curl
curl -L -O "https://augea.io/data/reports/card-vs-bank-study/v1/paired-exchange-gap.csv"
Dataset · Versioned release · v1.1.0 · Card vs Bank Study
Card vs Bank Study · Dataset
Machine-readable files for the Card vs Bank BTC buying cost gap study. Pinned to a version so a citation today still resolves tomorrow, ready to drop into pandas, R, JavaScript, or a shell pipeline.
Paired exchanges
21
both methods offered
Countries
13
cheapest-per-method rows
Files
4
17.0 KB total
License
CC-BY-4.0
attribution only
Dataset snapshot · Version lock
Study ID
card-vs-bank-2026-04
Version
v1.1.0
Window
April 2026 snapshot
Baseline
BTC · $1000 · US
Pin v1.1.0 in citations so downstream readers resolve exactly this edition. Future editions will ship at new versioned URLs and will not rewrite this one.
Downloads
Benchmark pack
Full study model, paired rows, country cheapest rows, hero stats, citation metadata.
JSON12.6 KBDownload →
Paired exchange gap CSV
Same-exchange card vs bank midpoints, ratio, and dollar delta on a $1,000 buy.
CSV1.9 KBDownload →
Country cheapest gap CSV
Per country: cheapest card option vs cheapest bank option, with delta columns.
CSV1.4 KBDownload →
README
Version lock, citation text, and reuse guidance.
Markdown1.2 KBDownload →
Column dictionary
- exchange_slug
- Canonical Augea exchange identifier.
- exchange_name
- Display name of the exchange.
- card_mid_pct / bank_mid_pct
- Midpoint of low and high fee plus low and high spread, as a percentage of the purchase notional.
- ratio
- Card midpoint divided by bank midpoint on the same exchange.
- gap_pct
- Card minus bank in percentage points.
- extra_usd_on_1000
- Delta in dollars on a $1,000 BTC buy.
- extra_usd_per_year_at_250_monthly
- Delta compounded across 12 monthly $250 purchases.
Preview: paired exchange gap CSV
First 6 rows of 21, sorted by ratio (descending). Percentage columns to 2 decimals; ratio in x-form.
| Slugexchange_slug | Exchangeexchange_name | Card %card_mid_pct | Bank %bank_mid_pct | Ratioratio | Gap ppgap_pct | Extra / $1kextra_usd_on_1000 | Yearly extra @ $250/moextra_usd_per_year_at_250_monthly | baseline_assetbaseline_asset | baseline_countrybaseline_country | baseline_buy_usdbaseline_buy_usd | window_labelwindow_label |
|---|---|---|---|---|---|---|---|---|---|---|---|
| kucoin | KuCoin | 3.06% | 0.15% | 20.00x | 2.91% | $29.07 | $87.24 | BTC | US | 1000 | April 2026 snapshot |
| mexc | MEXC | 3.06% | 0.15% | 20.00x | 2.91% | $29.07 | $87.24 | BTC | US | 1000 | April 2026 snapshot |
| woox | WOO X | 3.06% | 0.15% | 20.00x | 2.91% | $29.07 | $87.24 | BTC | US | 1000 | April 2026 snapshot |
| kraken | Kraken | 5.61% | 0.32% | 17.74x | 5.29% | $52.94 | $158.76 | BTC | US | 1000 | April 2026 snapshot |
| binance | Binance | 2.55% | 0.15% | 16.67x | 2.40% | $23.97 | $71.88 | BTC | US | 1000 | April 2026 snapshot |
| bybit | Bybit | 2.55% | 0.15% | 16.67x | 2.40% | $23.97 | $71.88 | BTC | US | 1000 | April 2026 snapshot |
Use it in code
Four drop-in examples for loading the paired exchange gap CSV directly from its pinned URL. Swap the path to pull the country cheapest CSV or the JSON pack.
Python (pandas)
import pandas as pd url = "https://augea.io/data/reports/card-vs-bank-study/v1/paired-exchange-gap.csv" df = pd.read_csv(url) print(df[["exchange_name", "card_mid_pct", "bank_mid_pct", "ratio"]].head())
JavaScript (fetch)
const res = await fetch(
"https://augea.io/data/reports/card-vs-bank-study/v1/paired-exchange-gap.csv",
);
const csv = await res.text();
console.log(csv.split("\n").slice(0, 5));R (readr)
library(readr)
url <- "https://augea.io/data/reports/card-vs-bank-study/v1/paired-exchange-gap.csv"
df <- read_csv(url)
print(head(df[c("exchange_name", "card_mid_pct", "bank_mid_pct", "ratio")]))Citation and reuse
Augea, Card vs Bank BTC Buying Cost Gap — Augea Data Event (April 2026), https://augea.io/reports/card-vs-bank-study, accessed [DATE].
Augea. (2026). Card vs Bank BTC Buying Cost Gap Data Appendix (April 2026). https://augea.io/data/reports/card-vs-bank-study