Article
AI Trading Code Review Checklist: Before Codex or Claude Touches Broker Data
Use an AI trading code review checklist before Codex or Claude touches broker data, chart exports, order logs, or journal workflows.
AI trading code review matters before Codex or Claude touches broker data because the output can look polished while still mishandling fields, assumptions, timestamps, order states, or review boundaries. The safest workflow makes human review part of the design.
Quick Answer
Before Codex or Claude touches broker data, define the data source, field meanings, allowed task, reject conditions, tests, and human approval gate. The AI should not decide what to trade or change risk without review.
For the main IBKR Codex workflow, use Using Codex or Claude Code With IBKR Chart Data.
Code Review Checklist
Review AI-generated trading code like operational infrastructure. The question is not whether the code looks smart. The question is whether a human can trace every field, assumption, and output.
If the workflow uses IBKR chart data, pair this checklist with Codex IBKR Chart Data Prompt Template (Copy-Paste).
| Review Area | Question | Reject If |
|---|---|---|
| Source data | What exact file, API, or export is read? | The source is vague |
| Field meaning | Does each field have a definition? | Fields are guessed or renamed silently |
| Timestamp logic | Are timezones and sessions explicit? | Time assumptions are hidden |
| Broker state | Are orders, fills, rejects, and cancels separated? | States are collapsed into one result |
| Risk boundary | Can AI change size, stops, or live behavior? | Risk logic is not human-approved |
| Tests | Can sample rows prove the workflow? | No manual or automated checks exist |
What Codex or Claude Can Safely Help With
AI can help draft parsers, schemas, documentation, dashboard tables, test cases, and QA checklists. It should stay inside supplied data and clearly mark assumptions.
For Claude-specific review workflows, use Using Claude Code for Your End-of-Day Trading Journal (Without Letting It Grade Your Trades).
- Normalize a sample export into a table.
- Identify missing fields in a broker-data handoff.
- Draft tests that compare source rows to output rows.
- Document assumptions and reject conditions.
- Create a human-review checklist for the next run.
Next Step
Take one sample export and ask AI for one narrow artifact. Then review it manually before adding another task.
If the workflow involves execution records, continue with Broker API Data vs Execution Data: Why Retail Trading Systems Need Both.
FAQ
What should an AI trading code review checklist include?
It should include source data, field definitions, timestamp handling, broker state handling, risk boundaries, tests, logs, assumptions, and human approval gates.
Should Codex or Claude make trading decisions from broker data?
No. They can support parsing, documentation, dashboards, and QA, but trading decisions and risk logic should remain human-controlled.
What is the safest first AI trading code task?
Start with a parser, field map, schema, documentation pass, or QA checklist that can be checked against a small sample file.
Sample Structured Chart-Data Exports
Review how chart drawings, annotations, OHLC, volume, and execution context become reusable structured data.