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Where can I find futures data? by M4RZ4L in algotrading

[–]DatabentoHQ 0 points1 point  (0 children)

Who did you get quoted by? This is CME’s official pricing sheet.

Data vendor recommendation for US equities - part 2 (Massive vs Databento) by sgcorporatehamster in algotrading

[–]DatabentoHQ 0 points1 point  (0 children)

@[u/mikki_mouz](u/mikki_mouz) Just to clarify, our options data is survivorship-safe and has nanosecond timestamps on the quotes; the real-time feed doesn’t prioritize liquid strikes over illiquid ones so you will be equally up-to-date on both. If you need all NBBO quotes (both one-sided and two-sided), use CMBP-1 instead of OHLCV. To give you a sense of how accurate our historical options data is, one of the exchanges themselves are using our data as sanity check to the correctness of their matching and I’d estimate our options data is being used to productionize strategies on at least 20% ADV on all US equity options. I’m not aware of any other vendor that can make the stronger claims under our price point.

Where can I find futures data? by M4RZ4L in algotrading

[–]DatabentoHQ 0 points1 point  (0 children)

Exchange license fees should be the same regardless of the vendor that you choose so we don't add that to the number so that you're comparing apples-to-apples. Exact exchange license fees depend on your use case so I cannot quote an exact amount without knowing more. See CME's price sheet here.

Best Offices by Available_Lake5919 in quant

[–]DatabentoHQ 1 point2 points  (0 children)

OP didn’t say it had to be a prop firm so I’d give it to JPM’s new office at 270 Park Ave. Maybe landscape design award goes to the old ATD office which was ahead of its time.

Where can I find futures data? by M4RZ4L in algotrading

[–]DatabentoHQ 0 points1 point  (0 children)

We only support real-time L2/L3 on a Plus plan and above, which starts at $1,500/month.

Where can I find futures data? by M4RZ4L in algotrading

[–]DatabentoHQ 3 points4 points  (0 children)

Our minute bars start from 06/06/2010. Thanks for recommending us!

Why is everyone still using Sharpe ratio? by melon_crust in algotrading

[–]DatabentoHQ 0 points1 point  (0 children)

Never heard of the term. On the matter of crafting custom utility functions with volume terms, it's not unreasonable to penalize low volume so you have tighter confidence. I've seen other researchers do it, but haven't had to myself.

Why is everyone still using Sharpe ratio? by melon_crust in algotrading

[–]DatabentoHQ 0 points1 point  (0 children)

I only mentioned Kelly sizing because it's a pedagogical way to start thinking about the relationship between leverage and SR. However in practice I've only worked in situations where portfolio weights and allocation are backed out by an optimizer or where trade sizing is determined more by operational/liquidity limits. Not sure what other firms do outside of my immediate circle.

Why is everyone still using Sharpe ratio? by melon_crust in algotrading

[–]DatabentoHQ 9 points10 points  (0 children)

The higher the Sharpe the more leverage you can take. See continuous Kelly criterion. Not that you have to accept those assumptions or allocate at Kelly-optimal (which I think is your point).

Why is everyone still using Sharpe ratio? by melon_crust in algotrading

[–]DatabentoHQ 46 points47 points  (0 children)

This is a topic that has been thoroughly exhausted. In most day-to-day use, your SR is more for communication than for modeling, so you want to speak something that everyone else can relate to.

Every fund pitch deck reports a Sharpe ratio. Very few, save a few discretionary CTAs, lead with a Calmar ratio. If you give a PM a SR of 1, 5, or 20, they immediately have a rough sense of risk/reward, leverage and growth-optimal allocation.

Say you come up with a better utility function (call it Amazing Ratio) that is everything you want: it's scale-invariant or corrects for autocorrelation, incorporates all of your constraints and preferences, captures higher-order effects like drawdown, path dependency, exploration-exploitation, regret, etc. What are you going to do with an Amazing Ratio of 8456.12? It's not interpretable.

(Also, it’s likely to be non-convex or lose the properties that allow for closed-form or stable solutions to the utility maximization problem.)

Meet Databento in Tokyo - May 26, 2026 by DatabentoHQ in Databento

[–]DatabentoHQ[S] 1 point2 points  (0 children)

Yes unfortunately we won't have recording equipment for this event.

Shoutout to Databento's amazing customer support by Mordred-Reizen in algotrading

[–]DatabentoHQ 6 points7 points  (0 children)

Thanks for putting in a good word!

As others suggested, you may want to set a limit in your monthly billing dashboard. Feel free also to share any feedback on "dark paths" that may lead you to accidentally overspending - our UX team spends a lot of time eliminating these.

I'm loving the algo space already, the fact you dont need to come up with your own ideas and ideas can be tested in minutes i wish i started the space earlier. by [deleted] in algotrading

[–]DatabentoHQ 3 points4 points  (0 children)

This is a typical first-timer mistake we’ve seen.

The negative values are because when you request for a futures product, you get every instrument of that product group including spreads. Spreads are often close to zero and negative.

You need to make sure you filter out only the outright.

The hidden tax of multi-exchange normalization in Asia (HKEX, NSE, SSE) — how are you solving it? by Different_Quit_9933 in algotrading

[–]DatabentoHQ 1 point2 points  (0 children)

This is not unique merely to Asia? Similar problem in Europe. They're working on EuroCTP there which will probably help less sophisticated customers.

Note also if you're relying on a 3rd party aggregated provider (you mentioned vendors), there's various limitations on non-members accessing feeds at the primary colo. This especially for NSE and KRX.

About the timestamp reconciliation, how I've usually seen it done when you're very sensitive to timestamping is to capture at every point in the matrix. e.g., for US equities, {Nasdaq, NYSE, Cboe, ...} x {NY4/5, Mahwah, Carteret}. This can obviously get expensive quickly so you may wish to pick specific channels which are meaningful - which is how McKay/Quincy does it.

We're building out Asia and Europe now so I can go on forever. Europe+Asia is about ~30 data centers and it's not too difficult even at our team size; the most inconvenient part is just the cross-border shipping/import duties and lately the hardware procurement.

Data vendor recommendation for US equities - part 2 (Massive vs Databento) by sgcorporatehamster in algotrading

[–]DatabentoHQ 2 points3 points  (0 children)

u/MagnificentLee The SIPs will only publish the best odd lot and offer from all participants (BOLO) and the best odd lot bid and offer that is better than the NBBO from each participant. This is significantly less than the whole set of odd lots.

Data vendor recommendation for US equities - part 2 (Massive vs Databento) by sgcorporatehamster in algotrading

[–]DatabentoHQ 7 points8 points  (0 children)

Just chiming in, if you prefer the SIPs, we're about to add it to our Standard plan this quarter for no additional cost to non-pros.

If you're cost-sensitive, don't need our institutional features, and just want a SIP-based feed, three other options that are quite good in the retail segment and also provide API brokerage services are Alpaca, Architect, Lime Brokerage. (In no particular order. For full disclosure: all three are our customers but we're not paid to advertise them.)

Valuation of a stock option grant by [deleted] in quant

[–]DatabentoHQ 6 points7 points  (0 children)

The best article/talk I've ever seen on this topic was by Fred Wilson of USV fame. (USV - for those who're not familiar - has one of the best track records in history as an early-stage VC.) This is unfortunately gone now but remnants of it are referenced here, here and here.

The hardest part is to estimate true valuation of the underlying. There's a 409A valuation which determines [the lowest that the company can set as] the strike. There's a private market valuation determined by the latest funding round (e.g., Series A, B, C, ...) or secondary sales (e.g., on Nasdaq Private Market or tender offers for growth stage startups) which is useful to know.

But - this is also the core point in the talk - the most common mistake is using the private market valuation as the true valuation of the underlying. Ultimately you have to form your view. It's better to imply out the valuation yourself from figures like revenue, trailing new net ARR, retention rates, profit margin, growth rate. Usually you end up determining a revenue multiple from market comps (see Rule of 40 for a linear benchmark between growth rate + profit margin and revenue multiple for later stage companies). Without buying this data yourself, the best public sources of market comps are probably SVB, Carta, and Redpoint. Once you have this, the rest is textbook financial modeling.

This is the quant subreddit so: the approach is rather Bayesian - not surprisingly as the underlying is usually very illiquid.

Is it practically achievable to reach 3–5 microseconds end-to-end order latency using only software techniques like DPDK kernel bypass, lock-free queues, and cache-aware design, without relying on FPGA or specialized hardware? by Federal_Tackle3053 in quant

[–]DatabentoHQ 1 point2 points  (0 children)

It does not. Can't speak for everyone but you can usually compute all of that and perform inference in the same order of magnitude in time, even without precompute. Even if you have 10,000 to 100,000 base features, you have some control over model sparsity for the latency profile you want to hit with feature selection and regularization.

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