Product roadmap

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.

Feature to schedule recurring batch download jobs (e.g. nightly/weekly) through the web portal. This would be helpful to users who are regularly submitting batch download jobs through our web portal so that they only need to visit Databento once to retrieve the data after the job is complete. (Users of our APIs and client libraries are already able to use batch.submit_job to automate creating batch download jobs.)

Data for Eurex, including all schemas (MBO, MBP, ohlcv, etc.).

All orders plus last look quotes from 35 major banks and non-bank LPs, on one of the largest FX venues.

From EEX CEF Core feed EEX EOBI feed. This will include order-by-order data as well as less granular formats, such daily settlements, definitions, etc.

Make our historical and live APIs easier to integrate from Java.

Databento's feed is based on CME's MBO feed and we do not overlay implied depth from the MBP feed. This creates the appearance of less liquidity and wider spreads compared to many vendors that are only using the MBP feed. Overlaying MBO and MBP creates several complications; we think using the direct book is better for signal generation and execution, and prefer not overlay implied MBP over MBO to form a composite book. At this time, users who are sensitive to implied orders can impute the implied book themselves. That said, we may expose the implied book for users who find this useful and prefer to compare our data to another reference.

https://conda-forge.org/ https://anaconda.org/

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.

Especially for US equities trading activity during non-US hours.

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)

Full depth of book feed for Cboe Futures Exchange (CFE). CFE contains volatility futures and corporate bond index futures, such as VIX futures (VX, VXM).

To extend support to browser-based applications.

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.

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