Week 9: Final Tests and Insights

With my final round of testing underway, this week was about making sense of the numbers and preparing to turn raw results into meaningful conclusions. After refining my model and experimenting with grouped financial indicators last week, I now focused on seeing how these strategies held up across different market conditions and stocks.

One of my key tasks was applying the model to fresh stock data—real companies, current trends—to assess how well it generalizes beyond the historical training set. I continued tweaking groupings of features, like combining long-term trends with short-term volatility, in hopes of finding more consistent signals. While some groupings still introduced noise, others showed potential—especially in stable markets where momentum indicators played a bigger role. It’s become increasingly clear that context matters, and no one-size-fits-all feature set works perfectly.

I also began documenting my results in more structured ways: plotting comparative charts, calculating error rates, and evaluating performance against the S&P 500. These visual summaries are helping to spotlight key takeaways—both strengths and limitations—that will shape my final research paper. My external advisor offered valuable input this week on how to frame these findings effectively, encouraging me to look beyond accuracy and think about practical usability.

Next week, I’ll shift my full focus to final deliverables: polishing the paper, designing my presentation, and tying everything together.