Taibo (and Thomas),
I suggest adding a test for goodness-of-fit (see here for an example). Then, one could examine (ideally plot) the goodness-of-fit parameter(s) versus time to get a sense if the strength of the model varies with time. Also, statistics can be used dynamically to select the best model versus time...over some time periods, perhaps only a straight-line fit (or constant) is justified by the statistics, but over other time periods, a quadratic model might be justified. The statistics can be used to pick the order of the general polynomial model.
Also, you might try running the algorithm on SPY to see if you can outperform the Quantopian S&P 500 benchmark with the same or lower risk.