Advice on learning path for the book Quantitative Trading For a Quantitative Finance club in my university, we are to read the book Quantitative Trading by Guo, Lai, Shek, and Wong; a very popular interdisciplinary book combining financial mathematics and computer science. Admittedly though, my understanding of prerequisite knowledge for the book (statistics as it relates to finance, Martingale Theory, Probability Theory etc) is rather lacking. For those of you who are reading the book now or have already completed a course using this book, would you be able to offer me advice on what books I should read to prep for this one, and what subjects I should make myself knowledgeable about?
 A: I admittedly haven't read this book but here is a list of references for Quant trading that I think are useful.
Arbitrage Theory in Continuous Time by Bjork is a very good reference for quant finance and if you want to dig deeper into the mathematics, I really like Stochastic Calculus for Finance 1 and 2 by Shreve.
It may also be useful to have a good background in statistics in general, I'd recommend Statistical Inference by Casella and Berger.
Quant roles are exceedingly expecting knowledge of machine learning/predictive modelling, for that I would recommend any of ESL, Murphy or Bishop.
Depending on your background, I'd also recommend doing lots of programming, for Quant trading it often useful to have a mix of python for prototyping, and C++ for production. 
Finally, a solid background in design and analysis of algorithms is crucial, CLRS is really good, and MIT has the following courses free online (with lectures recorded for the first two), in increasing level of difficulty: 6.006, 6.046, 6.854J
