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Wilmott - Why Julia Matters for Computational Finance

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Wilmott - Why Julia Matters for Computational Finance

Wilmott - Why Julia Matters for Computational Finance

Wilmott - Why Julia Matters for Computational Finance

Date Published

Feb 9, 2017

Feb 9, 2017

Contributors

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Date Published

Feb 9, 2017

Contributors

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In the November 2016 issue of Wilmott Magazine, Julia Computing’s Viral Shah and Simon Byrne explain why Julia is taking the field of quantitative finance by storm.

Why are so many quants from investment banking, insuranceasset managementfund managementforeign exchange analytics, commodity trading, energy trading, central banking and risk analysis switching to Julia?

Finance quants find Julia the optimal finance solution for a number of reasons:

  • Julia is the fastest modern language for financial, mathematical, statistical and scientific computing

  • Julia delivers lightning fast speed – speed improvements up to 11x for macroeconomic modeling, 225x for parallel supercomputing and 1,000x for insurance risk model estimation

  • Julia is the only modern financial, mathematical, statistical or scientific language that can handle massive datasets updated in real time, such as financial tick data

  • Julia runs on your desktop, laptop, enterprise server, private or public cloud

  • Julia is optimized for supercomputers with accelerators and parallel computing

  • JuliaFin is fully integrated with Excel and Bloomberg

  • JuliaFin includes Miletus, a custom finance package to design and execute real-time trading strategies

  • Julia is easy to learn with flexible syntax that is familiar to users of Python, R and Matlab

  • Julia integrates well with existing code and platforms

  • Julia code is elegant – advanced libraries make coding simple and reduce the number of lines of code – in some cases, by 90% or more - resulting in a solution that is faster, easier to code, analyze and debug

  • Julia solves the two language problem – because Julia combines the ease of use and familiar syntax of Python, R, Matlab, or Stata with the speed of C, C++ or Java, programmers no longer need to estimate models in one language and reproduce them in a faster production language. With Julia, these steps can be performed in a single high-level, high-capacity, high-speed environment.

No wonder users such as BlackRock, the Federal Reserve Bank of New YorkNobel Laureate Thomas J. Sargent, and the world’s largest investment banks, insurers, risk managers, fund managers, asset managers, foreign exchange analysts, energy traders, commodity traders and others are switching to Julia.

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Authors

JuliaHub, formerly Julia Computing, was founded in 2015 by the four co-creators of Julia (Dr. Viral Shah, Prof. Alan Edelman, Dr. Jeff Bezanson and Stefan Karpinski) together with Deepak Vinchhi and Keno Fischer. Julia is the fastest and easiest high productivity language for scientific computing. Julia is used by over 10,000 companies and over 1,500 universities. Julia’s creators won the prestigious James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.

Authors

JuliaHub, formerly Julia Computing, was founded in 2015 by the four co-creators of Julia (Dr. Viral Shah, Prof. Alan Edelman, Dr. Jeff Bezanson and Stefan Karpinski) together with Deepak Vinchhi and Keno Fischer. Julia is the fastest and easiest high productivity language for scientific computing. Julia is used by over 10,000 companies and over 1,500 universities. Julia’s creators won the prestigious James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.

Authors

JuliaHub, formerly Julia Computing, was founded in 2015 by the four co-creators of Julia (Dr. Viral Shah, Prof. Alan Edelman, Dr. Jeff Bezanson and Stefan Karpinski) together with Deepak Vinchhi and Keno Fischer. Julia is the fastest and easiest high productivity language for scientific computing. Julia is used by over 10,000 companies and over 1,500 universities. Julia’s creators won the prestigious James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.

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