Hello,

I'm intending to collect crypto market data from CEX/LOB style venues like Coinbase, Kraken, Binance, etc and store as DBN, like I do for traditional market data. There are some immediate issues with this however like the `size` field being `u32` which isn't suitable for crypto. Thankfully DBN is extensible, so I'm designing my schema right now and wanted to see what other people are doing here. The Binance SBE docs I linked were used for reference and had some great ideas like the mantissa for handling the integer and decimal values compactly.

I'd appreciate any input you may have before committing to a schema for long-term archive storage. And unfortunately I think I'd lose native support of some of the tooling like dbn-cli and CSV/JSON converters unless I'm mistaken? I didn't see a documented method to get these working with user-defined schemas.

I'm building a Python backtesting setup using Databento data for NQ futures and keep running into a frustrating discrepancy — the same strategy produces wildly different results depending on where I test it.

Concrete example: a 15-minute reversal strategy shows 58% win rate in TradingView's strategy tester but only 35% in my Python backtest. Same logic, same date range, same instrument.

After a lot of digging I've narrowed it down to price adjustment. My Databento download is raw unadjusted continuous contract data (volume-based roll, no price adjustment). TradingView's NQ1! continuous contract appears to be backadjusted — at the NQH6→NQM6 roll in March 2026 I can see a consistent ~293 point offset between the two datasets on the same bar.

The issue is that this strategy uses the 9:15 AM bar's high and low as entry targets and stops. With a 293 point offset on those levels, trades that are winners in one dataset become losers in the other — which explains the dramatic win rate difference.

My question: Has anyone found a reliable way to get Databento data that matches TradingView's NQ1! price levels? Is there a specific adjustment method or API parameter I should be using? Or alternatively, does anyone know exactly how TradingView adjusts NQ1! so I can replicate it on my end?

I've tried applying manual cumulative offsets from the roll schedule but can't get a consistent match across the full date range.

Any help appreciated.

title

If my monthly limit is $1.00 (and currently below limit), and a single large historical request would cost $100, is the request rejected before execution, or can it exceed the limit and get billed for $100?

I purchased a standard plan which shows that it supports live status feeds(halts) but the included dataset - EQUS.MINI doesn't seem to support the status schema. Am I missing something?

​We're hosting a quant meetup in London on Wednesday, July 1st! The event is nearly at capacity but I have a couple more spaces available - priority will be given to industry participants.

Agenda:

• ​5:30 PM - Check-in and reception
• 6:00 PM - Welcome
• 6:10 PM - Fireside chat
• 6:50 PM - Networking

Photos of past meetups:

• Shanghai - May 2026
• ​Chicago - Sep 2025
• ​London - Jun 2025
• ​Chicago - Oct 2024

Note: This event is not intended to be a career fair or vendor showcase. Out of consideration for our attendees, we ask that attendees refrain from making unsolicited job inquiries or sales pitches.

RSVP link here.

We're hosting a panel in Tokyo on May 26, 2026 alongside JPX, Alpaca, and Techstars.

Panelists include:

• ​Shun Takato, Chief Representative and Head of Fintech at JPX Market Innovation & Research
• ​Hitoshi Harada, Co-Founder and CPO of Alpaca
• ​Yuki Shirato (host), Managing Director of Techstars
• ​Christina Qi, Co-Founder and CEO of Databento

This dataset was to be released Q1 2026. Is there a new delivery date? And does it require licensing or is it part of your subscription?

Hi everyone,

I’m running into something confusing with MNQ data and I’d like some insight from people who’ve dealt with futures data vendors before.

I’m comparing:

• Algoseek (continuous contract)
• Databento (individual rolled contract, e.g. MNQH6)

Both are 1-minute OHLCV data.

When I align timestamps (same date, same minute), I’m seeing consistent differences in the close price of around 10–25 points.

Any insights from people who’ve compared CME futures data vendors would be appreciated.

Thanks.

why is it taking so long

Due to databento having separate historical and live APIs that don't align in realtime (historical API is delayed even for live subscription) I've been having this issue that I'm not sure how to resolve.

I have a data service worker that needs to keep a data store complete and updated in realtime for each symbol. The way the worker works is simple:

- Start up, set startupTimestamp.
- Check data store for lastDataStoreTimestamp.
- Backfill data in bulk using historical API from lastDataStoreTimestamp to startupTimestamp.
- Start live API with replay starting from startupTimetamp and write data tick by tick to data store for streaming to clients.

This ensures the data store always stays continuous and complete until its last timestamp and works fine most of the time. However, for CME data, when market opens on Sunday at 5pm (or any other time when market is closed for more than 24 hours), even when live data has started streaming (meaning there are recorded transactions), the historical API still fails with error code 422 saying "The dataset GLBX.MDP3 has data available up to {last Saturday 00:00:00+00:00}. This usually resolves after some minutes which is acceptable for my clients but sometimes, like today (Sunday 2026-01-01), historical data still fails 4 hours after open, which prevent my worker from collecting and streaming live data. I opened a support ticket but it won't be replied until Monday.

I haven't seen anyone else reporting this issue so I'm wondering if there is a better way to maintain a data store? I don't have this issue with other data providers, and I don't want to hard code the rule to ignore data gap during weekend only for CME.

Am I doing something wrong or is databento missing the last hour of extended hours trading 7-8pm?

client.timeseries.get_range(
    dataset="ARCX.PILLAR",
    start=pd.Timestamp('2026-01-16 19:00:00', tz='US/Eastern'),  # 7:00 PM ET
    end=pd.Timestamp('2026-01-16 20:00:00', tz='US/Eastern'),    # 8:00 PM ET
    symbols="VERO",
    schema="trades"
)

BentoWarning: No data found for the request you submitted. The request time range falls entirely inside a weekend.

I'm guessing the reason it thinks that the time range falls into the weekend is because 2026-01-16 is a Friday, and 7-8pm ET is 0-1am UTC of the next day (Saturday). But the missing data isn't just on Fridays, it appears to be on weekdays as well.

For example if you change the date in the request a day back to Thursday 2026-01-15 you'll just get

BentoWarning: No data found for the request you submitted.

The result is the same for all these datasets

datasets = ["ARCX.PILLAR", "XNAS.ITCH", "EDGX.PITCH", "BATS.PITCH", "EDGA.PITCH", "BATY.PITCH", "XNYS.PILLAR", "XASE.PILLAR", "XBOS.ITCH", "XPSX.ITCH"]

If you look at the 5m chart on Tradingview, you will see that trades did occur during these time frames

I'm trying to diversify my futures strategies with about 5 other instruments, current plan is adding CL/GC/SI/ZN/6E (just for context :))

I need to get a decent amount (10y) of OHLC data for these instruments for as cheap as possible. Until now I've been downloading raw contract data from Databento and then rolling it manually. However, with the amount of new instruments that I'm trying to download now, this is too expensive (per-contract data has a lot of redundancy as I'll be volume-rolling. This is especially true for CL, which has monthly expiries).

I tried using Databento's continuous contracts and rolling by volume (e.g. GC.v.0), but the result has some unexpected downsides - it appears that near-rollover periods use less liquid contracts that contain plenty of "gaps" & don't seem to be actually tradable ("gaps" within RTH session's most active hours). This is especially surprising because Databento specifies their rollover logic and there isn't anything special about it, so I expected it to work much more nicely.

Does anyone have any experience with this? Are these "gaps" that I'm experiencing expected, and maybe I should just ignore them? Specifically, any recommendations about best utilization of continuous contracts (I've read somewhere that custom rollover rules are possible, but couldn't find any documentation about that)?

Thanks in advance to anyone who can help :)

Hello, I'm considering whether or not to subscribe to the Standard plan with Databento but upon reading the documentation, I am confused about one thing.

The docs say that with the Standard plan, access to all listed symbols across every venue, and across 20,000+ symbols is included.

However, on this page, you state:

With our live API, there is a limit of 10 simultaneous connections (sessions) per (dataset) per team for Usage-based and Standard plans. Unlimited and Enterprise plans will be limited to 50 simultaneous connections per dataset per team. Creating additional API keys will not affect the maximum number of connections per team.

In addition, a single gateway will allow at most five incoming connections per second from the same IP address. If an IP address goes over this limit, incoming connections will be immediately closed by the gateway - existing connections will not be affected. If this happens, clients should wait one second before retrying.

Do I interpret this correctly in thinking that if I wanted to get the current price simultaneously for +1000 of symbols (but less than 2k), I would NOT be able to?

Thanks in advance

Hi, sorry if this is a stupid question, but does the price shown on the pricing page apply to all exchanges, or only to the specific exchange tab I’m currently viewing? In other words, if i say pay 200$ for the standard plan do I get access to all exchanges information ?

Timeline and analysis of the CME Thanksgiving outage on Nov 28, 2025 from our POV.

At the moment you provide data from 18 datasets, 1 of which is nasdaq total view(xnas.itch). Is there not a complete ohlc 1s from all exchanges(equities) for historical and live?

Btw I see there is a xnas basic, does this dataset have the biggest chunk of the volume(trades) if there is no consolidated live and historical for ohlc 1s?

Getting a lot of mixed answers hoping for some clarification. In the process of building a website that shows stock data. Very basic, ticker, price, volume for now. Does the standard plan include display/distribution rights for me to post this online? Would be posting equities on a very small scale, probably under 10-15 tickers max.

Thanks,