Research retrieval
Time-stamped market data, documents, provenance, freshness, and source-aware summaries.
AI TRADER LLM
Ai Trader LLM API
Architecture guidance for connecting language models to market research and supervised trading workflows without giving a model unrestricted account authority.
Developer overview
What this API category covers
An Ai Trader LLM API is best understood as an orchestration layer. It can connect a model to trusted data and narrow tools, but deterministic services should own permissions, account limits, order validation, approvals, and execution. The language model interprets and communicates; policy code authorizes and constrains.
Do not choose an interface from a marketing checklist alone. Verify schemas, units, timestamps, permissions, failure behavior, testing conditions, and data-use terms for the exact product and account. A production integration should be able to recover authoritative state after a disconnect without guessing.
Capability map
Core integration responsibilities
Use these responsibilities to define architecture, acceptance tests, and provider evidence.
Bounded tools
Typed functions for quotes, positions, analysis, proposals, checks, and approval requests.
Policy engine
Allowed instruments, order limits, exposure, restricted windows, and kill switches.
Human approval
Immutable proposal details, risk results, expiry, authentication, and review evidence.
Execution router
Restricted credentials, idempotency, reconciliation, state transitions, and errors.
Audit plane
Prompts, evidence, model version, tool calls, checks, approvals, orders, and monitoring.
Implementation
Production checklist
- Define research, recommendation, approval, and execution separately.
- Never give the model a raw brokerage credential.
- Expose narrow schema-validated tools.
- Treat external text as untrusted data, not policy.
- Run deterministic checks immediately before execution.
- Bind approval to an immutable expiring proposal.
- Use stable IDs and reconcile uncertain submissions.
- Test injection, stale data, disconnects, and rule violations.
Conceptual adapter
const adapter = {
instruments: "provider identifiers",
timestamps: "UTC + source context",
numbers: "exact decimals",
streaming: "snapshot + recovery",
orders: "idempotent state machine",
credentials: "server-side only",
evidence: "docs + tests + logs"
};
Related research
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A weighted evaluation framework for workflow fit, data, execution, security, recovery, and cost.
Read guide →Secure Trading API Keys
Server-side credential patterns, least privilege, rotation, monitoring, and incident response.
Read guide →FAQ
Ai Trader LLM API questions
What is an Ai Trader LLM API?
It is an application layer that connects language models to trading-related data and controlled tools for research, explanation, proposals, approvals, or automation.
Should an LLM submit orders directly?
A safer architecture routes structured proposals through deterministic authorization, risk checks, and any required human approval before a restricted execution service acts.
How should prompt injection be handled?
Separate untrusted content from system instructions, restrict tools, validate every action, preserve provenance, and assume hostile text may reach the model.
What should be logged?
Record relevant prompts, evidence, model and tool versions, structured outputs, policy results, approvals, provider requests, responses, and reconciliation events while protecting secrets.