This is a collection of R notebooks and Stan code showing the start-to-finish process of quantitative analysis on the buy-side to produce a forecasting model. The code demonstrates the use of Gaussian processes in a dynamic linear regression. More generally, Gaussian processes can be used in nonlinear regressions in which the relationship between xs and ys is assumed to vary smoothly with respect to the values of the xs. We will assume that the relationship varies smoothly with respect to time, but is static across values of xs within a given time. Another use of Gaussian processes is as a nonlinear regression technique, so that the relationship between x and y varies smoothly with respect to the values of xs, sort of like a continuous version of random forest regressions. I used Syracuse University’s High Performance Computing lab for the backtesting, and they wrote up a short interview with me here.

“Full Documentation”