AT: This year has been pretty rough overall for currency managers, and currencies have been stagnant for a while. Let’s talk about the evolution of online forex brokers. As this market grows, what do you think of the potential profit opportunities? Will the trend moves continue to dissipate and will that make it harder for currency managers to make money? Is a sea change in the market occurring?

PP: I’ve been around long enough to know that it’s not new. But on the other hand, the market is changing with regard to the distribution of prices. We know that market prices are not normally distributed, meaning there are sharp moves that don’t fit the normal distribution pattern — these “fat tails” in a non-Gaussian (bell) curve. This tendency is increasing, in my opinion.

All system trading ultimately relies on near-normal distribution. When this deteriorates, you have less opportunity for systematic trading. Things will be more difficult for system traders, and fundamental traders who have a view about price levels might come to the forefront.

On our Web site there’s a comparison between systematic and discretionary currency managers how they under perform and outperform each other. It shows that, currently, discretionary are ahead of systematic traders. If that will persist, I don’t know. I should like to think not.


AT: If it does, how would it impact your trading?

PP: It’s not going to. But we have to be more careful with the fine-tuning of the parameter spaces and be on the button with regard to monitoring how systems perform in general and how other traders perform.


AT: It wouldn’t lead to shortening your trading horizon, decreasing your size or some other change?

PP: No. We are very happy with the current model. We don’t, generally, reduce the position size if we lose money.


AT: So when you talk about adjusting parameters depending how the market evolves, what are you talking about changing?

PP: The parameters we change are “buffers,” which adjust to market volatility. But we tend not to change them, if we can avoid it.


AT: Are the buffers essentially volatility-based filters that dictate how much a market has to move after an initial signal is triggered before the signal is acted upon?

PP: Yes.


AT: What’s an average trade length?

PP: Two days.


AT: Is forex a unique market, in that trading currencies is different than trading stocks or bonds?

PP: Currencies don’t “scale” like other assets. Stocks, bonds and other asset prices scale logarithmically (i.e., on a percentage basis). What I’m saying is, the daily volatility of, say, soybeans trading at $12 will be higher than it would with beans trading at $3. It moves in percentage terms according to the price level. That’s normal.

I checked the average daily range or volatility of the British pound when it was trading around $1 vs. when it was around $2 or $2.50 and, funnily enough, there was virtually no difference in the daily volatility. It should have been half when the pound was trading at $1 vs. where it was with the pound at $2, but it wasn’t.

This is probably because currencies are reciprocal — you could say there’s a pound/dollar rate and a dollar/pound rate. By contrast, gold bullion is measured in dollars, but you never measure dollars in gold bullion.

One thing’s for sure: Currencies do not fluctuate more when they are at higher prices — at least as much as they should.

The medium of currencies is not comparable to the bond and stock markets, in which you have indices people can buy — a portfolio of bonds or a portfolio of stocks, like buying all the stocks in the S&P 500 — that serves as a benchmark. You can’t do that in currencies.


AT: I read you only trade the three largest currency pairs (EUR/USD, USD/JPY and EUR/JPY),
but you also have a study that discusses the superior trend characteristics of less mature currencies. If some of these less mature markets have better trend characteristics even if they’re less liquid — isn’t there enough price movement to justify doing some trading in them?

PP: The risk is just too big. Everything is fine and dandy on paper, but simulations don’t show everything — you find out there was a 50-point market gap that wasn’t factored in because most people work with hourly data, at best. Even if you had one-minute data, even then you wouldn’t even necessarily spot a “liquidity black hole” (a term created by Prof. Avinash Persaud). We’ve seen liquidity holes occur in time spans less than one minute. So even if you make a one-minute chart, the gap doesn’t show up.

And this is a risk that has increased recently in my opinion — I’ve never seen so many liquidity holes, particularly in the dollar/yen rate.


AT: Are there any currency pairs you are currently not trading but are watching because they are poised to become mature enough to trade?

PP: No.






Peter Panholzer: Currency system architect Part 4

AT: Also, do you think some of the less mature currency pairs are viable for individual traders, who don’t have to worry as much about huge liquidity?

PP: Sure. You can choose between many models when you buy a car.


AT: You seem to feel the Bank of Japan is especially responsible for the liquidity holes you talk about — you’ve even referred to their “manipulative moves” on your Web site. Why is that?

PP: The Bank of Japan simply has more discretionary powers than other central banks.


AT: What basic principles would you advise forex traders adopt given today’s market?

PP: After all my 31 years of trading, I’ve come to say that the best way is to trade systematically — in an unemotional way — and that can only be done using very rigorous system construction and exploitation of data statistics.

You can do this as an individual trader, but it takes a lot of resources. You can’t buy a good trading program, in my opinion. We certainly wouldn’t sell ours. The program has to fit you like your own clothing. In that sense we have a tailor — that is, a programmer who is, ideally, himself a trader — andwe work together with him to develop and monitor the systems. This is Adam Hartley, of SnapDragon Systems in Oxford, England.

I also believe simple rules, amazingly enough, make the best systems; complex rules don’t, necessarily. A complex rule, for example, would be the application of chaos theory. Rocket scientists were given millions of dollars to research and trade using fractals and non-linear dynamics, and they failed. Sophistication is not the solution.

Sometimes the simplest system will be the most useful because it is easy to understand and execute — the Turtle system, for example, or the very similar system I used long before the Turtles. (“Turtle”was the name given to the trend following disciples of trading star Richard Dennis, who made hundreds of millions of dollars in the 1970s and 1980s.) Decades ago I used a four-week breakout system (buy at the four-week high and sell at the four-week low), and that turned out to be the Turtle system, except the Turtle system entered at four weeks and got out at two.

Obviously it was very useful in very trendy markets in the 1970s when there was inflation. That’s another thing: The 1970s were unprecedented inflationary years. If that happens again, we’ll have some tremendous advantages. What you need is movement. Right now, there’s not so much in the currency market.


AT: If a general deterioration of trend moves is inevitable — not just cyclical — doesn’t that eventually spell doom for currency trend followers? Wouldn’t you eventually have to adopt a different approach?

PP: Currently these clouds seem far away. As [Former Canadian Prime Minister] Pierre Trudeau once said to an old lady who asked him about inflation, “The universe will unfold as it must.”


AT: Do you have any feeling about currencies over the next six months or so?

PP: I think 2005 will be a banner year for currencies. I also have a strong feeling the remainder of the year will show a strong improvement in conditions. There seems to be some kind of seasonality that October, November and December tend to be good months. But of course, that could be called witchcraft (laughs). Nevertheless, the statistics are there to show the last quarter is good.


AT: Why is 2005 going to be good?

PP: Because when you come out of a long sideways move, in general, you get a big move. This is natural.


AT: Do you think the high leverage sometimes offered by forex brokers is a good thing for smaller traders to use?

PP: I think it’s irresponsible to trade with only the margin money — in the spot market or in futures. But the exchanges and brokers have an interest to let the little guy trade with as little money as possible the margin. It’s not in the interest of the trader. It’s in the interest of the broker because it creates more commission per customer dollar.

All I can say is, in currency trading leverage is a very important thing. You can use no leverage or 100 to-1 leverage. And in general, you should always use less leverage than you think you should because we always overestimate our tolerance for risk.

We don’t always understand that risk isn’t always a money risk; there’s the risk of regret. Sometimes you regret you haven’t been in the market, sometimes that you got out too early or that you’ve taken a loss when it wasn’t necessary. It’s not only the money we worry about, it’s also our regret we worry about.

AT: What were your trading experiences in that period from 1973 to 1979? How long did it take you to develop your systematic approach?

PP: It took me from 1973 to 1977 that’s when my trading blossomed and I started to make oodles of money. It takes four years of tuition in any endeavor in life. If you want to learn golf, tennis, piano, a language — it always takes at least four years, doesn’t it? But before that I didn’t make money — I made it, I lost it. I survived. But that’s normal. Whatever I lost, I considered tuition.


AT: How did you initially approach the markets?

PP: I’ve been a contrarian since 1972. The figures published by Market Vane (Sibbet-Haddady) started in 1972, and ever since then I’ve been following them. But then to apply that information, that takes insight, too. So when I say it took me four years of learning, that includes learning contrarianism. All the money I made before I started system trading was through contrarian trades.


AT: What’s an example of the kind of trade you might have used in this regard?

PP: Take sugar, for example. After running up from 6 cents to more than 60 cents throughout the early 1970s, it had just collapsed back to 6 cents. Everyone considered it a bargain, of course.

Well, contrarian thinking taught me to go against “everyone.” I went short at 6 cents and everyone thought I was nuts — until I cashed in my profits at 4.5 cents. In relative terms, a move from 6 to 4.5 is huge. Nobody thought that way, but it’s a 25-percent drop, after all.

The following winter (1978) I holidayed in Mexico City, but, in true Jesse Livermore fashion, instead of visiting monuments I hung around brokerage houses and got to talk to a Mexican client who “confided” to me that his brokerage had lent him the required additional margin for his short cocoa contracts, which were then trading at 89 cents/pound. “We don’t want you to close out your position and let some gringo profit from your losses,” they had told him.

To me that sounded like a starting pistol. I immediately called my partner in Toronto and asked him to go long six contracts of cocoa in my account. It was a bumpy ride all the way up to above $2 when I took my profit.


AT: What ideas did you go through in that period that you ended up casting aside?

PP: Econometrics — what we might call one step ahead of fundamentals — didn’t work for me. For example, at our firm we had a curve that showed the average weekly movement of pork bellies in and out of warehouses. That has a seasonal pattern — it goes up around Easter, down in the summer and increases in the winter. You can average that over 20 years and get a smooth curve.

Then I compared the actual movements against the 20-year average to determine what that would mean in terms of a move in pork belly prices. That idea makes a lot of sense, but it doesn’t work. This is what led me to the numbers game.


AT: Is “the numbers game” what most people would consider technical, quantitative or statistical?

PP: Completely statistical. I do not believe in technical indicators per se, although some people use them as a tool — and there’s nothing wrong with that. Let’s say you have a hammer. If this hammer suits your hand particularly well and you can hit a nail right on, that’s a good hammer for you. For me, system trading is a good hammer.


AT: Is it fair to say that whatever you use has to be testable?

PP: Certainly. A lot of research goes into the stability of parameter spaces — which are ranges of system parameter values across which you want performance to be consistent as markets evolve.

But I think the most important thing I’ve learned in the past 25 years is that if you delegate trade execution to someone else, you’re more likely to succeed. I’m purely systematic. We now have what we call a “robot interface” with one broker. That account has no interference whatsoever. No resting orders are placed in advance, so the broker doesn’t know the robot’s “intentions.”

We prefer not to show our orders. I believe in that in principle. If a broker sees the order, there’s always the argument that brokers [execute] where the orders are bunched up, for example.

With the robot, the order sits on our computer server and the broker doesn’t see it. The moment our server sees the trade trigger, it executes at the market with the electronic broker platform.


AT: That sounds like a good idea for individual traders, too: Instead of placing limit orders, place the trade as a market order when you get a signal from your system.

PP: Yes, of course. All I can say is, in all likelihood, this is the best way to execute a system. Second guessing it is the biggest danger.


AT: What’s the TETRA program?

PP: It’s the program applied in most of the funds we manage. We started it in January 1998.


AT: Is it a trend-following approach? How does it work?

PP: It’s a hybrid system. A trend-following system normally has some kind of sliding protection something that keeps moving with the market; that’s how you follow the trend. We have a hybrid because we use both profit targets and sliding protection, in a systematic way.

We use four-hour (the primary time interval), hourly and daily data in the TETRA program. These intervals are not being changed. Some researchers, such as Richard Olsen (see “Richard Olsen: Tuned to high frequency,” Currency Trader, Oct. 2004 p. 42), have found you can use “tick time” that multiple hours of trading activity can be compressed into one hour, for example

A system consists of three things:
Entry mechanism, exit mechanism and then re-entry mechanism. If you don’t have a re-entry mechanism, you have no system.


AT: How did you determine these time intervals? How long a process was it?

PP: It was a process covering years of research and hard toil.

Forex might be all the rage in the trading industry these days, but most individual traders have begun to take note of the world’s largest market only fairly recently.

Peter Panholzer, head of trading and chief investment officer at DynexCorp and Panholzer Advisory Corporation (PAC), which together manage $53 million in the forex market, has been trading the currency market professionally since the late 1970s. He was currency before currency was cool.
Panholzer was something of a trailblazer, having started managing money exclusively in the currency
market at a time when common wisdom dictated doing so was a mistake because of the lack of diversification. Also, he was an early practitioner of systematic trading, which was a relatively novel concept in currencies at the time.

Panholzer, 61, who became a Canadian citizen in 1977, grew up in Vienna, where he earned a master’s degree in architecture from the Technical University of Vienna. This seemingly unrelated focus of study actually served his future career choice well. “I have a mathematical background,” he says. “In Vienna, the education for architecture is not just artistic, there’s a lot of engineering involved, and therefore a lot of math. I acquired my programming skills myself in the early 1970s.”

Panholzer worked as an architect for six years in Toronto, and he co-designed some award-winning
buildings in Canada. But when contemplating going solo, some of the inequities of his profession got
under his skin. His decision to try trading was the result of his belief in “objectivism, the philosophy of
Ayn Rand (author of the books Atlas Shrugged and The Fountainhead) that effort and talent should be
rewarded,” he says. “Unfortunately, in architecture, talent and effort are not, in most cases, rewarded. A bad architect who was a good businessman would be the richest architect you could find anywhere.” Panholzer continues. “Yes, there are some excellent architects — Frank Gehry and, in Canada, Arthur Ericson come to mind — who probably have been enjoying the benefits of Ayn Rand’s philosophy in the truest sense.

There you have two great talents who, like Frank Lloyd Wright, reaped the financial rewards of their talent. But there are great architects out there who are not rewarded accordingly. That bothered me.

“In trading that’s not the case. In the markets, you get rewarded — or slapped in the face — immediately.” Although 2004 has so far been a slap in the face for DynexCorp and most other currency funds, Panholzer has mostly been rewarded over the 25 years of his trading manager career. DynexCorp’s TETRA trading program was down roughly 15 percent this year through September, but Panholzer has had only three losing years since 1994 and DynexCorp was the No. 1 currency manager in 1998 and No. 3 overall from 1994- 1999, according to the Managed Account Report’s rankings of riskadjusted returns.

Panholzer began his market career in 1973 trading silver. He started trading currencies for his own account in 1977 and with such success that he began managing money in currencies exclusively in
October 1979. He’s been doing it ever since.

In 1979 he joined the Toronto branch of ContiCommodity Services (Canada) Ltd. as an account executive. He transferred to the Lugano, Switzerland branch office in 1981. Between 1979 and 1984, he ran the Magnum Currency Program (using currency futures contracts at the Chicago Mercantile
Exchange) at Conti, which produced an average annual return of 80 percent in its first three years.
After four more years at E.F. Hutton in the mid-1980s, he struck out on his own, founding PAC in
1988, to specialize purely in foreign exchange spot trading. Since 1992 he has lived in Monaco.

DynexCorp handles institutional business, which consists mostly of banks outside the U.S., while PAC,
which has an American office and is NFA and CFTC registered, handles individual and small corporate clients. The funds are traded in strict systematic fashion using Panholzer’s TETRA trading program.

“I didn’t start trading a strict system until 1979, which at the time was a fairly new approach,” he says. “And the attitude was trading currencies exclusively was a mistake because you weren’t diversified enough.” “We essentially have three traders looking after the signals,” he continues. “There is no discretion. They all use the algorithm that delivers the orders, which is very convenient because even a person who doesn’t know how to trade can internally monitor the program.”

Panholzer’s belief in systematic trading is based on two principles familiar to most mechanical traders: testability and objectivity. But he makes a distinction between “system trading” and “technical trading systems.” “System trading is different from technical trading,” he says. “In technical trading, you’re reading tea leaves — the indicators. Interpretation of technical indicators is often subjective and not programmable. But you can program a system.”

Panholzer sometimes sounds like a typical “quant” — talking about the trading in terms of distributions and other statistical concepts — but he also has a somewhat quirky sense of humor (which he occasionally uses to deflect inquiries into more specific aspects of his trading) and, like any good money manager, a talent for changing the subject. When asked what he thinks he does differently from other currency traders, he replied, “I do everything differently from other currency traders. The only thing that connects currency traders is that they never seem to sleep.”

WE: Why is such a large part of the forex market operating intraday?

RO: Very simple. The biggest thing that determines how long you have to be in a position is the spread you pay. If the bid-ask and the fees are .5 percent, then you’ll have to wait until price, on average, has moved at least .5percent.

Without people realizing it, that’s what dictates how long positions are held in any financial market. In foreign exchange, the spread is 0.02 percent, so you can be out with a profit — or a loss — after, literally, five minutes.


WE: Does the forex market have characteristics distinct from other markets?

RO: First, it is ideal for speculative trading, exactly for the reason many people have shied away from it — because it’s so efficient. But it also offers far more opportunity than the typical equity market.

Opportunity potential is defined by annual volatility divided by the current spread. For example, if annual volatility for an equity market is 30 percent and the typical bid-ask spread and ticket costs amount to, say, 0.3 percent, you get a factor of 100 (.30/.3). If you are correct every time you trade, with this 30-percent volatility you can do 100 times your spread.

Take the same approach in foreign exchange. Assume annual volatility is 12 percent and the spread is .02 percent — you get a factor of 600, which means is it is potentially six times more profitable to trade foreign exchange.


WE: Tell me about your money management firm, Olsen Invest.

RO: We’ve been doing it for two and a half years. Prior to that we were offering banks a kind of trading signal service, as a purely advisory activity. Our return, I think, is quite impressive. We run a Sharpe Ratio of 2.23. Last year’s return for a standard risk profile was 15 percent. This year we’re up around 8 percent.


WE: What kind of trading approach do you use for this?

RO: Purely quantitative. We have computer models that read in tick-bytick data, and take positions based purely on this data.


WE: What kind of time frame?

RO: The length of trade, on average, is six hours. Our objective is to shorten that. The maximum period might be up to several days, but in general we try to get in and get out as soon as possible.


WE: Can we discuss a representativestatistical observation or pattern that would provide the basis for a trade?

RO: Our trading strategy is based on a broader economic theory. We are able to earn money because we provide a service — or value-added — to the market and indirectly to the economy as a whole.

Our value-added is that we provide liquidity and are ready to warehouse risk when many other market participants are running for cover. We try to open positions whenever there is a price shock or “tail event” on an intraday basis. The size of a position is determined by the model’s overall assessment of the market environment: Are we in a trending or sideways market? We close out our position regardless of the overall market environment as soon as we have reached our profit objective. We do not believe in sitting on positions too long.

Positions are like inventory in a warehouse: We like to make sure we clear the inventory at a regular frequency. If our trading idea has proven to be premature and we incur an unrealized loss, we increase our exposure — provided the model has not changed its longer-term assessment. We continue to rebalance our positions to optimize the risk profile of our overall exposure. We do not like stop losses and use them only to prevent large-scale drawdowns.


WE: This sounds like you react to “order flow” — meaning, simply, the aggregate OANDA trading activity at a given moment by going long when the market had overextended itself to the downside and selling on the opposite condition. In effect, you’re profiting by being an effective self corrective mechanism for the marketplace, this being something that is difficult for the smaller retail trader to do. Am I in the ballpark?

RO: I have to stress that our models are exclusively driven by the price feed of OANDA FXTrade. Anyone subscribing to the OANDA price feed could build our type of model. Our models analyze microvolatility, which is volatility computed using tick-bytick prices, and on the basis of this makeinferences about market state and the likelihood of self-corrective mechanisms, as you call it, in the marketplace. The small retail trader is unlikely to build such an elaborate system as we havedeveloped. Having said this, the retail trader has other advantages over the professional money manager. He can adopt a higher risk profile and thus achieve a higher return, albeit at a higher risk. Long-term, I see a strong demand for sophisticated trading indicators, which incorporate some of the refinements that I have mentioned earlier.


WE: Does the money management work overlap with OANDA.com at all?

RO: We execute exclusively through OANDA. One important thing is the investment management is a completely independent business that we trade through OANDA. The reason I participated in the founding of OANDA was because, as a trader, I always thought it would be great if there was a brokerage that offered me these features.

An important feature of OANDA FXTrade is that every customer trades at the same price, whatever his position. So, Olsen Invest customers trade on exactly the same price as every other customer of OANDA. Olsen Invest does not take advantage of inside information to “trade against the customer order flow.”


WE: How does the discontinuity and other “inefficiencies” you might have discovered — in tick data translate into trade opportunities?

RO: The example I just gave is such an opportunity. The short-term discontinuity, or price extreme, is embedded in the long-term memory effect, or mean reversion. I use these terms in a fuzzy way. I want to refer to a broad range of different types of memory effects — they exist in many different shapes and forms. An overbought or oversold indicator is a means to quantify the size of a price discontinuity and determine when the price is ready for a bounce back.


WE: Based on your understanding of price behavior, what kinds of trading ideas or approaches — especially the better-known ones — do you think lack merit or effectiveness?

RO: Traditional moving averages are overrated — they fail to account for the dynamic nature of price evolution. Someone who uses such an indicator gets stuck in the rut of a particular analytical time range and forgets about the nonlinear interplay of market participants trading on very different time scales — day traders, regular speculators, portfolio managers, treasurers and central bankers.


WE: Do you concentrate on particular currency pairs?

RO: No, we spread our trading across a wide range of currencies.


WE: What kind of leverage do you use?

RO: We run programs with different risk profiles. Our standard risk profile has an average leverage of approximately 0.8.


WE: What do you think of the growth in the retail forex industry over the past few years?

RO: I was always frustrated by how the big banks discriminate against small traders, making it [prohibitively expensive] for them to trade. So I believe it’s a great opportunity because, in the end, why should only the big guys earn good money? So [the development of the industry] is not just good for the small trader, but for the industry as a whole because longer-term it will contribute to overall market stability. If the big guys all [rely on the same information] they’ll drive the markets in one direction. The more smaller players you have involved, the more diversity you’ll have in trading styles.


WE: Is a corollary of this that the massive long-term trends that used to develop will be diffused to a certain extent?

RO: Yes — this is my hope. I have to add a point here: My big surprise with the OANDA FXTrade platform was the observation that the trading behavior of the retail market was far closer to that of the professional market than I would ever have guessed. For example, from the very outset, we at OANDA FXTrade would have a low volume day, when UBS or Deutsche Bank had a low-volume day.


WE: Are there any risks unique to forex that typical stock traders should be aware of?

RO: All the defects a trader has in equity markets will be exaggerated in forex trading.


WE: Is that just a matter of the high leverage available in forex?

RO: Yes. That’s point No. 1. But it’s also because forex is a 24-hour market and prices move with unbelievable speed relative to the spread. The return and risk potential are very big. It’s like driving on the highway: you can get very far, but make sure your seat belt is on and you know how to drive.


WE: Have you applied your tick-data analysis to markets other than forex? Do you have plans to branch out into other markets?

RO: Yes, we’ve researched the fixed income, commodity and stock markets. They all obey the same rules, subject to complex intricacies, which are a result of their specific market structures and conventions. But for the time being, I want to remain focused on foreign exchange. As the saying goes,the devil is in the details and I want to excel in the foreign exchange market.


WE: Have you done much research regarding the specific trading applications of certain economic “events,” such as the response of forex to interest rate adjustments, trade balance numbers, GDP and other economic numbers?

RO: We’ve done some work. We know the “simple” ways don’t work — the linear indicators fail to perform.


WE: Meaning, a one-input, cause and effect, “What does Market A do in the two days after Report B is released?” type of trading model?

RO: Yes. Interestingly, the neural network type approaches people tried out over the past 15 years don’t work, either. I believe in using a staged approach. Like an archeologist, it’s important to identify different “layers” and resolve the complexities of each layer separately. The overbought-oversold indicator I mentioned has two layers — the definition of the actual indicator and, second, a method that accounts for the 24-hour seasonality of volatility. I definitely plan to develop specific trading applications for economic events, but a lot of work has to be done to make this a success.

I have lots of ambitions. I want to create a few successful companies and have some real fun. It’s so easy to be cynical, but I just want to do some good work.

WE: I have a question about “reengineering finance,” as you put it. Engineering a computer, a car or a toaster is one thing, but the market — consisting of all its flawed, emotional human beings — how can you engineer that?

RO: It has to begin with a different perception of how things work. If you read physics or chemistrybooks from 150 years ago, they have some strange stories in them. At the time there were very good reasons the stories were formulated that way, but in hindsight they were really just children’s stories. In the natural sciences, this vicious circle ended when people really started to analyze the data. Based on their insights, they suddenly discovered reality is much different from what our theories tell us. A very simple example is (British physicist) Ernest Rutherford, who revolutionized our view of how atoms are built. Previously, people thought atoms were a single object or body. It was only because of his very simple experiment that we learned the atom has a kind of core, but then there is a huge, empty space [around it]. In my view, the same thing is true in finance. If you really look at tick data, certain basic statements we make about financial markets prove to be wrong.


WE: For example?

RO: Let’s start with something simple you’ll find in any student’s basic economics or finance textbook: Demand and supply determines the price — singular, “the price.” But if you look at any financial market, there is a bid and an ask. You might see only one or the other, but there are definitely always two prices. So, the basic textbook definition has a fundamental mistake.


WE: I’m not entirely sure what you’re getting at because, regardless of the bid and ask prices at a given moment, there’s only one transactionprice. A market could be 10 bid and 20 ask, and a trade could occur at 10 or 20 — or 15 for that matter.

RO: From the moment of the transaction, of course, there is only one “real price,” but until that moment there is a range of potential prices. Understanding the market process requires more than just looking at supply and demand. Was the transaction traded at the bid or ask price? Was it a big or small trade? Was it an ‘outlier’ price — that is, how does this transaction price relate to the previous and subsequent prices? How was volatility priced at the time of the transaction? What were the interest rates at that moment in time? By oversimplifying the process and assuming demand and supply are the sole determinants of price, we divert our attention from the complexities of the price-formation process and fail to uncover the multiple forces at work. Natural science progressed because scientists had the patience to first describe natural phenomena without giving in to the temptation to explain it. They had the courage to assemble evidence and differentiate between areas they understood and those that were unanswered riddles. They were good [detectives]. In finance, we presume, too often, to explain. It’s important that we stand back and identify areas we don’t understand. What I’m arguing is, whenever there’s a disconnect with your basic model, take it very seriously. There are many other properties that are very different from what the textbooks suggest. Some of the stories people use to explain things in the market are quite contrived. For example, people in quantitative economics might refer to a time series as “continuous.” I say, if you plot that on a graph it might appear continuous, but financial markets are discontinuous. They make discrete jumps. And they can be big or small.


WE: You’re referring to analyzing things from a tick perspective, correct?

RO: Yes. For me, 20 years ago, it was a very obvious thing: Let’s put together a tick-by-tick database as a foundation and build upon that. Today, that’s still not taken for granted — very few people take this scientific approach.


WE: Are the “discreet jumps” you refer to in price or time – or both?

RO: Prices move in discrete jumps, as does time. Time is event-driven and, thus, is non-continuous. Time can come to a virtual standstill, for example, prior to news releases and then jump ahead in the seconds after the announcement. I think discontinuity is the most important statistical property in finance. It has practical implications on many levels.


WE: For example?

RO: First of all, discontinuity highlights the risks involved in trading. Prices can move far faster and in bigger jumps than intuition suggests. A related idea is that established relationships of the past need not hold in the future. Volatility and correlation regimes that have been valid for months or several years can suddenly break down. Finally, hidden behind the discontinuities are long-term memory processes that impact the future.


WE: What do you mean by “longterm memory processes?”

RO: Long-term processes are “memory effects” of, for example, market participants remembering extraordinary events, such as 9/11. The memory of these events influences current market behavior The discontinuities interrupt these long-term processes, creating a complex pattern of price movement that’s shaped by both extremely long-term memory effects and short-term price shocks. I’m highlighting the dichotomy of the simultaneous impact of extremely long-term and short-term forces in the markets.


WE: What are the insights tick data offers that are not available on other time frames — besides the discontinuity characteristic you mentioned?

RO: Related to discontinuities are non stable statistical properties, by which I mean properties that change depending on the observation interval. An example are the “fat tails” (referring to the greater preponderance of extreme values that occur in a data set vs. what a normal bell curve would imply) or price extremes. The shorter the time interval of observation, the bigger the fat tails or price extremes become. Only by having access to the full spectrum of data — from long-term to tick-by-tick price intervals — can we study the statistical properties at different time resolutions and evaluate their stability.


WE: But don’t you think price behavior is similar — or “fractal,” or however one chooses to define it — across time frames?

RO: Yes, without doubt. The fractal property is a key characteristic of financial market data. And technical analysis has leveraged this property without being aware of it. Technical analysis books explain that their tools can be used for every time frame from intraday to long-term — something that is only possible because the behavior of financial market prices is fractal.


WE: Do you consider what you’re doing a work in progress?

RO: Very much. I have only done a small fraction of what I really intend to do. To me, finance will become a kind of big industry that will be very comparable to the pharmaceutical industry, in which thousands of researchers analyze statistical properties and build complex models to understand certain features. It will be very different 10, 20 or 30 years from now than what it is today.


WE: With that level of sophistication, do you think there will still be a role for smaller individual traders?

RO: There is always a role for them, but they will have to adjust to the fact that their role will shift. Take the analogy of cars. Initially we all got around on foot, then we began to ride horses and today we drive cars. As this new technology evolves, the type of indicators we’ll be using will become increasingly sophisticated. Traders will have to learn about those indicators and explore the different ways to apply them correctly.



WE: Are you essentially talking about quantitative, statistical analysis of price movement, as opposed to what people usually think of as technical indicators?

RO: Yes. For me, though, technical indicators are, in part, an anticipation of the new type of indicators that will evolve. And I don’t see a divide between fundamental indicators and technical indicators they’ll all fold into one big thing.


WE: What’s your definition of an “indicator?” What you’re talking about might be different from what most people associate with that word.

RO: An indicator is a mapping of price or other information — such as expected news announcements— to constitute a signal. For example, overbought or oversold indicators are simple indicators. In traditional technical analysis packages, the overbought and oversold indicators are primitive. The quality of these indicators can be improved by taking into account the 24-hour seasonality of volatility.


WE: Is this a matter of calculating indicators on a tick basis?

RO: Yes. Currency markets — and, by the way, all other markets — exhibit different levels of volatility during the course of a 24-hour day. The currency markets are highly volatile when Europe and the U.S. are open at the same time, whereas volatility is at its low point during the Asian lunch break. When volatility is typically high, it takes a bigger price movement to generate an overbought or oversold signal than in a period that generally is very quiet, such as the Asian lunch break. With the simple overbought and oversold signals in use today, traders get false signals. This could be improved.


WE: Using the overbought-oversold indicator example, what might go into an “improved” calculation?

RO: If you compute the over bought over sold indicator on the basis of two moving averages — long term and short-term — you rescale the moving averages with the 24-hour seasonal volatility. For moving averages with hourly data, you rescale each hour with the seasonal volatility of that respective hour, or with five-minute intervals with the seasonal volatility for each five-minute interval.


WE: Where does OANDA fit into what you’re doing?

RO: Within the world of OANDA I’m interested in building the most efficient currency broker there has ever been in foreign exchange. That means offering the narrowest spreads in the industry and the most seamless transaction process. There also are some other features, such as second-by-second interest payments. If you actually look at how financial markets and typical currency markets operate, you discover the financial system pays daily rates of interest. This contributes to instability. The market would be much more stable if there were continuous interest rate payments. The explanation is very simple: Central banks use interest rates as a kind of lever to manage demand and supply. If a currency comes under pressure they can hike interest rates, and by doing so they can shift the demand and supply. But there’s a catch: This was fine as long as 80 percent of the market consisted of people taking positions for one or several days. But in the modern currency market, 90 percent of the volume is intraday. So when the central banks hike interest rates, they impact only about 5 to 10 percent of the market which means that when they do hike rates, they have to hike them very dramatically to have any impact at all.


Continued………………..

Richard Olsen wants to turn the markets into a car, or a computer. Or maybe a toaster.

That might sound like a non sequitur, but Olsen, chairman and CEO of the Zurich-based Olsen Group, as built a career out of looking at things a little differently than the people around him.

“What drives me — what fascinates me — is trying to make finance a true engineering science,” the Oxford-educated economist says. “What that means, in a way, is to make finance as sophisticated as your best gadget, such as your computer or even an elegant car — to make it as slick as some of the physical technology we encounter in our everyday lives.”

If it seems like a tall order, the 51-year-old Olsen is giving it the old college try. His specialty is analyzing “high-frequency” (i.e.,trade-by-trade, or “tick”) data in the forex market. He founded his first financial research company in this vein in 1985 and today has his hand in several financial research, trading and market-service companies related to the fruits of that labor.

In addition to Olsen Financial Technologies, which makes software tools for gathering, cleaning and analyzing high-frequency data, Olsen in 1996 founded (with professor Michael Stumm) OANDA (www.oanda.com), an online forex brokerage/market maker.

Olsen Invest (www.olseninvest.com) is a money management firm, that Olsen founded in November 2002. The company has six “reference” U.S. dollar accounts (each initially funded with $10,000) with varying risk profiles. It shows the balance of one of the accounts plus unrealized profit/loss, minus a 35-percent performance fee.

Olsen studied law before he got involved in the markets, receiving a law degree from the University of Zurich in 1979 before earning a masters in economics from Oxford University in 1980 and a Ph.D. in law from University of Zurich in 1981. “I studied law because my family thought economics wasn’t a real subject,” he says, laughing. “I didn’t realize for a while that law wasn’t a real subject, either.”

From 1983 to 1985 Olsen worked in research and FX dealing at a Zurich bank, but he quickly saw an opportunity to strike out on his own.

He started his own company (“little realizing what I was doing,” he says in retrospect) in 1985 with the goal of selling cleansed databases of FX tick data and forecasting services to banks.

“The underlying vision was to take the approach that has proved to be very successful in natural science and apply it to finance,” he says.

That scientific approach was based on the systematic analysis of forex tick databases, which Olsen says contain overlooked clues about price that often challenge popular notions of market behavior.

An Introduction to High-Frequency Finance, the book Olsen co-authored in 2001, outlines many of the principles that guide his work. The following discussion summarizes a few of its major concepts.

One of the big advantages of tick data is how much of it there is. Whenever you use statistical analysis, the more data you have to work with, the more confidence you can have in your results. Because tick data provides so many “observations” (An Introduction to High-Frequency Finance points out that a liquid market can generate as many data points in a single day as daily data can in 100 years), it provides more statistically reliable insights into price action.

Also, there is a consistency to tick data over select periods, even though a certain period might contain a multitude of data points. For example, it could be argued that comparing the day-to-day or week-to week price action of the Dow Jones Industrial Average today to its behavior 100 years go is inherently flawed because both the index and the marketplace have changed so dramatically over the past century. However, the number of “observations” in a tick database is huge over a relatively short time window — say, a month. The fundamental characteristics of the marketplace are much more stable during this period than they are for daily or weekly data spanning a century. This decreases the risk of making apples-to-oranges comparisons. Further, high-frequency finance holds that calculations such as volatility can be made more robust through the use of tick data. For example, shows the disparities between annualized volatility figures for the U.S. dollar-Japanese yen rate (USD/JPY) using the afternoon price during Japanese trading hours, the afternoon price during London trading hours and the 24 hour continuous tick data.

One contention of the majority of longer-term traders is the amount of meaningless market “noise” increases as the time frame shortens — tick data being the shortest time frame possible. However, the high frequency argument is that “low-frequency” data — daily, weekly, etc. actually hide important market dynamics observable only in high-frequency tick data, including the interaction of different market players operating on different time frames and in different geographic locations.

The book’s key findings regarding these aspects of tick data fall into three categories — scaling, seasonality and heterogeneity.

Scalability refers to the “fractal” properties of price action. For example, the book’s research showed the volatility patterns exhibited in forex prices at the 10-minute time frame are similar to those at the hourly and daily time frames. (This should sound familiar to any chartist who trades the same type of pattern on different time frames.) This concept of scaling also applies to how frequently price changes direction on different time frames.

The seasonal factor refers to regular patterns in, among other things, volatility, bid-ask spreads and tick frequency that emerge over a 24-hour trading day or over a week. These ten dencies are driven by any things, some as simple as the overlap of the end of the European FX trading session and the beginning of the U.S. session that typically produces the highest level of trading activity each day. Other examples include volatility and bid-ask patterns, such as the tendency for intraday forex volatility to peak between hours 12 and 18 (GMT) of the day and for spreads to increase on Fridays.

Market heterogeneity addresses the presence of different trading goals and styles among the differentplayers in the FX market. For example, market makers operate on the extreme shortterm, while central banks trade infrequently. Olsen contends this diversity causes news to be absorbed over time by the market, rather than all at once, as is popularly believed. In other words, an initial market reaction to an event is followed by a series of secondary reactions, which include market players reacting to each other’s reactions. You can’t predict the news, according to Olsen, but you can model the reactive process.

It is, as they say, a lot to “get your brain around.” In early September, we explored these ideas with Olsen and discussed their relevance for FX traders.
continued.......

We: Can you talk a little more about the connection you see between music and trading?

CJ: Music is full of symmetry, art is full of symmetry, nature is full of symmetry. Reading the natural world is really what pattern recognition is all about. I studied jazz — I majored in the trumpet — and had been learning improvisational playing since I was a kid. We’d play and study chord structures that had “resolutional” feelings attached to them as they climbed, climaxed, and descended. Market patterns are basically the same, but you need a method that allows you to analyze them, manage the risk, and then extract a profit from them. I look for symmetry in several areas to determine the [musical] “key,” if you will, the market is currently playing. It’s like there’s a current world view being established by the patterns, and as a manager, I am taking opportunity flow from those patterns. Once I have the current market “key,” I apply the notes — the trades — and hopefully make some music. I have to be flexible in my approach because although patterns repeat, the outcomes are random. Art is flexible, music is flexible, nature is flexible. Everything is moving, flowing, ebbing, and running, and I am following that flow.


WE: But you use a systematic approach to trade. How did you develop that part of your trading?

CJ: Going through all the hard knocks it takes to become a trader, I started to learn about the discipline and psychology of trading, and I decided I wanted to learn about system development. I already had a pretty extensive computer background and I started building systems for futures. I felt I needed cash data to design the systems because there were problems building systems using futures data, where the data had to be seamed at the end of every month (to construct a continuous price series out of individual contract months). You can’t build systems unless you have clean data. I don’t care who’s generating the data; if it has to be seamed in a specific fashion, that will lend itself to problems later in system construction. I was somewhat homegrown as far as doing this. I’m sure there are people who know how to get around that [problem], but I didn’t, so I set futures aside for a while and started doing a lot of research with cash markets. I found the models I was developing really lent themselves well to currencies, so I naturally gravitated toward them because there was lots of liquidity and plenty of cash data available. I started to do all my research in modeling currencies.


WE: Did the data situation push you toward forex, or were there other things that attracted you to it?

CJ: The availability of the data, but also the liquidity. I wanted to trade 24-hour markets because I thought it would give me more opportunity for profit. I wanted something that had some sort of pattern recognition feature, and the currencies are so huge and the liquidity is so deep, it just seemed like there was less interference with some of the patterns.


WE: In terms of the hard knocks you took, what lessons did you try to address in developing your systems?

CJ: I learned a lot about the psychological aspects of trading. Trying to trade from a discretionary base lends itself to a lot of emotional decision making. You can have a good run for a while, but eventually, if you’re making decisions from an emotional place, you can get yourself in trouble. And I found that for me, day trading was not the way to go. Making decisions from a long-term perspective was fine. I could be something of a long-term, macro trader — using a buy-and-hold view.

Taking risk wasn’t a problem — I could always take risk, and effectively manage it. The problem was day trading didn’t fit me, so I started developing models that would handle the execution for me. Creating ideas and putting them together in a mathematical algorithm that would express itself in the market was a fun challenge. But I found out that using just one system would work fine for a while, but inevitably the market would fall out of sync with that system. That was frustrating. On the one hand, discretionary trading was problematic emotionally. On the other hand, although I had learned that systematic trading was fine if you could construct it in a way that always fit the market, it didn’t always it the market. I ended up falling right in the middle — one approach being left brain and one being right brain. I would have a systematic method that I could control [discreetly]. In other words, I’d use the flexible left-brain energy to read the market from a macroeconomic perspective and look at how a system is performing, and I would use the systems in a very rigid, mechanical way to execute the trades themselves. That’s what the FX multi-strategy has become — a hybrid strategy that is fully systematic, with my discretion applied over the systems.


WE: How, then, does discretion come into play?

CJ: I have six different programs that I run across five different markets. I try to keep these engines running so they fit the markets in the proper perspective, relative to the currencies I’m trading. The systems running together create a portfolio matrix with its own characteristics. I think this is the neatest aspect of the program. Understanding the individual pieces and the relationship to the whole makes this method more of an art. It’s like a painter who understands the relationships of color, value, shape, and textures. Everything comes together to make a fine [piece of] art. In one currency I might trade one strategy for a period of six weeks. On another currency I might trade another strategy for a period of three weeks. As the market evolves and the liquidity shifts between currencies, I’m able to detect change in that liquidity flow, from my macro-fundamental approach to the market — which is all behavioral finance by the way, and one of the elements I use. The other element is pattern recognition — not necessarily pattern recognition of the market itself, but pattern recognition of the systems I’m trading. In other words, how a system is trading with a particular market.


WE: Are you talking about managing a system’s equity curve, to a certain extent — adjusting your trading depending on whether a system is going through a relatively good or bad phase?

CJ: Exactly. Basically I’m looking at the equity curves, excursion analysis, and a couple of other things that are proprietary, but I’m looking at the behavior of how the system is trading in a particular market to give me an indication of what’s next.


WE: So you’re looking for signs that particular system may be getting out of sync with a market?

CJ: Yes. And if you were just trading two markets you might not have enough information to go on. I’m trading five markets that are all interrelated.


WE: Which ones do you trade?

CJ: I trade the four major pairs and the Euro-yen. I basically have five confirmations of how a system is performing. I have the systems trading live and trading a “dummy” in the background on all the markets to give me the indications as to how to set the rotation [among the systems]. The systems themselves represent different ideas trading on different time frames. What’s unique is how I mix the systems in a portfolio context.


WE: What’s the range of holding periods?

CJ: I’m out the losers pretty quick — usually in one day. Winners will last as long as a week sometimes. The average trade frequency is 2.8 days.


WE: How many trades will you have on at a given time?

CJ: I’ll have five trades on all the time. I typically use four to one leverage — five-to-one, max.


WE: You’re never out of the market?

CJ: We’re always in, long or short, in all five pairs.


WE: What are your most important trading principles?

CJ: Years ago I talked to a lot of professionals — a lot of fund managers. I wanted to see what they were doing, because at the time I didn’t know if I was going to let them manage my wealth or do it myself. The story I got back then, and the one I got a couple of years ago, is that the most important thing is what you can say you have right now in terms of profits. They look at consistency and monthly profits. In other words, just stay profitable every month. So for me, I would say that’s become the most important thing, along with keeping drawdowns manageable. In June 2004 I de-leveraged the program by half to cut the drawdowns to a manageable level. That’s the only major change I’ve made. I look for consistent profits every month because that’s where I think investors are going to be. They want “alpha,” but they also want to know they’re not losing money. Since we retooled in June 2004 my biggest drawdown has been 5.75 percent.


WE: Do you ever use currency futures?

CJ: No. We look at some data in the futures, but we trade spot only. A lot of people have an issue about whether a trading program is better suited to futures and whether execution is better in futures. As an experienced trader, I’ll say futures aren’t liquid enough in the Globex session, which is when we are trading, for what we’re doing. Futures used to be much more tight than the spot market, but in the last few years the depth and tightness of the spot market has made it much more competitive that I think the futures are less attractive for what we do. For other people it may be different. If they want to be in the regulated exchanges for certain risk-related reasons, that’s fine, but I think the spot market is far better for trading, and I think most banks and fund managers would agree.


WE: Do you think that’s true for smaller traders, also?

CJ: On the retail level it may be a toss-up. When the spreads get wide in the spot forex market, it might make sense for a small trader to use futures.


WE: As far as market characteristics go, do you think trading currencies is any different than trading anything else?

CJ: I think a tradable market is a tradable market, as long as it’s liquid. I think our strategy in a couple years might lend itself to other markets. I don’t know for sure, yet — I’ll have to see. I have my hands full right now trading currencies. I might always be in currencies — that’s what I specialize in. For now, I plan on remaining a specialist.


WE: If 2005 turns out to be a down year for currency managers, it will be the first since 1994. Why do you think it’s been so challenging?

CJ: I can’t speak for other traders, but it appears that most of the [currency managers] are trend followers. The currencies have had some good trends, but those trends have occurred inside a very wide volatility envelope. They haven’t been able to capture those trends in their normal modeling.

Rather than pursuing a trend, I’m pursuing liquidity. That’s my main objective — to pursue money flows, not necessarily to use the trends as an indication of that. I do use some volume in my studies.

This year [many currency managers] are out of sync. There are some quality traders out there, for whom I have a lot of admiration, that are having losing years. For them, I think it’s reversion to the mean for their programs. And I have a deep respect for reversion to the mean because I had some of that myself in my program last year — and I didn’t like it. I wanted to pull the volatility out and keep the focus on staying profitable now — that’s what counts. [I’m only concerned with] making alpha this month. My next challenge is to set and maintain a target volatility for the program.

Clarkson Jones is president of Charlotte, N.C. based Monarch Capital Management, a forex money management firm that trades roughly $5 million in assets.

After gaining nearly 8 percent in both September and October, Jones’ FX Multi-Strategy trading program was up 38.52 percent in 2005. So far, the year has been a nice rebound from a 13.69-percent loss in 2004 (his largest drawdown has been -21.86 percent), which was his first down year since launching his program in Nov. 2001. Jones’ program posted annual returns of 78.23 percent and 164.15 percent in 2002 and 2003, respectively, and his fund’s total return since its inception in Nov. 2001 is 633.43 percent (a compound annual return of 64.56 percent).

Before trading full time, Jones, who turns 40 this month, was a civil engineer and owner of a company (from January 1991 to January 2002) that sold construction-related products and services. He first entered the markets in 1994, trading stocks and some treasuries. He then branched into commodity futures, which he found “somewhat challenging.”

However, his research in this area led directly to his discovery that “currencies leant themselves [more readily] to my methods — maybe because they are so liquid,” he says. “Other markets became less appealing once I discovered currencies.”

Jones is relatively tight-lipped about many of the specifics of his trading approach, but he has a diversified trading methodology that blends multiple trading systems triggered by both trend and countertrend signals on different time frames in five different currency pairs.

He stresses that he blends discretionary judgment with systematic trade execution. Jones studied music as well as mathematics and physics in college, and he says these seemingly disparate fields contribute to the way he approaches the market.

“I had two different majors — engineering and fine arts,” he says. “One is left brain and one is right brain. I think that’s actually helped me to be more creative and a little more, I guess, artistic with my approach to the market. “I studied music and learned about pattern recognition when learning how to read music,” he continues. “I also studied physics and mathematics and excelled in those areas — it came very naturally to me — and I became a civil engineer. I started my own company and made quite a bit money and started investing it for myself. I learned how to trade, and basically have evolved to where I am today.”

Richard Olsen is chairman and CEO of the Zurich-based Olsen Group, which comprises several financial research, trading, and market-service companies: OANDA (www.oanda.com), an online forex brokerage/market maker; Olsen Financial Technologies, which makes software tools for gathering, cleaning, and analyzing high-frequency data; and Olsen Invest, a money-management firm Olsen founded in November 2002.

Olsen Invest has six U.S. dollar accounts (each initially funded with $10,000) with varying risk profiles. Currency Trader interviewed Richard Olsen in its first issue (October 2004), an article that was published and updated in the December 2005 issue of Active Trader magazine.

We talked to him again in early October to discuss some developments with the Olsen Invest “Live Accounts.”


WE: The live accounts seemed to have suffered larger than normal drawdowns in September. Could you comment on the causes?

RO: In the wake of Hurricane Katrina, the U.S. dollar experienced a rapid down move against all other major currencies, especially the Euro. Our models handled the initial impact well.

At the time we were worried the dollar would continue to drop, so to manage risk, we increased the hedge of the dollar to protect ourselves against a further massive drop. But our expectation proved wrong and the dollar recovered. We have a parameter in the model that monitors the bid-ask spread. We use it for unexpected news events, such as 9/11, to switch the model into “conservative” mode whenever the model detects a widening of the spread, [it] adjusts its behavior and becomes more conservative. Typically, OANDA publishes a spread of 1.5 basis points. Temporarily, it halved the spread from 1.5 to 0.8 basis points, which had the effect of priming the model to become more aggressive, and [it] increased its positions significantly.

We don’t want to interfere with the model; we decided to let it work itself out of its positions. In hindsight, this was the wrong decision. In response to the outcome of the German election, the Euro made another significant drop that culminated in massive sell-offs in the Euro, Swiss franc, and other currencies on Friday of the previous week.

There was an additional 1-percent drop in the Euro in the morning on Monday, Oct. 3. The model had to rebalance its positions to maintain its maximum exposure limits. When, at last on Thursday, Oct. 7, the Euro bounced back, the model could not recoup its losses (as would typically have been the case) because it was off-balance due to the increased positions it had inherited.

On Monday, Oct. 10, we decided to cut our losses. We closed the existing positions and restarted the trading models in normal mode without memory of the incurred losses.

Here’s an analysis of these events: The trading models and infrastructure are comparable to a complex automated factory. The safety measures implemented to prevent unforeseen losses were insufficient, otherwise the event would not have occurred — our infrastructure was thus subject to Murphy’s law.

If the operational mishaps had not occurred, the trading models would have generated 0.3-percent return in September, and — including the Euro’s rebound on Oct. 7 – the models would have generated a 1.2-percent return in September and October, which is a very different result from the massive (-9.88 percent) drawdown.

We have restarted the models, but have lowered temporarily the return target of the standard profile from the standard 15 percent to 8 percent. We have started an immediate program to upgrade the trading model infrastructure and improve the defensive mechanisms. As soon as we have completed the enhancements, we will hike the return target to the standard 15 percent.

It’s important to emphasize that the drawdown was not because of the models themselves. The models did their job. The drawdown originated on the part of the defensive mechanisms. Over the past four weeks, we have learned many painful lessons and we are making every effort to enhance these safety mechanisms.

In the middle of July, we released new trading models that allow us to increase the trade frequency and improve the rebalancing of exposure between exchange rates. The models improve the stability of returns.

We are highly confident the long-term performance of our models will fulfill expectations, and the recent drawdown will, from a longer-term perspective, be a temporary (however painful) blip.

WE: Is it fair to characterize your approach as a shorter term breakout-type system?

MK: Yes. It involves a bit of breakout, a bit of Elliott Wave when it comes to counting oscillations, and support and resistance. The MACD (moving average convergence-divergence indicator) is involved as well, once again in counting oscillations. We’re not 100 percent governed by one thing.


WE: Support and resistance seem to play a pivotal role in your trading. How do you define these levels? Is it a discretionary process, or are you using some variation of standard channel breakouts, or something else?

MK: By looking at charts, really. It is more discretionary, because it tends to be the two of us looking at the chart and determining the levels. Daryl might say, “I think we need an order at 1.1980.” And I’ll say, “To me it looks like 1.1975.” And either I persuade him or he persuades me, or maybe we’ll split the difference. Let’s say we’re out of the market now (Oct. 4). Looking at the Euro/dollar, we’d probably have an order at 1.1880 on the downside and 1.1980 on the upside. We’re actually long the market at the moment at 1.1952, where the market is now.


WE: In terms of limiting your losses, how do you go about setting stop points?

MK: They’re based on support and resistance.


WE: So, relative to a support or resistance level you’re placing a stop?

MK: — between 30 and 60 points behind the market.


WE: What about taking profits?

MK: We’re fairly unique in that we try to run the position up until a set parameter.


WE: A profit target of some kind? approach? How long did it take you? Did you do any backtesting of any kind?

MK: I’m a great non-believer in back-testing, because it’s just very arbitrary. Initially, we traded our own personal account for about a year. That had big swings. We changed gears during that period playing about with leverage, and so on.


WE: So that was essentially an “incubation” phase — real-time trading instead of testing?

MK: Yes. We would alter trade size, leverage, and stop parameters. But from day one of the incubator phase, the idea of trailing orders behind a position was always one of the main cornerstones of the system, as was monitoring the market 24 hours a day.


WE: Your annual returns show that 2003 was the bad year. What went wrong that year and what did you learn from it?

MK: We were continuously involved in the market, and it just kept catching us, really.


WE: Do you mean you were getting whipsawed?

MK: Yes, quite a lot. Many breakouts were false and we’d go back into the market and the same thing would occur again. It was in the latter part of that year — probably around October — that we decided to take the approach of not being involved in the market throughout. Also, we decided the distance at which we were trailing stops was too far. There were times we had very good unrealized profits, but then we’d give them back. It’s all very well having an unrealized profit of 90 points or so if you only end up booking a 5 or 10-point profit.


WE: To prevent this, do you tighten the trailing stop as time goes by and trail closer to price the longer the trade is open?

MK: Yes, we use a mathematical formula to tighten the stop loss at a certain point and time.


WE: On the flip side, it looks like 2005 has been a very good year so far.

MK: Yes, we’ve been very consistent, very steady. At the moment, we’re up 19 percent for the year.


WE: What do you think your edge is?

MK: I think we are quite dedicated to forex, and we do monitor it extensively. There’s very little time when we’re not monitoring the markets, even when we don’t have positions.


WE: What kind of weight do you put on fundamental considerations?

MK: We’re obviously very aware of fundamental news — economic reports and events such as Hurricane Katrina. We do pay attention to fundamental news, but that’s not the main driver in determining what position we have at any point in time. That’s determined by the way the train moves — the way the market swings.


WE: So there’s never a situation when a big event — a change of central bank policy, for example would make you override any systematic trading rules you had?

MK: If the market reacts to it, we would get positioned accordingly by our stop-in order. Look at the Bank of China. They announced recently they were expanding the yuan’s range. There were a lot of people who thought the dollar/ yen rate would never go above 112, and it’s up near 114.50. (It rallied to nearly 116 in the weeks after this interview.) I have a very cynical approach to the market, to be honest. Ten people could tell me, “You have to buy this, you have to buy this,” but I’ll just sit back and see what happens.


WE: Is there any discretion other than determining the support and resistance levels?

MK: No; that’s it. And with that, it’s something anyone who knows a certain amount of technical analysis could more or less gauge.


WE: So what are your plans for the business?

MK: To get bought by Citibank (he laughs).

Mario Kelly and Daryl Swain, forex traders and principals of Wallwood Consultants Ltd., put to lie the adage, “Those who can’t do, teach.”

Formerly partners in a forex education and advisory service, Kelly and Swain taught forex trading before launching their commodity trading advisor (CTA) which, over nearly five years of trading, has posted some quite respectable numbers.

Through September, Wallwood was up 19.72 percent on the year, placing it at the top of the Barclay Group’s (www.barclaygrp.com) forex CTArankings. The firm’s total return since inception in January 2001 is 96.40 percent — a 19.29-percent compound annual return. Other than a 9.45 percent loss in 2003 (the year they suffered their worst drawdown of -32.27 percent), the firm has posted doubledigit gains each year.

Kelly and Swain, both 42, operate Wallwood from offices outside London and in Spain. They trade exclusively in the spot forex market (typically using 6:1 leverage) and currently manage $10 million. It shows a VAMI chart of the firm’s equity growth along with that of the S&P 500 and Barclay CTA Index.

Kelly and Swain both have held various positions in the forex industry, and immediately prior to starting their own business, they were execution traders at CMC Markets, a forex trading firm and brokerage. They launched what would eventually become their CTA when they were (as they say in Britain) “made redundant” in 1997. They decided to set up a forex advisory and educational service, initially for some of their former clients.

The business wasn’t intended to be a long-term proposition, according to Kelly. Although neither one of them had managed money before, he and Swain were set on it, and they eventually began trading their own funds to refine their trading approach.

“It’s all very well telling people how to trade, but we might as well do it ourselves and show them what we tell them to do does work,” Kelly says.

One of the advantages of their former position as brokers at CMC, according to Kelly, was they got to see the different missteps non-professional traders often make. “My impression is that a lot of people tend to have more money than sense when it comes to trading,” Kelly says. “They make really basic mistakes, like not having stops in place and leaving positions unattended.

They don’t realize what they’re doing. “The unique thing we had when we devised our trading system is that we saw a lot of [order] flows and a lot of different trading models,” he continues. “You try to take the good bits of most of them. Our initial trading model mainly looked at how clients traded and avoided the mistakes they made. It doesn’t work totally — we have had our drawdowns. It’s impossible to say you’ll win on every single trade.”

Kelly describes Wallwood’s approach as essentially systematic — with the exception of one discretionary analytical component — with a primary focus on limiting losses, which they accomplish partly by limiting their exposure to the market. They enter the market on price breakouts(long or short), and then use a position management approach that incorporates a trailing stop that tightens as a position progresses.

“We use a mathematically based algorithm that is, effectively, based on swing trading,” Kelly says. “If we experience a certain amount of losses, we pull out of the market and then wait for a period before entering again.”

The trade imbalance remains the issue that simply won’t go away. The U.S. can’t continue wracking up increasingly higher trade and current account deficits, some people argue. At various times over the past several years, market analysts and economists have warned that at some point the U.S. will have to pay the tab. Many analysts argue that will come in the form of a dramatically weaker U.S. dollar.

China remains the main culprit in this story, especially according to some of the folks in Washington D.C. The U.S. trade deficit with China is larger than with any other country, eclipsing the $200 billion mark in 2005.

“By all estimates, it seems the currency pair in most need of adjustment is dollar/yuan,” says Charmaine Buskas of Moody’s Economy.com. “The dollar is still very overvalued compared to the yuan. The U.S. sees China as having an unfair advantage due to a weak yuan.”

The Chinese authorities did revalue the yuan last year, bolstering its currency by 2.1 percent in July 2005. Since then, however, only modest appreciation in the neighborhood of 1.2 percent has occurred. The yuan continues to hover around eight per U.S. dollar ($0.125).

In 2005, the U.S. trade deficit screamed to a new record high at $723 billion, up from 2004’s $617.6 billion. Powell sees that expansion continuing. According to his data, the first three months of 2006 totaled $196.2 billion, vs. $172.1 billion for the same period in 2005.

Artificial intelligence, neural networks, chaos theory — these are all part of the discussion when talking forex with Bill Reid, manager of the Algorithmic Currency Fund (www.algorithmictradingadvisors.com). But along with these concepts, Reid shares some pretty basic ideas about trading currencies.

“I often tell people forex is the equity of one country compared to the equity of another country,” he says. “How that country is positioned in the world market determines its relative equity, and the value of this country’s equity at this instant makes the market very interesting. That makes it a fun market to learn about and participate in.”

Reid’s Fairview, Texas-based fund has posted 12 winning months out of 18 since its launch in September 2005, and its performance — while off its explosive early pace — has nonetheless outperformed both the S&P 500 index and the general universe of professional currency traders — which, as represented by the Barclay Group’s Currency Traders Index, posted negative annual returns in both 2005 and 2006. The Value Added Management Index (VAMI) graph compares the growth of a $1,000 investment in his fund to a $1,000 investment in the S&P 500. Reid currently manages $10.7 million through the fund.

Reid, 65, got involved in trading after taking early retirement in 2004 from IBM, where he worked for a decade in the artificial intelligence (AI) division, developing AI for a wide range of companies, including Boeing.

“All the jet engines use artificial intelligence to determine how well they’re doing,” he says. “After I took early retirement, someone asked me if I ever thought about trading commodities. It got me thinking that artificial intelligence was [mature] enough to be applied to trading. I started talking to people about the application of AI in the financial markets.”

For Reid, who has a master’s in computer science, the transition into trading turned out to be very much an extension of his education and varied professional background. He holds seven patents, including a device for landing on other planets that he developed for NASA early in his career. Forex represented a new challenge.

“I’ve always been interested in new things,” he says. “I wanted to see if we could develop a unique approach that could be patented in this crowded market space. I think we’ve accomplished that.”

Reid trades 10 currency pairs (EUR/USD, GBP/USD, NZD/USD, AUD/USD, GBP/EUR, USD/CHF, EUR/CHF, USD/JPY, EUR/JPY, and USD/CAD) using a trading model built around the concepts and processes he developed in his previous career. When he began researching trading, the Massachusetts Institute of Technology (MIT) was a prime source of ideas.

“I worked with MIT on several financial topics,” Reid says. “They’ve done a great deal of original research in the subject of science application in financial markets. A key part of their work is a wonderful book, Nonlinear Dynamics, Chaos, and Instability explaining how to analyze financial markets with chaos theory. Their analysis indicated the only market that is chaotic is forex.”

We: What does that mean, exactly?

BR: MIT’s mathematical studies found that treasury bonds are random, the stock market is correlated, and forex is chaotic, which refers to something that looks random, disordered, or irregular and has the potential of having some underlying order, if you can figure it out. Weather forecasting, epidemics, and the creation of new planets and galaxies, for example, are chaotic. The forex market is unique because it is bounded by central bank actions on currencies. If a particular country’s currency rises, other countries cannot buy their products. If a country’s currency falls, they cannot buy othe rcountry’s products. This chaotic behavior then applies to individual currency pairs. The bounded action of currencies probably contributes to why, mathematically, the forex market can be shown to be chaotic and exhibit the ability to be traded successfully both short and long term.


We: What attracted you to a market you’d classify as chaotic over one with a presumably higher degree of order, or correlation, such as the stock market?

BR: Chaotic means you can find the order both in the short term and the long term, so you can trade it both ways. The banks trade it long term, but we trade it short term. This short-term, long-term differentiation is not applicable to a correlated market, such as the stock market, because price moves in stocks can result from either general market movement or individual company success. Stock prices cannot differentiate between the market or specific company value driving the price. This is why equity hedge funds require a long funds lock-in period and why all financial advisors advise clients to look at mutual funds from a long-term perspective.


We: In regard to the long-term, short-term aspect of chaotic behavior, are you referring to fractal (see “Chaos theory”) properties — similar patterns or behavior appearing on different time frames?

BR: Fractal patterns certainly exist in 15-minute samples, one hour samples, and four-hour samples. In the longer term, a country’s economic position on balance of trade will be a major factor. Recently, at the Texas Hedge Fund forum, we showed that if you held the 10 currency pairs we trade for nine months, invested 10 percent of your funds in each pair and traded at 50-percent leverage, you would have made 17.7 percent annually — if you had picked the right starting position. Balance of trade positions move slowly.


We: So, how does AI and this concept of market behavior manifest itself in your trading program?

: We start with a large number of inputs around 50 — that go into a neural engine. Twenty-five percent of them are conventional indicators, such as accumulation-distribution, MACD (moving average convergence-divergence), the Relative Strength Index, or stochastics. Another 25 percent are non-linear indicators that have logic to detect the initial conditions. [These are essentially] indicators that look at what the initial conditions were when a certain pattern formed in various places throughout the data history we’re analyzing. It produces a forecast based on that information, and high predictability makes it tradable. Another 50 percent of the inputs are chaos-developed price patterns. These patterns can be individual or interrelated.


We: As in the relationship between two different currency pairs?

BR: Right. The model treats signals as geometric objects — for example, the double peaks that often occur in currency pairs. But the characteristics vary with the frequency, or time characteristics, of the prices. If the rise to a peak or the fall to a valley is short — that is, higher frequency — the likelihood of a double peak higher than the first peak, if it occurs at all, is greatly reduced. We developed a tool to look at the price history to develop these “chaos” patterns, and that actually came out of a model that MIT developed for analyzing chaotic markets.


We: You mentioned you designed a neural network to process all this information. Can you describe what that is?

BR: A neural network is an interconnection network with the ability to train a group of inputs to match a desired output signal.


We: Are the inputs the

different pieces of market information you look at? Is the output hopefully a profitable trade signal?

BR: Yes. We developed profit “fitness” criteria that a genetic algorithm (see “AI and neural networks,” above) uses to produce a profitable output signal. For example, one criterion is how far ahead on the signal we can look to see if this was a good trade. Generally this look-ahead is 10 to 15 periods. Looking ahead 10 to 15 periods generates an output signal that is 100-percent correct and makes large profits. The model’s optimizer adjusts the input signal variables to capture as much of that profit it can. In between the input and output processing elements are a number of “hidden-node” processes. Because of the complexity and interactions between the hidden nodes of a neural network, it is difficult to apply analytical techniques to understand how a decision is reached directly from the original inputs. So, we have to trust the output of the network blindly and have confidence in our design approaches.


We: Let’s go into the model a little more. When you talk about using standard technical indicators, how are you using them? How are look-back periods determined?

BR: We train them over 12 months. That model shows how well they’re doing, then we feed them into a genetic server, which takes the best inputs and optimizes the performance by figuring out what percentage of that signal should be used to work best over the past three months. We train them over 12 months in the neural engine and look back over three months in the genetic server to see how well they’re doing.


We: How long did you spend developing and testing your model before taking it live?

BR: A little over two years. There wasn’t a neural engine at the time that could handle that. What we developed was a context-memory neural engine.


We: Let’s look at some of your trade statistics, then. What’s your percentage of winning trades? BR: Over the past nine months the winning percentage has been 50.17 percent. CT: How about average or median profit vs. loss?

BR: The median profit was 1.478 times the median loss.


We: How long does a trade typically last?

BR: Two-and-a-half days on average.


We: How many different trades will you have on at a time?

BR: We have a money-management system that determines which pairs are doing well, and it turns off the ones that aren’t doing well. At any given time there are generally five or six pairs trading live and four or five that are turned off.


We: What’s the filtering criteria — that is, what does “doing well” in this context mean?

BR: When they aren’t winning as much as they are losing, we turn them off.


We: Is this a kind of equity curve management approach?

BR: Exactly.


We: Are you always trading your model or systems in the background on a simulated basis so you know when to turn certain currency pairs on or off?

BR: Yes.


We: How would you characterize the typical trade signal? Is it a breakout or trend-following trade, or an exhaustion or reversal type of trade? Or is there a mixture?

BR: In general, they tend be trend following. Just where on the trend it executes seems to be highly variable, though. The model takes positions in and out of the market. We don’t have profit limits. When we place a trade we also place a 50-pip stop, generally.


We: Regardless of the currency pair?

BR: Yes. We limit investor risk to 2.5 percent of his money, so it’s a constant risk model. It’s anywhere from 50 pips to 63 pips, depending on the model.


We: What kind of price data do you use in your trading model?

BR: Hourly data. To us, short term is hourly and long term is weekly.


We: Can you pick out a recent trade or two?

BR: A long trade in the NZD/USD, which was entered on March 15 at 11 p.m., is up 69 pips (as of March 20). It’s still on. We have another position on in EUR/CHF that started on March 16 at 8 a.m. and is up 85 pips.


We: Have you ever applied your modeling or trading techniques on other markets?

BR: Yes. We’re looking at the stock market now.


We: You had some huge monthly returns early on, followed by five up and five down months through September 2006. The last couple of years have been very tough in the forex market. How are you adapting?

BR: Well, the thing to be careful about when looking at our history, is that when hurricane Katrina came through, everybody got out of the U.S. dollar — that’s when we made that huge amount of money. We also have a new Fed Chairman (Ben Bernanke). He bounces things up and down, which is why November, December, and the beginning of January were a little bit different than we’d expected. But it looks like it’s turning around now.

CTWe: February seemed to be a good month or you (+17 percent). Did anything in particular contribute to that performance?

BR: The money management had turned the non-winning pairs off in January so we had a good-performing set of currency pairs in February.


We: How often do you adjust your trading model?

BR: We retrain it at the end of every month, over the weekend.


We: After a year or more, what have you learned about the markets and your model? Is it different from what you envisioned?

BR: We had to add what we call an “event processor.” We track nine different events — such as the first Fridays of the month (when many important monthly economic reports are often released). During the hour an event is supposed to happen, if a pair moves more than 30 pips in less than seven minutes, we’ll get out of the position. We update it every month.


We: Was that the result of taking big losses because of certain events?

BR: Yes, like on first Fridays — we learned that (laughs). That’s what some of the big drops last year were from. Forex is a market without any of the artificial market maker limitations, like short trading restrictions or the ability to drop through a stop position without executing. Banks have been in this market for centuries and now invest twice as much in the forex market as they do in the equities market. This is because they have developed effective training techniques of an intermediate time sample approach. Forex is nice in that it may be trading effectively on many different time samples.

For those who managed to stay awake during their high school history classes, the adage, “The sun never sets on the British Empire” brings to mind some historical precedence regarding global reserve currencies and how they can lose that status.

William Silber, author of the book When Washington Shut Down Wall Street: The Great Financial Crisis of 1914 and the Origins of America’s Monetary Supremacy notes that it took years for the U.S. dollar to topple the British pound as the world’s reserve currency. From the mid-19th century to the 1914-1925 period, Silber posits that the sterling was clearly the world’s reserve currency. He points to the outbreak of WWI in 1914 and the move in which every country but Britain and the U.S. went off the gold standard as the first element in that major shift.

“This put the dollar on the map,” Silber says. “It was an opportunity for America to behave like a financial superpower.”

However, the sterling just didn’t roll over and die.

“The sterling was the international medium of exchange,” Silber adds. “It is very difficult to overthrow a reigning king. There is staying power. Tradition is on its side. People like to use what they are used to.”

By the time Britain went off the gold standard in 1919, because of huge internal inflation problems, America had continued to make further inroads. Just a few years earlier, New York had taken over from London as the moneylender to the world, says Silber. The British, focused on paying for a war, no longer had capital to lend for international business projects.

“All the traditional British clients — Argentina, China, and Canada — had to go to New York because the British no longer had any more money to lend to the world,” Silber says.

Even by 1925, Silber admitted that the U.S. dollar had only “come even” with the British pound. Why?

“It is so hard to overthrow the international medium of exchange,” he says.

Which brings us to the question hanging over today’s marketplace.

Steve Misic first got into stock market investing in the early 1990s, but like many other traders he became more active as the tech-stock bubble expanded into 2000. Misic would often call in buy-and-hold trades from his cell phone while driving a bakery sales route. There wasn’t much to it, he remembers.

“I was trading from my truck,” he says. “It was one-way — you just bought it and it went up.”

As everyone knows, however, the ride didn’t last forever. When the bubble burst, Misic, like many others, rode most his holdings down. After that, he decided to learn how to trade.

“After I lost money in the bubble, I wanted to learn a way to make sure that never happened again,” he says.

He attended seminars and studied technical analysis. In early 2004, Misic decided to trade full-time. For many years he had worked nights, and even after he quit his job his body clock was still set to awaken in the early hours of the morning. Trading around 2 a.m in the forex market was a good fit for him. He relied on traditional technical analysis, basing his short-term trading on support and resistance concepts.


Most important lesson learned

“Trading makes a person humble. I’ll never get it all taken away from me again because I learned from the last bubble. Right now, momentum trading is all the rage again, but eventually there will be a sell-off. I’ve learned how to trade technically.”

The U.S. economy experienced a similar scenario in April of 2006. On Jan. 23, 2006, the 10- year T-note yielded 4.36 percent — one basis point lower than its yield on the first day of 2006. By April 26 (roughly the same time period as our current scenario), its yield had climbed 76 basis points to 5.12 percent, and it remained at or above 5 percent through July 28. Strong new home sales and robust orders for durable goods helped propel long-term interest rates higher.

How did the dollar perform vs. the Euro during this time period? On Jan. 23, the euro/dollar (EUR/USD) rallied strongly to close at 1.2303. By April 26 it had rallied to 1.2453 — meaning, the dollar had weakened vs. the euro. The EUR/USD pair continued to rally into May before pausing and eventually reaching a high of 1.2979 in early June 2006.

Arguably, the improving health of the U.S. economy should have been reflected in a strengthening dollar. As it turned out, U.S. economic activity was so robust the Federal Reserve raised the federal funds rate at both its May 2006 (to 5.00 percent) and June 2006 meetings (to 5.25 percent, where it currently stands).

At the same time, though, the Eurozone economy was quickly gaining steam and the European Central Bank (ECB) had embarked on a campaign to raise short-term interest rates. One possible explanation for the relative strength in the euro vs. the dollar is that traders were expecting the gap between short-term interest rates in Europe and the U.S. to narrow.

In theory and generally speaking, the dollar should strengthen when long-term rates are rising. But as shown here, other factors may outweigh or completely override any possible lift the dollar might expect to see from rising long- term rates.

Nonetheless, although short-term interest rates are usually the focus of currency analysis, long-term interest rates bear watching, especially in regard to fundamental economic concerns that drive the big-picture action in the forex market.