- extract/cftc_cot: refactor extract_cot_year() to accept url_template and
landing_subdir params; add _extract_cot() shared loop; add extract_cot_combined()
entry point using com_disagg_txt_{year}.zip → landing/cot_combined/
- pyproject.toml: add extract_cot_combined script entry point
- macros/__init__.py: add @cot_combined_glob() for cot_combined/**/*.csv.gzip
- fct_cot_positioning.sql: union cot_glob and cot_combined_glob in src CTE;
add report_type column (FutOnly_or_Combined) to cast_and_clean + deduplicated;
include FutOnly_or_Combined in hkey to avoid key collisions; add report_type to grain
- obt_cot_positioning.sql: add report_type = 'FutOnly' filter to preserve
existing serving behavior
- obt_cot_positioning_combined.sql: new serving model filtered to report_type =
'Combined'; identical analytics (COT index, net %, windows) on combined data
- pipelines.py: register extract_cot_combined; add to extract_all meta-pipeline
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- obt_cot_positioning.sql: replace final SELECT * with explicit column list
so linter can resolve schema without foundation.fct_cot_positioning in DB
- fct_weather_daily.sql: fix HASH(location_id, src."date") → located."date"
(cast_and_clean CTE references FROM located, not FROM src)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds extract/openweathermap package with daily weather extraction for 8
coffee-growing regions (Brazil, Vietnam, Colombia, Ethiopia, Honduras,
Guatemala, Indonesia). Feeds crop stress signal for commodity sentiment score.
Extractor:
- OWM One Call API 3.0 / Day Summary — one JSON.gz per (location, date)
- extract_weather: daily, fetches yesterday + today (16 calls max)
- extract_weather_backfill: fills 2020-01-01 to yesterday, capped at 500
calls/run with resume cursor '{location_id}:{date}' for crash safety
- Full idempotency via file existence check; state tracking via extract_core
SQLMesh:
- seeds.weather_locations (8 regions with lat/lon/variety)
- foundation.fct_weather_daily: INCREMENTAL_BY_TIME_RANGE, grain
(location_id, observation_date), dedup via hash key, crop stress flags:
is_frost (<2°C), is_heat_stress (>35°C), is_drought (<1mm), in_growing_season
Landing path: LANDING_DIR/weather/{location_id}/{year}/{date}.json.gz
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Delete 6 data raw models (coffee_prices, cot_disaggregated, ice_*,
psd_data) — pure read_csv passthroughs with no added value
- Move 3 PSD seed models raw/ → seeds/, rename schema raw.* → seeds.*
- Update staging.psdalldata__commodity: read_csv(@psd_glob()) directly,
join seeds.psd_* instead of raw.psd_*
- Update 5 foundation models: inline read_csv() with src CTE, removing
raw.* dependency (fct_coffee_prices, fct_cot_positioning, fct_ice_*)
- Remove fixture-based SQLMesh test that depended on raw.cot_disaggregated
(unit tests incompatible with inline read_csv; integration run covers this)
- Update readme.md: 3-layer architecture (staging/foundation → serving)
Landing files are immutable and content-addressed — the landing directory
is the audit trail. A raw SQL layer duplicated file bytes into DuckDB
with no added value.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Replace brittle ICE_STOCKS_URL env var with API-based URL discovery via
the private ICE Report Center JSON API (no auth required)
- Add rolling CSV → XLS fallback in extract_ice_stocks() using
find_latest_report() from ice_api.py
- Add ice_api.py: fetch_report_listings(), find_latest_report() with
pagination up to MAX_API_PAGES
- Add xls_parse.py: detect_file_format() (magic bytes), xls_to_rows()
using xlrd for OLE2/BIFF XLS files
- Add extract_ice_aging(): monthly certified stock aging report by
age bucket × port → ice_aging/ landing dir
- Add extract_ice_historical(): 30-year EOM by-port stocks from static
ICE URL → ice_stocks_by_port/ landing dir
- Add xlrd>=2.0.1 (parse XLS), xlwt>=1.3.0 (dev, test fixtures)
- Add SQLMesh raw + foundation models for both new datasets
- Add ice_aging_glob(), ice_stocks_by_port_glob() macros
- Add extract_ice_aging + extract_ice_historical pipeline entries
- Add 12 unit tests (format detection, XLS roundtrip, API mock, CSV output)
Seed files (data/landing/ice_aging/seed/ and ice_stocks_by_port/seed/)
must be created locally — data/ is gitignored.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- config.yaml: remove ambiguousorinvalidcolumn linter rule (false positives on read_csv TVFs)
- fct_cot_positioning: use TRY_CAST throughout — CFTC uses '.' as null in many columns
- raw/cot_disaggregated: add columns() declaration for 33 varchar cols
- dim_commodity: switch from SEED to FULL model with SQL VALUES to preserve leading zeros
Pandas auto-converts '083' → 83 even with varchar column declarations in SEED models
- seeds/dim_commodity.csv: correct cftc_commodity_code from '083731' (contract market code)
to '083' (3-digit CFTC commodity code); add CSV quoting
- test_cot_foundation.yaml: fix output key name, vars for time range, partial: true,
and correct cftc_commodity_code to '083'
- analytics.py: COFFEE_CFTC_CODE '083731' → '083' to match actual data
Result: serving.cot_positioning has 685 rows (2013-01-08 to 2026-02-17), 23/23 tests pass.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
