Martin Lueck co-founded Aspect Capital in September 1997. Lueck oversees the research team, which is responsible for generating and analyzing fundamental research hypotheses for development of all Aspect’s Investment programs. Prior to founding Aspect, Lueck was with Adam, Harding and Lueck Limited (AHL), which he co-founded in February 1987 with Michael Adam and David Harding. Man Group plc completed the purchase of AHL in 1994. Lueck holds an MA in Physics from Oxford University.¹

Michael:

I’m thinking of all the assorted conferences that I’ve been at and I was wondering if we have crossed paths [in person].

Martin:

The one I remember seeing you at . . . there’s a picture of it on your website. You had Larry Hite and Ed Seykota [on stage in Chicago].

Martin:

You had him on stage with . . . it wasn’t a ukulele . . . he got out his banjo.

Michael:

Yes, “The Whipsaw Song.” You were in the audience for that?

Martin:

I was in the audience for that.

Michael:

That was probably an interesting audience. When you’re on stage for one of those things you’re in your own little world 
and you don’t realize that’s a pretty interesting audience [all trend followers at an annual event] at that moment in time.

The burnt customer certainly prefers to believe that he has been robbed rather than that he has been a fool on the advice 
of fools.²

Fred Schwed 
Where Are the Customers’ Yachts?

Martin:

Exactly. It’s voyeuristic because you guys are having so much fun and you could be in a bar, the three of you, and the rest of us are like voyeurs.

Michael:

I sometimes wonder if it is something that comes with age when you loosen up, or do you think guys like Larry and Ed were always that way?

Martin:

A bit of both. Obviously I don’t know Ed at all but Larry’s always been older than me and that will never change. Probably he’s always been wiser than me. And actually I haven’t seen him for a while, so whether he still gets wiser or whether he has just sort of plateaued and that wisdom is enough to carry him through, I don’t know.

The genesis of any [trading] idea, has to be a hypothesis about market behavior.

Anthony Todd

Michael:

Yes, I have interviews with just me and Larry in his office that I can’t release . . . they’re awesome wisdom but there’s a lot of color there that I can’t put out.

Martin:

Yes, I can believe that . . . Michael, listen, it’s a pleasure finally to speak with you. I have your book on my desk and have owned this for many, many years, so thank you for everything you’ve done for the industry.

Michael:

Well, thank you. Thanks for the nice words.

Martin:

I’m surprised we haven’t spoken.

Michael:

Yes. I should share a quick story with you. The first time I met one of your old friends, the head of Winton Capital, I was in his office and it was in 2005. And we had just met. It was just me and him and his dog. He started pulling up a screen showing me equity curves of assorted well-known U.S. CTAs and then superimposing his equity curve and then just giving me that wry smile of like, “Did you somehow miss me?” That was my first inkling of the London CTA scene, so to speak.

Martin:

Yes.

Michael:

There’s been an exponential explosion of all of you guys . . . 
that’s just out of this world.

Martin:

It’s an interesting story.

Michael:

When did you first know in your mind, this quantitative style, this systematic style of trend following trading, coding it and trading it over a diversified basket of markets, when did you know in your heart of hearts this had changed your life or at least you knew this could make some money?

Martin:

Wow. Michael [Adam], David [Harding], and I never quite had the luxury, if you could call it that, of having a role model. It wasn’t as though we looked out at the investment universe and said, “I want to be that person,” or, “I want to do that thing.” Certainly, for Michael and I, because we go back further than our relationship with David. We were at boarding school together from age 13 and then we were at Oxford together. After Oxford, Michael went to work for his father who ran a small physical commodity broking business in London. I went and took my first job at Nomura trying to sell Japanese equities to European investors. And I didn’t know what an equity was when I started that job, clutching my physics degree.

You know the saying, “Human see, human do.”

Julius 
Planet of the Apes (1968)

I was just fascinated by the analysis that Michael had started doing, prompted by his father. Michael’s father said, “Here boy, buy one of those newfangled computers.” Personal computers were just beginning to reach the shores of the U.K. in the 1980s and Michael bought a Hewlett-Packard 9816 [personal computer]. His father handed him a book of technical trading methods and said, “See whether there’s anything in this.”

It’s really been so gradual. Just over time I left Nomura, and joined Mike. This whole time-series analysis just lent itself to the nerdy physicist’s approach. We just discovered some things. We tested every model that we could encode from that great book. We distilled them down to some fundamental rule sets. We tried them on a range of markets and at that time, the range of markets that we had available to us sitting in the commodity world of London. . . what did 
we have? Cocoa, coffee, sugar, aluminum, copper, silver. Something like that.

The financial commodities were really in their infancy. Only a few years later, and largely once we’d met David, we started expanding the application of these models to a more diverse set of markets.

Michael:

People love that depth. People want to know what’s going on inside the mind [of early guys].

Martin:

There is no one moment where we said, “Ah yes, we have created the great imitator of . . .” who was around in those days? Richard Dennis, John W. Henry, the Mint gentleman [Larry Hite]. But, we didn’t know that. As we started things we didn’t know there was that U.S. industry.

Michael:

Mid-late ’80s?

Martin:

Even early ’80s. I started working with Mike in 1984. He was working for his father from early 1983. The earliest AHL track records stretches from 1983. We met David, I want to say in ’85 or ’86, and there was a bit of a tussle. David was working for Saber Asset Management and was the understudy to a great chartist, a man named Robin Edwards who ran a fund—very systematic but no computers involved—and David was a Cambridge-trained scientist. And he immediately saw the potential for using computers to encode Robin’s chart approach.

The Saber folks tried to hire Mike and me out of Mike’s father’s business and that wasn’t going to happen, so in the end we got David to join us in Mike’s father’s business. And then at the beginning of 1987 there was a little family tiff which resulted in the three headstrong boys leaving with one client and setting up AHL.

Michael:

But at the moment you set up AHL, had you made enough money at that moment in time that you felt like, “I’ve got a little breathing room,” or was it still kind of, “We’re not really sure what’s going to happen yet.”

Martin:

We didn’t have a bean, not a bean. In fact, blood is thicker than water. Michael’s dad, had built his business over the years, and it had morphed from that commodity broking firm into a small asset management business based on models the three of us were developing. He had a very, in those days, parochial or patrician’s approach to managing money and to the clients. And it was just a difference of opinion. We said. “Look, you can’t behave that way with your client’s money, you can’t take a paternal view of it, you have to be completely transparent,” and we set off on our own.

Everything is worth what its purchaser will pay for it.

Publilius Syrus

But we didn’t have a bean and Michael’s father actually lent Michael £20,000 that we spent on computer gear and rent until we had generated enough fees that we could go and rent our own office and pay his father back. Gosh, it was a very long time before we had confidence in what we were doing actually.

Michael:

A two-part question: What were you calling the style of trading amongst yourselves that you were executing? And number two, talk about some of the early coding and how you went about the early coding. This was not the age where you could walk out and buy TradeStation or Mechanica software. This was hard 
coding in the bowels, so to speak.

Martin:

Two parts to that. What did we call it? I don’t know. Yes, it was trend following and it wasn’t long before we woke up to the fact that there was an industry here because we had friends in the brokerage community—through Michael’s father’s connections—there was a metal broker in London back in the ’80s called Rudolf Wolf.

The roots of [our] managed futures was a client would open a managed account and give authorization for the manager, in our case AHL, to manage that money. It was brokers like Rudolf Wolf and some of the early physical commodity brokers that helped us and educated us in what the rest of the industry was doing. We knew pretty quickly that this was trend following and we started to get exposure to a much broader set of markets.

The first principle is that you must not fool yourself and you are the easiest person to fool.

Richard Feynman

In terms of the modeling and the development, you had in Michael and me in particular real techie wonks. Certainly I would not get a job now as a developer but back in the day that’s what we loved to do. Those Hewlett-Packard 9816s . . . actually the operating system was written in Pascal. And we learned Pascal and built our models in Pascal, and very quickly we built an environment that allowed you to encode trading ideas much more simply.

You didn’t need to write them all in Pascal and compile the darn thing and blah, blah, blah. It was effectively an inline simulation language which got developed in the days of AHL once we became part of the Man Group, and it was a well-funded exercise. We had code writers, developers just working on that interpreted language, so it was a precursor to many of those TradeStations. And for a while in the period between AHL and Aspect, certainly, Mike Adam had a version of that. Actually it wasn’t his but it was a similar product that he was marketing commercially. The software development was very much intertwined with the model development.

Michael:

I was thinking about the notion of achievement. Everyone, if they’re pushing in some way, is achieving, we’re all striving and it’s sometimes hard to reflect on that because you’re saying, “Hey, we just started doing this and it starts working and we just keep at it. We keep our nose to the grindstone and there’s some ups and there’s some downs and next thing you know, 30, 40 years later, boom, boom, boom, something interesting is there.”

Then for outsiders they look back now and see the very successful firm Aspect today but they don’t really think about the progression, the evolution.

Martin:

That’s so true of so much in life. It’s very hard to join dots when the dots don’t exist. You can join the dots with hindsight. If I was being really glib, there was a while where [it was just] the three of us [and] none of us got those fancy jobs in investment banking that all of our slick friends at university got . . . There was a period where [we were] three scientists, nerding around with the models and the simulations, the back data, some of Michael’s father’s money, and, “Heavens, this stuff works!”

The more we got into it, the more animated and the more excited we got . . . so it was revenge of the nerds for a little while. Then it was an investment bank in a box because . . . in the early days of AHL we did a number of different things—not only model development and asset management; Michael was already keen on commercializing that piece of software. I spent a fair bit of time using the software to provide consultancy services for financial businesses and what came to light . . . was that you could fairly easily model the behavior and the profitability of investment banks.

I grow old learning something new every day.

Solon 
Athenian statesman

We did a piece of work for a London gilt trading house. We modeled how many people, their risk limits, what kind of investment horizon . . . they had the front book traders that were making a market, and they had the back book traders that were holding the house book if you will, and we modeled the behavior of those things and said, “Roughly this looks like the profitability of your business,” and the folks, the executives, their jaws dropped. They went wide-eyed because we basically modeled their business.

We went around telling everyone, Goldman, Salomon, just get rid 
of all of your traders, they’re very expensive and they have hangovers, you can build it all on computers. We believed ourselves of course but everyone thought we were completely nuts. By and large [now] that’s what’s happened.

Michael:

Today Aspect is 100 percent systematic. I’m guessing that there was a moment when you’re doing all this homework, in the early stages of this industry, and seeing the results, but when did you have that “aha!” moment, “Wow, we should really take the human discretion out and automate this.” When was that moment?

Martin:

I draw attention to two things. The first was just an awareness that your ability to rationalize the available information, if you could do that pretty quickly and get rid of the noise, we’d all be much better traders. I tell a story about those early AHL days. Mike and I came up with a game and it was just a piece of code that would randomly sample one of the markets in our database, and it might invert it and it might multiply it by a random factor. You couldn’t tell what the market was but it would obviously keep the integrity of the price series and it would present a chunk of time series to you. It would then ask you to buy or sell and you’d buy or sell. Then it would move forward a day and then you’d buy or sell, and move forward a day and so on and so forth.

The 50–50–90 rule: Anytime you have a 50–50 chance of getting something right, there’s a 90% probability you’ll get it wrong.

Andy Rooney

And with that kind of rapid-fire decision making, actually, we were pretty good discretionary traders. If you blow each of those ticks up into a 24-hour period, with news coming at you and fear and greed and the chaos of normal life, you become a lousy trader. That was the first inkling that taking the emotion out of it, just reducing it to the raw information, has to be a good thing.

The second thing turns out to be the importance of risk management, because what a lot of people focus on in any model development is the models. Are you going long? Are you going short? How do you feel about this market or that? And that’s all well and good, and you obviously need to develop those models and be able to articulate what the underlying drivers are, but what many people miss is the risk management component.

What a lot of people do is focus on systematizing models, and then portfolio construction or/and the risk management piece they leave to discretion. Somewhere in the genesis of Aspect Capital we realized that’s absolutely a crucial thing to get right. You need to be able to systematize not only your models that interpret the price data and determine the confidence you have in that particular trend, but also you need to be able to systematize your portfolio construction process and your risk management process.

The manager that says “I am 95 percent systematic and 5 percent discretionary” is 100 percent discretionary. That’s not necessarily a bad thing. I’m not saying there aren’t geniuses out there who are discretionary, but it means you can’t rely on the scientific process. You can’t rely on the quality and the integrity of the simulation and research process if there’s the hint that you’re just going to step in and down-gear the portfolio when the going gets tough, or you’re going to knock out a few markets when they seem to be a little off the charts. Because you can’t build that into your simulations and you can’t know what you’re going to do in the future.

Although we knew at the outset that it was a good thing to systematize, I’ve become more resolute in how important it is across the entire investment process.

Michael:

You talk about that notion of 95 percent systematic and some portion of discretion. That was a very common marketing line in disclosure documents of trend following’s CTAs for a long, long time. It was this mysterious we’re 95 percent systematic but there’s also this magical 5 percent discretion which is the reason you’re giving us money.

Let me remind you of the particular characteristics of all of these behavior systems that I am trying to focus on. It is that people are impinging on other people and adapting to other people. What people do affects what other people do.

Thomas C. Schelling

I always used to think that didn’t make any sense. Now, because you and other peers, associates, et cetera have gone the opposite direction and said, “Hey hold on,” I don’t think you could 
probably get away today saying in the trend following space, “We’re 
95 percent systematic.” People that want to invest would probably say, “Explain that.”

Martin:

It’s really dependent who you were talking to, and we all lived in fear in the early days of the black box label. Two things: 
I think that 5 percent discretionary was also to give some investors a sense there was a thought process and it wasn’t just this ignorant machine clunking away. But also I come back to the point that people built the models and then . . . the overall risk target of the portfolio was something they would set discretionarily on how it felt that week, day, month, or epoch.

Michael:

It was a weird point in time where we had not yet got to the point where there was an acceptance of the 100 percent systematic. There was this gray area in the marketing and for good or bad reasons, perhaps, as you’re saying.

Martin:

That’s exactly right. And now it needs to be used judiciously so the idea of being systematic, the idea of being research driven as an investor, as a consultant doing your due diligence, you’ve got to scratch at that. Because as I give a bright young graduate an infinite amount of data and an infinite amount of processing power, they’re going to come up with the works of Shakespeare or they’re going to come up with models that just look staggering. And you and I know that you wouldn’t put your money in them.

There is a difference between 100 percent systematic models that are based on curve fitting, back fitting, cloud patterns, data mining versus 100 percent systematic models that are based on rigorous hypothesis extraction testing and a whole barrage of statistical tests to make sure that you aren’t fooling yourself—big, big difference.

Maybe we should teach schoolchildren probability theory and investment risk management.

Andrew Lo

Michael:

I have to give a presentation shortly in a country in 
Asia . . . a pretty successful city. And I assumed they knew what I was going to talk about, and they wrote me and they said, “Hey, can you bring these particular charts for your presentation and tell us whether or not these charts are in a trend or not in a trend?”

It’s amazing that people still . . . want to fixate on their markets. They’re excited about their markets and they’re not even thinking about diversification. That’s not even on their horizon. They’ve just got their markets they’re happy about and they want me to tell them something exciting about them. That’s not how you look at it. You are saying, “What are the targets of opportunity, when that opportunity arrives we’re going to do something with it but we can’t force it.” And I still think today, it’s still not a widely understood concept.

Martin:

I think that’s right, and I’ve been in exactly those situations where, whether it’s the local conference organizer who’s asking you to tell them how great their local market is, or it’s the drinks party where somebody’s saying, “What do you think about gold?” I don’t even know what country I’m in and which way up your stock index is, but I know it’s in my portfolio. And I know it’s one of the 150-plus assets that we monitor 24 hours a day that we always have a position in, and depending on how those trends have unfolded we will have a large position. And depending on how those trends have unfolded we will have a long position or a short position, and that’s the beauty of it.

Many managers will have a different approach from ours, but I start from the very high level premise that all assets have an equal opportunity to manifest trends. The whole model building process is an attempt to preserve that opportunity and make it as broad and robust as possible.

What do I mean by that? What I mean is that if you build your model such that it captures some of the characteristics of different markets, you trade hogs subtly differently from how you trade Treasury bonds, for example. You can persuade yourself that you’re building in some features of those markets, because clearly the world of hog traders is different than the world of bond traders.

But the dynamics of those markets at that level of resolution, there is, in our opinion, no persistency. All you can say about hogs and bonds is they have the potential to demonstrate trends. If I had looked at, say, equity markets over . . . if my dataset was, this is somewhat spurious, but say U.S. equity 2002 to 2007, I could conclude that equities don’t go down, or not for long.

What if I look at bonds over the last 10 years? Bonds don’t go down, do they? Never. You could build into your model certain biases or certain scenario expectations and that’s what we try and eschew. We try and avoid those built-in biases. I am indifferent as to whether your regional equity market is in a roaring bull trend, which I know is what you want, conference organizers. Or whether it’s in a terrible bear trend. I’m agnostic as to whether it’s going up or it’s down and I don’t look at how different markets have performed historically and say, “My models are better at trading commodities than they are at trading financials and therefore I overweight the financials.”

I try and keep it as completely agnostic directionally and asset allocation wise so that it can grab hold of any opportunity that presents itself.

Michael:

When you’re talking to people after all these decades, what percentage of the educated financial audience do you think grasps what you do at Aspect?

Martin:

Fortunately, many more now. This is a terrible generalization and therefore probably not true, but from my small vantage point, 2008 was the “aha!” moment and before that, marketing what we did could sometimes be a struggle, and you know that . . . we [also] didn’t help ourselves with the absurd fees that we used to charge back in the ’80s.

The evolution of that to making ourselves look respectable and “I want to be a hedge fund too,” all the way through to 2008, was where as a result of performance in 2009 the phone started ringing and the pension funds and the pension fund consultants that wouldn’t take our calls up until that point said, “Explain again how this stuff works.” And then they were really receptive.

Of course, you can’t make it up because then, ironically, in the aftermath of the global financial crisis, you get a period where this stuff didn’t perform as well as it had done historically and as well as our expectations would have led us to believe it can and it will. I think the vast majority of people now get what we do, but it wasn’t until relatively recently that that’s been the state 
of the world.

Michael:

I put this in one of my books that TV Ben Stein said, “If you made money in October 2008 you were doing something wrong.”

I know that two and two make four, and should be glad to prove it too if I could, though I must say if by any sort of process I could convert 2 and 2 into five it would give me much greater pleasure.

George Gordon

Martin:

Really? [Laughter]

Michael:

I know where he’s coming from. I understand from his understanding of assorted trading strategies or investment strategies, and if you only believe those, then that’s a fair thing to say. You’re leaving some things out to say that, but I just love that line.

Martin:

That is a great one, and none of us in our industry should think—and the respectable ones of us don’t think—that we’ve ever finished. It’s not like you’ve ever unlocked the secrets of the markets and my model is my model and now 
I’m off to the beach. Because (a) the markets never leave you alone and (b) there’s always some new thing that you hadn’t thought about.

I think very topical at the moment is the evolution of portfolio construction. The theory and the practice of portfolio construction. 
The Swensen model of diversification was not just have U.S. equities, have global equities too. There you go, now you’re diversified. I’m oversimplifying it, but certainly he has had and as far as I know has had no interest in quant strategies and at the time that was perceived to be a very valid and very useful form of diversification. And in 2008 that’s why you get lines like, “If you made in money in October 2008 you were doing something wrong,” Because the world view was so rigid.

It’s exciting. It keeps me young. And it keeps my research team young and energized, because there’s always some new worldview that you should explore.

Michael:

I’ve had some of the brightest and most accomplished behavioral economists on my podcast, and what I really find amazing in talking to them is their work seems to be the embodiment of traders like yourself, your peers, the other associates in the industry. Going down the systematic quant trend strategy path was in many ways capturing what Daniel Kahneman was winning the Nobel Prize for, or what Vernon Smith was winning the Nobel Prize for.

But when I talk to them, there seems to be this disconnect where all these behavioral economists should be waking up to these quant trend strategies and saying, “Wow, what a wealth of interesting data and evidence for us to sink our teeth into.” But there still seems to be a wall where they’ve not yet pulled the hood of the car up and looked under there and said, “Interesting.”

Some of the greatest, most revolutionary advances in science have been given their initial expression in attractively modest terms, with no fanfare.

Daniel Dennett

Martin:

I agree, Michael, and, “vive la différence,” I’m very happy that those folks and the folks at Google have not woken up to it. But you talk about behavioral economists—one area that has sort of fascinated me is the building of agent models. If you can define players in your complex system, who are the hedgers? Who are the speculators? Who are the counter trend traders? All of that. You’d think you could come up with a bottom-up model for the markets, which perhaps, but I don’t know, is that [what] behavioral economists are trying to do?

In my limited experience, candidly, don’t bother. It’s a very elegant mathematical model but it’s very unlikely to be able to make you any money. The thing about what we do is that it’s a bit rough and ready. The models are very sophisticated of course, and the mathematics is complicated, but you almost start from the premise that all markets will at some point display trends, and it’s our job in these models to be able to capture those trends efficiently and not lose money in the periods where we’re participating in those markets and they’re not trending.

There you go. I’ve just defined what it is we do, but you’ll notice that I haven’t dwelt on, “And it’s my job to tell you why those trends exist, where they come from and where they’re going.” If you fixate too much on that, which is the role of the behavioral economist—very fascinating field—but it’s not what we do.

Michael:

They seem to do a great job of really putting aside the efficient market theory and offering that human beings are not always rational, and bubbles exist, so I look at that very basic premise. Their work gets more complicated than that, but their basic premise, it dovetails right into your world in the sense that it’s a foundational explanation for why you might be successful.

Martin:

Yes, and it is, and it makes for great reading, and often there are “aha!” moments as you say, “Yes, well that’s why we sophisticated monkeys behave the way we do, and long may it continue.”

The past can’t hurt you anymore, not unless you let it.

Alan Moore 
V for Vendetta

Michael:

The world is a very interconnected place. Everyone is coming online. If a country doesn’t have a liquid futures market, they’re thinking about it. They would like one. How do you go about the process of bringing new potential trading opportunities into Aspect? I imagine you always stay excited because you’re like, “Hey, we don’t even know the next group of markets from the next country that’s going to come online that can possibly offer us opportunity.”

What’s great about trend following in many ways, it is the Indiana Jones of trading. Once you have your models, your systems and you know these markets in these countries are liquid and viable, you can go in.

Martin:

Yes. It’s an ongoing process and it’s something we devote a fair bit of time and a fair bit of money to, because you’ve got to keep your head up. It’s relatively easy to get started with the 
50 most liquid markets out there in the world, and if you just ignore all of the new stuff that you’re referring to, you miss a huge opportunity set.

Whenever there is a simple error that most laymen fall for, there is always a slightly more sophisticated version of the same problem that experts fall for.

Amos Tversky

As a business we have a regular cycle of reviewing news alerts and our brokers keep us prompted of what’s new, what’s coming. We like to have some back data, so we’re not going to be market participants on day one of an exchange opening, because we’ve got to get the sort of characteristic heartbeat of liquidity patterns within that market before we can parameterize our execution algorithms. We’ve got to establish a little bit of history and then we’ve got to establish a threshold of viability. A threshold of liquidity because it’s got to be worthwhile at a certain point having a one-basis-point allocation to a market where it isn’t viable in the program.

There’s a liquidity threshold. There’s something like 6,000, is that right? Six thousand futures markets globally. You can very quickly cut off 80 percent of those on liquidity basis as being viable for what we do and then it’s an evolving cycle. As liquidity picks up we will adapt our allocation in the portfolio. And the other feature of course is there may be constraints. For example, we can’t currently trade the Chinese futures markets for our investors because external investors are not allowed to participate in those markets.

Similarly, access to Brazilian futures markets, which are vastly liquid, but there’s a taxation situation that makes them extremely difficult and expensive to trade. So all of those markets we’re tracking, we’re collecting data, we are simulating the models on them and we’re ready to go at the drop of a hat should the legislation change. We welcome that. We love the additional diversification that it affords the portfolio, because back to the starting point, if you just traded the same liquid 50 markets that maybe we started with in the 1980s, if that was your portfolio now and all other things were equal, you’d have less diversification in the portfolio. There’s a long-term secular trend towards markets becoming more homogeneous over time, I believe. We are hungry for new access and new opportunities of diversification.

Michael:

Why do you think you’re so passionate?

Martin:

I’m a one-trick pony. [laughter] This is what I’ve done in 
my career. I can’t claim to be a scientist or a physicist, but that’s what I studied. I got into that because I’m inquisitive, 
I like precision, I like answers, I like engineering solutions to things. I know myself and I would be . . . a lousy discretionary trader. I like the application of sophisticated mathematical techniques and theories to extract signal from noise and I love working with a bunch of really, really smart, talented people. All of that keeps you passionate to keep learning and to keep competitive and to keep doing what we’re doing better and better.

Michael:

Even if the economics of this was much less—let’s say it was good enough to earn a living—I get the feeling talking to you that this still would have been your passion and your path.

You build on failure. You use it as a stepping stone. Close the door on the past. You don’t try to forget the mistakes, but you don’t dwell on it. You don’t let it have any of your energy, or any of your time, or any of your space.”

Johnny Cash

Martin:

Absolutely. This is just great fun. I didn’t set out on day one to be a great trader at all. As I said, I got into this working for Michael’s father because I was fascinated by the application of computers to time-series data. That’s what I’ve done my whole career. 
It doesn’t really matter that the rewards have been very good. That’s a nice feedback loop . . . actually, markets are a very swift judge of how precise and how honest you have been. So . . .

Michael:

It’s how to keep score.

Martin:

It’s how to keep score, exactly. That means that I’m not in an amorphous world of where people could say, “Wow, what a great article you’ve written.” There’s a scorekeeper.