The Black formula is arguably the most important function in quantitative finance, and is either used directly (for pricing) or indirectly (for quotation), in most pricing calls. This article presents benchmarks for evaluating the formula.

Elementary functions are those that implement basic mathematical operations like exponentiation and logarithms. Swapping out numerically equivalent elementary operations is a cheap and easy way of improving runtime performance. This article presents benchmarks for three important elementary functions.

The SimianQuant library provides a Fluent API on top of its symbolic engine to implement functions with feedback. Technical indicators are a classic example of the usage of this pattern. This article presents the results of benchmarking the generated implementations (in C++) of common indicators.

A data parallel operation is one in which the same function is applied to different inputs. Vectorizing data parallel operations is an important problem with applications in market risk, full portfolio evaluation and numerical schemes. This article compares different approaches for vectorizing the Black Scholes formula for a call option.

The sequence of operations that convert input to output can be visualized as a graph. Graph compression is the family name for a suite of SimianQuant algorithms that rewrite the computational graph to reduce the time and/or memory required.