On Day 29, we conducted our out-of-sample test on the four strategies and found that the adjusted strategy came out on top. We made this conclusion after ranking a cross section of the following metrics: cumulative return, Sharpe Ratio, and max draw...

The moment of truth has arrived! On Day 28, we iterated through all the metrics we had previously used to identify and analyze the robustness of our strategy. We found the new adjusted strategy performed better than the original and adjusted strateg...

On Day 27, we had our strategy enhancement reveal. By modifying the arithmetic behind our error correction, we chiseled another 16% points of outperformance vs. buy-and-hold and the original 12-by-12 strategy. All that remains now is to run the pred...

On Day 26, we extended the comparative error analysis to the original, 12-by-12 strategy and showed how results were similar to the unadjusted strategy relative to the adjusted one. The main observation that emerged was that the adjusted strategy pe...

The last five days! On Day 25, we compared the peformance of the adjusted vs. unadjusted strategy for different prediction scenarios: true and false positives and negatives. For true positives and false negatives, the adjusted strategy performed bet...

On Day 24, we explained in detail how the error correction term led to somewhat unexpected outperformance relative to the original and unadjusted strategies. The reason? We hypothesized that it was due to the the error term adjusting the prediction ...

On Day 23 we dove into the deep end to understand why the error correction we used worked as well as it did. We showed how traditional machine learning uses loss functions and then hypothesized how our use helped improve predictions through its effe...

On Day 22 we saw a meaningful improvement in our strategy by waiting an additional week to quantify model error and then using that error term to adjust the prediction on the most recently completed week of data. What was even more dramatic was comp...

On Day 21, we wrung our hands with frustration over how to proceed. The results of our circular block sampling suggested we shouldn’t expect a whole lot of outperformance in our 12-by-12 model out-of-sample. To deal with this our choices were, back ...

On Day 20 we completed our analysis of the 12-by-12 strategy using circular block sampling on the 3 and 7 blocks. We found the strategy did not outperform buy-and-hold on average and its frequency of outperformance was modest – in the 28-31% range –...