Product roadmap

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.

This feature would allow the user to request trading calendar information (such as trading session start/end times) via our API. This is especially useful when considering trading sessions that can span multiple UTC dates (and hence the possibility of having multiple trading sessions within a single day). Keywords: Market calendar, trading holidays.

Currently, indices are indirectly supported through tradable index instruments on CME futures, ETFs, etc. and we don't provide the index values (non-tradable) themselves. This may be sourced from a feed like the Cboe Global Indices Feed or NYSE Global Index Feed.

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.

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

This serves as a master list of all other snapshot-like features on our roadmap. The scope of this ticket is potentially very large and ambiguous so we've broken this down into smaller tickets that you can follow separately. (Historical only) https://roadmap.databento.com/b/n0o5prm6/feature-ideas/add-historical-endpoint-for-latest-snapshot-of-any-schema. This would allow a user to get the latest published value of any given schema, within the boundaries allowed by licensing/entitlements/historical embargo window. The main benefit of this is for creating ticker tape or latest quote features, e.g. on a web app, after we start exposing intraday data over the historical/HTTP API (https://roadmap.databento.com/roadmap/expose-intraday-and-current-trading-session-historical-data-over-historical-http-api-and-clients). Likely endpoint names for this would be either timeseries.get_last or timeseries.get_snapshot. (Historical only) https://roadmap.databento.com/b/n0o5prm6/feature-ideas/provide-snapshots-as-of-specified-time-in-historical-api. Likely endpoint names for this would be either timeseries.get_last or timeseries.get_snapshot.(Live only) https://roadmap.databento.com/roadmap/add-periodic-mbo-book-snapshots-to-live-api. This allows a user to get the last published value of any given schema at a specified time. The main benefit of this would be to allow customers to subsample the data on server side and reduce cost, though the benefit is diminished with feature 5 on this list. Note that this would allow a user to emulate (1) relatively well since a user could potentially just pass in their current clock time or some time slightly ahead of the clock time. However, their underlying implementations would be different and (1) and (2) would likely be released separately. Likely endpoint names for this would be either timeseries.get_last_asof or `timeseries. (Live only) https://roadmap.databento.com/b/n0o5prm6/feature-ideas/allow-live-api-clients-to-request-for-mbo-snapshot-recovery. This provides resilience to gaps or data errors originating from Databento side. It could also be used for recovery of book state caused by client-side issues or disconnection, but would be less quick than feature (4) on this list.(Both historical and live) https://roadmap.databento.com/roadmap/fixed-interval-mbp-1-summaries-eg-1-minute-bbo-or-subsampled-bbo. The purpose of this is more to provide customers a convenience over fetching or subscribing MBP-1 and subsampling and forward filling the MBP-1 data themselves, which could be very expensive given the size of MBP-1 data and how the customer has no idea how far to look back for the "last" MBP-1 update prior to the 1 second or 1 minute refresh interval. Some of these are in development, hence the status of this entire ticket, however you should check on each individual one in case the specific feature you're looking for is still in Considering state.

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

It might be possible to obtain CME trading session hours systematically in historical captures of the instrument definition messages, as embedded in tag-1682=MDSecurityTradingStatus. This ties to another proposed feature here.

Documentation for how to use the the order book from MBO data for visualizing the evolution of limit order book over time as heatmap. For instance every 10 seconds a snapshoot of the order book of historical 6E futures data is taken. Now a heatmap (exp.: Seaborn) is generated, showing price levels on y axis and timeincrements of 10 seconds on the x axis. The color intensity of the boxes depends on the size of the limitorders. Maybe this idea is an good example for implementing the heatmap with json, d3, ...

Data for: Copenhagen Stock Exchange (Nasdaq Copenhagen) Stockholm Stock Exchange (Nasdaq Stockholm) Helsinki Stock Exchange (Nasdaq Helsinki) Iceland Stock Exchange (Nasdaq Iceland) Tallinn Stock Exchange (Nasdaq Baltic) Riga Stock Exchange (Nasdaq Baltic) Vilnius Stock Exchange (Nasdaq Baltic)

Hello, I'd really like high quality equities data for european exchanges like xetra, so I created this feature request. Maybe others are interested in that as well. Even EOD would be nice as a start. Kind regards,

Several equities prop feeds provide market participant IDs (MPIDs). Currently, our normalized MBO format is lossy at the moment and fails to expose these MPIDs, and our only partial solution is to provide raw pcaps from which these can be extracted. This ticket will also partly facilitate this: https://roadmap.databento.com/roadmap/raw-data-via-api

Full order book feed for LSE securities — including full depth pricing data, auction imbalance, and instrument trading status.

Data for Deutsche Boerse Equities, including all schemas (MBO, MBP, ohlcv, etc)

Currently, we only support the standard Python versions and not the free-threaded interpreters. Supporting this will require some work in databento-dbn and perhaps dependencies of the databento package.