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.

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

The legacy live usage-based plans allowed users to access L2/L3 data. However L2/L3 were pulled from the Standard plan. One possibility to increase the value of the Standard plan is to offer limited access to L2/L3, perhaps gated by a quota on symbol subscriptions per account, etc.

Hi, is there any chance that we will see Euronext paris futures expansion (CAC40, Milling Wheat, Corn, Rapeseed ... futures) thanks

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

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.

B3: Brazil Equities and Futures MBO data. Keywords for search purposes: Brasil Bolsa Balcão S.A., Brazilian Mercantile and Futures Exchange (BM&F), BMF, Bovespa (São Paulo Stock Exchange), Cetip (Central of Custody and Financial Settlement of Securities for the organized OTC market).

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.

Equity data from TSX with broker IDs (TSX is one of the few markets out there with post-trade transparency)

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,

Exchange fees, e.g. clearing, rebates, etc. (e.g. for CME Group and Eurex) Also in parallel, a similar dataset that's useful would be historical borrow costs.

Daily net fund flows resulting from ETF creation/redemption mechanism, indicating the real capital entering or exiting the fund. This ticket also tracks shares outstanding and the net asset value (NAV) for ETFs.

Standard plan users have a hybrid setup where they get included history with their subscription, but requesting any data outside of the included range will be subject to usage-based rates. It may be hard to know exactly the cutoff time and schemas that are included with the subscription and accidentally overspend on metered usage while on a Standard subscription. This proposed feature will involve adding some functionality for users to disable metered billing on the billing dashboard and perhaps also when they've completed activating a Standard subscription plan. Timing If accepted, we'll only be able to implement this feature after we've rolled out some upcoming features in Q2 2025: OPRA subscription plansOPRA upgrades (extended history, CBBO, CMBP-1, etc.)EEXEurexICE USICE Europe Financials

Request for supporting Japanese market data. Tokyo Stock Exchange (TSE)

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.