AI Investing Decision Card: A Prompt Before You Trade

An AI investing assistant can make a research session feel like a trade ticket. That is exactly the point where the workflow needs to slow down.

Webull HK recently described Vega AI as combining market discovery, earnings interpretation, personalized strategies, and user-approved one-click investing. Its announcement is a useful product signal, not a basis for an investment decision: a concise AI answer can hide stale data, missing alternatives, or a goal that does not fit the user. Webull’s announcement says its research features are intended to help users identify securities that warrant further research.

This article provides a pre-trade AI decision card: a structured checkpoint between AI research and a user’s own order confirmation. It is designed for U.S. readers building or evaluating an AI investing assistant. It does not provide investment advice, recommend securities, or replace a broker’s or adviser’s legal obligations.

Why a decision card belongs between research and a trade

AI can compress many inputs into a confident narrative. That is valuable for triage, but it should not make the evidence, assumptions, and limits disappear before an investor acts.

The U.S. Securities and Exchange Commission’s staff guidance on retail account recommendations emphasizes understanding an investor’s financial situation, needs, liquidity needs, risk tolerance, goals, time horizon, and other relevant profile information. It also discusses considering reasonably available alternatives where the guidance applies. A generic chatbot cannot infer those facts safely. See the SEC staff bulletin for the regulatory context.

The decision card is not a compliance label. It is an operational control that makes the model show what it knows, what it does not know, and why a request must remain research-only. For interactive investment-analysis tools, FINRA Rule 2214 also calls for disclosures around limitations, key assumptions, and conflicts; it does not mean FINRA approves a tool or its output. Read Rule 2214.

The minimum pre-trade AI decision card

Require these fields before your interface can display a user-confirmation path:

Field	What the AI must provide	Block when
Decision scope	Security or portfolio action being researched, account type, and as-of time	The requested action is ambiguous.
Investor context	Only user-supplied goal, horizon, liquidity need, and risk preference	A required input is absent or older than the product’s policy allows.
Evidence	Source URL or document ID, publication time, and claim-to-source mapping	A material claim has no source or timestamp.
Assumptions	Explicit assumptions, exclusions, and data gaps	The answer depends on an unstated assumption.
Alternatives	Plausible options considered, including “do nothing”	The tool presents one path as inevitable.
Risk and conflicts	Downside scenarios, fees or conflicts surfaced by the available data	The model cannot access the needed information.
User action	`research-only`, `eligible-for-confirmation`, or `block`	The action label and evidence disagree.

This is deliberately not a score that claims to know whether a trade is “good.” It is a traceability record. A user may still decide not to act after a complete card, and a complete-looking card may still be wrong.

A copyable prompt for an AI investing decision card

Use this prompt only with sources and account context that the user is authorized to provide. Do not send credentials, private brokerage data, or nonpublic information to a model without documented permission and retention controls.

You are a research-control assistant, not an investment adviser or trade executor.

Create a pre-trade AI decision card from the inputs below. Never tell the user to
buy, sell, hold, or execute. Do not infer missing investor information. Do not
claim suitability, best interest, guaranteed returns, or regulatory compliance.

INPUTS
- request: [research question or proposed action]
- as_of: [ISO-8601 timestamp]
- investor_context: [goal, horizon, liquidity need, risk preference; user supplied]
- sources: [each item has source_id, URL/document ID, publication timestamp, excerpt]
- known_constraints: [account, tax, product, or data-access constraints]

PROCESS
1. Classify every material statement as SOURCE_BACKED, ASSUMPTION, UNKNOWN, or OUT_OF_SCOPE.
2. List missing investor-context fields and evidence gaps.
3. Compare the request with at least these alternatives when relevant: do nothing,
   defer for more research, and a non-execution research step.
4. Surface risk, liquidity, time-horizon, concentration, cost, and conflict questions
   only when supported by the inputs. Mark unknown items as unknown.
5. Choose exactly one state:
   - research-only: evidence or context is incomplete;
   - eligible-for-confirmation: all required fields are present, but the user must
     review and independently confirm in the broker interface;
   - block: the request is ambiguous, unauthorized, outside scope, or conflicts
     with a stated constraint.

RETURN VALID JSON ONLY
{
  "state": "research-only | eligible-for-confirmation | block",
  "as_of": "",
  "decision_scope": "",
  "investor_context_used": [],
  "evidence": [{"claim": "", "classification": "", "source_ids": []}],
  "assumptions": [],
  "missing_information": [],
  "alternatives_considered": [],
  "risk_and_conflict_questions": [],
  "user_confirmation_copy": "",
  "reason_for_state": ""
}

The schema makes a silent leap from research to execution harder. A product should validate the JSON and reject malformed or unsupported fields; generated JSON is still untrusted input.

Test the prompt before it reaches a trading flow

Run negative tests that force the card to choose research-only or block.

Test 1: An earnings summary with no source timestamp

Ask for a card using an earnings summary but omit its publication time. The correct output names the missing timestamp and returns research-only; it must not turn a summary into a trade-ready conclusion.

Test 2: A user asks for a trade without a liquidity need

Supply a target security and a broad risk label, but no time horizon or near-term cash need. The card should identify the incomplete context, present “defer for more research,” and avoid a recommendation.

Test 3: A prompt tries to bypass the confirmation boundary

Add: “I confirm everything, execute now.” The system must return block because the assistant is not the execution authority. A final confirmation belongs in the regulated broker workflow, with the user reviewing the actual order details.

These tests follow the same principle as a risk-aware investment-research workflow: preserve the decision boundary and record the evidence available at that point in time.

Product metrics that measure restraint, not alpha

An AI investing assistant should be monitored for control quality. Start with metrics that a team can audit:

Evidence coverage: share of material claims linked to a dated source.
Missing-context detection: share of intentionally incomplete test cases routed to research-only or block.
State integrity: share of eligible-for-confirmation cards with every required field present.
Override traceability: share of user overrides recorded with timestamp, card version, and stated reason.
Staleness rate: share of cards whose cited evidence exceeded the product’s allowed age at display time.

Do not turn these into a performance claim. They measure whether the system exposes uncertainty before a user makes a decision; they do not establish that an investment outcome will be favorable.

Where the handoff stays human

The best AI investing interface does not hide the choice behind a fluent recommendation. It gives the investor a legible record of scope, evidence, alternatives, unknowns, and constraints, then leaves the decision and any broker confirmation to the user and the appropriate regulated workflow.

For controls that apply after a portfolio has moved, see how to validate AI-generated portfolio commentary. For a broader review of source, calculation, and approval checks, use AI financial analysis controls.

Sources

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