How to Learn Algorithmic Trading
Readers seeking to learn quantitative / algorithmic trading often ask where to begin. As with any discipline, best approach is to get mentored by an expert. Short of that, read all the seminal works; ideally several times each.
The following is a reading list intended for retail traders introducing standard terminology and introductory topics, with bias to equity, exchange-traded derivatives, and FX. A caveat of this list is acknowledgment of the old adage that “there are no good books” and certainly none capture anywhere near the current state-of-the-art; that said, some books are better than none. This list focuses on those texts which build intuition, in preference to mathematical rigor. Each of the following is recommended, and a valued member of the Quantivity library.
Readers are encouraged to comment on their favorite omissions.
This post is the first in a series on learning algorithmic trading. Part 2 covers foundations of mathematical finance. Part 3 covers modern financial modeling and analysis. Readers familiar with systematic trading are encouraged to proceed to Part 2. Readers new to systematic trading (usually coming from either fundamental or discretionary technical analysis worlds), the following nicely motivate further study. Due to focus on retail quant / algo, the following is knowingly weak in structuring and modern asset pricing (with complete absence of exotics).
Two quick notes in advance, based upon reader comments (to which Quantivity defers, having not formally worked in the industry):
- Terminology note: “algorithmic trading” within the industry usually refers to optimal order execution (e.g. blocks at VWAP). In contrast, this post intends to capture the union of the larger universe of systematic, quantitative, and algorithmic trading from a retail perspective
- Intent: purpose of this post is to cover a diverse set of seminal literature, of which only a subset is applicable to any particular quant specialization (retail, prop, buy side, sell side, etc) and certainly should not be considered necessary for getting an entry-level job
Without further ado…
Flaunt academic finance and begin with behavioral finance (thanks to quant.this, for this concise list from a large literature):
- Reminiscences of a Stock Operator, by Lefèvre: classic speculator introduction via Livermore
- When Genius Failed, by Lowenstein: popular recantation of the LTCM fiasco
- Predictably Irrational, by Ariely: popular introduction to behavioral economics
- Behavioral Investing, by Montier: snippets of common wisdom
Dive into systematic trading systems (entry, exit, holding, Kelly betting, money management, etc), with a focus on the concepts (rather than mechanics, which are dated):
- Trade Your Way to Financial Freedom, by Tharp: standard retail overview, ignoring the ridiculous title
- Mathematics of Money Management, by Vince: standard retail introduction to money management
- Intermarket Trading Strategies, by Katsanos: random mix of trading strategies
- Advanced Trading Rules, by Acar and Satchell: survey of trading strategies
- Applied Quantitative Methods for Trading and Investment, by Dunis et al: survey of trading strategies (including a hint of Burgess statarb)
- Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson: introduction to modern algorithmic trading
Finally, for completeness, review a bit of technical analysis (TA) with a skeptical eye—while recognizing it is the predecessor of modern algo. Although Quantivity does not recommend TA in general (as much is subject to lookback bias), several concepts are seminal; for example, modern mathematical finance and microstructure formalizes and expands ideas such as moving averages, convolution / filtering, behavioral (e.g. overbought / oversold indicators), and moment derivatives (e.g. momentum and acceleration). Given that caveat, the following single reference is offered:
- Technical Analysis from A to Z, by Achelis: standard reference text on TA
With this introduction, readers are encouraged to dive into Part 2: foundations of financial mathematics, followed by Part 3: modern financial modeling and analysis.
Note: this post has been updated from its original draft, incorporating numerous insightful comments and recommendations (as retained below). Thanks to awwthor, quant.this, Josh Ulrich, Gappy, and Bjørn for their comments and recommendations.
Trackbacks
- Quantivity on learning algo « Coding the markets
- How to Learn Algorithmic Trading: Part 2 « Quantivity
- How to Learn Algorithmic Trading: Part 3 « Quantivity
- Trading the Unobservable « Quantivity
- How to Learn Algorithmic Trading: Part 1 | Ivy Capital Ltd
- Unmasking the Mystery of Machine Learning | William Victor's Blog
- Qualt Shop » Blog Archive » Learn Quant, and You Can Too!
- RSS Digest: Week Ending 02-Sep-2011 | Zahid Qureshi
- P-Q Convergence « Quantivity
- High Frequency / Automated Trading Systems Research | Roshan Ratnayake – Solutions Architect
- Links 7 Mar « Pink Iguana
- Quantitative Finance: What are the mathematical methods I can learn independently, and applied to finance, as opposed to spending $20K to earn a CQF certificate from Wilmott that essentially teaches these methods? - Quora
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- fx and futures trading – new learning materials – 07/05/13 | atoast2trading
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Quantivity 
Fantastic post! At the very least, it should give readers a good idea of the things they’ve got to learn about in order to ultimately understand algo trading, which is basically the confluence of a number of distinct fields. OTOH, I feel like some of those resources are probably not relevant to algo trading in the sense that your readers think about it (a distinctly buy-side forecast-centric way) since some of the books were about decidedly sell-side activities (e.g. market making).
If I were to do a post like this, I would probably recommend that readers learn about: a.) behavioral finance and microstructure, b.) macroeconomics and the financial system, c.) machine learning and statistical methods, d.) all things arbitrage and e.) modern portfolio theory (not necessarily in that order).
Also, I find it’s helpful to read anything you can get your hands on from one of the world’s big famous quant funds (RenTec, Bwater, GSAM, AQR, and D.E. Shaw according to NYM’s “Hedge Fund Elite”). They often aren’t open about what they do but they’ll drop hints that will help you get your thinking on the right path. For example, you’ll hear a lot about liquidity indicators and very little about technical indicators, if you catch my drift.
@awwthor: thanks for your comments. The buy/sell-side confluence was deliberate, seeking to acknowledge that limit order exchanges blur the classic institutional distinction. Microstructure is an extreme example, assuming traders will equally take either side. Retail selling option premium is a more classic conflated example.
Rumors suggest big quant funds are believed to run strategies which often are side agnostic, given public knowledge suggests intraday strategies which are essentially psuedo-MM—especially those which are flat overnight. Readers with factual knowledge of these strategies are certainly welcome to correct any misperceptions. 🙂
Definitely agree behavioral warrants inclusion. Adding classic MPT text now, as that is indeed an unintended omission.
What texts do you recommend, particularly for (a) , (c), and (d)?
I would think technical indicators for stock movement would be more important than liquidity indicators because when you ask the question what does liquidity have to do with knowing when to get in or out of the market, liquidity doesn’t answer the question, only a technical indicator would do that which is a logical domclusion. I think that is what you mean do you get my drift?
Hi!
Another great post. Still, a bit depressing since I have loads of reading to do 🙂 Tsay’s book is really good, and Hamilton is a classic!
When it comes to financial engineering, I really like Martingale methods in Financial engineering, by Musiele/Rutkowski. It does the theory a service actually.
How about some comments om implementation? Like programming skills, SQL, systems, networks etc.?? I would really like to see some discussion on this part of the problem.
B
Some other recommendations:
Signal Generation (Stat arb, HFT, options, intermarket) –
Quantitative Trading – EP Chan
Inside the Black Box – Rishi Narang
High Frequency Trading – Irene Aldridge
Day Trading Options – Jeff Augen
Intermarket Trading Strategies – Markos Katsanos
Advanced Trading Rules – Acar & Satchell
Applied Quantitative Methods for Trading and Investment – Dunis, Laws, Naim
Trading System Development –
The Encyclopedia of Trading Strategies – Jeffrey Owen Katz
Evidence Based Technical Analysis – David Aronson
Computer Analysis of The Futures Markets – LeBeau
Behavioral Finance & Financial History –
Predictably Irrational – Dan Ariely
Behavioral Investing – Montier
Fortune’s Formula – William Poundstone
When Genius Failed – Roger Lowenstein
@quant.this: thanks for recommendations. Given all the excellent comments, I will be refactoring this post into several and including coverage of the topic which you include.
Excellent post!
I would add:
A Guide to Econometrics, by Peter Kennedy: excellent overview of all things econometrics
to “Time-series econometrics”.
And how about works on money management, e.g. Money Management Strategies for Futures Traders, by Nauzer Balsara?
Two other great econometric books are “Using Econometrics” by Studenmund and for those of use who use EViews “Introductory Econometrics for Finance” by Chris Brooks.
As for the post below, I agree that you need to learn to trade, learn your style and then create a system based on that style. For that I recommend to anyone who wants to start algorithmic trading to read “Trade your way to financial freedom” by Tharp.
The next step would be to create a mechanized technical system that fits your style and mostly mimics how you would trade without emotion and then take it a step further and start applying quantitative theories to the system. At the end of the day 99% of algo trading (non micro structure related) are enhanced empirical technical systems. VWAP has been around a long time.
Also to speak to trading your style is 100% right. Remember that a lot of the guys who started the HFT craze were all market makers and already trading micro-structure. The computers just made them better at their jobs.
@quant.this: I agree. I would further your “empirical technical systems” to posit that quant/algo (esp retail, but equally institutional beyond sell-side MM-inspired HF and buy-side market impact / liquidation) appears to lack unifying intellectual frame; in other words, there is no agreement on intellectual optimality criterion (beyond some quant measure of profit, which has eluded generalization into a more abstract theme for guiding system development).
Thus, appears many people find challenge in evolving a TA-inspired mechanical system into greater sophistication. While Tharp does a good job at outlining the terrain, seems the methodology for evolving systems remains black magic. At best, the standard practice appears to be to incrementally try and plug in more fancy statistics and optimization. By analogy, what quant seems to be lacking is what BGI / Grinold provided for framing quant port management.
I think the post is really about “how to learn the basics of equity-based investment”. But frankly, even this way the list is a bit unfocused. Volatility trading is overweighted, for example, and a lot of vol trading is not algorithmic in nature anyway. Regarding introductory texts, I think that Campbell-Lo-McKinlay is an essential reference. Singleton’s Empirical Asset Pricing is also essential. I also know very successful algorithmic traders who don’t haven’t studied even one third of these references, but what they know (numerical optimization, statistics, online algorithms, large data analysis), they do know very well. Most of their knowledge is based on academic papers.
My view is that a prospective trader should use his/her competitive advantage. If they have to start with reading Tsay or Friedman-Hastie-Tibshirani, then they are hopeless. It will take too long to learn how to apply the concepts well, and it’s all too easy to misapply them.
In this list there are several books I haven’t read, but of those have read, several have very little marginal value, and definitely don’t recommend them. Gençay’s book on wavelets is especially bad. Taleb’s book is also horribly organized and not informative. Hull’s book is necessary for a structurer, but for an equity trader is not so useful. Grinold-Kahn is essential reading for PMs, but their framework becomes less relevant at shorter time scales, and I don’t know many small player doing any sophisticated portfolio construction. When there are no agency problems and no weird constraints, the transfer coefficient is not a top concern.
I would not recommend tackling optimizations problems with GAs, and definitely Goldberg’s book is near to useless. No strategist or PM I know uses GAs. Rather, I recommend a solid intro to optimization, like Luenberger, Bazaraa-Sherali-Shetty, or Boyd-Vandenberghe (available online).
Regarding market impact-shortfall models, I don’t think Almgren’s work is relevant to the retail investor, since both transaction size and holding period assumed in the literature are much bigger than what a day trader experiences.
Sorry to sound like a nihilist.
@Gappy: thanks for comments; I fully agree with all. I admit, this post snuck up a bit and came out reading far more ambitious than originally intended. I am working to juggle this post into several, both to call out the logical progression (esp emphasis on edge) and to untangle the “unfocused” nature of this topic. I will add a section on optimization, as I concur that is an important area.
At least partial focus on equities I posit is unavoidable, for two reasons. First, my sense is majority of retail focuses on: equities, exchange-listed derivatives (i.e. few exotics), FX, and a small bit of commodities (mostly metals). Second, quant coverage in the literature of both FX and commodities is thin and quite TA-centric, given the lack of theoretical economic theory (macro seems hopelessly broken, given even UIP appears not to have explanatory power and whose effect remains overwhelmed by carry trade inbalances).
@quantitivity: Yes, Van Tharp is to help people see the merit of system based trading and gets one to think about creating a robot trader version of yourself and why that is advantageous, it also talks about some system testing. David Aronson’s book is much better and basically shows TA becoming QA. I don’t consider myself a technician since I would never buy a “cup and handle” or be able to properly define it (fuzzy logic does help here) and back test, forward test and random test it. But when you start getting into MA’s, Wavelets, PCA, and different forms of FFT and testing those for return and robustness of returns, all of the sudden you are in quant land. In the end, technical traders where trading mean reversion way before the quants.
Don’t know if I said this already but love the blog and thank you for getting the mental juices flowing. Look forward to seeing more on regime changes and how that affects different markets and instruments.
I have a few book recomandations also:
Useful even for low frequency trading: An Introduction to High-Frequency Finance
How to do a proper backtest and avoid curve fitting: The Evaluation and Optimization of Trading Strategies, Robert Pardo
@Gigi: thanks for your comments and recommendations; your first recommendation is indeed already included in How to Learn Algorithmic Trading: Part 3. The latter I have not yet read, but have now ordered and will read.
Absolutely the first read for anyone considering trading via TA should be:
“Evidence Based Technical Analysis”
By David Aronson, Wiley 1996.
This book will not only save TA “NEWBIES” money in the long run by setting a back-testing polar star….
But more importantly, the book is capable of de-programming the voodoo most TA “EXPERTS” practice daily.
Although long on words (the book could have been 100 pages, rather than 544), the backt-testing methods presented will allow a CLOSE reader the ability to cut through TA bull with GPS-like precision.
— 850mph
conclusion. sorry for the typo.
@quantivity
You have a done a great service by posting this excellent article. Please, update it atleast once a year to keep it relevant and timely. Thanks.
@think.open: thanks for your kind comment. Journal review of recent quant techniques will be forthcoming in Parts 4 and 5 of the series, which I am slowly drafting.
Hi, can you tell me how a college student majoring in computer science should get started learning about this kind of stuff?
You say that your list is intended for retail traders, but I know pretty much nothing about trading yet. Do you recommend any good books for learning retail trading, so that I can get to that level before diving into quantitative trading?
Awesome post! Going to read the other two now…
Not to add to the carping. But you forgot Step Zero — which is learning the basics of trading interfaces. Until your computer can read market data and send instructions to a broker, you can’t trade.
I was recommended this blog by my cousin. I am no longer positive whether this post is written via him as no one else recognise such designated about my problem. You’re amazing! Thank you!
A good example can be seen here: http://www.collective2.com/cgi-perl/system72525278#
Respected Sir,
Let me first congrats & thank you for your wonderful post .sir my name is amit,from india, i am a NanoTech.professional,i also wanna to learn analytics for trading,please guide me how to start from scratch as i am novice in this field .your little guidance will change the life of many layman like me.
Warm Regards
AMIT
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@Quantivity : An Interesting and refreshingly open-minded introduction, looking forward to being able to get into Parts 2 and 3. However, speaking of intuition rather than mathematical rigor, I have an intuitive feeling that you and many others feel that Technical Analysis is the predecessor to Algo in the way that Alchemy preceded Chemistry as a flawed but perfectible process. I have yet to be convinced that Algo has anything to do with approaching a science or mathematical rigor. As Jim Cramer once said, it all only works if one is willing to lie. The broader problem of Wall Street investing is finding that part of the Grand Casino that is not fixed.
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Reblogged this on atoast2trading.
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@Jayson: thanks for your kind words and offer to include in blogroll.
Aw, this was an exceptionally good post. Taking the time and actual effort to generate a really good article… but what can I say… I put things off a lot and never seem to get nearly anything done.