Selling trading data to AI labs

Trading firms generate distinctive data: not just prices and fills, but the research, reasoning, risk discussions and operational workflows behind them. AI labs building systems that can analyze, reason about and support financial work are interested in examples of how professionals actually do that work. Trading data is a specialized category that we evaluate case by case, because it comes with significant restrictions.

This guide is general information, not legal, regulatory or investment advice. Financial firms are subject to securities, commodities and privacy regulation as well as contractual data restrictions. Involve compliance and counsel early.

What kinds of trading data exist

Market data

Prices, quotes, order books and trade prints from exchanges and vendors. Most firms license market data from exchanges and data vendors under agreements that restrict redistribution. Raw market data you received from third parties generally isn't yours to license, and it's widely available to labs directly anyway.

Proprietary trading records

Your own orders, executions, positions and P&L history. This is more distinctive but also sensitive: it can reveal strategies, and in some contexts it may relate to client activity.

Research and reasoning

Research notes, models, trade ideas, investment memos, post-trade reviews and the discussions around them. For AI labs, the reasoning (how professionals form views, weigh risks and make decisions under uncertainty) can be the most interesting part.

Operational workflows

Trade support, reconciliation, settlement breaks, corporate actions processing, compliance reviews, risk limit monitoring and escalations. These back-office and middle-office workflows are complex, rule-bound and poorly represented in public data.

What makes trading data valuable

• Decision-making under uncertainty, documented in research and chat
• Process rigor: reconciliation, controls, approvals and exception handling
• Specialized vocabulary and domain knowledge
• Long histories that span different market regimes
• Linkage between research, decisions, execution and post-trade review

Restrictions to work through

Third-party data licenses

Exchange and vendor agreements usually prohibit redistributing their data, including derived data in some cases. Exclude raw third-party market data unless your agreements clearly permit sharing, and check whether derived datasets are restricted too.

Client and counterparty information

If you handle client orders or accounts, client information is subject to privacy and confidentiality obligations, and potentially to financial privacy regulation. Client identities and account details should be excluded or rigorously de-identified, and client-confidential activity may need to be excluded entirely.

Regulatory considerations

Depending on your registration status, securities and commodities regulators' rules on recordkeeping, confidentiality and the handling of material non-public information (MNPI) may apply. Research or communications that could contain MNPI should be excluded. Firms with recordkeeping obligations should confirm that exports don't conflict with retention requirements.

Strategy protection

Your edge is your business. Recent positioning and live strategies should typically be excluded, and older data can be delayed, aggregated or generalized. Contracts should prohibit using the data to trade against you or to replicate your strategies.

See is it legal to sell company data? for the broader framework.

Techniques that preserve value while managing risk

• Time delay: license only data older than an agreed cutoff
• Normalization: convert positions and P&L to normalized or relative figures
• Instrument generalization: replace specific instruments with categories where precision isn't needed
• Pseudonymization of traders, clients and counterparties
• Focusing on workflows: operational and research-process data is often more valuable and less sensitive than raw trading records

Our de-identification guide covers the general techniques.

Example workflows that stand out

Described generically, with all people, clients and instruments pseudonymized or generalized, here are some examples:

• A post-trade review in which a desk discusses why a position underperformed, separates bad luck from bad process, and changes its risk limits as a result.
• A reconciliation break that operations traces through several systems to a corporate action processed incorrectly, followed by the fix and a control change to prevent recurrence.
• A research process in which an analyst's initial thesis is challenged in review, revised with new data, and documented in a memo before a decision is made.

These show reasoning, controls and coordination: the parts of financial work that are hardest for AI systems to learn from public sources.

Who is a good fit

• Proprietary trading firms and desks with long, well-documented histories
• Investment firms with rich research and decision documentation
• Financial operations teams with mature middle- and back-office processes
• Firms with established compliance functions that can review scope

Structuring the deal

Given the sensitivity, terms matter even more than usual: narrow permitted uses, strong confidentiality, prohibitions on re-identification and on trading use, security and audit requirements, and clear deletion obligations for raw data. Non-exclusive versus exclusive structures can significantly change price. See licensing vs. selling data and the licensing agreement checklist.

Getting started

Share rough estimates: which systems you use (OMS/EMS, research platforms, chat, email, reconciliation and risk tools), approximate volume, years of history, headcount and years in business. DataOffer reviews trading datasets case by case, helps you scope around third-party licenses, clients and strategy, and brings you competing offers. Nothing is shared until you approve the buyer, price and terms.

This guide is general information, not legal, tax or financial advice. Figures and ranges are illustrative; talk to qualified advisors about your situation.