Julia Featured In Wilmott Magazine
Automatic for the Greeks
Fast and accurate price sensitivities with Automatic Differentiation (AD) using Julia, a dynamic language whose features make the use of AD both straightforward and highly performant.
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With JuliaFin, front office quants can now deploy in production what they develop. In the fast moving world of finance, Julia enables the complete quantitative workflow - algorithm design, data feeds, backtesting new strategies, risk management - all of which can be driven from Microsoft Excel.
Tim Thornham
Solvency II compliant models in Julia
David Weiss
Large Scale Stochastic Simulation Using Julia
Erica Moszkowski
Federal Reserve Bank of New York
Julia at NY Fed
Aman Thind
Best X
Powering Financial Analytics

JuliaFin was developed for finance users including investment banks, hedge funds, insurers, asset managers, risk managers, analysts and traders in stocks, bonds, commodities, foreign exchange, options, derivatives and more. It can be installed on your enterprise server, your private cloud, the public cloud (e.g. Amazon Web Services, Microsoft Azure, Google Cloud), or on individual desktops and laptops.

Integration with Excel and Bloomberg
Miletus, a domain specific language for defining and executing financial contracts and trading strategies
Available with Amazon Web Services, Microsoft Azure and Jupyter Notebook
Time series analytics
who is making the move to julia?
From FinTech to RegTech, algorithmic traders, fund managers, macroeconomic modelers, analysts and regulators are flocking to Julia. Among others: Nobel prize winner Thomas J. Sargent, the Federal Reserve Bank of New York, the Bank of England, the Brazilian Development Bank (BNDES), BlackRock (the world’s largest asset manager), and many of the world’s largest insurers, fund managers, hedge funds and foreign exchange analysts.
julia users, partners and employers hiring julia programmers
Why are they choosing Julia?

It’s simple: Speed + Performance + Scalability + Ease of Use.

In the private sector, BlackRock, the world’s largest asset manager, is using Julia to upgrade its analytics capabilities.

Nobel prize winner Thomas J. Sargent says Julia is critical for his work because the next generation of macroeconomic models is very computationally intensive with large datasets and large numbers of variables. These macroeconomic models and their forecasts help solve large constrained optimization problems using massive datasets to inform policy analysis.

Economists at the Federal Reserve Bank of New York are porting their Dynamic Stochastic General Equilibrium (DSGE) models to Julia (DSGE.jl by Erica Moszkowski, Micah Smith, Pearl Li et. al.). They said they chose Julia because, “as the models that we use for forecasting and policy analysis grow more complicated, we need a language that can perform computations at high speed. Julia boasts performance as fast as that of languages like C or Fortran, and is still simple to learn. We want to address hard questions with our models—from understanding financial markets developments to modeling households’ heterogeneity—and we can do so only if we are close to the frontier of programming.”

They reported a 10x increase in the speed of model estimation, a 6x increase in speed for another algorithm and an 11x speed increase for the FRBNY’s ‘solve’ test - which is crucial because this test is run hundreds of thousands of times.

Aviva, one of the world’s largest insurers, is using Julia to comply with the European Union’s Solvency II regime and reported speed increases from 20x upto 1000x compared to their existing implementations. Furthermore, they reduced the code from 14,000 lines of a proprietary analytics language to 1,000 lines in Julia. This doesn’t just increase speed, efficiency and productivity - it also reduces errors and time spent checking and debugging code.

How does Julia improve on legacy systems?
  • Tight loops and easy parallelism for Monte Carlo simulations
  • Increase speed up to 1,000x
  • Eliminate the need for different languages for prototyping (e.g. Python, R) and deployment (e.g. C, C++)
  • Use a single language for model estimation and deployment
  • Shorter, tighter simpler code - more efficient, easier to check and debug
  • Ease of use - simple to learn, simple to program
  • Faster time to market for new models, and faster updates and upgrades which provide an important advantage in a highly competitive marketplace
  • Big data - as the number of variables, complexity and computational intensity increases, Julia - provides critical improvements in capacity and productivity
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Julia Computing, Inc. was founded with a mission to make Julia easy to use, easy to deploy and easy to scale. We operate out of Boston, New York, London, Bangalore, San Francisco, Los Angeles and Washington DC and we serve customers worldwide.
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