Product roadmap

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

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.

e.g. Shares outstanding, short interest, market capitalization, P/E ratio etc.

This ticket tracks all Euronext cash and derivatives markets. This includes, for example: Euronext Milan (formerly Borsa Italiana)Euronext Paris futures (CAC40, Milling Wheat, Corn, Rapeseed, etc.)Euronext Paris equitiesEuronext AmsterdamEuronext OsloEuronext BrusselsEuronext LisbonEuronext DublinLuxembourg Stock Exchange (LuxSE)

All Cboe Europe on-book equities markets - BXE, CXE, DXE. Captured at Equinix LD4.

Currently, Trade Busts and Trade Corrections are dropped by Databento's normalization. In the scope of this feature request, add the trade bust/corrections to the API, either as a separate schema or inline in the MBO schema (and downstream).

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.

Only regular trading hours are supported currently.

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.

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

At the moment, options data users have to rely on fetching the definition schema and filtering for symbols that they're interested in using fields like expiration, asset, underlying_product, instrument_class, group, and strike_price. It would be convenient to fetch the options or options chains with particular conditions on expiration and strike price without going through the definition schema. This would be similar to smart symbology for futures. Note that even after this feature is released, we still recommend users to use definition as it gives more control and transparency over the symbology resolution.

Securities and futures data.

Stocks and derivatives data from the Singapore Exchange (SGX)

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