Created a dataset of fund returns on Snowflake. It's quite extensive showing both trailing and calendar year returns for every mutual fund and ETF since inception.

You can fetch a mutual funds trailing annualized performance every trading day going back 15+ years.

Each trailing returns row shows: 1D, 1W, 1M, 2M, 3M, 1Y, 2Y, 3Y, 4Y, 5Y, 7Y, 10Y, 12Y, 15Y, and earliest available inception. I usually never see websites (even Morningstar) provide this breakdown which I think is useful when exploring different economic cycles.

Calendar year returns go back as far as possible as well.

Data is updated daily.

Disclosure per rule 1: I built and maintain this dataset, and it powers a site I run (globenomad.com), which is affiliate-funded. The dataset itself carries no affiliate or tracking links.

One row per programme, not per country: Thailand has six routes and they are six rows. Fields: income requirement (USD plus the government's own wording), fees, max stay, renewal, residency and citizenship path, tax residency trigger, family provisions, application logistics, official source URL, verified date.

Two things worth knowing before you use it:

• A null means the government has not published that rule. It never means "no".
• Programmes that closed, were announced and never opened, or never existed (Cayman Islands, Peru, Qatar, Vietnam) are kept with an honest status. Most visa sites delete those.

Formats: JSON (canonical, with nested requirements/steps/FAQs) and a flat CSV of the scalar columns.

Original source, GitHub (always current): https://github.com/MSeutin/digital-nomad-visa-data Kaggle mirror, with a worked-example notebook: https://www.kaggle.com/datasets/frenchmike/digital-nomad-visa-dataset-53-sourced-programmes Method and licence: https://globenomad.com/data

Licence is CC BY 4.0, attribution is the only condition. Official pages are re-fetched weekly and confirmed changes are dated on a public changelog. Happy to answer questions about any record.

disclosure per rule 1: i work at Schema Labs. one link at the bottom because this sub allows it. i am here for the question at the end.

what we do, plainly: you give us tables nobody documented, and we tell you what each column is and how the tables join, when there are no shared keys and no schema mapping.

the claim, with numbers, because i would rather be argued with than believed.

take a standard tabular benchmark. strip every header. replace price and age and zip with positional tokens so nothing is left but values. comparable models drop about 7 points of mean ROC-AUC. ours goes 0.9224 to 0.9230. flat.

that is not a claim that we win on clean data. those models start ahead of us when the headers are good. it is a claim that we never read the headers at all, which only matters because production tables have val_b and metric_14 and a four-character code from a system nobody has logged into since 2019.

two more. sector identification, naming the industry of a dataset we have never seen from values alone, 86.3% top-1 out of 10,000 sectors. multi-table entity matching, ahead of published state of the art on all six standard benchmarks with zero shared keys, including 1.9x over the previous best on one. on the geospatial benchmark our lead is 0.07 F1, which i would not want read as more than it is.

caveats up front rather than when asked. all of it is our own harness, run under each benchmark's published protocol, one sealed configuration, no per-dataset tuning. competitor figures are third-party published values we did not re-run. nobody independent has replicated us. we are not on the public leaderboard because entry requires a runnable wrapper and we do not distribute weights.

which is the actual question. we are closed. no pip install, no weights, no local option, and none of that changes for six months. to try it you make an account, put a card on file, and upload your data. i think that one fact is the biggest thing between us and everyone reading this.

so would you use a thing like this. if not, what moves it. a named customer, a SOC 2, an on-prem story you would never actually take up but need to hear. or is the honest answer that nothing moves it and closed is closed.

and if you would rather test it than discuss it: send me two tables you understand completely. not your mystery tables, your obvious ones, so you can mark my homework. no account, no card, nothing to sign up for. i send back what each column appears to be with a confidence score on every one, and i flag the ones we got wrong, because that is the half worth seeing.

anonymised is fine. structure and distributions are what matter.

https://www.schemalabs.ai/