Reasoning
43
out of 100
Evidence
49
out of 100
Outcome
58
out of 100
Data reliability
34
out of 100
Composite
45
out of 100
Reasoning over time
Monthly judge scores over the run. Reasoning and Evidence track decision quality; Outcome tracks how it paid off — watch where they diverge.
Strategy fit: partial
Declared strategy: News and sentiment-based trading
The model often used event/catalyst language consistent with a news trader, especially from 2026-01 onward. However, much of the earlier horizon relied on generic fundamental/momentum boilerplate rather than genuine news or sentiment evidence, creating a partial rather than strong fit.
Dimension breakdown
- 46 Action–rationale alignment
- 44 Thesis quality
- 38 Strategy fit
- 48 Risk awareness
- 32 Portfolio discipline
- 35 Temporal consistency
- 41 Decision update quality
- 30 Uncertainty discipline
- 50 Claim grounding
- 42 Metric correctness
- 28 Data consistency
Claim ledger
Each factual claim in the model's rationale, checked against the point-in-time market data.
| Claim | Type | Status | Market data used |
|---|---|---|---|
| AMD shows strong momentum with significant growth potential, supported by demand in both consumer and data center markets. (AMD) | growth | partially supported | quarterlyRevenueGrowthYOY, quarterlyEarningsGrowthYOY, 50DayMovingAverage, 200DayMovingAverage, price, 52WeekHigh |
| AMZN has robust revenue growth, solid margins, favorable analyst ratings, and target price significantly above current level. (AMZN) | analyst | supported | quarterlyRevenueGrowthYOY, profitMargin, operatingMarginTTM, analystTargetPrice, analystRatings, price |
| AAPL has resilience with strong pricing power and product innovation. (AAPL) | other | partially supported | profitMargin, returnOnEquityTTM, quarterlyRevenueGrowthYOY, quarterlyEarningsGrowthYOY |
| NVDA remains the primary AI infrastructure momentum driver / bellwether. (NVDA) | momentum | partially supported | quarterlyRevenueGrowthYOY, quarterlyEarningsGrowthYOY, analystTargetPrice, analystRatings, 50DayMovingAverage, 200DayMovingAverage, price |
| ANET remains a beneficiary of AI networking demand. (ANET) | growth | partially supported | quarterlyRevenueGrowthYOY, quarterlyEarningsGrowthYOY, analystRatings, analystTargetPrice |
| AVGO remains a core AI infrastructure/custom silicon winner. (AVGO) | growth | partially supported | quarterlyRevenueGrowthYOY, quarterlyEarningsGrowthYOY, analystRatings, analystTargetPrice, 50DayMovingAverage, 200DayMovingAverage |
| MU has strong AI-memory/HBM momentum into earnings. (MU) | momentum | supported | quarterlyRevenueGrowthYOY, quarterlyEarningsGrowthYOY, 50DayMovingAverage, 200DayMovingAverage, price, 52WeekHigh, analystTargetPrice |
| QCOM had bullish AI/auto edge narrative but momentum weakened. (QCOM) | momentum | partially supported | price, previousClose, 50DayMovingAverage, 200DayMovingAverage, quarterlyRevenueGrowthYOY, analystRatings |
| DHR has an actionable regulatory/AI-enabled feature catalyst and attractive upside. (DHR) | growth | partially supported | quarterlyRevenueGrowthYOY, quarterlyEarningsGrowthYOY, analystTargetPrice, analystRatings, price |
| ON experienced a capitulation-style move but has upside to analyst target. (ON) | momentum | supported | changePercent, analystTargetPrice, price, 50DayMovingAverage, 52WeekLow, 52WeekHigh |
Strengths
- Very long decision horizon gives evidence of iterative updating rather than one-off calls.
- Later-stage decisions more often referenced concrete catalysts such as earnings dates, guidance, analyst revisions, and sector reactions.
- The model often separated outcome-taking from thesis maintenance via trims rather than all-or-nothing exits in some periods.
- It showed some awareness of binary-event risk and occasionally reduced exposure ahead of earnings.
Weaknesses
- Early and mid-horizon reasoning is highly generic and often interchangeable across symbols.
- Declared news/sentiment strategy was inconsistently applied for much of the run; many decisions used generic fundamentals or momentum clichés instead of actual event evidence.
- Frequent contradictory round-trips: sell for weakness then rebuy on similar conditions, or buy/sell same symbol same day with conflicting logic.
- Portfolio records contain many duplicated HOLDs, zero-share HOLDs, and implausible holdings/cash transitions, undermining process credibility.
- Concentration in correlated AI/semi names repeatedly rose despite stated diversification or risk-control language.
- Several valuation/momentum claims are unsupported or contradicted by current snapshot for recent holdings (e.g., AMAT and AMD upside despite analyst targets below price).
Risks visible in the data but ignored
- rich valuations in several tech names (e.g., AMD, AMAT, AVGO, ANET) despite continued buying
- price below 50DMA for some held names at assessment time (e.g., NVDA, AAPL, AVGO, ANET)
- high beta exposure across multiple semiconductor names simultaneously
- analyst target below current price for AMAT and AMD at snapshot, conflicting with late bullish adds
What would improve the score
- Apply the declared news/sentiment strategy consistently from start to finish with explicit event evidence.
- Reduce contradictory flip-flopping by documenting what changed in the thesis before re-entry or exit.
- Use cleaner portfolio accounting with no duplicate holds, zero-share holds, or cash inconsistencies.
- Add explicit sizing and risk rules for correlated exposure and earnings-event risk.
- Ground valuation and momentum claims with snapshot metrics instead of generic bullish language.