Product roadmap

Support Parquet as a form of encoding, aside from dbn, CSV and JSON.

Add a schema for calculated metrics like options greeks, e.g. implied volatility, delta, etc.

e.g. Shares outstanding, short interest, market capitalization, P/E ratio etc.

It would be great if Japan Exchange Group (Nikkei 225, etc) could be supported. CME Nikkei 225 volume is too low.

This ticket tracks all Euronext cash and derivatives markets. This includes, for example: Euronext Milan (formerly Borsa Italiana)Euronext Paris futures (CAC40, Milling Wheat, Corn, Rapeseed, etc.)Euronext Paris equitiesEuronext AmsterdamEuronext OsloEuronext BrusselsEuronext LisbonEuronext DublinLuxembourg Stock Exchange (LuxSE)

Currently, Trade Busts and Trade Corrections are dropped by Databento's normalization. In the scope of this feature request, add the trade bust/corrections to the API, either as a separate schema or inline in the MBO schema (and downstream).

Currently, equities is supported via individual prop feeds of each venue. While NASDAQ is sufficient for getting NBBO for most of the time, some users prefer something that will be more in line with actual NBBO from SIPs. This feature request tracks 3 possible modes of consolidation for both historical and live data: Databento server-side consolidation of multiple proprietary feeds Consolidated data from proprietary feed like Nasdaq Basic in lieu of SIP Consolidated data from CTA/UTP SIPs We plan on implementing 1-2 of these three options.

Only regular trading hours are supported currently.

Could we add support to make the result of DBNStore.to_df a Polars dataframe as well? Perhaps the function signature could just be overloaded with a to_polars: bool argument. Something like: In Python @overload def to_df(self, to_polars: Literal[False]) -> pd.DataFrame: ... @overload def to_df(self, to_polars: Literal[True]) -> pl.DataFrame: ... Or, maybe to_df is split into two different functions to_pandas and to_polars. Either way, it would be helpful to avoid having to do pl.from_pandas(store.to_df().reset_index(drop=False)). Plus, Polars can convert to pyarrow-Pandas zero-copy, but not the other way around.

At the moment, options data users have to rely on fetching the definition schema and filtering for symbols that they're interested in using fields like expiration, asset, underlying_product, instrument_class, group, and strike_price. It would be convenient to fetch the options or options chains with particular conditions on expiration and strike price without going through the definition schema. This would be similar to smart symbology for futures. Note that even after this feature is released, we still recommend users to use definition as it gives more control and transparency over the symbology resolution.

This client library makes all our historical and live features easier to integrate in R on Windows, Linux, and Mac OS. R is currently already supported through our HTTP API and Raw TCP protocol, which are both language-agnostic.

The earliest that our tick history (L1 and better) goes back to is 2018, which is the start of our Nasdaq TotalView-ITCH coverage. While backfill of daily/OHLCV/L0 data is in progress, which will bring daily history back to at least 2010, we have no immediate plans to backfill our tick history yet. The root issue is that we cannot find a quality source that we trust for this granularity of data prior to 2018. While several sources exist, most of them do not meet our accuracy/quality control standards. This ticket tracks backfill of US equities - especially tick history for those venues - back to before 2018.

This ticket tracks the setup of Databento's layer 1 network architecture. We anticipate this in Aurora I initially, then several venues in NY4, followed by FR2. At the moment, our data takes at least one layer 3 hop before getting to our capture server. Hardware timestamps are taken on the host. This results in some additional latency (about 500-550 ns for the layer 3 hop) and timestamping jitter. This also results in more inconsistency (typically at the sub-microsecond level) across hosts. The new layer 1 architecture will replace the layer 3 hop, cutting down 500-550 ns to 5 ns, achieve better timestamping consistency between our hosts, and lower jitter from the exchange handoff.

CME has "LimitsBanding" messages which provide updates during the weekly session to high & low limit prices for instruments. These are currently excluded from normalization, with the high_limit_price and low_limit_price in our definitions schema being provided by the instrument definition messages, where CME says "This value is indicative only and may not reflect the actual real-time low limit price." The price band limits may either be added as new records in the statistics schema. Internal tracking: D-3303

All Cboe Europe on-book equities markets - BXE, CXE, DXE. Captured at Equinix LD4.