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, insurance, asset management, fund management, foreign 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 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
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 York, Nobel 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.