Academic program
The data your peer reviewers will recognize
The same archive that two US regulators use to monitor markets, available to academic researchers at pricing structured for university budgets. algoseek has supported quantitative finance research at universities since 2015. We understand grant cycles, ethics board timelines, and what a journal reviewer looks for in a data section.
Trusted by 20+ universities and research institutions
University finance and economics departments
Quantitative finance master’s programs
Computational finance research labs
Centers for empirical asset pricing
Market microstructure research groups
Financial engineering programs
A growing list of departments and research groups, from research-intensive R1 universities to specialized quantitative finance programs. Specific institutional logos available on request after application review.
Why algoseek for academic research
Survivorship bias, point-in-time reference data, exchange-level granularity, and reproducibility. Get any of them wrong and the paper doesn’t hold up. Three reasons algoseek’s archive handles all four.
Data the regulators trust
Two US regulators chose algoseek after evaluating the alternatives. Research that cites algoseek as the data source is citing what the regulators themselves use to monitor markets, an unusually strong provenance for academic work.
Survivorship-bias-free
Delisted tickers, mergers, ticker changes, share class events, and corporate actions are tracked from the first day they happened. Reproducible empirical research requires the dataset to reflect the universe as it was, not as it is now, and that is what algoseek’s reference data layer provides.
Granularity for microstructure
Tick-level TAQ, exchange-level depth, condition codes, and minute bars with up to 90 quantitative fields per bar. Microstructure research, execution research, and high-frequency studies are all in scope on the same archive that supports lower-frequency work.
The data your peer reviewers will check
Published quantitative finance research is read carefully. Reviewers ask whether the data is the consolidated SIP feed or a derived approximation. They ask whether the security master tracks ticker changes, mergers, and delistings, or whether it silently introduces survivorship bias. They ask how corporate adjustments were handled.
algoseek’s archive answers those questions in your favor. The same archive that two US regulators use to resolve disputes about what actually happened in the market.
The actual CTA and UTP consolidated SIP feed for US equities, not a derived best-of-feeds approximation. Security masters built and maintained in-house from multiple sources, with cross-referencing against FIGI, ISIN, and others rather than third-party pass-through. Three levels of corporate adjustment data, from basic factors to full event detail with dollar amounts and share exchange ratios.
For a paper aiming at a top finance journal, the data citation alone matters. For a graduate thesis defended in front of a committee that knows what to ask, it matters more.
What academic clients use the data for
A non-exhaustive list of research areas where algoseek’s data is supporting active academic work. The dataset’s breadth covers most quantitative finance research questions, from the slowest cross-sectional asset pricing studies to the fastest microstructure papers.
Empirical asset pricing
Factor research, anomaly studies, and cross-sectional return predictability
Reference data, adjustment factors, and survivorship-bias-free coverage for replicating canonical results and extending them with new factors or longer horizons.
Market microstructure
Liquidity, price impact, and order book dynamics
Exchange-level depth and condition-coded TAQ preserving venue and reporting detail for questions about where and how trading happens.
Options research
Implied volatility surfaces, options pricing, and derivatives studies
Full OPRA coverage with 60+ field minute bars, daily analytics, and contract security masters that track every option through its lifecycle. The options data covers every listed US equity option since 2012.
Machine learning in finance
Feature engineering, model evaluation, and out-of-sample testing
Up to 90 quantitative fields per minute bar for a rich feature space, with survivorship-bias-free history reflecting the actual investable universe.
Algorithmic trading
Execution algorithms, transaction cost analysis, and slippage studies
Tick-level data with venue, condition codes, and quote context for studying execution quality at the level of individual fills.
Regulatory and policy
Market structure analysis, regulatory impact studies, and surveillance research
The same archive used by US regulators, available for academic research into market quality, regulatory interventions, and surveillance methods.
How academic pricing works
Academic pricing is a separate path from commercial pricing. Universities and research institutions have different constraints, and the engagement is structured to match.
Who academic pricing is for
Faculty and PhD research
Tenured faculty, post-docs, and doctoral candidates conducting research with academic publication as the primary deliverable. Single-investigator and group projects both qualify.
Sponsored research
Grant-funded studies, including those with budgets that include data acquisition. Industry-sponsored research with academic outputs is reviewed case by case.
Master’s thesis projects
Graduate-level research projects supervised by faculty, where the deliverable is a thesis or working paper. Smaller scope than PhD research, with terms scaled accordingly.
University research centers
Quantitative finance institutes, financial economics centers, and applied research groups within universities. Centre-level licenses available for ongoing programs.
What does not qualify. Academic pricing is for strictly academic research. It does not cover trading conducted alongside research, internal use by a university endowment, or research funded primarily by a commercial sponsor where the sponsor receives the output. Those use cases route to standard institutional pricing.
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Is academic pricing strictly for research and publication?
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Yes. Academic pricing covers research conducted by faculty, PhD students, and graduate researchers for publication, dissertations, and academic study. The data may not be used for commercial trading, advisory work, or any revenue-generating activity. The license terms are research-only. For mixed-use research groups that publish but also have commercial spin-out activity, the engagement structure is different and the conversation starts with sales rather than this form.
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What does algoseek ask in return?
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Acknowledgement in any public papers that result from the research. Standard data-source citation is sufficient. The recognition supports the next round of academic relationships and is the simplest way to keep the program sustainable.
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How is academic pricing structured?
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Most academic engagements run on annual or multi-year arrangements that align with research grant cycles. Pricing is configurable around the specific datasets needed for the research program rather than a flat per-package rate. This keeps the cost specific to the project.
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Do academic clients get the same data and infrastructure?
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Yes. Academic clients access the same datasets through the same delivery infrastructure as commercial clients: S3, ArdaDB, RESTful API, and the data sandbox. The infrastructure is identical because the research benefits from the same tooling production work uses. Reproducibility starts with using the same environment papers will eventually be reviewed against.
Acknowledgement standard
What we ask in return for academic pricing
The single non-financial condition of the academic program is acknowledgement in published research. The form of the acknowledgement is up to the author and the journal, but a standard data-source citation in the paper is sufficient.
“We thank algoseek for providing the equity TAQ data used in this research.”
Substitute the specific dataset or datasets you used. Any equivalent wording the author or journal prefers is fine.
The acknowledgement supports algoseek’s reputation in the academic community and lets us keep extending the program to new research groups. It costs the author nothing and helps the next group through the door.
Apply for academic pricing
A short form to start the conversation. We respond within a few business days, usually with a discovery call to scope the dataset against the research program.
Already engaged with the research community?
If you’d rather have an introductory conversation before applying, that route is open. We have walked through dataset selection, grant timelines, and license structuring with research groups across most of the patterns. A short call usually clarifies fit before any application is needed.