Python for Finance: Analyze Big Financial Data

Conclusions

Nowadays, the Python ecosystem provides a number of ways to improve the performance

of code:

Paradigms

Some Python paradigms might be more performant than others, given a specific

problem.

Libraries

There is a wealth of libraries available for different types of problems, which often

lead to much higher performance given a problem that fits into the scope of the

library (e.g., numexpr).

Compiling

A number of powerful compiling solutions are available, including static (e.g.,

Cython) and dynamic ones (e.g., Numba).

Parallelization

Some Python libraries have built-in parallelization capabilities (e.g., numexpr), while

others allow us to harness the full power of multiple-core CPUs, whole clusters (e.g.,

IPython.parallel), or GPUs (e.g., NumbaPro).

A major benefit of the Python ecosystem is that all these approaches generally are easily

implementable, meaning that the additional effort included is generally quite low (even for

nonexperts). In other words, performance improvements often are low-hanging fruits

given the performance libraries available as of today.