the grammatical ones from the ungrammatical ones with great skill.

Reber referred to this as implicit learning, because it appeared that the subjects had truly learned something about the patterns presented to them, but that this learning was not conscious and self-directed. Reberand subsequent researchers in the field, such as Axel Cleeremans in Brussels, suggest that many performance skills, such as riding a bicycle and learning a language, are acquired in just this way. In such cases,we learn complex competencies, but cannot fully verbalize what we know or reduce our knowledge to a set of patterns or principles.

Such implicit learning has been demonstrated in the laboratory across a variety of tasks. Cleeremans and McClelland, for example, flashed lights on a computer screen for subjects, with the lights appearing at sixdifferent places on the screen. The subjects had to press a keyboard button corresponding to the location of the light on the screen. There were complex rules determining where the light would flash, but theserules were not known by the subjects. After thousands of trials, the subjects became very good at anticipating the location of the light, as demonstrated by reduced response times. Significantly, when the lightswere flashed on the screen in a random pattern, no such reduction in response time was observed. This was a meaningful finding, since the patterns picked up by the subjects were not only outside their onsciousawareness—they were also mathematically complex and beyond the subjectsomputational abilities! (Like the markets, the patterns were actually “noisy”—a mixture of patterns and random events.)

It appears that much repetition is needed before implicit learning can occur. The thousands of trials in the Cleeremans and McClelland study are not unusual for this research. Moreover, it appears that the state ofthe subjects’ attention is crucial to the results. In a research review, Cleeremans, Destrebeckqz, and Boyer report that, when subjects perform the learning tasks with divided attention, the implicit learning suffersgreatly. (Interestingly, conscious efforts to abstract the rules from the stream of trials also interfere with learning). This has led Cleeremans to speculate that implicit learning is akin to the learning demonstrated byneural networks, in which complex patterns can be abstracted from material through the presentation of numerous examples.

The implicit learning research suggests a provocative hypothesis: Perhaps expertise in trading is akin to expertise in psychotherapy. While therapists say their work is grounded in research and makes use oftheory-based techniques, the actual factors that account for positive results are implicit, and acquired over the course of years of working with patients. Similarly, traders may attribute their results to the research orpatterns they are trading. In reality, however, the research and patterns serve as rationales that legitimize the absorption of markets over a period of years. It is the implicit learning of markets across thousands of“trials” that makes for expertise, not necessarily the conscious strategies that traders profess.

Implications for Developing Expertise in the Markets

Such an implicit learning perspective helps to make sense of Schwager’s findings. There are many ways of becoming immersed in the markets: through research, observation of charts, tape reading, etc. Thespecific activity is less important than the immersion. We become experts in trading in the same way that subjects learned Reber’s artificial grammars. We see enough examples under sufficient conditions of attention and concentration that we become able to intuit the underlying patterns. In an important sense, we learn to feel our market knowledge before we become able to verbalize it. While simply “going with yourfeelings” is generally a recipe for trading disaster, I believe it is also the case that our emotions and “gut” feelings can be important sources of market information.

The reason for this is tied up in the neurobiology of the brain. In his excellent text The Executive Brain: Frontal Lobes and the Civilized Mind, New York University’s Elkhonon Goldberg summarizes evidence thatsuggests a division of labor for the hemispheres of our brains. Our right, nonverbal hemispheres become activated when we encounter novel stimuli and information. Our left, verbal hemispheres are more active inprocessing routine knowledge and situations. When we first encounter new situations, as in the markets, we tend to process the information non-verbally—which means implicitly. Only when we have made thesepatterns highly familiar will there be a transfer to left hemisphere processing and an ability to capture, in words, some of the complexity of one’s understandings. As we know from studies of regional cerebral bloodflow, the right hemisphere is also activated under emotional conditions. It is not surprising that our awareness of novel patterns, whether in artificial grammars or in markets, would appear as felt tendencies rather
than as verbalized rules.

o finally we get to the traditional domain of the trading psychologist! How do we know when our feelings convey real information for trading and when they merely provide interference from our conflicts oversuccess/failure, risk/safety, etc.? Developing trading expertise is not so simple as following such slogans as “tune out your emotions when you are trading”. Much of what you might know about the markets maytake the form of implicit knowledge that is encoded nonverbally and experienced viscerally.

This is an area that I am currently researching, and I welcome readers to stay in touch with me about the results. I will make sure updated information is posted in a timely way to my personal page atwww.greatspeculations.com. I also hope to have my own book out on the topic early in 2003; my page will also keep readers abreast of that development. But in the remainder of this article, allow me to engage in afew speculations of my own regarding the implications of implicit learning for trading success.

1. Many are called, few are chosen – I believe the implicit learning perspective helps to explain why so few traders ultimately succeed at their craft. Quite simply, they cannot outlast their learning curves. If,indeed, it takes thousands of trials to generate successful implicit learning, a great number of traders would have been bankrupted by then. Many others might not survive that number of trials simply due to the timeand energy required. It is impossible to hold a full-time job and generate the degree of immersion in the markets needed for implicit learning. On the other hand, it is impossible to obtain a full-time income fromtrading without developing the mastery conferred by years of experience. Part-time traders never develop expertise for the same reason that part-time chess players or athletes are unlikely to succeed. For purelypractical reasons associated with raising a family, making a living, etc., few people can undergo the “starving artist” phase of skill-building.
2. Emotions interfere with trading – This is a near-universal observation among full-time traders and captures an important understanding. Fear, greed, overconfidence, self-blame—all of these can undercut eventhe most mechanical trading. Indeed, when Linda Raschke and I surveyed 64 traders for their personality and coping patterns, the factor of neuroticism—the tendency to experience negative emotions—emerged asa major factor associated with trading difficulties. This makes sense from an implicit learning perspective. To the degree that a trader is focused on his or her fears, self-esteem, fantasies, etc., attention is drawnaway from the learning process. The problem may not be emotionalism per se; there are many highly emotional, but successful traders. Rather, the issue may be the degree to which emotions interfere with one’scognitive processing by competing for attention. Focusing on negative emotions may be a much larger problem than actually experiencing them. Many outstanding traders “explode” when they make a rookie error.For them, however, the storm blows over quickly; less successful traders appear to be less able to let the issue go. As a result, they become caught in a cycle of blame, increasing self-consciousness, and furtherblame. As a psychologist, my leaning is to help traders experience their frustration and get over it quickly, rather than “overcome” it altogether. (In my chatroom session with Linda Raschke, I will be addressing how
   to accomplish this).
3. The advantages of learning trading vs. investing – If the internalization of complex patterns requires many thousands of observations across different market conditions, the challenge for the trader is makingthis process as efficient as possible. My sense is that there may be an advantage to learning trading, as opposed to investing, simply because short-term traders are apt to observe many patterns in the course of asingle day or week. The investor, conversely, may note a pattern every few months or years, greatly extending the amount of time needed for implicit learning. This dynamic would help to explain why many of themost successful traders I have met have had experience working on the exchange floors. In the fast-paced environment of the floors, a trade may last seconds to minutes, with many trades placed per day. Complexresearch strategies and chart analyses fly out the window when time frames are compressed to that degree. Instead, traders become so immersed in the markets that they acquire the (implicit) ability to read
   moment-to-moment patterns of momentum and price change. This creates an ideal implicit learning environment; having so many patterns to read per day makes the development of expertise much more efficient.Ironically, it also might help account for difficulties floor traders often experience when they attempt to trade off the floor. Without the contextual cues that help them process those price and momentum shifts, floortraders lose their edge—even though they may think they are employing their same, successful trading methods.
4. Developing technologies for training traders – If we look at how experts are trained in other fields, we notice a common factor: an intensive period of apprenticeship in which the student works under a masterand obtains continuous instruction and practice. Consider, for example, the cultivation of expertise in the martial arts. Many years will be spent in the dojo studying under a sensei before the black belt is conferred.Instruction alternates with practice; rehearsal of techniques alternates with the application of techniques in real-life (tournament) conditions. The online medium has created a variety of promising strategies fortraining traders, such as Linda’s chatroom, real-time market commentary via weblog, and services that allow simulated online trading. My sense is that we will see an accelerated shift from services that emphasizetrading techniques to comprehensive trading “dojos” that incorporate real-time instruction, practice, and coaching. Already we are seeing expert instruction modules built into conventional software programs such as
   Metastock. This move toward implicit learning environments strikes me as a most promising application for peer-to-peer networks, as traders share research resources and trading experiences and learn from eachother. (See www.limewire.org for more information on Gnutella and P2P networking).
5. Developing technologies for facilitating learning – This is my primary research interest in trading psychology. A broad array of research suggests that learning is mediated through the brain’s prefrontalcortex, which also controls attention, concentration, planning, and other executive functions. We also know that children with learning disabilities are significantly more likely than others to possess neurologicaldeficits associated with the frontal lobes, including attention deficit hyperactivity disorder (ADHD). Elkhonon Goldberg cites considerable research that indicates we can improve the functioning of our frontal cortexthrough structured exercises, much as we can build our muscles in the gym. Such exercises have been used, for example, in delaying the onset and progression of Alzheimer’s disease. Is it possible, however, todevelop super-states of concentration and learning in a mental gym the way that bodybuilders can hone their physiques in a weight room? I believe we can. I am currently working with Dr. Jeffrey Carmen onbiofeedback strategies that directly measure regional cerebral blood flow to the prefrontal cortex. Utilizing infrared sensors to detect heat changes in the forehead (reflecting increased frontal blood flow), it ispossible for traders to know exactly how much of their mental processing power is available to them at all times. Moreover, it is possible for them to learn strategies for increasing their frontal activation andmaximizing their optimal learning states. This would allow traders to process each trading day (or lesson) as thoroughly as possible, creating more efficient learning.
6. My research to date suggests that the state of mind induced by the biofeedback exercises is not unlike the state that people enter during hypnotic induction or meditation. It is a state of relaxed and focusedconcentration. Such a mind frame minimizes the impact of emotional interference at the same time that it quiets the verbal, internal dialogue that permeates much of our cognitive lives. Following Goldberg’shypothesis, I believe that the capacity to enter such states of consciousness may allow us to efficiently process novel information by facilitating right hemispheric activation, even as it dampens emotional arousaland the interference of critical, verbal thinking. This very much fits with psychologist Mihalyi Csikszentmihalyi’s observations of “flow” states among highly creative and successful individuals. The learning ofexpertise may depend as much upon the mind state of the learner as the quality of the instructional materials.

Conclusion

I began this article with a straightforward question: How does one gain expertise as a trader? We have seen that expertise is often described as the outcome of an explicit research process or as an explicitacquisition of knowledge about recurrent patterns. Much skill-based learning, however, is acquired implicitly, as the result of processing thousands of examples. Small children learn language, for example, longbefore they can verbalize rules of grammar and syntax; we learn complex motor skills, such as hitting a baseball, without ever being able to capture our expertise in a way that could be duplicated by another person.

While immersion in research and in pattern recognition can indeed produce trading expertise—a finding made clear by Schwager—the key ingredient in trading development may be the immersion, not the researchor the patterns per se. If this is true, efforts to find the best trading system or the most promising chart pattern are off the mark. The what of learning trading may be less important than the how. If you want to become a proficient trader, the most promising strategy is to immerse yourself in the markets under the tutelage of a master trader. You need to process example after example under real trading conditions, withfull concentration, to develop your own “neural network”.

I believe the most exciting frontier for trading psychology is the development of tools and techniques for maximizing implicit learning processes. Such techniques would assist in the acquisition and utilization ofexpertise by training individuals to sustain states of consciousness in which they are open to implicit processing. As I hope to demonstrate more thoroughly in my forthcoming book, there are reasons forbelieving that experienced traders possess greater expertise than they are aware of. This tacit knowledge, to use Michael Polanyi’s memorable term, reveals itself during “hot streaks” in trading and thosewonderful experiences where we just “know” what the market is doing and place winning trades accordingly. Too many traders look to emulate others. The secret to success, conversely, might well be to gaingreater access to the expertise we have already acquired implicitly and learn to become the traders we already are when we’re at our best.

Well, if you’ve followed me thus far through a lengthy article you no doubt have much of capacity for attention and concentration needed to become a master trader! In the coming months, I hope to elaborate manyof the ideas and techniques alluded to in this article, and I encourage you to stay in touch regarding new directions and developments.

With that, I will part with a last research finding from Reber. Remember those artificial grammars that people had to learn, such as MQTXG? Letters were displayed to subjects that either followed the grammar (i.e.,Q could only follow M; T could only follow Q, etc.) or that did not. The subjects did not know the rules of the grammar, but over many trials could figure out which combinations of letters were right and which werewrong. Suppose, however, that the grammar is changed in the middle of the experiment, so that the new constructions follow the rules of NRSYF instead of MQTXG. Will subjects continue to display implicit learning?

The answer is enlightening. After many trials with the initial grammar, without knowing the rules, subjects will choose “MQ”, “TX”, and “QT as grammatical constructions while rejecting “QM”, “XT”, and “TQ”. Oncethe grammar is switched, the subjects’ learning goes out the window and their guesses retreat to chance levels. But with enough new trials, subjects pick up the new grammar and are able to recognize “NR”, “SY”,and “RS” as grammatical and reject “RN”, “YS”, and “SR”. In other words, people not only learn complex patterns implicitly; they continue their implicit learning when the patterns shift. This has major implicationsfor the development of market expertise. The markets are always changing, but as long as we stay in our optimal learning modes, we can adapt with them.

Brett N. Steenbarger, Ph.D. is Associate Professor of Psychiatry and Behavioral Sciences at SUNY Upstate Medical University. Dr. Steenbarger is an active trader and author of The Psychology of Trading (Wiley, 2002). He writes feature columns for the MSN Money website (www.moneycentral.com) and several trading publications, including Stocks Futures and Options Magazine (www.sfomag.com). These articles and a daily trading weblog are linked at www.Greatspeculations.com.