Every bank exports its CSV differently — different column names, different date formats, comma vs. dot as the decimal separator, debit and credit split across two columns or folded into one signed amount. The moment you try to merge several accounts or feed them into a budgeting tool, you drown in that mess.

Ledgerly turns those wildly different bank CSVs into one clean, unified and automatically categorized transaction list — as a single, dependency-light command-line binary. No bank login, no cloud: it only ever touches the files you hand it.

A raw ING export:

Buchung;Valuta;Auftraggeber/Empfänger;Buchungstext;Verwendungszweck;Betrag;Währung
02.01.2026;02.01.2026;REWE SAGT DANKE;Lastschrift;Einkauf Lebensmittel;-43,12;EUR

After ledgerly convert:

date,value_date,amount,currency,payee,purpose,account,category,reference
2026-01-02,2026-01-02,-43.12,EUR,REWE SAGT DANKE,Einkauf Lebensmittel,ing,Groceries,

Same row, but now with an ISO date, a dot-decimal amount, a stable column order and a category — ready to merge with any other account.

• Bank profiles (YAML): map a bank's columns onto one unified schema, including date and number formats. Ships with ING, DKB, Sparkasse and PayPal.
• Rule-based categorization ("REWE"/"EDEKA" → Groceries, "Netflix" → Subscriptions), with a sensible default rule set you can extend or override.
• Merge multiple accounts into a single chronological timeline.
• Duplicate detection for overlapping export ranges.
• Output as unified CSV or JSON.
• A short terminal summary (totals per category and per month).

With a Go toolchain (1.22+):

go install github.com/SciTee/ledgerly/cmd/ledgerly@latest

Or grab a prebuilt binary from the releases page and drop it on your PATH. Ledgerly is a single static binary with no runtime dependencies.

Convert one statement, auto-detecting the bank from its header:

ledgerly convert --auto-detect statement.csv > unified.csv

Or name the profile explicitly and write JSON:

ledgerly convert --profile ING --format json -o january.json ing-january.csv

See which profiles are built in:

ledgerly profiles list

Pass several files; ledgerly reads each with the chosen profile, stamps the account label (the file name by default), merges everything into one timeline and drops duplicate rows from overlapping exports:

ledgerly convert --profile Sparkasse giro-jan.csv giro-feb.csv > giro.csv

Use --account to set an explicit label instead of the file name.

Tip: convert each bank separately (different profiles), then concatenate the unified CSVs — they all share the same schema.

Categories come from an ordered list of rules; the first match wins. The built-in set covers common German merchants. To add your own, write a rules file and stack it on top of the defaults with --rules:

# my-rules.yaml — checked before the defaults
rules:
  - { match: "Lieblingsbäcker", category: "Dining Out" }
  - { regex: "miete|kaltmiete", field: purpose, category: "Rent" }

ledgerly convert --profile DKB --rules my-rules.yaml statement.csv

A rule matches a case-insensitive substring (match) or a regular expression (regex) against the payee, purpose, or both (field: any, the default). Anything no rule matches is left as Uncategorized. Pass --no-categorize to skip the step entirely. The full rule reference is in docs/rules.md.

The unified schema has a fixed column order in CSV and the same fields in JSON:

Built in: ING, DKB, Sparkasse, PayPal. A profile is just a small YAML file, so you can point at your own with --profile ./mybank.yaml. See docs/profiles.md for the full schema and a walkthrough.

Adding a profile for your bank is the most useful contribution. Drop the YAML in internal/profile/builtin, add a small sample export and a golden test, and open a PR — details in docs/profiles.md.

• No bank API / online-banking login — strictly file-based.
• Not a full accounting or budgeting app — Ledgerly is the clean preprocessing step in front of one.

MIT — see LICENSE.