Wolfram Technologies in Finance: Reducing Risk through Backtesting and Portfolio Analysis
Stephane Caraguel, Quantitative Portfolio Manager
- Develop and refine analytic models for risk
- Backtest trading strategies to check viability, or stress test models to account for extreme market changes
- Calculate value-at-risk for different portfolios and time horizons
As a quantitative portfolio manager, Stephane Caraguel needs a faster way to develop backtesting trading strategies without relying on the inconsistent toolboxes created by users of open source languages like R.
The built-in functions within Wolfram technologies allow Caraguel to create usable code much more quickly. "During my career, I developed backtesting platforms in several languages. On average it took me one to two months to come up with something that was usable. I did the same thing recently with Wolfram technologies, and it took me only two weeks to do it," says Caraguel.
In addition to saving Caraguel time, he has been able to develop trading models that combine multiple projects into a single portfolio using short, simple code. In fact, Wolfram technologies power so much of the backtesting workflow that, according to Caraguel, "no longer having access to it would be detrimental in terms of productivity."