How Quantitative Trading at Brown uses SiftingIO for empirical U.S. equity research

Quantitative Trading at Brown is combining proprietary alternative, non-financial data with historical intraday U.S. equity market data. The research examines whether events identified in those alternative datasets are associated with changes in market returns, volatility, and trading volume.

The work is empirical and exploratory. Members are testing whether such a relationship exists, not asserting one. Candidate signals are put through robustness checks and out-of-sample testing before any conclusion is drawn, the standard bar for treating a result as meaningful rather than an artifact of one particular sample.

Intraday granularity matters here. Studying returns, volatility, and volume at the intraday level lets the group line up market behavior with the timing of the events in its alternative data, rather than collapsing a trading day into a single close. That is what makes it possible to ask whether an event is associated with a measurable move, and over what window.

SiftingIO provides the historical OHLCV market-data layer for this work. Members are building research datasets containing millions of intraday market observations across U.S. equities, then aligning those bars with the club’s own proprietary alternative datasets so the two can be studied together.

Working at this scale keeps the analysis honest: returns, volatility, and volume are measured against a broad, consistent record of intraday activity rather than a handful of hand-picked days. The market data arrives already normalized under a single schema, which is documented in the data methodology, so the club can spend its time on research questions rather than on reconciling formats.

Two things made SiftingIO a practical fit for a student research group. The clean API makes it straightforward to integrate market data into the research workflow, so pulling historical bars is a small, well-understood step rather than a project of its own.

Separate accounts and API keys allow members to work independently without sharing credentials. Each contributor runs their own queries against their own key, which keeps parallel research tracks cleanly isolated as the group grows and as members join and graduate across academic terms.

Quantitative Trading at Brown is a student organization at Brown University. This research is student-led and independent, and SiftingIO’s role is limited to supplying the historical market-data layer described above.