*Open Source for Quantitative Finance*
Open source software (like Python, R and Julia) is revolutionizing how quantitative financial workflows and applications are developed and deployed. Not only that times-to-insights and IT costs are significantly decreased, productivity of quants increases substantially as well. The right use of open source software therefore represents a measurable competitive advantage.
The Python Quants (http://tpq.io) focus on making the best of open source for quantitative finance. To this end, we have developed the *Quant Platform* (http://pqp.io) which allows for the most efficient, scalable and flexible deployment of modern analytics technologies across whole organizations and across regions. The platform makes collaboration on projects within teams easy, facilitates code sharing and also instant financial application roll-outs. It can be used on any kind of infrastructure, from the smallest cloud instance to the biggest servers.
With our open source library *DX Analytics* (http://dx-analytics) we bring global valuation and modern risk management approaches to the front office and illustrate the power of modern analytics technologies. Value and risk manage in near-time option portfolios with 1,000+ positions and 100+ underlyings on a single standard compute node while maintaining scalability to even much larger derivatives books.
In addition to our technology and books, we also provide *consulting, development and support services* as well as standard and customized *trainings*.
Why Python for Finance and Quants?
10 years ago, Python was considered exotic in Finance – at best. Today, Python has become a major force in Finance due to a number of characteristics:
Syntax: Python syntax is pretty close to the symbolic language used in mathematical finance (also: symbolic Python with SymPy)
Multi-purpose: prototyping, development, production, sytems administration – Python is one for all
Libraries: there is a library for almost any task or problem you face
Efficiency: Python speeds up all IT development tasks financial institutions typically face and reduces maintenance costs
Further benefits of using Python in a Quant context ares:
Performance: Python has evolved from a scripting language to a 'meta' language with bridges to all high performance environments (e.g. LLVM, multi-core CPUs, GPUs, clusters)
Interoperability: Python seamlessly integrates with almost any other language and technology
Interactivity: Python allows domain experts to get closer to their business and financial data pools and to do interactive real-time analytics
Collaboration: tools like Python Quant Platform (http://quant-platform.com) allow developers and financial data scientists to collaborate across companies and business departments, to share code, data and their analytics insights easily
PYTHON QUANT PLATFORM
dx python dx_example.py