Not done is nothing taught me a lesson. Publicly saying "I didn't complete this because I lost interest" doesn't feel good and provides motivation to finish. So I've returned to the Kelly Criterion explanation I was working on, added a little bit, and provided return calculator that optimizes.
Next I need to add some linear algebra, some multi-variable calculus, then implement something that finds maxima using Newton's method on a convex polytope defined in any number of dimensions using a set of linear inequalities. You can think of this as a non-linear version of the problem solved by the Simplex Algorithm. The fun part is that I need it to good enough to find the optimum point in an n-dimensional polytope sitting in n+1-dimensional space. (The actual polytope I will need is defined by 0 ≤ xi &le 1 for i=0..n, and x0 + ... + xn = 1.) That means I have to deal with issues like finding my way back to the region after round-off error takes me away from it.