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Migrated from monorepo during Forgejo registry consolidation 2026-05-07
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Viktor Barzin e72fd22a17 col: simulator auto-adjusts spending to local prices via Numbeo+Expatistan 
The Monte Carlo used to compare jurisdictions at a flat London-equivalent
spend, which silently overstated the cost-of-living for any move to a
cheaper region. Now every cross-jurisdiction simulation auto-scales
spending_gbp by the real Numbeo/Expatistan ratio between the user's
baseline city and the target city.

Architecture:
- fire_planner/col/baseline.py — 22 cities with headline Numbeo data
  (source URLs + snapshot dates embedded) — fallback when scraper fails
- col/numbeo.py + col/expatistan.py — httpx async scrapers, regex-parsed,
  polite 1.1s rate-limit, EUR/USD anchored
- col/cache.py — PG-backed cache (col_snapshot table, 1-year TTL)
- col/service.py — sync compute_col_ratio() for the simulator; async
  lookup_city_cached() with source reconciliation for the refresh CronJob
- alembic 0005 — col_snapshot table, UNIQUE(city_slug, source_name)

Simulator wiring:
- SimulateRequest gains col_auto_adjust=True (default), col_baseline_city,
  col_target_city. Defaults pick the jurisdiction's representative city.
- _resolve_col_adjustment scales spending_gbp before path-building.
- SimulateResult surfaces col_multiplier_applied + col_adjusted_spending_gbp.

CLIs:
- python -m fire_planner col-seed — loads BASELINES into col_snapshot
  (post-migration seed step)
- python -m fire_planner col-refresh-stale --within-days 7 — used by the
  weekly fire-planner-col-refresh CronJob

268 tests pass. Mypy strict + ruff clean.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-22 14:14:57 +00:00
alembic
fire_planner
frontend
tests
.dockerignore
.gitignore
.woodpecker.yml
alembic.ini
Dockerfile
PLAYBOOK_VIKTOR.md
poetry.lock
pyproject.toml
README.md

README.md

fire-planner

Risk-adjusted, tax-minimised FIRE retirement planner. Consumes today's portfolio, savings rate, and RSU vest schedule from sibling services (wealthfolio, payslip-ingest, hmrc-sync) and returns the after-tax probability of success for each combination of jurisdiction, withdrawal strategy, and "year you break UK tax residency".

Layout

  • fire_planner/ — package
    • tax/ — per-jurisdiction tax engines (UK, nomad, Malaysia, Thailand, Cyprus, Bulgaria)
    • returns/ — Shiller 1871+ data + block bootstrap sampler
    • strategies/ — Trinity 4% SWR, Guyton-Klinger guardrails, VPW
    • ingest/ — pulls from wealthfolio / payslip-ingest / hmrc-sync
    • simulator.py — vectorised NumPy MC engine
    • scenarios.py — Cartesian product over (jurisdiction × strategy × leave-UK-year × glide)
    • app.py — FastAPI on-demand /recompute
    • __main__.py — click CLI: ingest, simulate, recompute-all, migrate

Common commands

poetry install
pytest -v
mypy .
ruff check .
yapf --recursive .

# Run migrations against the local DB:
DB_CONNECTION_STRING=postgresql+asyncpg://... alembic upgrade head

# CLI
DB_CONNECTION_STRING=... python -m fire_planner ingest
DB_CONNECTION_STRING=... python -m fire_planner simulate --scenario=cyprus-vpw-leave-y3
DB_CONNECTION_STRING=... python -m fire_planner recompute-all

Schema

Six tables in fire_planner schema on pg-cluster-rw:

  • account_snapshot — daily NW per account (Wealthfolio)
  • scenario — Cartesian-product scenario definition
  • mc_run — execution metadata + summary stats per (scenario, run_at)
  • mc_path — sparse storage (top decile, bottom decile, median)
  • projection_yearly — deterministic point projection per scenario
  • scenario_summary — denormalised fast-read for Grafana