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.

Trades in US equities that happen off-exchange are reported to one of three TRFs. NYSE provides information on trades reported to their TRF through their "NYSE Trades" feed. More information available here: https://www.nyse.com/markets/nyse/market-info

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.

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, 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.

e.g. for S&P 500.

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.

Historical and live market news.

Australian Stocks with ASX and CBOE trades

This request is to make the API documentation available for download so it can be referenced offline.

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

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.

Original request from Juan Linares: "Great product but please add dark mode." There are two separate parts to this: Dark mode for the portal and main website (databento.com, databento.com/portal) Dark mode for the docs We can consider this only after Q1 2025 since we're doing a major rebranding of our website which is expected to finish by early April 2025. The new colors will make it easier for us to implement a dark mode.

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