As someone who was a trader on a derivatives prop desk I would suggest those interested in finance with solid coding skills skip being a 20-year old's peon and:
(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.
Make sure to investigate the firm and, in particular, their enforcement of non-competes before you join to ensure you are comfortable with their terms. One of the three in your bullet (1) has a long history of enforcing (and backing up with Full Legal Might) an 18-month non-compete on even somewhat competent developers and traders.
Or an effective lifetime non-compete if you're talking about Renaissance. Then again, their flagship fund, which is now only open to employees, regularly beats 50% annualised returns after fees.
Such a person might be interested in attending a class Wes McKinney (http://pandas.pydata.org) will be teaching on April 16 in NYC.
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).
Hmm...challenging question since the topic it's asking about, finance, is so broad.
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.
I'll second that Quora recommendation. There is a great community there of quants, general hf engineers, MFE students, and others looking to get into the field.
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.
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.
Oh man, I wish this was available when I was in school. This field has always interested me, but it seems that the bar of entry is super high (trading account).
An Interactive Brokers trading account with a minimum 10K USD deposit will get you access to their trading API. If you can deposit 25K, you'll be OK with pattern day-trader status and gain 4x leverage for your account.
They also offer a paper-trading account for testing purposes. They give you $1M in funny money to trade with to test your algos.
Even cheaper, if you want to trade futures, the margin requirements are even lower, $500-1000 minimum, and you can use Ninjatrader as your trading platform for certain brokerages. Ninjatrader has a pretty good C# interface to trade with. You have to pay for Ninjatrader if you want to trade, but you can sim trade and write your own trading strategies and backtest for free until you're ready to trade for real.
For everyone lamenting not being able to take this class. Taking the class is the least important aspect. Going to college or equivalent and learning and practicing how to think hard are all you need classes for. Anyone who has completed a degree in any hard science /math /engineering can learn this material from the books and websites and forums.
This is actually one of the hottest fields in C/C++ software development. Either high frequency trading or machine learning based transactions are in very high demand. I wish I could take this class.
You may want to check out the forums at wilmott.com or the forums at nuclearphynance.com
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..
(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.