- 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>
Incremental serving model for 12 coffee-growing locations. Adds:
- Rolling aggregates: precip_sum_7d/30d, temp_mean_30d, temp_anomaly, water_balance_7d
- Gaps-and-islands streak counters: drought_streak_days, heat_streak_days, vpd_streak_days
- Composite crop_stress_index 0–100 (drought 30%, water deficit 25%, heat 20%, VPD 15%, frost 10%)
- lookback 90: ensures rolling windows and streak counters see sufficient history on daily runs
Co-Authored-By: Claude Opus 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>
- serving/ice_aging_stocks.sql: pass-through from foundation, parses age
bucket string to start/end days ints for correct sort order
- serving/ice_warehouse_stocks_by_port.sql: monthly by-port since 1996,
adds MoM change, MoM %, 12-month rolling average
- analytics.py: get_ice_aging_latest(), get_ice_aging_trend(),
get_ice_stocks_by_port_trend(), get_ice_stocks_by_port_latest()
- api/routes.py: GET /commodities/<code>/stocks/aging and
GET /commodities/<code>/stocks/by-port with auth + rate limiting
- dashboard/routes.py: add 3 new queries to asyncio.gather(), pass to template
- index.html: aging stacked bar chart (age buckets × port) with 4 metric
cards; by-port stacked area chart (30-year history) with 4 metric cards
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
