1. The vast majority of firms only hire people with CS or math degrees from top 10 schools, with an MS being the minimum. There are few exceptions.
2. Nobody is making as much money as they used to.
3. Depending on what firm you end up at you will probably spend a lot less time building cool strategies and a lot more time trying to shave off a few microseconds here and there.
4. Be prepared for a pretty rough work environment. Swearing, violent outbursts, people being secretive and not sharing ideas.
5. If you're lucky, management will be STEM people. If you're not, they will be former floor traders who have managed to stay alive this long.
Put concisely: if you can't handle seeing a smaller paycheck next month because a guy on your team pressed the wrong button and lost $50k in 10 seconds then trading is not for you.
If you want to get in I would recommend as much math and programming as possible. A PhD probably isn't necessary, an MS from a top school will do. You'd better be prepared to do programming, math, AND arithmetic in interviews, though. I once had a options trading interview that included ten "please multiply 148 by 72 in your head, we'll wait" questions.
Sadly this advice only applies to USA... E.g. UK has such an over-abundance of IT workforce that tech salaries are significantly below US despite comparable cost of living.
Source: I was offered a spot at CTC, wound up at a large bank. Maybe the better wording would be, "truly understand market making and market mechanics, and where improvements there could be valuable, and then see if algos could solve that problem." Algos have solved lots of problems but when you have a hammer everything looks like a nail. Technology was the limiting factor in markets for awhile, and right now the technology isn't the bottleneck, it's the politics and the people - see Bitcoin. But before you go full Marc Andressen, remember that finance is fundamentally about using the massive trust and resource allocation system within the human society, and so to think every valuable problem in the space can be solved by an algo may be dated thinking.
Let me state flat out that I don't think there are any good books about algorithmic trading all all.
Most will talk at surface level about what they are doing but non will give you a start to finish example that can be deployed with a brokerage like interactive brokers.
Trading & Exchanges I see was recommended. I'd skip it, most(All?) trading strategies that the average person will come up with won't be market micro structure related, and if it is then I'm going to flat out state that you've lost before you started.
If you really want to get into it, then please don't start with machine learning.
I've said this many times but ask your self:
- "what machine learning techniques could I apply that 100 fresh PHD's haven't done on their first week at a hedge fund?"
- "What data source do you have that the average hedge fund doesn't have access to"?
- "What market insight do you have that someone whose done this for years doesn't have?"
If after all that you still want to get started then honestly your best bet is to start with quantopian. Don't look at market data changes at a granularity of less than 1 day until you can create a strategy on your own that makes money.
Quantopian can give you access to a backtesting platform and clean market data, which is the step most people get stuck on, and usually quit at.
Once you've found a strategy that makes money, put your money into an account with Interactive brokers, If after 3 months you still want to continue then start looking at market data slices of less than one day.
- first step is don't
- second step is to focus on time slices of 1 day or more
- third, put your own money into action on you strategy for 3 months
- fourth step, there is a very small chance you'll make it this far, look at time slices of less than a day. At this point you can start to apply machine learning and build your own software. Even at this stage you are more likely to be an ATM for a hedge fund than you are to make money.
Feel free to reach out to me, personal email in profile if you'd like to chat.