Contents
Why observation discipline matters
Most financial systems fail before reasoning begins. They treat raw inputs, headlines, model outputs, sentiment scores, and operator impressions as equivalent. They ingest without structure, attribute without rigor, and reason without knowing what kind of information they are consuming.
An observation is not a fact until it can be identified, attributed, time-bounded, and truth-state labeled. If an observation cannot meet this standard, it should not be allowed to influence downstream reasoning. Garbage in is not a data problem. It is an architecture problem.
This standard defines the minimum viable record for a market observation. It is the intake valve of the intelligence system. If it is loose, everything downstream is contaminated.
Required fields for a valid observation
Every observation record must contain the following fields before it can be admitted into the reasoning pipeline.
Observation ID
RequiredMeaning: A unique, immutable identifier for this observation record.
Why it matters: Without identity, observations cannot be traced, audited, or referenced in downstream reasoning.
Timestamp Observed
RequiredMeaning: The moment the observation was captured by the ingestion pipeline.
Why it matters: Freshness is relative to capture time. Stale data cannot be detected without an origin timestamp.
Source Type
RequiredMeaning: The category of origin: exchange feed, regulatory filing, news wire, transcript, alternative data, model output, operator entry.
Why it matters: Different source types carry different reliability profiles and must be handled by different governance rules.
Source Attribution
RequiredMeaning: The specific origin: Bloomberg ticker feed, SEC EDGAR filing 10-K, Reuters headline ID, earnings call transcript vendor, model version, operator name.
Why it matters: An observation without attribution is an orphan. It cannot be verified, disputed, or retracted.
Instrument / Entity
RequiredMeaning: The security, company, market, or entity the observation refers to, using a normalized identifier.
Why it matters: Ambiguous reference creates mapping errors and correlation failures.
Market Domain
RequiredMeaning: The domain: equity, fixed income, FX, commodity, derivative, macro, credit, crypto, private.
Why it matters: Domain determines the relevant regulatory boundaries, liquidity assumptions, and risk models.
Observation Type
RequiredMeaning: The class of observation: price, liquidity, event, filing, narrative, macro, positioning, system-health.
Why it matters: Type determines the appropriate truth-state, freshness window, and allowed downstream usage.
Raw Claim
RequiredMeaning: The unaltered original text, number, or signal as received from the source.
Why it matters: Raw claim preserves evidence. Any subsequent interpretation must be traceable back to the original input.
Structured Summary
RequiredMeaning: A normalized, schema-compliant extraction of the raw claim for machine consumption.
Why it matters: Downstream systems consume structured data. The summary must be reversible to the raw claim.
Truth-State
RequiredMeaning: The epistemic label: Live, Stale, Inferred, Simulated, Incomplete, Disputed, Unknown.
Why it matters: Truth-state is the boundary between observation and assumption. No observation may proceed without it.
Freshness Window
RequiredMeaning: The maximum acceptable age for this observation type before it must be downgraded to Stale.
Why it matters: Different signals decay at different speeds. A price is perishable. A corporate charter is not.
Confidence Posture
RequiredMeaning: A structured statement of confidence: high, medium, low, or quantified where available, with justification.
Why it matters: Confidence must be explicit, not implied. Downstream systems must know when to weight an observation heavily or lightly.
Context Tags
OptionalMeaning: Relevant metadata: earnings season, macro release window, options expiration, geopolitical event, sector rotation.
Why it matters: Context tags enable pattern matching and anomaly detection across time and regime.
Conflicts Detected
Required if applicableMeaning: A flag indicating whether this observation contradicts another authoritative source.
Why it matters: Unresolved conflicts must freeze downstream action until resolution or operator override.
Missing Dimensions
Required if applicableMeaning: A list of known absent fields, sources, or contexts that would normally be required.
Why it matters: Incomplete data is dangerous when its incompleteness is hidden. Explicit gaps reduce blind spots.
Operator Notes
OptionalMeaning: Free-form annotations by a human operator reviewing or overriding the observation.
Why it matters: Operator notes capture judgment, skepticism, and institutional memory that structured fields cannot.
Escalation Flag
Required if applicableMeaning: A boolean indicating whether this observation has triggered a review or blocking condition.
Why it matters: Escalation flags prevent contaminated or anomalous observations from silently entering the reasoning pipeline.
Observation classes
Observations are not monolithic. Each class carries different truth-state expectations, freshness requirements, and downstream permissions.
Price / Quote Observation
A direct market observation of a price, bid, ask, spread, or volume figure from an exchange or market data vendor.
Liquidity Observation
An observation of market depth, order book structure, slippage estimates, or funding conditions.
Event Observation
A discrete market event: earnings release, merger announcement, regulatory action, central bank decision.
Filing / Disclosure Observation
A structured document observation from a regulatory filing, proxy statement, or mandatory disclosure.
Narrative Observation
A market narrative, sentiment signal, or media framing observation. Must be labeled Inferred unless directly quoting a primary source.
Macro Observation
A macroeconomic data point: inflation, employment, GDP, policy rate. Often subject to revision and multiple source reporting.
Positioning Observation
An observation of market positioning: short interest, futures positioning, options skew, flow data.
System-Health Observation
An observation about the state of the intelligence system itself: feed latency, model drift, pipeline failure.
Operator Hypothesis
An operator-generated idea or conjecture. Must be explicitly separated from observation and labeled as inferred or simulated.
Allowed vs prohibited inputs
Allowed inputs
- —Direct observed facts with clear attribution
- —Clearly labeled inferred content
- —Clearly labeled simulated outputs
- —Partial observations labeled incomplete
- —Conflicting reports labeled disputed
- —Operator hypotheses labeled separately from observation
Prohibited inputs
- —Anonymous unsupported claims treated as fact
- —Stale observations presented as current
- —Inferred or simulated content presented as direct observation
- —Operator opinions recorded as fact
- —Unattributed summaries entering downstream reasoning
- —Missing-dimension observations without explicit flags
Observation lifecycle
An observation that fails any lifecycle check must be escalated, not silently discarded. Escalation ensures that anomalies, conflicts, and boundary cases are visible to operators and available for audit.
Failure modes
What breaks when observation discipline is absent:
Narrative contamination
Unlabeled narrative observations enter reasoning as if they were primary facts, distorting downstream synthesis.
Stale-data drift
Observations age past their freshness window but remain in active memory, silently degrading model accuracy.
Source confusion
Multiple sources report the same variable with different values, but only one is retained without logging the conflict.
Simulation/fact collapse
Simulated or inferred outputs are stored as observations, erasing the boundary between model output and market reality.
Operator overreach
Operator opinions are recorded as observations without proper labeling, injecting unexamined bias into the system.
Hidden uncertainty
Missing dimensions, unknown variables, or disputed sources are not flagged, creating false confidence in incomplete pictures.
What this means for Veldarium Capital
Before Veldarium Capital builds research engines, simulation consoles, or operator interfaces, it is defining the observation standard those systems would have to obey. This framework is the intake specification.
Every planned module—from the Ingestion Layer to the Truth-State Classifier to the Audit Ledger—will be evaluated against this standard. If a system cannot produce observations with full attribution, truth-state labeling, and freshness control, it does not meet the minimum bar for deployment.
Observation discipline is not a feature. It is the precondition for intelligence.
Applied example
See a complete, fictional observation record that applies this standard in practice.
View sample observation recordDisclaimer
This framework is for research, educational, and product-development purposes only. It is not investment advice, not a recommendation to buy or sell any financial instrument, and not an offer to manage capital.
Veldarium Capital is a research and software initiative of Veldarium Technology Systems LLC. It does not manage outside capital, provide personalized investment advice, or offer trade recommendations.