Product roadmap

Submit dataset requests and feature ideas here. For bug reports, use our chat support or issues tracker instead.

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  1. Eurex EOBI dataset

    Data for Eurex, including all schemas (MBO, MBP, ohlcv, etc.).

    Renan Gemignani

    13

  2. WebSocket API for live data

    To extend support to browser-based applications.

    Tessa Hollinger

    6

  3. Parquet encoding

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

    Tessa Hollinger

    11

  4. London Metal Exchange

    futures and futures options OHLC, OI and volume from the LME

    Felix E

    0

  5. Singapore Exchange (SGX) data

    Stocks and derivatives data from the Singapore Exchange (SGX)

    Carter Green

    0

  6. CBOE Complex Options Order Feed

    Adding support for the CBOE Complex Options Order Feed, which provides detailed insights into complex order activity across CBOE markets. This includes multi-leg strategy orders, spread data, and intricate order types that are vital for understanding advanced options trading strategies. Key Features: -Real-time and historical data on multi-leg orders. -Visibility into pricing and execution of complex options strategies. -Detailed breakdowns of spread and combination order flows.

    Eric M Duncan

    0

  7. ICE iMpact: ICE Futures Europe - Financials

    Currently our ICE Futures Europe coverage only includes commodities and not financial products like Euribor, as stated here. This ticket separately tracks a future dataset that includes the financial products. ICE Futures Europe financial products include: STIRSFTSE 100London Stock OptionsBond DerivativesEuriborSwapnoteEuroswissLong GiltMedium GiltShort GiltSOFRSONIA

    Tessa Hollinger

    6

  8. B3 UMDF Dataset

    Brazil Equities and Futures MBO data.

    Renan Gemignani

    2

  9. Add Polars support to `to_df` method

    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.

    Aidan L

    1

  10. Provide adjusted continuous contract

    Our continuous contract symbology does not behave the same as continuous contracts provided on retail charting apps, which create a continuous series by applying a constant offset on each rollover month to the lead month contract. Our philosophy is generally to provide raw prices because: (a) adjustments are opaque and may introduce vendor errors, (b) this gives you flexibility to do your own custom rollover adjustments, (c) the adjusted prices will throw off certain feature/signal calculations, (d) in practice you can't hold an instrument through the roll anyway, so adjusted prices may underestimate slippage that you'll experience from crossing the spread on the legs or the listed spread. (e) there's no single rollover rule that we expect to be preferred by all customers for all symbols, e.g. rollover rule for a symbol with term structure like SR3 or a physical commodity with seasonality and more than the common quarterly expiration schedule. However, we may consider providing something like this either: a) as a convenience feature to support legacy use cases that require adjusted continuous contracts b) as a code example to show how the user can compute the appropriate price adjustment themselves

    Tessa Hollinger

    2

  11. Real-time and historical index 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.

    Tessa Hollinger

    24

  12. Trading calendar information

    This feature would allow the user to request trading calendar information (such as trading session start/end times) via our API. This is especially useful when considering trading sessions that can span multiple UTC dates (and hence the possibility of having multiple trading sessions within a single day)

    Renan Gemignani

    5

  13. CFE Book Depth

    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).

    Zach Banks

    10

  14. Consolidated US equities data

    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.

    Tessa Hollinger

    12

  15. Cboe FX ITCH (forex, foreign exchange)

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

    Tessa Hollinger

    13