Investor flows cause price movements. We provide unparalleled real-time insights into investor flows.
Our research finds institutional flow explains about 63% of quarterly S&P 500 variance, and each net dollar of institutional buying goes with about $7 of S&P 500 market value. XTech reports that flow by investor type in US equities, ETFs, options and crypto ETFs, and by investor group in global futures.
Our data partners and research clients
Investor Flow by Investor Type
Our flagship, and the data behind our research: net buying and selling by institutional and retail investors in US equities, ETFs, options and crypto ETFs, from 1-minute bars to weekly totals, in real time, 15 minutes delayed or at end of day.
Global Futures Flow by Investor Group
Daily positioning by investor group across global futures, by position size, trade size and turnover rate, with groups aligned to CFTC COT categories. Our research compares it with the weekly COT report.
Factor Library: Ready to Test
Flow turned into ready-to-test factor columns: Institutional Participation Share, Retail Participation Share and the Short Interest Indicator, among others. Our factor guide shows how the two participation factors behaved in testing.
Global Macro Forecasts
US CPI forecasts evaluated against consensus since 2017, the first about 20 days before the print, plus other macro releases.
Reference Data: Security Master and Historical SIP Bars
A point-in-time US security master (221,545 listings, 21 venues, corporate actions) and 1-minute and daily SIP bars from 2016, delivered as files with a Python API.
The same point-in-time data our research uses
Our papers use the same point-in-time data you license: a backtest sees only what was knowable at the time, and history is never revised. Query it through the REST API, ask questions through the MCP service, or explore it in the Tesseract portal, like the CPI chart here.
Where the numbers come from: our published research on institutional flow, 13F filings and futures
The figures above come from our white paper "Decoding Real-Time Order Book Dynamics to Measure Market Inelasticity" (S&P 500, 2007–2025 data). Our other papers show how cumulative institutional flow anticipates 13F filings and how futures flow tracks CFTC positioning between reports.
Each finding below has a short summary on our blog, where you can request the full white paper.
In our 2007–2025 data, institutional flow explains 62.6% of quarterly S&P 500 variance, and each net dollar of institutional buying goes with about $7 of market value (multiplier 7.17).
Institutional Flow Moves the Index
Across S&P 500 stocks (2015–2025), cumulative institutional flow called the direction of the next 13F change with 65.5% average accuracy, rising to 71.1% on the 222 names where the signal is significant.
Access the white paper
13F Filings, Months Before They Are Filed
Daily futures flow anticipates the weekly CFTC report: direction matched about 71% of the time for managed money and 74% for producers, and about 48% in financial futures.
Futures vs the COT
Our first US CPI forecast arrives about 20 days before the release and the last about 5 days before, with forecasts evaluated against consensus since November 2017.
CPI Forecasts, Weeks Ahead
How It Works
Our datasets come with the research behind them, so you can judge them before you test them.
Start with a white paper, request sample data through our contact form, then connect the data to your stack.
Our research team supports the evaluation and answers questions on coverage, history and delivery.
1. Read the research
Our datasets are backed by research we publish: how much institutional flow moves the S&P 500, how far ahead it anticipates 13F filings, how futures flow tracks the weekly CFTC report and how our CPI forecasts compare with consensus. Each paper has a short summary on our blog, where you can request the full white paper.
2. Test it on sample data
Request sample data through our contact form for the datasets you want to evaluate, and tell us what you plan to backtest. Our research team helps you set up the test, and can walk you through the data live on your own tickers, sectors and sessions, so you see what it shows before you license it.
3. Plug it into your stack
Flow, factor and macro datasets come through one delivery layer: pipelines call our Unifier REST API, AI agents use the MCP service, and discretionary desks explore flow and forecasts in the Tesseract portal without writing code. Depending on the dataset, data arrives in real time, 15 minutes delayed or at end of day.
4. Backtest point-in-time
Flow history is point-in-time and never revised: each record carries the moment it would have reached a live feed, so a backtest sees only what was knowable then. Our papers study these same datasets, and we tell you each dataset's known limits before you test it.
"We found that the IC results from the XTech dataset were significantly stronger than both our prior expectations and what we typically observe from comparable datasets."
Name Witheld
PM, Multibillion Dollar Quant Fund
"The XTech Options Flow Dataset is a very clean and high quality dataset and was very easy to use. You did a great job distilling a very complex dataset down into something very easy to digest."
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Name Witheld
Head of Data Sourcing, Top 10 HFT Fund
"Actually, yes, it's quite impressive, to be honest… The history from 2011 is also great. Because it allows lots and lots of testing… so far so good… You definitely are going in the right direction."
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Name Witheld
Quant Researcher, Top 3 European Investment Bank
Latest research
New white papers, monthly CPI forecasts and flow case studies for institutional investors.
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