What our research shows
In our 2007–2025 data, institutional flow explains 62.6% of quarterly S&P 500 variance. Price and volume alone cannot show who drove it.
Our papers also measure how early that flow shows up in 13F filings and how futures flow tracks CFTC positioning. Every record is point-in-time and never revised.
Price and volume
+ investor flow
What quant teams check first
Three things decide whether a dataset adds information to your models: who is behind each trade, whether the history is clean, and how easily it fits your stack. Here is how XTech answers each one.
Flow by investor type, each minute
XTech separates institutional and retail buying and selling in every US-listed stock, ETF, option and crypto ETF, in dollars and trade counts, at 1-minute granularity. In global futures, positioning is broken out by investor group, daily. Price and volume alone cannot show who moved the market.
The same measures across assets, delivered your way
Buy and sell dollars and trade counts by investor type, across equities, ETFs, options and futures. Pipelines use the Unifier REST API, AI agents the MCP service and desks the Tesseract portal, in the cloud or on-premise.
Point-in-time history, never revised
Each record carries the moment it would have been available in a live feed, and history is never revised, so a backtest sees only what was knowable at the time. US equity flow history starts in 2007.
CPI forecasts, about 20 days before the print
Our first CPI forecast arrives about 20 days before the release, the final one about 5 days before. The record against consensus is in the paper.
Evidence first: questions quant teams ask before testing
Short answers to the questions buyers ask first. Methodology and full data dictionaries are available under NDA.
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What evidence is there that investor flow adds information?
Our published research: institutional flow explains 62.6% of quarterly S&P 500 variance (2007–2025); cumulative flow called the direction of the next 13F change with 65.5%† average accuracy; daily futures flow matched the direction of the weekly CFTC report about 71% of the time for managed money.
What is in the data, and how far back does it go?
Buying and selling by investor type, in dollars and trade counts, 1-minute to weekly. US equity history starts in 2007.
Which assets do you cover?
US equities, ETFs, options and crypto ETFs, split into institutional and retail; global futures by investor group; US macro releases led by CPI, plus PCE, payrolls, ISM, retail sales and sentiment; and a point-in-time US security master with SIP bars.
How is the data delivered?
Through the REST API for pipelines, the MCP service for AI agents and the Tesseract portal for discretionary desks, in real time, 15 minutes delayed or at end of day. Reference data also comes as Parquet files with a Python API. Deploy in the cloud or on-premise, on AWS, Azure, GCP or Oracle.
How can I test the data before I sign a licence?
Request sample data through our contact form and tell us what you want to test. Our research team supports the evaluation. Licences: team data licence or enterprise data licence.
Latest research
New white papers, monthly CPI forecasts and flow case studies for institutional investors.
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"XTech’s advanced AI capabilities, powered by Prosper’s forward-looking consumer data, are transforming how investors anticipate economic trends. Our collaboration delivers predictive insights—like highly accurate CPI forecasts—that prove the power of consumer-centric analytics in financial decision-making."
Gary Drenik
Co-founder
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Head of Data Sourcing, Major Global Hedge Fund