(I'm not in a position to hire so sorry to digress but) Are there any good texts you can recommend for quantitative finance, 'for engineers' sort of thing? I studied engineering (EE / CS) and am interested (in a non-work sense) in finance / economics, but haven't read anything that bridges the gap and don't know where to start.
I suppose the problem really is that I don't know, just that it interests me and that the field I do know more about does actually have quite an overlap, but pretty much none of my knowledge is in the intersection, st least as far as I know.
On the financial side I've only ever read things like the Economist, FT, consumer investment stuff. I'm keen to try a more quantitative/engineering textbook, but I'm naïve even of the right search terms for an entry point.
Then I can mention some recommendations (in no particular order):
* https://www.goodreads.com/en/book/show/7346602-algorithmic-t...
Really dry (and thick) book, but it's comprehensive in its treatment of execution algos. This was written by a guy who worked on the sellside, so this is written in the context of receiving large orders from buyside clients and trying to execute them efficiently and with minimal market impact. There's an overview here: http://www.algo-dma.com/2007/12/overview.html
Main criticism is that it's kind of a dated book, but the fundamentals (how to benchmark, dealing with multiple venues) are still relevant.
* Robert Carver's books and blog: https://qoppac.blogspot.com/p/about-me.html
His background is a buyside algo trader whos' now retired. Mostly focuses on futures, still trades on his own account. I'd say his books are entry-level, but they have one big useful rule - keep your system simple and be paranoid about overfitting your parameters.
* AQR's Asness writes some interesting stuff:
https://www.aqr.com/Insights/Perspectives
The main caveat there is that he has a vested interest in pushing for smart beta, as that is AQR's bread-and-butter. Still, clever guy making interesting points.
For relaxed (and arguably information-free) reading, you can look at:
Flash Boys - Main criticism here is that the book is arguably PR for the IEX, so they paint HFTs in an almost exclusively negative light. So take that with a big grain of salt. Still, nice book to get some context about the HFT world.
More Money Than God - super naff title, but ok read; covers the stories of various successful fund managers, especially in the algo space.
As a final thought, I'd say, the quant space is really opaque (and still changing fast even today) and largely misunderstood by outsiders. I find that academics, reporters and pop-culture authors usually get things quite wrong. Insiders, like Asness and Carver know more, but sometimes have vested interests in selling you something. There are a lot of books on Amazon about "systematic investing" and most of them are thin on substance or, even worse, just wrong.
Quantitative finance is a field that takes from many different topics and combines them, so one way to start instead of studying the field directly it helps to learn a handful of different subjects slowly over time.
Most people start with basic programming and basic stock market indicators. Many people start by hacking together some python scripts using some framework and then backtesting to see if their assumptions are correct.
On the more advanced level there is C++, CUDA, and Rust, and similar tech that needs to be fast. When developing a custom algo for an NP problem, you need a lot of speed. On this front, auditing MIT's old Artificial Intelligence class might be a good place to start, which goes over how to optimize NP algorithms and builds up the thought process of how to automate difficult problems. https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP...
Writing a bot that plays the stock market when boiled down is either 1) pattern matching the market, having a hypothesis, and then writing some code to test that hypothesis or 2) writing a machine that pattern matches for you, finding meta-patterns of sorts. Is closer to Data Science and machine learning.
Quantitative finance can be as simple as what is often done in Data Science work. That is, hacking together some python scripts and testing your assumption, or it can be as complex as being an expert C++ programmer and building up a custom system, combining Software Engineering and Data Science.
Furthermore, in the DS world there is a lot of research at places like https://arxiv.org/ like an existing path of half-solutions that can be applied, tested, and verified. In the Quant world, information is private, so you don't get that luxury. Likewise, most models in the DS world don't work in the Quant world, for at least two reasons: 1) Most machine learning today isn't timeseries based. Eg, a self driving car may frame by frame identify what is in each picture, but it doesn't do a good job retaining data in the larger scene making it difficult to use standard machine learning to solve problems in the financial market. and 2) Most machine learning algos are based on floating point numbers and approximations. When dealing with money you need perfect precision, so even if there is the perfect machine learning library, you might end up writing your own version.
Eh, I'm probably rambling a bit too much here. Either way, quant finance is tons of fun! Also, I've only done projects independently, so I have no idea how different my approaches are to the industry.
Also, another field that might help is DSP, though I admit it's something I don't know enough about, so I can't say with absolute certainty how much it would help.
And of course, there is understanding how the market works itself. This often involves getting your feet wet, usually by paper trading. For many this means learning option trading.
