What changed during the research
1. Astra retains its lead as the research budget grows
Across all completed refinement runs, Astra's mean gain over the common seed is 23.20 percentage points at sixteen evaluations and 26.32 at sixty-four. Sonnet reaches 21.90 at sixty-four. Astra has the highest observed mean at sixteen, thirty-two and sixty-four evaluations; uncertainty remains too large for a definitive ordering.
Why it matters: an effective researcher must both find useful improvements and retain them while exploring further. Total performance counts all progress from the same starting strategy, so an early breakthrough remains part of the result.
2. One improvement came from trading the idea less aggressively
In Astra’s first clean refinement run, the researcher had combined price momentum with short-interest information. It then tested a specific hypothesis: daily changes in the combined signal could create costly trading noise. The next submission kept the signal construction and changed only its smoothing weight.
Before
Use 100% of today’s computed signal.
After
Use 10% of today’s signal and retain 90% of the previous smoothed value.
| Recorded outcome | Before · evaluation 10 | After · evaluation 11 |
|---|
| Net profit | $93 | $817 |
| Gross profit | $869 | $1,097 |
| Traded notional | $1,037,938 | $328,907 |
| Declared trading costs | $776 | $280 |
Traded notional fell by 68%. Net profit rose from $93 to $817 on the fixed $10,000 development book. Both higher gross profit and lower costs contributed. The hypothesis was recorded before the test, and the parent–child code comparison confirms the isolated smoothing change.
Why it matters: the researcher did more than search for a stronger predictor. It tested how an existing signal should be expressed over time. That is a concrete example of research on implementation and trading economics.
3. More complex models were not always the useful next step
In Astra’s first origination run, an early short-interest signal produced $171 net. Three subsequent public-trained tree-model submissions lost money after costs in development. The researcher returned to a simpler momentum signal, then combined it with short-interest, short-volume and insider-purchase information. The final submission reached $451 net.
Why it matters: the useful behavior was not a straight line of successful guesses. The history records an unsuccessful branch of research followed by a change of direction. The best-so-far curve hides that detour; the submitted strategies reveal it.
4. Some breakthroughs arrived late—and independent searches overlapped
In one Terra refinement run, the best result after ten evaluations was still a $1,263 loss. The first profitable submission arrived at evaluation 13, at $111 net, after switching to a signal based on short-interest days-to-cover. Stopping at ten tests would have missed that outcome.
Separately, Astra and Luna independently originated signals favoring stocks with lower days-to-cover, a measure of short-selling positions relative to trading volume. They used different formulas but both recorded $171 net. This suggests overlapping discoveries in this task; it does not prove identical portfolios or a general investment effect.
Why it matters: research needs enough room to change direction, but additional researchers do not automatically produce different ideas. Those observations motivate testing longer horizons and, later, whether sharing findings improves search efficiency.
The code examples are selected after observing the runs. Each describes one adaptive trajectory or comparison; independent 95% confidence intervals are not estimable for these individual changes. Aggregate means and intervals remain the primary model comparison. Full trajectories, hypotheses and source code ↗