..so in these past months I have had the opportunity to realise that the paramount skill to have in order to pass quant developer (no ML) interviews, is a thorough knowledge of classic algorithms theory. Second place comes data structures, and how to implement them in your language of choice (
Python or
C++).
Quantitative finance concepts needed are the very basics, and what is necessary to go through
C++ finance books like Joshi's or Duffy's. For interview purposes however, these books are useful to the extent that they expose you to the kind of data structures and programming style you will be expected to know when interviewed.
Going back to our past discussions on here, about raw pointers in C++98/11, that's the subject of chapter 3 of Scott Meyers' Effective
C++, the first book. It's all explained in that chapter basically, the one on RAII resource managers (smart pointers, and custom types for resources that are not pointers).
The book has a lot of interesting tips and insights, however I have found it somewhat tough to digest, if while coding one has never encountered the difficulties that the book tries to address. I am guessing a lot of it bols down to knowledge of software design patterns and how and to which situation apply them.
I have noticed many of the changes introduced with the new C++11/14 standard were already available to users, by way of the TR1 library.
At this point, as I understand it, the main novelties introduced by the new standard can be boiled down to 4: the 'auto' types can be used when object is initialised, the 'move' mechanics (remind of auto_ptr behaviour?), lambda expressions to use as function objects in lists of parameters of <algorithm>s, and classes helping manage concurrency (that I cannot really take advantage of atm).
I also have the 'Effective Modern
C++' book on C++11/14 by the same author, which I guess will present the best 'design patterns', and in what specific situations it is useful to take advantage of these improvements in the language.
As weird as it could have sounded to me a few months ago, it would probably be more useful to learn some rudiments of
Python by completing online courses such as '
Python for data science' and the likes, than to go through Joshi's 'Concepts and Practice...' book.