If you want some practical discussions, "Trading and Exchanges" by Larry Harris is a bit dated, but nothing comes close. Most of the descriptions are still valid today (even if some of the mechanics have changed)
If you want something more along the lines of what a financial engineer would know:
- "Options, Futures, and other Derivatives" by John Hull (goes over basic models without delving too deeply into theoretical math aspects)
- "Stochastic Calculus for Finance II" by Steve Shreve (goes over the basic stuff but has enough stats to keep a grad student happy)
- "Monte Carlo Methods in Financial Engineering" by Paul Glasserman (much more practical, and goes over subtleties of monte carlo simulation and other stuff like low discrepancy sampling)
- "Modelling Fixed Income Securities and Interest Rate Options" by Robert Jarrow (walks through how to perform certain simulations, covers lots of little details most theoretical books skip)
If you want something more theoretical:
- "Introduction to Stochastic Calculus Applied to Finance" by Lamberton and Lapeyre (Nice little intro)
- "Arbitrage Theory in Continuous Time" by Tomas Bjork (Slower discussion, larger breadth)
- "Brownian Motion and Stochastic Calculus" and "Methods of Mathematical Finance" by Karatzas and Shreve (Solid theoretical foundation, for the more mathematically inclined)
There was a good non-measure theoretic discussion of financial models but the name escapes me ATM.
Nassim Taleb's "Dynamic Hedging" is the finest book I'm aware of on the subject of actually trading/managing vol portfolios. It's REALLY good. And dense.
"Paul Wilmott on Quantitative Finance" vol 1-3 are worth having as a reference, perhaps.
"Analysis and Use of Financial Statements" by White, Sondhi and Fried.
It has nothing to do with computational finance but knowing more than nothing about financial statements and how to think about what a company actually IS can only help you in the long run. Also, it's quite interesting.
Quantitative finance is all math, so in order to really understand it, you need a solid background in probability and statistics. Even the most basic concepts make heavy use of probability, so without strong fundamentals you won't get very far.
There are other concepts, such as risk-free returns, etc that also play a big part in quant finance. A book that I really enjoyed that gave a good background in a lot of these concepts was "Trading Strategies for Capital Markets".
One of the basic concepts of quantitative finance revolves around the Black-Scholes equation, which calculates a price for an option. I would suggest first looking this up as well as how it was derived, to see if you want to pursue this further. If you're having a hard time with this, then quant finance may not be what you're looking for.
A video tutorial website that seems decent for quant finance is Nathan's Lessons:
If what you REALLY mean is you want to program trading algorithms, that's a bit of a different beast. For that you really need to understand how algorithmic trading works, how the market microstructure works, how to place trades, etc. For this there are a bunch of books but the best introductory book is definitely "Trading and Exchanges: Market Microstructure for Practitioners". It's a bit old, but still largely relevant and gives you a lot of the history. I really wish they would update this book, because I would buy it again. Another decent book is "Algorithmic Trading and DMA".
I'm finding this a more interesting and detailed read than _Capital Markets for Quantitative Professionals_. For instance, the book traces through everything that happens when placing, executing, and settling typical trades.
On the other hand, I've taken the Canadian Securities Course, so perhaps Capital Markets is a better introduction for total beginners.
It's a few years out of date, but not painfully so. Madoff was still a huge market maker, not a scandal, and it makes no mention liquidity rebates.
Edit: I should add that I had no trading background before reading the book, I was just an engineer at a finance startup who still thought "order" and "trade" were synonyms.
This book, "Algorithmic Trading and DMA: An introduction to direct access trading strategies" (http://www.amazon.com/Algorithmic-Trading-DMA-introduction-s...) is pretty darn good if you are a programmer. However, if you really meant "quant finance," then others can give you better suggestions :)
edit: I just remembered, Paul Wilmott and Hull have several introductory books if you are interested in what is usually called "quantitative finance." This means how to price options, futures, etc. From what I recall, this does NOT mean using statistical correlations to trade two similar stocks, making market-making models, etc.
For example, here's a tiny scala pricer I coded up in 5 minutes to spit out the price of a google at-the-money call expiring 47 days from now, on Dec 17 using a 10k Monte-Carlo simulation.
val risk = 0.28 // google has a 28% implied vol
val T = 47.0/365 // 47 days annualized
println((1 to 10000).toList.map(_=>Random.nextGaussian).map(x=>595 * exp((-risk * risk/2) * T + risk * sqrt(T) * x)).map(x=>max(0,(x-595))).sum/10000)
Output: $23 & change ( its selling at about $23 right now )
To veyron's excellent list, I'd add Brandimarte's "Numerical Methods in Finance".
A big part of algorithmic trading and stat arb is portfolio management, including deriving alpha, building risk models, etc.
The bible is: http://www.amazon.com/Active-Portfolio-Management-Quantitati...
Also, http://quant.ly/ is essentially Hacker News for quants and often has interesting articles.
Note, scroll way down to see the content, the formatting is screwy. Note 2, I'm not a quant but the math I do overlaps tremendously.
edit: previous HN discussion:
The "careers" forum on nuclearphynance.com is also pretty good, and might also shatter some preconceptions about how easy it is to waltz into the industry. (note: nuclearphynance seems to be down at the time of writing)
If there's a SE, discussions on HN, thousands of MFE's being minted, and so many books on quant finance topics, you really have to wonder if the quant labour market isn't saturated.
There will, however, always be lucrative jobs for those who are truly brilliant and willing to work on stuff which is kinda boring.
Jane Street still jingles its bells up at MIT, but for those who are less than six stdev's IQ or don't live in NYC / London, I would say focus on petroengineering instead of quant finance. (Petroengineering is the highest paying college major.)
But I suspect it's just like those who finish a law degree and then can't get a (good enough) job in law: not the university's problem, and they're ashamed they ended up in the bottom 50% of their class.
Chan's blog is informative as well,
Evidence Based Technical Analysis:
An Introduction to High Frequency Finance:
Trading and Exchanges: Market Microstructure for Practitioners:
I also wrote a couple of very introductory articles, sadly I never got past part #2:
I am could mentor 1-2 HN readers that are serious about getting into quant trading. Just let me know you are interested :)
Great book. It's what I recommend to my new developers when we hire them without previous financial services experience.
Here is his page on actex, take a look at some of the previews and I think you will be sold. http://www.actexmadriver.com/contributorinfo.cfm?ContribID=8...
Evolution, Complexity, and the Radical Remaking of Economics
don't let the false prophets of equilibrium theory and efficient market theory bend your ear too far ;)
if you get into looking for a job, there are a few books that list common questions for quant jobs also...
- 'The Concepts and Practice of Mathematical Finance'
- 'C++ Design Patterns and Derivatives Pricing'
Also of note is Baxter & Rennie:
- 'Financial Calculus: An Introduction to Derivative Pricing'
Once you've studied those and have a good grasp of Measure Theory, you'll want to tackle Shreve, Vol II.
And a brief plug of my (slightly out of date!) quant finance website, Quantstart.com.
also this one: http://news.ycombinator.com/item?id=1238906 with a comment by pg there
The above will help you design trading strategies. For modelling derivatives and such, you'll have to check out books on stochastic calculus (not my thing, so can't help there).
If you are looking into getting into high frequency trading take note that the world's exchanges are changing to crack down on some of the practices.
Quantitative finance is very complex math used to model and backtest data. It is used by hedge funds, long-term traders, and shops that analyze data to find securities that they believe are mispriced.
(This is obviously a highly simplified description of the field.)
But from what I'e read, the size of the derivatives market was approximately 5-6X global GDP in the mid 00's. Now, it's over 20X global GDP. If that market ever crashed, we'd be looking back at the crash of '08 as 'good times'.
Also, its not like if the market crashed, all of the value of the derivatives go to zero. Derivative holdings always have counterparties. If you make 100MM, someone else loses 100MM and vice versa. this isn't foolproof, as there is counterparty risk, but it makes the system a lot more stable than your statistics make it seem.
TLDR: The total risk of the derivative market is nowhere near 20X GDP.
"The brilliance behind Buffett’s investment in Berkshire is astounding. He effectively used (and uses) Berkshire as the world’s largest option writing house. The premiums and cash flow from his insurance business created dividends that he could invest in other businesses. But Buffett wasn’t just buying Coca-Cola and Geico as many have been led to believe. Buffett was placing some (short-term AND long-term) complex bets in derivatives markets, options markets, and bond markets."
They should be teaching kids the basics of this stuff in schools...
This is what happened with AIG, where people thought they were adequately hedged on their position because they bought a CDS with AIG, and then it turned out that AIG itself had written so many CDSs that it couldn't pay back its obligations, which exacerbated the entire financial crisis.
Remember, exchanges benefit from greater volume (more fees).
With regards to the quants being trashed, you will always need people who can rigorously describe a system (I do realize that rigor and accuracy are different). The quants are not going anywhere :)