Product roadmap

Add a schema for calculated metrics like options greeks, e.g. implied volatility, delta, etc.

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).

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

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.

To extend support to browser-based applications.

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.

Data for: Copenhagen Stock Exchange (Nasdaq Copenhagen) Stockholm Stock Exchange (Nasdaq Stockholm) Helsinki Stock Exchange (Nasdaq Helsinki) Iceland Stock Exchange (Nasdaq Iceland) Tallinn Stock Exchange (Nasdaq Baltic) Riga Stock Exchange (Nasdaq Baltic) Vilnius Stock Exchange (Nasdaq Baltic)

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.

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.

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,

https://www.cftc.gov/MarketReports/CommitmentsofTraders/index.htm

Full order book feed for LSE securities — including full depth pricing data, auction imbalance, and instrument trading status.

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.

Data for Deutsche Boerse Equities, including all schemas (MBO, MBP, ohlcv, etc)