Product roadmap

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.

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

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)

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,

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)

As described in this issue ticket (https://issues.databento.com/b/6vrl98vl/feature-ideas/incorrect-scaling-of-strike-price-in-definition-schema-for-some-cme-options), CME has inconsistent rules on strike price scaling that varies with product group. There is no systematic way to correct for this and adjustments must be made by hand - this is a faulty issue with CME's own security definition data. Most options trading firms maintain a table of these adjustment/display factors by hand. We decided to make this adjustment under the hood to known products affected by this, so that most of our customers who do not have their own internal adjustment table or are unaware of this underlying CME behavior will not be impacted by this issue. As a downside however, it can be counterproductive for more sophisticated firms that do have their own adjustment/display factors and need to know which strike prices have been adjusted by Databento. At the moment, it appears that the best possible follow-on solution is to provide a separate table of the adjustment/display factors applied by Databento, so firms can revert the data back to the original data.

It would be great if you guys could provide this data, even just starting from the past couple of years. There aren't any good sources online for retail.