Research, Compliance & Risk
AI Stock Research Agent
Join prices, fundamentals, and corporate actions without mixing entities, periods, currencies, or observation times.
Featured Providers
Verify endpoint coverage, fields, freshness, entitlements, and licensing before production use.
Alpha Vantage
AI Stock Research Agent
Build a research agent that joins prices, fundamentals, and corporate actions without mixing identities, periods, currencies, or observation times—and returns a memo every claim can trace to evidence.
Identityissuer, listing, security
Timeperiod and observed-at
Valuescurrency, units, adjustments
Evidencesource and validation
Direct AnswerResolve identity Separate time axes Preserve provenance
How should an AI stock research agent join financial data?
An AI stock research agent should never join fundamentals, prices, and corporate actions on ticker alone. Resolve a stable issuer and security identity first; keep market observations separate from fiscal-period facts; apply splits only where appropriate; and preserve source, publication time, retrieval time, currency, unit scale, and validation status on every record.
Map issuer, security, listing, exchange, ticker, CIK, LEI, and ISIN where available. Store ticker history instead of treating a symbol as permanent.
Keep trading timestamp, fiscal period end, filing date, publication time, and retrieval time as distinct fields. They answer different questions.
Attach provider, source URL, raw field, transformation, unit, and validation result so every memo claim can be reconstructed.
Reference Architecture1. Source adapters 2. Canonical evidence store 3. Validation and answer layer
Architecture for an evidence-first AI stock research agent
Use five layers: capability discovery, raw evidence capture, canonical normalization, validation, and memo generation. Keep raw responses immutable; derive normalized facts separately; and let the language model read only records that carry provenance and validation state.
Discover and call quote, filing, fundamentals, corporate-action, transcript, and news capabilities. Record the request, response hash, and retrieval time before transformation.
Normalize identity, time, currency, unit scale, fact type, and source while retaining the provider's original field and value.
Reject incompatible joins, label warnings, compute derived metrics only from accepted inputs, and cite evidence in the final memo.
Use SEC EDGAR and the SEC EDGAR API documentation as primary references for US filing identity and timestamps.
Example data schema for prices, fundamentals, and corporate actions
Consider a user asking, “Explain why this stock moved after earnings.” The agent must join a security, a market observation, fiscal-period facts, and any split or dividend that changes comparability. The following sequence prevents a plausible-looking but invalid merge.
Step 01Resolve security identity
Map the user ticker to issuer, security, exchange, listing currency, and stable identifiers. Confirm that each provider refers to the same listing.
Step 02Capture raw evidence
Store quote, filing, statement, corporate-action, estimates, transcript, and news responses with retrieval time and content hash.
Step 03Normalize contracts
Represent market timestamps and fiscal periods separately; standardize currency, unit scale, statement type, and adjustment status.
Step 04Validate the join
Join only compatible identities and periods. Apply splits to per-share and price series according to source methodology, not to reported revenue or cash.
Step 05Derive and answer
Calculate ratios from accepted facts, keep formulas and citations, label uncertainty, and send conflicts or missing primary evidence to human review.
Canonical research bundle
{
"security": {"issuer_id": "...", "security_id": "...", "ticker": "...", "exchange": "...", "currency": "USD"},
"market_observation": {"observed_at": "...", "session": "after_hours", "close": 0, "adjustment": "split_adjusted"},
"fundamental_fact": {"concept": "revenue", "fiscal_period": "FY2026 Q2", "value": 0, "unit": "USD", "scale": 1000000},
"corporate_actions": [{"type": "split", "ex_date": "...", "ratio": "2:1"}],
"provenance": {"provider": "...", "source_url": "...", "published_at": "...", "retrieved_at": "...", "raw_hash": "..."},
"validation": {"status": "accepted", "checks": ["identity_match", "period_match", "currency_match"]},
"derived_claims": [{"claim": "...", "formula": "...", "evidence_ids": ["..."]}]
}Join rule
A ticker is a lookup input, not the database key. Use stable issuer and security identifiers, then require compatible periods, currency, unit scale, and observation time. If any join key is unresolved, return a warning instead of manufacturing a complete bundle.
Data Contract
Minimum schema an AI stock research agent should preserve
The exact provider fields can vary, but the canonical layer should preserve enough context to reproduce every join and calculation.
| Entity | Required fields | Validation rule |
|---|---|---|
| Security identity | issuer_id, security_id, exchange, ticker history, CIK/LEI/ISIN when available | Do not join records only because the current ticker matches. |
| Market observation | observed_at, session, currency, price field, adjustment status, source | Never mix delayed, live, regular-session, and after-hours values silently. |
| Fundamental fact | concept, fiscal period, filing date, value, unit, scale, statement type, source | Reject incompatible periods and distinguish reported, estimated, and calculated values. |
| Corporate action | type, announcement date, ex-date, effective date, ratio or amount, source | Apply adjustments only to affected series and retain the unadjusted value. |
| Evidence record | source URL, provider, published_at, retrieved_at, raw hash, validation status | No material claim reaches the memo without at least one evidence ID. |
Quality Controls
Validation checklist and common failure modes
Judge the agent on evidence quality and workflow discipline, not on whether a later price move happens to agree with its narrative. Test identity collisions, period mismatches, double-adjusted prices, stale quotes, missing evidence, and look-ahead leakage before production use.
Freshness and point-in-time integrity
Record when every quote, estimate, filing, and news item was observed. Never evaluate an old decision with information that became available later.
Source provenance
Keep provider, source URL, document date, retrieval time, and execution ID. A polished paragraph without provenance is not a research artifact.
Period and unit consistency
Verify fiscal period, currency, split adjustments, per-share definitions, and whether values are reported, estimated, or calculated.
Coverage and missing data
Report which requested evidence was unavailable. Missing consensus or transcript data should remain missing—not silently become zero or “neutral.”
Claim support
Require at least one cited fact for material claims and label interpretation separately. Unsupported causal language should fail review.
Human decision boundary
Use the agent to organize evidence and surface exceptions. Portfolio actions, suitability, and high-impact conclusions remain human responsibilities.
Pass condition: every material number has a period, unit, source, and observation timestamp.
Fail condition: the memo mixes delayed and live data, hides disagreement, or presents interpretation as a verified fact.
Regression test: rerun historical cases with frozen evidence and confirm the output schema and calculations remain stable.
Escalation rule: route conflicting values, missing primary documents, and low-confidence explanations to a reviewer.
FAQ
AI stock research agent schema FAQ
What is the minimum data schema for an AI stock research agent?
Separate security identity, market observations, fundamental facts, corporate actions, provenance, and validation. Every material value needs a period or timestamp, currency, unit scale, source, and retrieval time.
Can I join stock prices and fundamentals by ticker?
No. Tickers can change, be reused, or refer to different listings. Resolve a stable issuer and security identity, retain exchange and ticker history, and validate period, currency, unit scale, and adjustment status.
How should an agent handle stock splits and corporate actions?
Store each action separately with announcement, ex-date, effective date, ratio or amount, and source. Adjust only affected price or per-share series and retain the original value and methodology.
How do I prevent look-ahead bias in an AI research agent?
Preserve publication and retrieval timestamps, freeze the evidence available at the decision time, and never use filings, estimates, or news published later to evaluate a historical output.
Where does QVeris fit in the architecture?
QVeris can provide discovery, inspection, and calling across financial and document capabilities. Your application should still own the canonical schema, validation rules, evidence store, licensing checks, and human review boundary.
Build a research agent with evidence, not guesses
Use QVeris to discover, inspect, and call financial capabilities before your model writes.