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.

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

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.

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.

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

Hi there! Ive only been working with your service a short time, but im already commited to your platform. Python is a GREAT general purpose language. But I think i am justified when I say that for certain use cases, a "compile to native binary" language like Go is superior for the speed and simplicity (no package dependencies to manage, easier to build out dockerized/container applications) Justification: While there are other compile to native languages (RUST, Haskell, an Elixir i have heard of), Go has a significantly higher developer community. Its will be alot easier to crowdsource for bug, QA, enhancements and fuel adoption...I believe only the bareminimum functionality should be "made official" from you guys. While i think there are some things that should be made simpler than what you provide in your Databento Python SDK, I believe its more important to keep a consistant "bare bones functionality" across all your SDK offerings for accessing your core datasets GO (if you werent aware) allows exposing packages/modules that Python can consume. So you should (in theory) be able to design a top level/ 1 to many code base that can be re-purposed for python (and any other languages) ....Instead of maintaining 2 SDK's... you maintain the one... and port to the other via package exposure)...attached a screenshot (While you provide a pip install package, this approach would allow also make it easier for you to push to other package managers like homebrew, nuget, NPM....without having to refactor and maintain code bases for multiple languages.. Im a long time Go developer... So i 120% would like to volunteer to assist with development and design efforts. Ive already done quite a bit on my own project that I believe others would find useful ( I believe you'd also benefit from having an end user and consumer, aid in designing the final consumer product(s)... You know what they say about starship designers, never actually getting to go into space LOL) Long story short, an official Go SDK would offer "Minimal effort, maximum coverage" and enhanced scalability Thanks in advance!

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.

Note: This is not typically included in corporate actions (which are available) or fundamentals (https://roadmap.databento.com/b/n0o5prm6/feature-ideas/equities-reference-fundamental-and-static-data).

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.

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.