Open prop firm dataset

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The files are rebuilt every time the site is published, so they always match the pages. Firm data was last read between 26 Oct 2026 and 4 Nov 2026; each row carries its own last_verified date, and the JSON has a generated date at the top.

What is in firms.csv

One row per firm that sells challenges today, ordered by PA Score. Lists are separated by semicolons. A blank cell means the firm does not publish that value, never that we estimated it. Median completeness is 90% of tracked fields.

Fields too nested for a flat file (fee per program and account size, payout methods, per-field sources and check dates, score components) are in firms.json. How the score is built: methodology.

Licence and how to cite

The data is published under Creative Commons Attribution 4.0 (CC BY 4.0). Use it for research, articles, apps, models or anything else, commercial or not. Credit PropAnalyse with a link to this page or to the firm page you used. Our page text is not part of the licence; quote it in short extracts with a link, as our terms set out.

Suggested citation:

PropAnalyse, "Prop firm dataset", 4 Nov 2026. https://propanalyse.com/data/ (CC BY 4.0)

How it is collected

We read each firm's own website, help centre and terms, record the page URL and the date, and enter the value as the firm states it. Third-party review and affiliate sites are never used as a source for rules or fees. Trustpilot score and review count are read from Trustpilot itself. Firms are re-checked on a rolling basis; rule, fee, payout and code changes go into the change log the day we spot them (rule changes, 40 edits to existing values so far). Errors can be reported with a source through corrections.

FundedXYZ is owned by the people behind PropAnalyse. It is in the dataset with the same fields, sources and scoring as every other firm.

Quick start

Top ten active firms by score, in Python:

import pandas as pd
firms = pd.read_csv("https://propanalyse.com/data/firms.csv")
active = firms[firms.status == "active"]
print(active.sort_values("pa_score", ascending=False)[["name", "pa_score", "fee_from", "split_max_pct"]].head(10))

Crypto-exchange firms from the JSON, on the command line:

curl -s https://propanalyse.com/data/firms.json | jq '.firms[] | select(.category == "crypto-exchange") | {name, pa_score, fee_from}'

Many common questions are already answered on prop firm statistics, which is computed from these same files on every build.