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Amazon Accounts Receivable Aging Dashboard

Automates the Finance team's month-end Amazon Accounts Receivable Aging workbook: upload the month's Amazon Custom Unified Transaction files → get a structurally- and financially-identical Accounts Receivable Aging workbook, with validation, settlement reconciliation, exceptions, drill-down, and an audit trail.

Amazon only (no Walmart/TikTok/Temu/Shein/eBay/Shopify). Reverse-engineered from the Jan-2026 files; the USA receivable reproduces to the penny (11,110,433 = Detail!D11). See docs/accounts-receivable-logic.md.

Layout

backend/
  app/core/        # streaming parser + receivable engine (stdlib + openpyxl only)
  app/api/         # FastAPI app: routes, auth (login), deps
  app/services/    # jobs, persistence, FX-rate fetch, controls, retention
  app/db/          # SQLAlchemy models (20 tables) + engine (SQLite / MySQL)
  tests/           # 150+ tests: engine, API, auth, FX, dedup, Jan-2026 integration
  cli.py           # process files from the command line
  manage.py        # admin: add-user / set-password / dedupe-files / …
  migrate_sqlite_to_mysql.py   # one-time data migration for the AWS cutover
frontend/          # React + TS + Vite dashboard (nginx-served in production)
scripts/           # launchers: start.ps1 / start.bat (Windows) · start.command (macOS)
deploy/            # DEPLOY.md runbook + backup.sh (nightly mysqldump + S3 sync)
docs/              # system guide · AR logic · audit reports
docker-compose.yml            # dev stack        docker-compose.prod.yml  # production
.env.example                  # every setting, LOCAL + PRODUCTION sections

Production deployment (AWS)

One 8 GB server runs the whole stack with automatic HTTPS, MySQL, per-user login, and nightly S3 backups — see deploy/DEPLOY.md for the full runbook (provisioning, user creation, SQLite→MySQL migration, backups, updates). ≈ $50–55/month.

cp .env.example .env.production    # fill the PRODUCTION section (domain, passwords, AR_SECRET_KEY)
docker compose --env-file .env.production -f docker-compose.prod.yml up -d --build
docker compose --env-file .env.production -f docker-compose.prod.yml exec backend \
  python manage.py add-user <user> --name "Full Name"

Key production behaviors:

One closing per month — every month stays saved and selectable (month switcher in the closing header); creating a second closing for an existing month requires an explicit override.
Duplicate-proof uploads — re-uploading a filename replaces it; identical content under another name is skipped. A month can never count a file twice.
Login (AR_AUTH) — per-user accounts via manage.py add-user; journal review/approval and FX confirmations record the signed-in user's verified name.
Exchange rates — "Fetch month-end rates" pulls central-bank rates (Frankfurter, free, keyless; AR_FX_PROVIDER); fetched rates still require human confirmation (Control C5).
Completed closings are locked read-only; corrections need an explicit Reopen.
Crash-safe jobs — a restart mid-processing marks the closing as interrupted instead of leaving it stuck; generated exports are purged after AR_RETENTION_DAYS (uploads never are).

Run the app (development)

# 1. Configure the environment (SQLite by default — no database setup needed)
cp .env.example .env   # then fill the LOCAL section (AR_SECRET_KEY at minimum)

# Option A — Docker (backend + Vite hot reload)
docker compose up --build
# → http://localhost:5173  (API on :8000; uploads/exports on the ar_data volume)

# Option B — local processes
make install          # backend deps + npm install
make backend          # terminal 1 → FastAPI on :8000
make frontend         # terminal 2 → dashboard on http://localhost:5173

# Option C — Windows one-click (ports 8010/5174)
scripts\start.bat     # or: powershell -ExecutionPolicy Bypass -File scripts\start.ps1
# macOS one-click:
scripts/start.command

Then open http://localhost:5173 → New Closing → pick the month → drag in the three Amazon files → Run processing → review → Download Full A/R Aging Excel.

Uploads and exports are stored under AR_DATA_DIR (default backend/data locally, /data in Docker). The app connects to MySQL using MYSQL_* variables from .env.

Run the engine headless (CLI)

cd backend && pip install -r requirements.txt
python cli.py "/path/USA…01 to 10 January,2026.xlsx" \
              "/path/USA…11 to 20 January,2026.xlsx" \
              "/path/USA…21 to 31 January,2026.xlsx" \
              --month-end 2026-01-31 --lag 2 --reserve-standard 125.44 --out AR_Aging_Jan26.xlsx

Tests

make test        # 16 fast tests (engine, export, API, robustness)
make test-all    # + Jan-2026 reconciliation & sample-comparison (integration, ~4 min)
# point tests at the sample files if not in the repo root:
AR_SAMPLE_DIR="/path/to/samples" make test-all

Status — all phases complete (incl. v2 multi-market)

✅ Engine — settlement classification + receivable; USA = 11,110,433 verified to the penny
✅ Multi-marketplace — all 13 markets (CA/UK/AU/IE + localized FR/DE/IT/ES/NL/PL/SV/TR) reconcile to the penny vs the Jan-26 workbook; per-market currency & FX; settlement-owner logic for cross-market EU chains; helper-row & pivot-sheet detection
✅ AR roll-forward — opening balance (auto carry-forward) + net revenue − payouts = closing; AR Ledger · Finance Summary · Journal Entry (per-marketplace) · Reconciliation Control w/ sign-off
✅ Header mapping — localized alias tables + admin rules UI (/api/mapping-rules); unmapped amounts are never silently excluded
✅ Storage-fee detection — canonical + description-based (potential/missing storage exceptions)
✅ Excel — Full audit workbook + Summary Finance pack
✅ Tests — ~60 fast + integration (USA reconciliation, sample comparison, 13-market benchmark)

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