What the agent
learned this week.
Every Monday, an autonomous Opus pass examines the fund's biggest winners, biggest losers, and the names we missed — extracting patterns to guide the next decisions.
The portfolio's biggest wins came from leaning into confirmed AI-infrastructure leaders breaking out to 52-week highs — Micron returned +79% and Palo Alto +56% on stable, high-conviction scores. Where we failed was on AMD: every signal screamed buy (price at 99.7% of 52-week range, direct AI-chip catalyst headlines, strong momentum) but our win-probability specialist was spooked by valuation and kept the score oscillating, so we never built a real position despite a +43% move. We correctly avoided AI-displacement losers like Workday and Five9, where the bear thesis was confirmed by collapsing scores and on-point disruption news. The system-level lesson: when thesis, catalyst, and momentum all align with a clean breakout, valuation-based caution should be capped rather than allowed to veto entry — and our winning holdings should be sized larger when their scores stay pinned in the high 90s.
Learning archive.
Our biggest wins (MU +51%, MRVL +19%, PANW +46%) shared a clean fingerprint: stocks breaking out to 52-week highs with direct, ticker-specific AI-infrastructure news (memory demand, optical interconnects, AI-agent security). Our losers (ETN, DLR) looked similar on the chart — also near 52-week highs — but lacked the direct catalyst and carried richer multiples, which the model belatedly recognized as scores collapsed from 80s to 40s. The most painful miss was Datadog (+72%), where the risk specialist's allergy to a 421x PE overrode an unambiguous 'observability for agentic AI' catalyst that mirrored the PANW setup we did capture. The system-level lesson: when a thematic news cluster is already producing a held winner, peer names in that cluster deserve an automatic second look rather than being filtered out by single-component vetoes like high PE or low momentum. We should also size our highest-conviction, persistently top-scoring names more aggressively — MU earned a 13-15% weight, not the standard book slot.
Our biggest wins came from direct AI silicon exposure bought at fresh 52-week highs — Micron returned 98% and Marvell 65% on memory and interconnect demand tied to named hyperscaler contracts. The system worked when momentum and a concrete catalyst lined up; it failed when we substituted data-center landlords (Digital Realty, Equinix) as cheaper AI proxies and got single-digit returns instead. The most painful miss was AMD, which rallied 95% while our win-probability specialist kept vetoing it on valuation despite a near-perfect technical and momentum profile — we even briefly bought it and flipped back to skip. The lesson is that in a thesis-confirming tape, valuation-based risk scores need a ceiling, and once a stock's daily score crosses 88 we should not whipsaw back to skip within three days. On sizing, our highest-conviction names earned their weight, but mid-conviction names where the score climbed sharply after entry (Broadcom, Marvell) suggest we should scale in as conviction confirms rather than fix size at day one.
Our biggest wins (Micron +98%, Marvell +56%) came from a tight, replicable formula: stocks breaking out to new 52-week highs while news headlines tied them directly to AI infrastructure spending. The system held these correctly even when traditional 'win probability' scores were only moderate. Our biggest blind spot was AMD, which delivered +105% — we sat out because high valuation triggered a veto, even though every other signal screamed buy. The lesson: when thesis, momentum, and breakout all align on a confirmed AI-demand story, valuation concerns are usually already priced in and shouldn't block entry. On the defensive side, the system correctly avoided three AI-displacement victims (Chegg, Five9, WPP), recognizing that being cheap is not protection when your business model is in the crosshairs. We need to loosen the valuation veto for confirmed-breakout names and tighten our skepticism on data-center holdings where the news flow is generic sector hype rather than company-specific catalysts.
Our biggest wins came from AI-semiconductor names — Micron, Marvell, and Broadcom — that were breaking out to fresh 52-week highs with concrete catalysts like Nvidia partnerships and memory-demand upgrades; we held them through 35-75% gains. The system also correctly avoided three AI-disruption victims (Chegg, Five9, WPP) where the news flow plainly predicted the businesses being eaten. Our clearest miss was AMD, which traded in lockstep with the chips we owned but was blocked by an over-cautious win-probability score reacting to valuation; a simple rule that floors win-probability when a peer in the same news cluster is already held would have caught it. The system-level lesson: high-quality, high-conviction names with weak near-term catalysts (META, Equinix, Digital Realty) are dead money in a momentum tape, and we should size them down rather than hold at full weight when catalyst_pct stays below 60.
How it works: each Monday the system identifies the past month's biggest winners (whether held or not) and biggest losers across the watchlist, then asks Opus to extract concrete, replicable patterns. The patterns feed back into the Portfolio Manager agent's prompt for the following week's decisions.