Programming for Quantitative Developers

Almost every quantitative developer application starts the same way: an auto-graded online assessment, two to four problems, sixty to a hundred and twenty minutes, on HackerRank or Codility or CodeSignal. Nobody reads your code at that stage. A test harness runs it against inputs you never see and returns a number.

That format decides what is worth practising, and it is not what a generic algorithms course teaches. It rewards a solution that is correct on the empty input and the single-element input, that holds its complexity bound when the input is a hundred times larger than the example, and that can be written in twenty minutes rather than sketched in an hour. Later rounds invert the emphasis completely: a human reads the same code and asks what happens when a requirement changes.

This course covers the Python side of that pipeline. It opens with what the hiring funnel actually looks like at the top trading firms, including which of them ban Python outright, then works through the material the assessments keep returning to: the data structures you are expected to implement rather than import, the parsing and data-hygiene problems that generic platforms never set, algorithms chosen against an explicit complexity bound, the numerical Python that survives a review, and finally order books and matching, which is the one domain problem these firms ask and nobody else does.

Its companion, Systems Programming for Trading, covers the C++ half: internals, concurrency, and compile-time programming. Start here if you are preparing for an assessment in the next month, and there if the role says C++ on the hot path.

Every lesson ends with the problems from our coding catalogue that drill it, so the theory and the keyboard stay in the same session.