Python for Finance: Analyze Big Financial Data

In the past, decision makers could rely on structured, regular planning, decision, and

(risk) management processes, whereas they today face the need to take care of these

functions in real time; several tasks that have been taken care of in the past via

overnight batch runs in the back office have now been moved to the front office and

are executed in real time.

Again, one can observe an interplay between advances in technology and

financial/business practice. On the one hand, there is the need to constantly improve

analytics approaches in terms of speed and capability by applying modern technologies.

On the other hand, advances on the technology side allow new analytics approaches that

were considered impossible (or infeasible due to budget constraints) a couple of years or

even months ago.

One major trend in the analytics space has been the utilization of parallel architectures on

the CPU (central processing unit) side and massively parallel architectures on the GPGPU

(general-purpose graphical processing units) side. Current GPGPUs often have more than

1,000 computing cores, making necessary a sometimes radical rethinking of what

parallelism might mean to different algorithms. What is still an obstacle in this regard is

that users generally have to learn new paradigms and techniques to harness the power of

such hardware.

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