This page provides a high-level explanation of how StocKnoWhere organizes research inputs. It is intended to explain product behavior in plain language rather than disclose proprietary implementation details.
The workflow is intentionally structured around review priority. A signal, score, or AI narrative should start a research check, not end it. Users should inspect the chart, data context, and visible drivers before forming a view.
The Research Lab presents common chart studies such as VWAP, moving averages, and MACD so a user can compare short-term price action against broader trend and momentum context. The goal is to reduce mode-switching between separate tools.
Bias readouts summarize multiple study inputs into a compact directional view. These readouts are meant to be quick orientation aids, not substitutes for the underlying chart. Users should verify whether price structure, study alignment, and time frame choice support the summary.
Anomaly workflows are designed to flag unusual conditions based on configured thresholds, cross-checks, and the data available at scan time. Scores and labels are heuristic outputs. They indicate review priority, not certainty.
A higher score means the setup is more unusual under the platform's current logic. It does not mean the setup is automatically bullish, bearish, safe, or profitable. Driver details should be reviewed before interpreting the score.
Where possible, the platform exposes drivers or explanations behind the score so a user can understand why a symbol or portfolio is being highlighted. This is intended to make the workflow more auditable than a single opaque number.
AI summaries and briefings convert the visible workflow context into concise written text. These summaries can compress research time, but they may omit nuance or reflect limitations in the underlying data. The correct use is to treat them as a starting point for review.
AI output is generated from available context and should be checked against the underlying data. It may be incomplete, stale, or overly confident if the input context is incomplete. The platform includes responsible-use language because summaries are research aids, not personalized investment recommendations.
Portfolio tools interpret saved positions together instead of viewing each ticker in isolation. That enables checks around concentration, repeated exposure, and portfolio-level anomaly patterns that may not be obvious from a single-chart workflow.
Research outputs depend on market data availability, external APIs, processing timing, and provider reliability. A missing update, delayed refresh, provider outage, or changed data field can affect what the platform displays. Users should treat timestamps, stale-data warnings, and missing fields as part of the research context.
None of the above constitutes investment advice, a recommendation, or a guarantee of market outcomes. All outputs depend on data availability, configuration choices, and user interpretation.
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