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Private BBX catalogue research and cellar management.
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LifestyleTypeScript · PLpgSQLGitHub @rjc54nOnlineOwner unconfirmed
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Extract and filter listings on the BBX wine auction platform and set alerts
Taken from the repository README
Loading the full README. The saved excerpt is shown below.
Tools for finding below-market fine wine on the BBX (Berry Bros. & Rudd)
trading exchange and managing private cellar evidence:Read the full README ↗Tools for finding below-market fine wine on the BBX (Berry Bros. & Rudd) trading exchange and managing private cellar evidence:
The Streamlit application and hourly scanner are thin wrappers over the shared
core/pipeline.py scan pipeline. The Next.js application is a separate reader
and private-data workflow over Supabase.
Three phases, ordered cheapest-first:
prod_product index (stock_origin:'BBX', optional new_to_bbx window, colour, price band, format).getBiddableCprStock, which returns one entry per format (06-00750, 01-01500, …) for each parent SKU. Every metric is resolved to the discovered listing's own format: the ask is the effective-lowest per-format price from Algolia, while market_price and last_bbx_transaction come from the REST entry whose format matches (never entries[0], which is an arbitrary format). Records failing the market/last-transaction discount thresholds are dropped here.A single wine can appear once per format; the pipeline keeps ask, market, "next" and the displayed case_format all pinned to the same format so a magnum's price can never be attributed to a case (or vice versa), and notification dedup is keyed by parent_sku + format_code.
Threshold semantics: pct_market is always enforced; pct_last and pct_next are enforced only when computable (a wine with no last transaction or no competing seller passes on pct_market alone).
The prod_product index truncates any single query at 1,000 hits (10 pages × 100; larger hitsPerPage is clamped). The full BBX book is ~15,000 listings, so a broad query (e.g. "All BBX") cannot be paged in full by one filter set.
fetch_listings handles this by sharding: when a filter set exceeds the cap it splits by facet (region → colour → price band → vintage), recursing only into shards still over the cap, with a NOT query per level to catch records missing the facet, and de-duplication by objectID. Sharding is lazy — narrow queries (the hourly bot's "new in last 1 day") never shard and cost the same ~3 requests they always did.
Politeness is built in: a jittered sleep after every request, exponential backoff on 429/5xx, short-page early exit, and reuse of parent facet counts so under-cap shards skip their own count query. A full-book sweep is a few hundred requests over ~3 minutes.
bbx/
apps/
streamlit_app/streamlit_app.py # UI wrapper
arbitrage_bot/run_arbitrage.py # CLI/CI wrapper + Slack + dedup
daily_sweep/run_sweep.py # full biddable-universe snapshot
web/ # Next.js catalogue and private cellar app
core/
pipeline.py # shared 3-phase scan funnel (ScanConfig, run_scan)
fetch_listings.py # Algolia fetch helper
fetch_bbx_variants.py # GraphQL order-book helper
slack.py # Slack webhook helper
notification_state.py # dedup rules + S3/local state persistence
data/
payload.json # GraphQL payload template
tests/ # pytest unit tests (dedup, discount maths, sharding)
docs/
ROADMAP-2026-07.md # dated product roadmap and rationale
CELLARTRACKER-IMPLEMENTATION.md # fourth dataset implementation
CODEBASE-REVIEW-2026-07-31.md # dated defects and maintenance review
supabase/
migrations/ # deployed database schema history
tests/ # database access and behaviour tests
.github/workflows/arbitrage.yml
.github/workflows/daily_sweep.yml
.github/workflows/database-migrations.yml
ALGOLIA_APP_IDALGOLIA_API_KEYStreamlit reads these from .streamlit/secrets.toml; the bot reads environment variables.
The Next.js application also reads them from its server environment for the
owner-only release-price and CellarTracker matching pages. Do not prefix either
variable with NEXT_PUBLIC_.
SLACK_WEBHOOKS3_BUCKET, S3_STATE_KEY (plus AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_REGION)When the S3 variables are unset, state falls back to a local file at data/arbitrage_state.json (gitignored).
streamlit run apps/streamlit_app/streamlit_app.py
Requires .streamlit/secrets.toml.
See apps/web/README.md for environment variables,
owner bootstrap, local development and release checks.
export ALGOLIA_APP_ID="..."
export ALGOLIA_API_KEY="..."
export SLACK_WEBHOOK="..."
python apps/arbitrage_bot/run_arbitrage.py
Runs hourly 08:00–23:00 UTC via .github/workflows/arbitrage.yml, with a concurrency guard, unit tests before each run, and a Slack alert if the job itself fails.
pip install -r requirements-dev.txt
python -m pytest tests/ -q
The Next.js checks run separately:
cd apps/web
npm run lint
npm test
npm run build
.github/workflows/ci.yml runs the Python and web checks on every pull
request and on push to main. The database migration workflow
(database-migrations.yml) runs separately, only on changes touching
supabase/**. The scheduled scanner and sweep also run the Python tests
before their production jobs.
Implemented in core/notification_state.py:
REMINDER_INTERVAL_DAYS → notify (reminder).The dated implementation roadmap is
docs/ROADMAP-2026-07.md. For the full set of
specs, phase implementation history and dated reviews, see the index at
docs/README.md.
Agents (and anyone else) making changes here should read
AGENTS.md first — it records operational rules learned from
real incidents, including how database migrations actually reach
production.