Academic program
The data your peer reviewers will recognize
The same data two US regulators use to monitor markets, at pricing structured for academic budgets. Supporting quantitative finance research since 2015.
Trusted by 20+ universities and research institutions
From elite universities to specialized quantitative finance programs.
Why algoseek for academic research
Survivorship bias, point-in-time reference data, exchange-level granularity, reproducibility. Get any of them wrong and the paper doesn’t hold up.
Data the regulators trust
Two US regulators chose algoseek after evaluating the alternatives. Citing algoseek means citing the data regulators themselves use.
Survivorship-bias-free
Delisted tickers, mergers, ticker changes, and corporate actions tracked from day one. The dataset reflects the universe as it was, not as it is now.
Granularity for microstructure
Tick-level TAQ, exchange-level depth, condition codes, and minute bars with up to 90 fields. Microstructure to low-frequency work, one archive.
The data your peer reviewers will check
Reviewers ask whether the data is the real SIP feed or an approximation, whether the security master quietly introduces survivorship bias, and 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, not a best-of-feeds approximation. Security masters built in-house and cross-referenced against FIGI and ISIN. Three levels of corporate adjustment data, down to dollar amounts and share exchange ratios.
For a top-journal submission, the data citation matters. For a thesis defense, it matters more.
What academic clients use the data for
Research areas where algoseek data supports active academic work, from cross-sectional asset pricing to the fastest microstructure papers.
Empirical asset pricing
Factor research, anomaly studies, and cross-sectional return predictability
Survivorship-bias-free coverage and adjustment factors for replicating canonical results and extending them.
Market microstructure
Liquidity, price impact, and order book dynamics
Exchange-level depth and condition-coded TAQ preserving venue and reporting detail.
Options research
Implied volatility surfaces, options pricing, and derivatives studies
Full OPRA coverage since 2014, with 60+ field minute bars and contract security masters tracking every option’s lifecycle.
Machine learning in finance
Feature engineering, model evaluation, and out-of-sample testing
Up to 90 fields per minute bar, with 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, down to individual fills.
Regulatory and policy
Market structure analysis, regulatory impact studies, and surveillance research
The regulators’ own archive, applied to market quality, interventions, and surveillance methods.
How academic pricing works
A separate path from commercial pricing, structured around academic budget constraints.
Who academic pricing is for
Faculty and PhD research
Tenured faculty, post-docs, and doctoral candidates whose primary deliverable is publication. Solo and group projects both qualify.
Sponsored research
Grant-funded studies. Industry-sponsored research with academic outputs is reviewed case by case.
Master’s thesis projects
Faculty-supervised projects delivering a thesis or working paper, with terms scaled to scope.
University research centers
Quant finance institutes and applied research groups. Center-level licenses for ongoing programs.
What does not qualify. Trading alongside research, endowment use, or commercially sponsored work where the sponsor receives the output. Those route to standard institutional pricing.
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Is academic pricing strictly for research and publication?
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Yes. The license is research-only: publication, dissertations, academic study. No commercial trading, advisory work, or revenue-generating use. Mixed-use groups with commercial spin-out activity start with sales instead of this form.
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What does algoseek ask in return?
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Acknowledgement in published papers: the dataset named in the methodology, a formal citation, and an attribution line on figures built from algoseek data.
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How is academic pricing structured?
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Annual or multi-year arrangements aligned with grant cycles, priced around the specific datasets the research needs rather than a flat per-package rate.
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Do academic clients get the same data and infrastructure?
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Yes. Same datasets, same delivery: S3, ArdaDB, RESTful API, and the data sandbox. Reproducibility starts with the same environment production work uses.
Citation standard
What we ask in return for academic pricing
Published research names the algoseek dataset in the methodology, includes a formal citation in the bibliography, and adds an attribution line to every figure and table built from algoseek data.
Apply for academic pricing
A short form to start the conversation. We respond within a few business days, usually with a discovery call.
Already engaged with the research community?
Prefer a conversation before applying? A short call usually clarifies dataset selection, grant timelines, and license fit.