Powered by Eveningstar AI
Methodology
Scott Capital uses concentrated, multi-asset research with scenario analysis and AI-assisted signal synthesis. The methodology is built to keep evidence, uncertainty, and human judgment visible.
The public research system starts with a small number of visible positions and asks what matters most for each one. The goal is not to produce constant trading signals; it is to organize thesis evidence, risk, exposure, and scenario framing in a way that can be reviewed calmly.
Inputs may include market prices, technical indicators, portfolio exposure data, research notes, volatility and risk context, and anomaly signals. Inputs are treated as evidence, not as automatic conclusions.
Signals are grouped by trend, momentum, volatility, anomaly, technical level, thesis or risk checkpoint, and macro or portfolio context. A useful signal can strengthen a thesis, weaken a thesis, or simply mark an area where the evidence is conflicted.
Bear, base, and bull scenarios are research tools, not forecasts. They help separate downside risks, central assumptions, and upside optionality so a position can be evaluated under more than one market path.
Eveningstar AI helps synthesize and organize evidence across market data, position notes, risk drivers, anomaly checks, and scenario language. It supports research judgment; it does not replace it.
Final published research language and portfolio judgment are human-reviewed. AI-generated summaries are treated as workflow inputs that require review for accuracy, uncertainty, tone, and investment-advice risk before publication.
Live feeds may be unavailable, snapshots may be delayed, and market context can change quickly. Each module should display the latest successful update when possible, and stale or unavailable information should be labeled rather than hidden behind fragile loading states.
The process has meaningful limits: model uncertainty, market risk, stale data, false positives, false negatives, incomplete context, and the possibility that a scenario framing is wrong. Nothing on this site should be treated as personalized investment advice.
Signal Taxonomy
Directional evidence across price, exposure, or thesis progress.
Whether recent behavior is strengthening, fading, or becoming unstable.
Risk context that can change position sizing, scenario emphasis, or review urgency.
Unexpected moves, conflicting evidence, or regime behavior that deserves human attention.
Support, resistance, range, and invalidation areas used as research context.
Position-specific evidence that can confirm, challenge, or reframe the working thesis.
Rate, liquidity, risk appetite, and cross-asset conditions that may affect the book.
Concentration, sleeve exposure, correlation, cash reserve, and risk-bucket implications.
Freshness and Labels
The module is showing a recently successful update and should include a visible timestamp when available.
The module is usable, but the displayed data may trail the latest market state.
The module is showing the latest saved research state rather than a live feed.
The feed or input is unavailable, while the broader research view remains accessible.
Methodology descriptions are provided for transparency into the research process. They are not a promise of performance, not a recommendation, and not an offer to buy or sell any security, token, fund interest, or advisory service.