Product roadmap

Macroeconomic data releases, such as: Jobs Inflation, e.g. CPI FOMC

The current coverage for EEX (XEEE.EOBI datataset) does not include settlement price and open interest for EEX Japanese power futures.

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.

We've received some requests recently for Binance data. Please upvote if this is of interest. We're still determining whether this is worth the risk.

futures and futures options OHLC, OI and volume from the LME

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

Currently, multi-leg products (spreads, strategies, combos) on CME/ICE are hard to use because our instrument definitions do not provide metadata about each underlying leg. The user has to infer the legs from the symbol. This is a form of lossy normalization, since CME/ICE does provide this in their security definitions in a repeating group, but our fixed instrument definition schemas are forced to discard this—they only provide the the instrument_id of the first underlying instrument through underlying_id. In the meantime, our recommendation to users is to either infer this from the symbol OR download the raw security/instrument definitions from the exchange (e.g. CME's is free on their FTP) OR get a pcap subscription from us. If you need historical secdefs copied from CME (since their FTP site only gives 1 day history), we can provide a courtesy backfill of these for a fixed cost.

National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) data.

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.

Stocks and derivatives data from the Singapore Exchange (SGX)

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.

FedWatch data. As found on https://www.cmegroup.com/market-data/market-data-api.html

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.

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).

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.