Historical and real-time
tick data

We provide up to four timestamps per event, each in nanosecond resolution.

1. Exchange timestamp: A lossless capture of the primary timestamp from the trading venue—typically tag 60 (TransactTime), but sometimes tag 52 (SendingTime), depending on the venue. Used to sequence and correlate matching engine events.

2. Sending timestamp: If both tags are available, we include both. Useful for estimating matching engine latency.

3. Wire capture timestamp: A high-precision timestamp taken at the exchange boundary when the packet is pulled off the wire. Synchronized to a GPS clock using PTP, it enables accurate backtesting and simulation of the matching engine round-trip delay. Also useful on venues that provide only millisecond-resolution timestamps that are sometimes out-of-order. This is the same as packet receive timestamp found in our PCAP data, and can be used to validate our normalized data against raw market data.

4. Databento sending timestamp: Available via our real-time gateway, this shows when Databento sent the data, useful for measuring internal latency and data staleness.

Learn more in our timestamping guide.

Databento offers tick data across futures, options, and equities, covering 70+ global trading venues.

Raw tick data is captured in its original wire format, encoded in the native protocol used by the exchange or trading venue. Normalized tick data derived from raw tick data into a consistent format that unifies fields across different wire protocols for easier use.

Some vendors label their normalized data as “raw,” but this can be misleading—normalization is usually lossy, potentially discarding details like original packet order.

We offer raw tick data through our PCAP service, and normalized tick data via our API and browser GUI.

There's no industry convention on what's considered "tick data". Many market data providers pass off a feed or dataset with each individual trade (a subset of L1 market data) as "tick data". In many cases, what they call "tick data" isn't even the most granular form of data available—which is usually either in PCAP or raw MBO format. In our case, we provide all three levels of granularity in our "tick data" offering.

We offer both historical and real-time tick data in normalized form.

Historical tick data is accessible via API or our no-code browser GUI. You can stream it into application memory or download it in batch as binary or CSV flat files over HTTP or FTP.

Real-time tick data is delivered through our streaming API.

Yes, usually.

Even for low-frequency strategies, most large price moves and trade opportunities occur within sub-second bursts. OHLCV data often obscures when your trade would’ve actually executed within a bar—making it hard to model slippage or fill timing accurately. Market makers also widen spreads during volatile moments, so constant spread assumptions often underestimate true slippage. It’s generally easier to model these effects directly with tick data than to rely on parametric approximations.

Also, since low-frequency strategies are especially sensitive to execution and market impact, sophisticated practitioners still often rely on tick data for transaction cost analysis as they develop their low- to mid-frequency strategies.