Product roadmap

Support Parquet as a form of encoding, aside from dbn, CSV and JSON.

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.

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.

Databento's feed is based on CME's MBO feed and we do not overlay implied depth from the MBP feed. This creates the appearance of less liquidity and wider spreads compared to many vendors that are only using the MBP feed. Overlaying MBO and MBP creates several complications; we think using the direct book is better for signal generation and execution, and prefer not overlay implied MBP over MBO to form a composite book. At this time, users who are sensitive to implied orders can impute the implied book themselves. That said, we may expose the implied book for users who find this useful and prefer to compare our data to another reference.

Kraken spot and derivatives instrument catalog, order book (L1, L2, L3) and trade data for crypto, stable/FX, perps, xStocks, etc. https://docs.kraken.com/api/docs/websocket-v2/book https://docs.kraken.com/api/docs/websocket-v2/level3 https://docs.kraken.com/api/docs/websocket-v2/trade https://docs.kraken.com/api/docs/futures-api/websocket/book https://docs.kraken.com/api/docs/futures-api/websocket/trade

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.

To extend support to browser-based applications.

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.

Currently, US equities data prior to 2018-05-01 is not available.

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

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.

Especially for US equities trading activity during non-US hours.

We're adding live auction imbalance data for NYSE, NYSE Arca, and NYSE American. These feeds will be included with all Plus and Unlimited plans for Databento US Equities. While real-time imbalance data can be licensed as part of the full Integrated feeds for NYSE, NYSE Arca, and NYSE American, this costs upwards of $7,500/month in non-display fees. The Order Imbalances feeds start at $1,000/month and offer a more cost-effective option to license this data.