- 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>
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>
- 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>
extract: wrap response.content in BytesIO before passing to
normalize_zipped_csv, and call .read() on the returned BytesIO before
write_bytes (two bugs: wrong type in, wrong type out)
sqlmesh: {{ var() }} inside SQL string literals is not substituted by
SQLMesh's Jinja (SQL parser treats them as opaque strings). Replace with
a @psd_glob() macro that evaluates LANDING_DIR at render time and returns
a quoted glob path string.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
