How to apply Python to complex financial markets

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  • Per Eriksson, Senior executive, Enterprise risk and valuation solutions, Fincad
  • Gary Collier, Chief technology officer, Man Group Alpha Technology
  • Ronnie Shah, Director and head of US quantitative research and quantitative investment solutions, Deutsche Bank
  • Artur Sepp, Head of research, Quantica Capital
  • Moderator: Joel Clark, Contributing editor, Risk.net

The unprecedented proliferation of data in derivatives markets has led to a rise in popularity of Python, a multipurpose programming language known for its versatility and flexibility. Undoubtedly, the increased adoption of Python has helped enable greater collaboration and customisations for valuation, risk modelling and reporting.

Python may be more accessible and easier to learn, but it is not without its challenges. It is not as fast as other languages, making it unsuited to high-frequency trading and other speed-sensitive practices. Programmers must know how to implement the language properly to put it to best use, and upgrades or changes must be properly managed to avoid disruption.

Risk.net convened a panel of experts to discuss the application of Python within financial markets, the benefits it can bring to businesses and the challenges associated with adopting and extending its use.

Among the topics discussed were:

  • The advantages of Python for analysing and valuing derivatives
  • The scope of Python’s usage
  • The challenges, risks and limitations associated with implementing and using Python for data-intensive processes 
  • Whether certain processes are better suited to other programming languages
  • The best practices for maximising the benefits of Python while mitigating inherent risks and challenges
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