For a quant, gaining access to another market, venue, or period of history can create a substantial data-engineering task before research begins. The team may need to source the data, map identifiers, reconstruct order books, normalize time zones, and provision storage and compute.
In this webinar, Peter Simpson, OneTick Product Owner, demonstrates a different workflow. He issues OneTick SQL queries from a q session, runs them against OneTick Market Data, and returns the results as q tables in KDB-X.
The result is a practical way to investigate selected datasets without first backloading the underlying history into local infrastructure.
What you’ll see
- Historical, intraday, and latest-value queries across market datasets.
- Trades, quotes, NBBO data, and order book depth.
- Order book snapshots filtered by depth, size, value, spread, or price skew.
- Equities and futures, including individual and continuous contracts.
- Queries using exchange symbols and supported identifiers, including Bloomberg symbols, FIGI, ISIN, and CUSIP, subject to applicable licenses.
- Time zone selection, filtering, aggregation, and alternative output structures.
- Corporate action adjustments and consolidated views of fragmented markets.
From cloud query to q table
The demonstrated module sends OneTick SQL queries from KDB-X to OneTick Market Data. Query results are returned in Arrow format, converted into q tables, and made available for further analysis in KDB-X.
For quantitative teams, this can shorten the path between identifying a research question and interrogating the relevant data. Researchers can retrieve a defined market, symbol universe, date range, or analytical view without first building a local copy of the complete dataset.
For desk and research leaders, the relevant question is not simply whether the data is available. It is how much engineering work, infrastructure, and elapsed time are required before a researcher can use it.
Market microstructure and execution research
The webinar demonstrates access to trades, quotes, NBBO data, and full order book depth. Users can retrieve individual order updates or use snapshot and summary functions to analyze a filtered view of the book.
Queries can return the book:
- At a specific point in time.
- At regular intervals.
- After each book update.
- To a selected number of levels.
- In different row and column structures.
- Using depth criteria such as shares, trade value, spread, or price skew.
The session also discusses book-depth analytics for estimating how far into the book a trade would need to execute and examining the corresponding volume-weighted average price on the bid and ask.
Research across fragmented markets
Liquidity is distributed across venues, and the appropriate consolidated view varies by region. The webinar explains how OneTick Market Data provides consolidated datasets for selected fragmented markets and how users can query regional BBO or NBBO-style views.
Researchers can use the consolidated view supplied with the data or define a subset based on the venues relevant to their trading activity.
Historical continuity and data adjustments
The session covers several issues that affect the validity of historical analysis:
- Corporate action adjustment factors.
- Adjusted and unadjusted price histories.
- Futures data stored contract by contract.
- Continuous contracts based on expiry.
- Front-month alternatives based on volume or open interest.
- Queries across supported symbologies.
These controls matter when a backtest or historical comparison depends on consistent treatment of instruments through time.
Access and current product boundaries
The webinar uses sample data to demonstrate the setup and query workflow. Broader historical coverage is available through subscription.
Real-time data uses the same query pattern, but access depends on venue permissioning and signed exchange agreements.
The module demonstrated is specific to KDB-X. It does not currently support kdb+, and remote queries use OneTick SQL rather than QSQL.
Raw datasets can also be made available as partitioned Parquet files in an Amazon S3 bucket. Raw-data access requires permission for the complete venue, while API access can support symbol-level requirements.
Who should watch
- Quantitative researchers evaluating new markets, datasets, or signals.
- Quantitative developers building research and market data workflows.
- Trading analytics and transaction cost analysis teams working with quotes, NBBO data, and order book depth.
- Market data engineers responsible for data sourcing, normalization, and delivery.
- Heads of desk and research leaders assessing the time and infrastructure required to make new datasets usable.
Continue exploring
Learn how KDB-X supports high-performance analytics across Python, SQL, and q, or explore the market coverage available through OneTick Market Data.