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.
(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.