Thanks for the suggestions! I might implement a couple in a future post. Numpy can handle a 10k by 10k matrix - in this case, you wouldn't want to print it to

Thanks for the suggestions! I might implement a couple in a future post. Numpy can handle a 10k by 10k matrix - in this case, you wouldn't want to print it to the spreadsheet, but you could still run the optimization. I've tested to 100 tickers and there isn't a noticeable increase in computation time. (I'm sure there would be at 10,000, but it's probably doable; the constraint here might be fetching that much data.) The optimization is done with a Nelder-Mead simplex algorithm [1], but SciPy has a lot of different functionality built in. There's a different function, fmin_bgfs[2], that does use BFGS optimization. Computations as a service is something we're definitely looking into; if there's enough demand we'll do it. [1] http://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method http://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method [2] http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_bfgs.html#scipy.optimize.fmin_bfgs http://docs.scipy.org/doc/scipy/reference/generated/scipy.op...