or, The Reading List That Will✱ Make You Rich.
Added, 2019: I’ve meant to go through this post many times over the years as it’s probably the blog I get the most e-mail about. It’s worth keeping in mind, if you read the below, that I started blog.hiremebecauseimsmart.com explicitly to try to get a better job. I had run a small business, gone to university, worked numerous crappy jobs, and didn’t feel I was getting ahead. I read in the newspaper that someone had made a website about why the Target Corporation or someone should hire her and was successful (As much as I enjoy posting my stupid philosophy and economics opinions, I’m also quite content to keep them to myself. And my goal of posting readable material about tensors or whatever higher-mathematics would have probably waited another 10–20 years for me to figure out what I’m talking about. I would rather write a book like Steve Shapin’s The scientific revolution, Roger Penrose’s Road to Reality, Roger L Geiger’s History of American Higher Education Bill Thurston’s Geometry and Topology of 3-Manifolds, Hilbert & Cohn–Vossen’s Geometry and the Imagination, and so on — themes that the author has maturely and fully digested — than the sort of stuff I’ve been doing here. But, you never know how long you are going to live, there’s a real danger of losing one’s way, following the path another has macheted through, — and, this is the 21st century, no college graduates know how to get jobs and I’m no ashamed of trying something out-there to try to get myself out of the position I was in: having lost my small business by being cheated by a web venture capitalist, losing my apartment, borrowing money from my romantic partner, and picking up a crap job to pay it back. Failure is not the kind of thing one wants to broadcast, since irrational confidence and optimism seems to be what gets people ahead, but I’m also not ashamed to say that it happens to many people, it happens to me, and in particular, don’t trust web venture capitalists. There is still plenty of real, actual business going on, no matter if all the way up to Treasury Secretary Mnuchin is obsessed with internet advertising companies (FB, GOOG). There’s much more to say about quant land, including the decline of hedge funds, re-regulation of banks, products and desks that only seemingly succeeded and in the long-run were shown to be unsustainable, and the levels of honesty and happiness in the financial industry writ large. Do MD’s and up succeed when they venture outside of the banking industry? Is trading a good career to give your life to? Does Emanuel Derman’s my life as a quant or Edward O. Thorpe’s a man for all markets hold any relevance for the average millennial unemployed mathematics graduate? Are people on the buy side brilliant and fun to work with? Which prop firms are chop shops and which are insanely good? This is not the place to say it and I’m not the person to say it. If you’re interested in getting a job with a bank, an MFE may or may not be the way to do it, and the bank job may or may not make you happy. Keep your ears and your mind open, is the only advice I should rightly give to anyone.
Many of my views have changed (I hope to the smarter) and my advice to someone 10 years younger would be quite different than what I wrote here, even if the times hadn’t changed. Before going into detail with my new recommendations (which themselves are things I learned years ago but didn’t take the time to write down), I should emphasise thatby my own yardstick, I failed at the DIY MFE. I posted this around the time I saw Joy Pathak blogging from his Baruch MFE program. Since we were both writing about the same subject matter at the same time, I chose to “race” against him, and I lost. Joy did the Baruch MFE, seemed to fall in with a shady academic, but then got a job at a bank and within a few years was a rates trader. Success. My path was much more scraping together contracts from horrible people, with a few good people mixed in, but not in a risk-taking role. I should also mention that I didn’t want to move to NYC or London without a job offer. My thought now is that this is one of the catch-22’s of life that the internet can’t really solve—or at least, that my approach didn’t solve. You can’t get experience without experience, and you can’t get a City job without already being wealthy enough to live in a $2500/month apartment.
I also ended up working for an MFE grad—I forget which bank(s) he worked at, but it was on the delta-one desk. And note which direction the hiring went: him with the money, me with the quant knowledge. The knowledge and skills you need to come up with good trading ideas are much, much better taught by other traders around you, live, than trying to piece things together from what’s been written down. I think I knew this in 2010 when I wrote this, but am just really averse to paying for things—even if it feels wrong and illogical to pay for a job, maybe that’s just what you have to do.
There are some better sources of knowledge than what I posted below, which you can do for free, let’s say before stepping into the $25k-job-application.
anything that makes you better at Excel
look up actual option contracts on NASDAQ. Spent some time actually watching a ticker and reading about it. How come the last price can be below the ask?
read P Z Maymin’s Financial Hacking. (Expensive, but concise.)
watch TastyTrade videos. Don’t get your options theory from Terry Tao, get it from a veteran floor trader who gets the theory wrong.
Ditto, buy Sheldon Natenberg’s book (older edition is better). The theory is wrong, which is a good thing. (Compared to eg Helyette Geman, below—when my wheat-trader friend flipped through Geman’s book she immediately decried it as academic crap.)
Bet real money on PredictIt. You’ll get the feeling of sitting in front of a screen, you’re watching a real curve, and the prices are low enough that without being rich “put all your chips in”. I think the buy-in is $250, which is earnable at a service job, even if earning it sucks.
Run through Simon Gleadall’s volatility simulator gauntlet, Volcube. If you’re wanting to leverage mathematical ability, I can’t emphasise enough how little university-level stuff matters relative to calculation ability. Taleb pointed out in his surprisingly good 1999 book (vanilla hedging) that the derivatives that matter on a whole book differ greatly to what matters at the individual option level. (This can be understood as a derivative at a different tangent space, but like I said, don’t worry about that!) With the Volcube software you can practise actual arithmetic with actual numbers of the kind you would see in the field. Maybe not hold everyone’s trades and prices, in order, in your head, like an old school floor trader, but at least do option arithmetic with realistic numbers until you don’t confuse long with short.
Read Coyote’s HLT (high latency trader) blog on Posterous, Matt Hurd’s sumohurd.blogspot.com, Kipp Rogers’ mechanicalmarkets.wordpress.com, and Dale Rosenthal’s course notes. Don’t waste your time with Asness, Buffett, any of the Buffett clones (Klarman, etc), or Ray Dalio. Don’t watch too many of the Opalesque TV and definitely don’t listen to Barry Ritholtz. Acrossthecurve.com was good, I don’t know about his twitter. Listen to a Gundlach show at least once.
Be aware that literally everyone, except possibly yourself, can see what you are trying to do. Think about the junior risk manager in Margin Call versus the old one (Stanley Tucci’s character). Tucci was a bridge engineer who got pulled into finance by high money during mid-career because bankers have a fetish for engineering. The young guy just had no direction or interests in life, so — as Schopenhauer remarked two centuries ago — was driven naturally to money. In the intervening 10 years as I learned and identified more with this field, I heard from umpteen zillion young men who had the exact same thought: “I need money … I have no idea what to do … hmm, who makes money? … Those hedge fund guys do!” It’s very clear to people senior to you in the field when this is what you’re doing and what your motivation is. There really are other things in life, don’t forget that. (But then again, successful finance people do make 10x or more what others do. Also true, and senior people forget it … until their bank lets them go.)
Programming. Programming skill is incredibly broad, so broad that the opportunities you see expand well outside of trading or banking.
There are many places to start, perhaps too many. nodeschool.io was a decent place I recommended as a starting point ~5 years ago. Ruby, Perl, and Python are all good languages to start with. But it would have been helpful to know that they’re replacements for Bash. See tldp.org. Eventually (maybe 3–5 years in) you will want to learn C (not necessarily C++—the operating system is written in C, and C++ has a lot more linguistic theory baggage). So buy K&R at some point. Look at the UNIX tools, why UNIX was made the way it was, what glibc is (zines.jvns.ca = best starting point), even what musl-libc.org is. And what the operating system is (bottomupcs.com, MIT xv6 course + lions unix commentary), what memory is, I wish I would have learned these things before all of the object oriented theory, type theory, Haskell monads, and so on — because to get things done you write to a database, write to memory, and write files, maybe write to other processes (IPC), and write to (TCP) sockets. You have to compile libraries so make and nm | ld | ar come up sooner in reality than I was ready for them based on reading. Aiken’s compilers course (stanford) also makes my top 5 of “deep fundamentals” — which aren’t the same as getting a working perl script, installing npm toys or gem install and playing around in the shell, or curling things — but for me have both a far more lasting and far more immediate value than debating programming languages or frameworks (and especially text editors and spacing. get a life if you do this.).
I don’t know what database to tell you to start with. Maybe postgres? Maybe redis? Don’t waste too much time choosing.
If I would have thought more deeply about my career direction 10 years ago, then instead of “finance” (because it’s advertised by master programs and because I studied a certain topic as an undergraduate), or “weather” (because it sounds cool and computational), I would have taken a more serious look at various file formats—video and audio, for starters. In trading there is the FIX format which has annoying aspects you can dig your heels into and become expert at. Well, MPEG-4 probably has its quirks too. Unicode and CJK characters have quirks as well. DNS is a specialty area as well (look up github.com/miekg/skydns). Compression does tend to have some faux mathematicality about it, but encodings and compression are probably also a lot of common sense and experience with the format. Yes, there are firms that compete with each other on a single day for the right to be Youtube’s next compression algo. Some people even retire early on compression company acquisition dollars. On the other hand, people are watching and recording more content online not less, and as banking went through a regulatory crisis and hedge funds had a bad decade, netflix-n-chill is more popular than ever, very well produced videos about everything on the planet being transmitted through youtube, musicians earning buy-a-house money from youtube and spotify ads —— all of this has specialty formats to it which don’t require you to show off your Oxford-ness or even to have any Oxford-ness. Just a thought, I can’t back it up.
Don’t take machine learning too seriously. Look more into SLAM and OpenCV (like real-time driving) than training a perfect model forever and entering it into a Kaggle contest, your specific tuning parameters never to actually be used at the company.
Commonly across job postings I’ll see that people want you to be able to parse a UDP feed. So look up NASDAQ BMX or at least IEX TOPS and try to parse the feed. At the very least this is both a gauge of your programming ability and will familiarise you with real market data as opposed to Bloomberg “entertainment” noise. (If you listen to business news for fun, either you are like me, desperate for a better job and fantasising about it or trying to learn the bare basics, or you should broaden your interests. Consider buying something published by Penguin, or look at Herzog’s rogue film school reading list.)
Speaking of Bloomberg, there are various fake finance companies under the heading of fintech. (I don’t think this was as common 10 years ago, iirc). I don’t care if they have a few customers, I don’t care if they have the money to pay for tickets to stupid conferences, have some self respect and don’t even consider applying to anything fintech or bitcoin. Dan Davies did a good post on fintech on medium.com a few years ago but the issue runs much deeper than that. I said I wasn’t going to explain capitalism in this follow-up post, but it’s a fact that irrational things happen due to people’s stupidity, even mass stupidity. (Don’t even dive into the economist theory about this, it’s not even worth addressing. Just let it die.) I can’t tell you when bitcoin will pop, what the pop will look like, or what FinTech investors are thinking. I can tell you that even if you “win” by getting a job with ZipLine Financial or Bayesian Capital Management or whatever, you lose. Respect yourself enough to say no to things that sound attractive. There is a proviso on this in that I’ve heard IEX has a great tech team, which would be worth being a part of even if the management thesis is flawed. Just don’t do zipline.
Speaking of Dan Davies, buy his book and lend it to a suffering business/economics graduate, it’s more realistic than Mankiw’s textbook.
I still have no idea what the actual success rates of graduates are. Collating and sharing this information is something I have been working on during 2019. Every bit of statistics and machine-learning I studied has been not just worthless, but worse than useless, in this project—as well as, generally, getting results that clients want during the last 10 years of contracting. Beware Greek symbols bearing gifts.
On to the original “omg world I seriously went to cawllege I can prove it somebody pleeeze gives me a jawb” posting.
What follows is very much a field report of one particular DIY Masters in Financial Engineering. That brings drawbacks (like idiosyncrasy and personal narrative) – and advantages (I’ve actually read the stuff I’m going to tell you about). The report is half why-I-didn’t-get-an-MFE and half what-I-did-instead.
Like a good postmodern, I’ll just present the story and let you make up your own mind.
DON’T SMART PEOPLE GO TO GRADUATE SCHOOL?
When I took a graduate-level economics class (optimal control) in my final year of school, I noticed a few things:
(a) the deeper the maths, the worse the professor
(b) nobody in my class really understood the lectures
© my favorite maths professor (who was teaching himself molecular biology at the time) said he never went to class, he just taught himself from textbooks
(d) at least half of my classmates were in graduate school to avoid the real world
and most important, to me:
(e) classes and exams after age 22 were grinding the creativity out of all those who had it to start with.
All of my own ideas, too, smelled like a sausage-grinder that puréed together fuzzy logic, equilibrium / optimisation models, and vector calculus with whatever talk I had most recently gone to. “Fuzzy lexical preference orderings in convex optimization” actually sounded like a good idea to me, I sh*t you not. Also, the meat-grinder never gets cleaned. (because I never went anywhere without a book in hand or an idea in my head).
WHY SHOULD I PAY FOR THAT?
I said to myself:
“I don’t learn much in lecture. I could do just as well by getting course syllabi from the Internet and reading the books myself.”
Here is a typical course list for a money mint, I mean MFE program.
0. ‘Remedial’ maths
1. Probability theory / stochastic processes
2. Numerical methods; Finite element method and Monte Carlo
3. Time series
4. Functional analysis
5. VAR, cVAR, CAPM, Greeks
6. The Heat Equation a.k.a. Black Scholes Parabolic PDE
7. Programming
8. Financial stuff like defining what various product areas are. If you DIY you can focus on the area you want to do.
9. Meeting practitioners.
Well DIY really can’t do that, so (9) might be a reason to enrol in a course. Maybe if you live in NYC or London you can show up at some parties and act like you belong. “My name is Paul Allen” kind of thing.
We live in the age of the Internet. That means free sh^t. Not even counting the open-source educational efforts by tip-top universities like MIT, Yale, and Stanford, there are 𝓞(1 bazillion) papers on xxx.lanl.gov. If you want to be on the cutting edge of machine learning, numerical methods, or time series analysis, you should be reading preprints, not textbooks.Added, 2019: This was the worst part of my predictions. The arXiv has become a horrorshow of fake research. (read Zak David’s blog for detailed examples; Matt Hurd also has one). There are still actually scientific channels, like math.AG, math.RT, math.GT, and so on, but anything with deep learning or finance has a high risk of garbage. Short papers from Facebook have seemed ok although they don’t relate to anything I need to do (for example a different memory management strategy in their deep learner). By the way, ead Eugenio Culurciello’s blog for a good overview of deep learning—but I can’t recommend diving very deep into this quasi-academic quasi-commercial cesspit. Back to finance, q-fin and allied channels seem to mostly be academics hoping that if they smooch up enough and act overconfident enough, some hedge fund will give them a job and they can leave academia. So, very much not worth your time. Solid textbooks with a very strong history of being cited and a long list of approvals, have been worth my time. Axler’s Linear Algebra Done Right — with not a single nod to “the many applications of today’s accelerating acceleration” — is a prime example. With regard to trading, although electronic markets are almost unrecognisable compared to Shelly Natenberg’s time, Natenberg’s volatility book is more “practical” than, e.g., the FBI’s “analysis paper” of the flash crash — despite the highly frequent datasets used in the modern, up-to-date paper.
If I can’t teach these things to myself, it’s because ultimately I lack either the interest or the aptitude. In both cases, I’ll be better off finding that out BEFORE spending two years of my life and $100,000, rather than after.
HUMILITY
Yet another reason to self-educate is that fresh graduates are always over-confident in the power of their mathematical methods.
Emanuel Derman asks MFE graduates why they believe they can get a price for a particular security and (lamentably) they answer, “Girsanov’s theorem.”
Mark Joshi’s “So you want to be a quant?” also says that fresh graduates usually propose a needlessly complicated math solution rather than use common sense, better evidence/research, or a quick, satisfactory math solution. And Sylvain Raines notes that quants who bring up matrices during sales talks are more likely to close doors than to close deals.
Finally – here is something I’ve never understood about the putative value of an MFE. So you’re trying to get a job where you face the market everyday and disagree with it. In other words you have to think for yourself, think differently, and come to a different conclusion than the average bettor. But you pay someone to indoctrinate you with exactly the same methods and formulae that all of your competitors are using. Then you expect to come to not just a different conclusion, but a market-beating conclusion?
That just sounds dumb.
I’m not just skeptical of graduate school, I’m also skeptical of my own skepticism of graduate school. So I did actually talk to admissions staff at MFE programs and the Wilmott CQF people.
I even applied to the CQF and took their test. It was hard enough to make you feel smart for knowing that the third moment of a distribution is its skewness, but easy enough that many many people could pass and win the prize of paying $23,000 to 7city Learning.
ANSWER MY QUESTION BEFORE I FORK OVER 100 G’s
Whenever I pressed admissions staff for actual data on the earnings of graduates, they demurred.
My criterion was that the lowest decile of graduates should be using their degree (directly) and earning a salary that would justify the time and money expense of the degree. In other words I wanted a guarantee that paying $150k would get me an interesting, high-paying job with excellent growth prospects. But I was met with (a) excuses / obfuscation, (b) possibly massaged figures, and © questions as to whether I was in fact smart enough for the program.
My conclusion is that there are enough gullible people with physics degrees, that the admissions staff don’t really have to work hard to fill the ranks. Just design good flyers and website, and let the tao-of-not-selling do your selling for you.
Maybe I’m wrong and everybody from the MFE programs I talked to does really well. Baruch College actually brags about its 88% placement rate, as if that’s a good number when you’re forking over several years’ salary for the purchase of more work.
If the MFE is legit, then it was a good trade and I missed the opportunity. I’m preparing for a career characterized by missed opportunities. But learning from Jesse Livermore, I’m not going to make that trade unless I’m confident; the evidence does not make me confident. I can see how the sellers of the degree will make money, but not how the buyers will.
OK, here is what you probably came here for. Not narrative but data. The reading list that will✱ make you rich.
Here’s the course I assembled for myself, as well as what I learned. I have read part or all of the following works.
Ito Calculus at MIT. Rigorous. Necessary.
Exploratory & Robust Data Analysis. Robust data analysis is the sh*t. Just ask yourself, would your method of analysis break if just one of the data you feed into the computer had an extra digit at the front? If the answer is yes, check yourself before you wreck yourself. Added, 2019: Absolutely not necessary. There is a mathoverflow post explaining the basics of the Ito calculus. The robust stuff is an academic sideshow.
Gilbert Strang, Advanced Calculus for Engineers. These are some fun videos aimed at working engineers. You can brush up on linear algebra and differential equations by learning something new about them, while also picking up some numerical methods. Have you noticed how many job postings require Finite Elements Method? Bonus: the calculus of variations is how your computer solves an OLS regression. You probably want to understand that.Added, 2019: I’m still wringing value out of Strang’s videos. I would add Axler’s book and 5+ other linear algebra books (so that would include “geometric” algebra, meaning with exterior=wedge products — like Sergei Winitzski’s book; chapter 5 iirc of Spivak DG1, even some actually good Real Mathematics — like Griess’ 12 Sporadic Groups or Mumford+Series+Wright’s Indra’s Pearls ). Linear algebra is something I come to again and again, always building with whatever else I’ve learned (both quant & mathematics), never too much, never irrelevant. Strang wrote Too much calculus, a critique of university mathematics curricula, 3 decades ago, it hasn’t been adopted, and it should have been.
John Kruschke, Bayesian Data Analysis. Well, you’ve gotta keep your mind open and not be bound to least-squares land. Everyone has to leave the Shire at one time or another. Kruschke’s book is aimed at lab scientists but he teaches you to Actually Do Bayesian Data Analysis, using R and BUGS. He also lambastes p-values quite heavily.Added, 2019: Everyone drinks the Bayesian cool aid for a bit. I got started with princeton.edu/~bayesway. Unfortunately, having cool theoretical obsessions is a waste of your life.
Angrist & Pischke. Now that you’re doubting everything you ever learned in econometrics class, it’s time for some reassurance. Things you can legitimately, robustly conclude from your regressions. Added, 2019: I still recommend this to people. It’s the top econometrics book I recommend, along with Pattern Theory by Mumford for the only rational machine learning book.
Time Series by John Cochrane. This is written for MBA students so it’s quite easy to read. Lays out basic time series analysis in plain English. Added, 2019: I no longer trust or respect Cochrane. But this is the best explanation of ARIMA I know of. The major insight being that you use a lag operator on the “index”. ARIMA and GARCH are like a Markov chain, a very basic calculation which you can iterate. Don’t take it too far or too seriously. Also props to Cochrane because he made me realise how to explain calculus to cops in a bar. I have tested it and it works. Being able to make somebody who thinks they are dumb realise that an intellectual diadem is actually personally within their reach feels better than a lot of money.
Cosma Shalizi, Intro to Complex Systems Science. Shalizi draws parallels between machine learning, statistical mechanics, and econometrics. It’s not his best work stylistically but Cosma is still a charmer. He also covers overfitting, V-C dimension, penalization. Added, 2019: I don’t use the Shalizi stuff. This was very inspiring as a student but as a little older I have less than zero respect for the Santa Fe Institute. Such is intellectual romance, you look back and wonder what you were thinking, but at the time a fervent zealot. twitter.com/gappy3000 has posted Roman Vershynin’s book in a few iterations. It covers some of this stuff.
Nonlinear Dynamics and Chaos. MIT OCW. It’s about weather but we all know the similarities between meteorology and economics. Useful. And if the finance thing doesn’t pan out, maybe I can try fluid dynamics. (I also read a geophysics text from MIT OCW … same story, I want to keep the petro options open. Maybe I’ll trade oil futures and have an understanding of geology as well as finance.)
Terence Tao, Functional Analysis. Short and good.
Ed Leamer, Let’s Take the Con Out of Econometrics. Classic, just plain fun. There is lots of good Ed Leamer stuff to find while you visit.
Brad Osgood, Fourier Analysis. The signal processing perspective is not really valid for financial data in my opinion, but it’s good to see the same mathematical object analyzed from a totally different perspective. Also you’ll get a better sense of generalized functions and along with that come tools for manipulating probability distributions and MGF’s.
Roger Penrose, The Road to Reality. Even though this is a physics book, it’s a great reference to give you the basics on a mathematical object. Also a handy, short calculus review. Added, 2019: It’s 2019 and I’m still reading this book. But now I finally understand the tensors and manifolds bits.
Solid Shape by Jan Koenderink. Just to get a little deeper into algebra and geometry, a.k.a. Wisdom, and give yourself a break from the diff eq’s. Added, 2019: Great book, I must have felt like a library shelfie would substitute for a CV and was just mentioning random good books. I still wish I could do this. Is anybody interested in starting a movement to send shelfies instead of hoary list of educational awards on CV’s?
Wikipedia. I like Wikipedia better than Mathworld because they use simpler language, especially with algebraic geometry (and when they don’t I change it). It’s the right level of depth for non-mathematicians. Also, academics frequently promote themselves on Wikipedia via links to relevant papers, for example look at the nodes around “Chirplets”. Wikipedia hunts can get too broad though, so it’s important to close the laptop from time to time and look at physical paper (books, printouts) to get the depth.
Reminiscences. I “read” this as book-on-tape while I was driving to meet my girlfriend’s family. It’s charming, and it answered a lot of questions I had about when to trade if the market is supposed to be efficient. Essentially, trade when all signals are go – your gut and your analysis both say that the current price of some asset is fuckin stupid. The book also gives you a sense of how the market has changed – Livingston heard stories about `great plungers’, rallies, depressions, wars, inflations from the 1880’s, we hear about him. Livingston also wishes he had sector-specific and market-total ETF’s available in the 1910’s.
Liar’s Poker. Don’t read it, it’s merely OK.
When Genius Failed. Don’t read it, I have no idea why anybody recommends this except they must not have read it themselves.
Black Swan. Also not really necessary. It’s just a trendy book. Do you want to think like everybody else?
My Life As A Quant. I didn’t read this and I’m not going to. The very existence of this book is what made me doubt that an MFE would get me anywhere (it signifies that many math people must try to enter Wall Street and make millions with their smarts).
Commodities and Commodity Derivatives by Helyette Geman. Covers weather, oil, nat gas, electricity, emissions, and precious metals. Nassim Taleb’s thesis advisor opens with a volley of Black-Scholes theory for mean-reverting processes and then she hands it off to some real traders. Also covers real options theory (just think about that jargon for a second).Added, 2019: Horrible.
Beat the Forex Dealer by Agustin Silvani. I got the feel of a modern trader’s mindset from this. I don’t know if the trading ideas still work but the KINDS of trading ideas he puts forward gave me a realistic idea of what kinds of trades to look for. As the title suggests, some of the trades are for retail forex traders to screw over their dealers. He aims to be half-academic and half-practical.
Volatility Trading by Euan Sinclair. Just started this. It’s decent so far. Volatility trading seems like a good thing to be aware of in cross-asset strategies or even if you’re just looking to shield your strategy from cross-asset destruction.
Robust Portfolio Optimization and Management. Really easy math and it actually takes taxes and trading costs into account. It’s “robust” in the sense I outlined above for Robust Statistics. That’s very good. This was the best portfolio management book out of several from the nearby university’s library. Also Frank Fabozzi is the guy who puts his imprimatur on all the fixed income stuff so if you’re into fixed income that’s another reason to read this.
Beyond the J Curve. You don’t really need this but I read it because I used to work in venture capital. Added, 2019: Academic crap that should not even be a university topic. Similar to much microstructure academics and stock market theory overall. Professors can be the best at Shakespeare scholarship, but never at business knowledge. Brian Z Tamanaha’s book Failing Law Schools gives a short history of why clinical professors of law were displaced by philosopher academicians (who are still paid like $350k/year. In case you had any remaining faith in “the market” rationally setting wages.) Occasionally you can find a clnical professor of finance and then the notes will actually be realistic. Dale Rosenthal is an example. But the violent tsunamis of capitalism leave little tidepools of island-hypertrophic Talmudic publishing, specifically around Wiley Finance books and O'Reilly Programming books. Anything, no matter how bad the quality, still has a buyer, because there’s some uninformed Indian / Kazakh / etc who thinks he can read about Wall Street and get a career there. And thus the gods of money bring you, finance academics, arXiv.q-fin, and Federal Reserve economists (not all bad). And the Frank Fabozzi series.
Financial Economics course by Bob Shiller. Easy introduction to dealers, bankers, brokers, shorts, futures, options, etc. Includes interviews with Andrew Redleaf, Carl Icahn, and Steve Schwartzmann. (!)It’s fine. Some exposure is better than none. Geneakouplos is an interesting character because, along with P Z Maymin (the libertarian candidate from Greenwich who says taxes “hurt the poor in ways they don’t understand” and “I don’t need to know any facts, I just apply my moral calculus to whatever someone gives me”), he lost a bunch of other people’s money trading mortgage-backed paper, from Greenwich. But he also (a student of Debreu iirc, or maybe Arrow) has written up one of the best, simplest finance models to explain the crash of 2008, namely that leverage goes up in good times as banks chase yields and thus offer more credit, and then exactly when traders don’t need their margins yanked out from under them, exactly that happens and everything pulls back too much at once. For mathematicians reading this, Geneakouplos’ leverage model is for me as high quality as the Cayley-Dickinson construction.
New York Supreme Court Case no. 603839/03. Renaissance Technologies Corporation versus Millennium Partners, LP (Pavel Volfbeyn and Alexander Belopolsky, Defendants). In the spirit of Reminiscences, some real-life gunfire to complement the academic stuff.
Blogs by traders, stat arbs, chartists, more. These are your opponents and if they choose to mouth off their thought process then that will only help you predict their trading choices. Plus you can get plain old useful info like practical considerations that traders actually think about. The only problem with this is too many stupid traders and PM’s. But from a beginner, yes, this was a fine place to start. Juhani Huopainen (morelivers.blogspot.com) wrote a good “war story” that I frequently share. And macroman.blogspot.com always does fun Sherlock Holmes posts. No idea how he keeps up that pace or maintains an interest in global macro though.
Wilmott Magazine. Kent Osgood’s articles on “Iceberg Risk”. Still not sure what I think about Osgood. His piece about the wiggle of the wiggle is too gARCH worshipping. But the points in Iceberg Risk are probably valid—just, how important are they?
Wilmott Magazine. “A Quant in King Arthur’s Court.”
Reading Wilmott magazine isn’t necessary. I just enjoyed the sense that I was reading the same thing stuff as real, paid quants.Still a good article that I think about. Also his Minyanville posts about the meaning of money and derivatives, his poker theory course at MIT covering the same, his book covering the same, and his book recommendations — particularly Daniel J Usner’s Indians, Settler, and Slaves and Williams The Economic Function of Futures Markets.
Algorithmic Trader magazine. Same deal.
Janet Tavakoli. Get the skinny on synthetic CDO’s from someone who structured them. Didn’t go to school for finance (so not doctrinaire).
Benjamin Graham. Warren Buffett and a handful of other investors have become billionaires by following Benjamin Graham’s method over their lifetimes. Charlie Munger <link> says that the techniques they used are no longer valid but you can get an idea of how these guys picked winners from the public market. Very anti-EMH stance. If you decide to play equities this is one school of thought of your opponents. Nobody else seems to like Tavakoli. I like her just as much or more. Never read her CDO textbook but I read her Wall Street suicide book and her recommendation of William Worthington Fowler is a fine complement to the more famous Jesse Livermore. Dr Tavakoli also makes the point, necessary in the United States, that Jamie Dimon having a lot of money doesn’t make him right, smart, or a good person. This is more generally applicable outside trading, Americans assume that being right has a financial reward and being smart means you should trade stocks to prove it. No on both counts.
Andrew Lo. A non-random walk down Wall Street. Just perused this bad boy. It’s very academic, by which I mean it addresses the Efficient Markets Hypothesis all the time and is clearly written by a statistician rather than a trader. His trading ideas have the flavor of Applied Quantitative Research – statistical anomalies. Stay the hell away from Lo. His assessment of systemic risk is bogus, his returns are subpar, and iirc he and his son were investigated by the SEC for defrauding investors. You can look it up.
Wikipedia is so-so for finance. You can find out some dubious or some comprehensive details. Usually not very in depth but it can be. Compare article on the CME to the list of S&P 500 companies.
My financial reading list is really personal to my interests. For example I’m intrigued by commodities just because it’s associated with the salt-of-the-earth. Volatility trading seems like a good thing to mix with cross-asset arbitrage strategies.
C++ Design Patterns for Derivatives Pricing by Mark Joshi (comes with a forum where Joshi responds quickly and helpfully to reader questions) This is a surprisingly good design patterns book, perhaps because of the focus on one problem. It served my personal purpose of demarcating when I understood C++ “well enough”. I recommend it to non-finance programmers as well. Rest in Peace MJ.
reddit.com/r/CarlHProgramming is a good introduction to C and then … you’ll need something else for the object-oriented features of C++ and I don’t have a good recommendation No. K&R and isocpp were better. Some of the coursera’s may be better as well. Even golang might teach you about pointers and addresses better. I’m not really sure how to help you with this one, but seeing how pointers solve a software-architecture problem in a real programming project (and I forget where I first saw this helpfully explained) is the goal, not understanding the definitions, virtual functions, inheritance, and so on.
Bearcave. This goes more under finance but this is the story of a programmer who took the signal + noise view of the market, thought he could use wavelets to pull out the noise, and ultimately failed. Good story. It’s a good story to prove algorithmic trading can waste a decade of educated man’s life, and that wavelets have the infinite capacity to charm educated men into believing they can do anything. Particularly when the inherent unpredictability of markets can disguise failure.
Wikipedia is not a really good source for a beginning programmer. You can skim and find out what a term means, but not find tutorials and often the language is too technical. Remember that Wikipedia is made and maintained by geeks.
Plus I’ve done lots of googling and read various library sources (none really superb) on the following topics:
Bash / shell scripting More important than I knew.
another programming language if you feel like going there. Python and VBA seem to be popular but who really knows. Jason Victor Dartmouth wrote a trading software that you can program in Ruby. You can interface R with iBrokers now. No right answers as far as I can tell. I probably spent too much time with R and within the R community. It’s an excellent calculator (especially dplyr/tidyverse and data.tables). But the community is weak on general computer knowledge and, when I went to an R conference in NYC, it was all unemployed/unemployable PhD science candidates who found out too late that there are no jobs in academia, even for Princeton PhD’s.
“REMEDIAL”
If you didn’t major in math, maybe you can get what you want out of two lecture series from MIT:
Arthur Mattuck, Ordinary Differential Equations
Gilbert Strang, Linear Algebra
and a statistics course. You can get notes to my econometrics class (Bill Becker’s) at indiana.edu/~e471. Not really useful except for bullshitting.
Two good, difficult books that cover the calculus-and-linear-algebra combo that marks the beginning of erudition in mathematics, are:
Advanced Calculus by C H Edwards I don’t know what calculus book I would recommend now.
Calculus 2: Linear and Nonlinear Functions by Flanerty and Kazdan Search through my isomorphismes.tumblr.com/tagged/linear tag on this blog, it’s pretty good. As I stated at the top linear algebra is the most useful part of mathematics, it’s not quick to understand, but if you are interested in certain things, it will pay you back more than any other mathematical topic. It can be in graphs, projections, rotations, dynamical systems, even algebraic geometry (or homological/commutative algebra). No single recommendation other than Strang.
If you get more curious about complex numbers in the process, read Tristan Needham’s “Visual Complex Analysis”. I hope to post a lot more about ℂ because it’s not as complicated as you think, and more interesting than you think. The first step is to know that ℂ doesn’t have to be constructed by adjoining a √–1. Re + Im √–1 is isomorphic to the matrix [Re –Im \\ Im Re] and to [Re Im \\ –Im Re], whichever conjugate you want to choose (√–1 vs –√–1). Watch this space isomorphismes.tumblr.com/tagged/ℂ and see
magical-realist ℂ
Borges was walking through the Library of Babel and came across an hourglass. Like most hourglasses it had two chambers but this also had *two* connecting tubes.
— isomorphismes (@isomorphisms) December 13, 2018
Borges was nearly blind when he entered the Library, so he primarily used his ears and his hands to feel the Devil’s Clock. Pressing his forehead to first one tube, then the other, he felt that the sand flowed upward at exactly the same rate as down.
— isomorphismes (@isomorphisms) December 13, 2018
Borges’ clock is isomorphic to a complex number ℂ, with the real component corresponding to the equal grains of sand in each chamber. Imaginary time flows as fast as the equal-up-and-down grains of sand.
— isomorphismes (@isomorphisms) December 13, 2018
as well as my answer to what is ℂ in terms of potatoes on quora. https://www.quora.com/How-can-you-explain-complex-numbers-in-terms-of-potatoes/answer/Lors-Soren?share=1 Ahlfors’ matrix ℂ is much easier to digest metaphysically, especially if you use a graph as your matrix.
The above list is not a comprehensive list of everything that I read in my DIY MFE. You will no doubt stumble across things that interest you and those will give you a different background than me and everybody else. Just wait until my strategy kills yours because I read a paper on a Lp metric that takes into account the sequential nature of time series.
* And, why am I calling it The Reading List That Will Make You Rich? I’m poking fun at myself. I have this stupid belief, not explicitly stated, but that if I simply learn these things, that I will get a high-paying job.
Other materials that I haven’t read but want to:
Paul Wilmott on Quantitative Finance I, II, III f**k no
L C Evans, Partial Differential Equations meh
L C Evans, Stochastic Differential Equations lecture notes meh
Evidence-based Technical Analysis still haven’t read, but might actually.
Elements of Statistical Learning not great, neither were Bishop or Murray. His young co-author (a young professor in I think the Pacific U.S. state of Washington) said it all in one of their DataCamp videos: people get into statistics because they can’t choose a field. Nuff said. Except I could add that statisticians will also spend (check the R ?density function here) academic eons debating the length of a Parzen window to smooth your kernel density plot (I think there are 5 options in R, triangular, epanechnikov, gaussian, few others) — all very similar, and in reality you can just run the function a few times with different widths if it didn’t work in reality. Don’t waste your time on machine learning and definitely don’t do free research for entitled white men so you can get a pub and they can sell a trading algorithm. (true story, I have met more entitled white men as a quant contractor than I could wish on any human being.)
Strogatz. Nonlinear Dynamics and Chaos. not great
A.W. van de Vaart. Time Series. didn’t read. no longer care.
J P May. A concise course in algebraic topology. read some of it. the length is good but I’ve lost my passion for AT.
Master the Markets by Tom Williams of TradeGuider Systems. wow. never read this one, and I’m honestly surprised I posted it.
It’s not easy to distinguish validity of a trading technique simply by the pedigree of the person who writes it. I read stuff outside of the approved sphere to get a different perspective. Also, how is a quant supposed to model real traders if the quant only reads quant books by quants, for quants? The Turtle Traders book is another book that exemplifies the way non-quant traders think. Do I need to say this? You have to be able to predict what non-quants will do for your algo to beat them.
El Aleph por Jorge Luis Borges, in the original Spanish.
What? You’ve gotta maintain your soul while you’re filling your head up with this stuff. Yes, wouldn’t it be nice if beign into literature got you a job.
Random Walks and Diffusion. MIT OCW who cares
Intro to Numerical Methods. MIT OCW only fake useful
Infinite Random Matrix Theory. MIT OCW probably awesome, but still haven’t watched it
Wavelets, filter banks, and applications. MIT OCW not gonna watch it unless I need a review for a project
Statistical Learning Theory, MIT OCW nah
Nonparametric statistics, MIT OCW bad
Several books on the Theory of Distributions wasn’t good
Several books on Wavelets Hell No
Several books on Machine Learning they were all bad, but had pretty pictures mathematicians should learn from
Andrew Ng lectures on machine learning AcademicEarth.org not great, and what ever happened to his Baidu contract? I looked up the numbers at one point (and tweeted them), his course has like 500,000 subscribers for what are apparently 100–500 jobs, and none of them actually would use statistical theory (more just the blue-collar parts of actually getting data to work)
Intro to Python on AcademicEarth.org didn’t do python
Danny Duffy programming book never read it, never met him
dbphoenix’s epic post on EliteTrader never read it, or at least not that I recall, and I will probably spend some more time snooping EliteTrader someday but it’s very low on priority list
The Volatility Surface was bad. I’m not sure what Jim Gatheral’s deal is. Colin Bennett’s volatility book was way better.
Iceberg Risk see above, not sure what to think
The Blank Swan still never made it through, although I really wanted to. Probability is an area where philosophers could really solve some problems. I posted one of Aaron Brown’s strongest points about it somewhere on this blog: you need to find someone who will take the other side of your trade, and usually it’s not just that they need to hedge, it’s that they believe the opposite of what you believe. At least in Eminis this is the case.
WHAT EMPLOYERS SAY THEY WANT
See top, learn to parse a UDP feed and why not use one from an actual market such as KOSPI CBOE BATS JPX or NASDAQ. You would also be that many steps closer to self-sufficiently actually trading your own strategy without needing firm infrastructure, paying ibrokers fees, or worst of all, punting on random oscillators through ninjatrade.
Of course you need to meet people to actually get a job, but you can do that without paying $150k for a masters degree, if you’re clever and personable. I got into an SPSS conference this fall by saying that my company was considering buying SPSS software. Then I met a bunch of statisticians. Just one example.
Anyway you will find similar book lists as the above coursework if you look at, e.g., DRW’s careers page or google for various quant reading lists.
Some employers are only interested in math / CS PhD’s, or more specifically people with a numerical methods background. That’s another reason to be skeptical about the value of an MFE.
The best reason to be skeptical of its value, though, is that as much money as was made by mathematicians in the markets over the last 20 years, the career opportunity may have been gone as soon as Emanuel Derman’s book hit the shelves.
WHAT INSIDERS SAY ABOUT THE JOBS MARKET
To quote a founding member of NuclearPhynance:
the job market is competitive and tight at the junior level and almost non-existent at the senior level compared to several years ago, likely to not return anytime soon. The quality people are staying put / being retained and the less-than-quality people are free and exponentially increasing their linkedin connections. Think long and hard about why you really want to move to this space.
WHAT OUTSIDERS SAY ABOUT THE JOBS MARKET
To paraphrase some random guy on /r/physics:
If you’re good at math, companies will hire you and teach you all the finance stuff.
Actually that is a paraphrase of literally quadrillions of people who have never traded or even thought much about finance. Quant-fin is just some stuff that’s trivial for mathematicians and physicists, but if they sink so low as to want to earn tens of millions of dollars per year, the money is there for the taking.
If you think I’ve missed something, leave a comment with book or paper title, a link to it, and your justification for why we should read it. If I agree with you I’ll add it to the list. If I disagree I’ll just taunt you in the comments.
YO, PEOPLE WHO ARE CRITICIZING ME
This post was really too long to ask people to read closely.
I am not claiming that this book list is good or complete. It’s just what I have read.
I don’t know for sure that the M.F.E. is a ripoff. But would you bet on a security with the payoff characteristics of these degrees? It’s just too risky.
I am not claiming that what I did was the right thing to do. It’s just what I did and therefore what I can talk about.
I now believe that programming know-how is much more important than mathematical erudition or familiarity with quant models. The job postings I have seen have all been for computer scientists, numerical programmers, people who can speed up existing strategies or at least program their own ideas.
I also found it worthwhile to do some paper trading. (I used ThinkOrSwim because iBrokers requires $10k minimum to use the system for fake trading or otherwise. ToS / TD Ameritrade lets you play around in the past.) The job I’m shooting for is research rather than trading, but I found it too easy to get lost in theory-land by reading paper after book after course notes. The Greeks, basic combinations of options, spreads, “psychology” or “emotions”, are much more meaningful after feeling the market whip my fake $100,000 up and down. Theoryspace is a probabilistic realm; live markets (even live past markets) are an actual realisation.
Also, it makes sense that jobs on the buy-side will want to see positive results of successful strategies that worked in the real world, not just GPA’s and theses. (Paper trading falls short here but is better than theory.)
Here’s the job requirements list for one “quantitative strategist” job (at Tradeworx). From what I’ve seen this is typical:
PhD maths/physics/ee/econ/finance/and so on
knowledge of machine learning
knowledge of unix / linux / bsd
c/c++ stl and, ideally, two of the following languages: python, bash, awk, tcl/tk, perl
experience in finance is not required
Added: A lot of young people have written to me asking for personal advice based on reading this article, which was linked on HN and single-handedly drove PageRank from 0 to 5.
First of all, I’m flattered but I can’t advise you on your life. I wrote this to draw attention to myself and to increase scepticism about masters degrees. There are a lot of expensive crap masters degrees these days.
Still, if you want my opinion, the CFA is the best of the bunch among the things I was considering. It is relatively cheap and the material seems sound. You can look up CFA salaries yourself; they look high to me. There is some FUD written by CFA’s but most essays I’ve seen on the CFA website are serious and fact-based. The CQF and MFE seem based on a labour market (for people who can price complex derivatives) that has expired, and was very competitive even in the early 00′s. I think I got the idea to do an MFE from some mathematics professors who said “You can always just go work on Wall Street”. Of course, they say this to all mathematics students, in part because they don’t know much about non-academic labour markets and need a zinger. Instead of doing more grad school, I would recommend: 1) learning to program (just scripting, not SW Eng), 2) learning statistics and some ML, 3) learning how finance actually works. The CFA can help with #3, in a way that finance or economics PhD won’t. Those PhD’s spend a lot of time on theories and are still geared toward preparing you to publish in academic journals. The CFA verifies that you know basic facts about how various markets actually work. For example ask your PhD friend how the Fed changes interest rates, and compare the level of detail to the Wikipedia article on the topic. Or ask in what ways markets are different today than they were 5 or 15 years ago. Of course if you go to grad school you can meet cool people. But you can also do this at work.
You are entirely responsible for your own decisions and I will not be held liable for them. But that’s my opinion: a CFA is sober, cheap, and appears to have high-paying jobs associated with it.
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