Financial Software Projects (C++) - NYU Fall 2011
cs.nyu.edu
cs.nyu.edu
(1) Join a quant fund, e.g. Renaissance, Two Sigma, Citadel, etc., or,
(2) Join a finance tech start-up, e.g. Palantir, Addepar, SecondMarket, Wealthfront, etc. who are liable to turn the industry over in the near future
Both options require you be more than a "developer" (in the Wall St sense). Coding is a means by which to express your edge in data analysis and algorithmics. A coder who knows some finance is a "dev". A dev who knows statistical methods is a "quant". A quant who can engineer new trades is a Master of the Universe (and one to put the phone monkeys to shame at that).
Note about the course books: this explains a lot about our devs. They have you implement VaR and other theoretically sophisticated programmes while giving a ram-jet flyover of finance that would leave you helpless with more the intricate articles in The Economist; it's the equivalent of learning CS theory with no coding experience or math through memorisation.
Note about note: I'm not criticising the course per se. It does a fine job of preparing a CS student for being a developer on Wall St. I'm targeting this more to the HN community, who I feel would be wasted talent in that role.
Wikipedia the time value of money, valuation of perpetuities and annuities, modern portfolio theory, mean-variance optimisation, and related topics. Really understand these. Also check out the efficient market hypothesis and behavioural economics. Doing this via Wikipedia is probably better than some pre-packaged finance textbook because it's hard.
Start with bonds: Fabozzi's Handbook of Fixed Income Securities.
For equities first try Pricing the Future - it gives a rare historical context to the Black-Scholes equation. I suppose reading McKinsey's Valuation is good for understanding cash flow valuation. Hull's Options, Futures, and Other Derivatives is the cornerstone piece of the field, followed closely by Paul Wilmott on Quantitative Finance. If you get volatility you understand the liquid equity markets.
Now you understand basic theoretical finance and the entire capital structure (Google that).
Final building block is global macro (not college macroeconomics - you'll need to grab a textbook for that). For this I don't know of a good book. Fortunately, the IMF puts out solid Article IVs, analysts and economists write stuff everywhere, and the Fed, World Bank, WEF, IMF, and a host of other acronyms publish enough data that you can play with to get your feet wet.
From there it literally involves typing things into Amazon, and failing at that, Google, and failing at that, LinkedIn. More Money Than God gives a nice history of hedge funds. The Quants is a fun read of the newer players. You can Wikipedia banks' histories and financial crises.
Go through material because you're curious, not to get through it. Follow your curiosity down branches.
The Quora community has done a lot of good at fleshing out these questions.
Reading questions within the topics of Trading, Quantitative Finance, and High-Frequency Trading should give you a decent understanding of the basics and provide some good reading materials for beginners.
Introduction to Python for Financial Data Analysis (90 minutes)
This class will introduce Quantitative Analysts to the Python environment for rapidly prototyping financial models. The objective is to demonstrate the research environment and introduce essential libraries. Attendees should be familiar with basic concepts of quantitative finance and data analysis, but experience using Python is optional (material will be accessible to beginners, but language basics will not be taught).
All I can say is classes always look better at paper than they do in real-life. I learned absolutely nothing of substance, and found it a waste of time. But YMMV.
They also offer a paper-trading account for testing purposes. They give you $1M in funny money to trade with to test your algos.
I'd recommend people read it for pleasure.
A lot of work in finance is also done in statistical programming languages like R. Some firms have also made big investment in vector processing / APL type languages like q (or k) and time series databases like kdb+.
Event processing / correlation platforms and programming is also gaining a lot of traction in finance. Progress Software's Apama platform, Streambase, and a bunch of other start ups are competing in this space as well.
Technology in finance is huge. It goes from building pricing models for derivatives and bonds (post under discussion), high frequency trading (FPGAs, event correlation,etc), to trade processing (high throughput transaction processing), to web app development (retail trading platforms, ebanking).... I could keep going on and on. It is wide field and if you are interested in working in technology in finance, it is safe to say you could find some nice in which you could use your skills. Obviously pay grades, job quality, etc. vary..
Sans hat tip.