The unprecedented proliferation of data in derivatives markets has led to the rise in popularity of Python, a multi-purpose programming language known for its versatility and flexibility. Undoubtedly, the increased adoption of Python has helped enable greater collaboration and customisations for valuation and risk modelling and reporting. But, while Python may now be more accessible and easier to learn, it is not without its challenges.
In this on-demand webinar held by Risk.net, expert panellists discuss the application of Python within financial markets, the benefits it can bring to businesses and the challenges associated with adopting the language and extending its use more broadly than in the past.
Questions covered include:
- What are the advantages of using Python to analyse and value derivatives?
- How widespread is the use of Python?
- What are the challenges, risks and limitations associated with implementing and using Python for data-intensive processes?
- Are certain processes likely to be better suited to other programming languages?
- What are the best practices to maximise the benefits of Python while mitigating inherent risks and challenges associated with adopting the language and extending its use?
- Joel Clark, Contributing Editor, Risk.net
- Per Eriksson, Senior Executive, Enterprise Risk & Valuation Solutions, FINCAD
- Gary Collier, CTO, Man Group Alpha Technology
- Dr. Ronnie Shah, Director and Head of US Quantitative Research and Quantitative Investment Solutions, Deutsche Bank
- Artur Sepp, Head of Research, Quantica Capital AG