Agri Positioning Matrix (crop condition × COT)
Physical crop reality vs paper positioning: USDA Good+Excellent condition momentum joined to CFTC speculative-positioning z-score per crop, weekly, across 8 crops - corn, soybeans, wheat, cotton, rice, oats (all with both condition and the dominant CFTC futures market) plus barley and sorghum (condition only, no listed futures). Crops deteriorating while funds are net-short flags short-squeeze setups in food commodities.
- Source
- derived
- Licence
- Redistributable open data
- Updates
- Weekly
- Data through
- 2026-07-26
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Condition momentum + spec-positioning z per crop, weekly, 8 crops
- Spec-z for corn, soybeans, wheat, cotton, rice, oats; condition-only for barley, sorghum
- Maps each USDA crop to its dominant (max-OI) CFTC futures market per week
- Source: USDA NASS + CFTC (public domain, derived)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | Crop | 100.0% | 9 |
| ts | timestamp (UTC) | Week | 100.0% | 2,813 |
| variable | str | Metric | 100.0% | 2 |
| value | float | Value | 100.0% | 4,736 |
| unit | str | Unit (mixed) | 100.0% | 2 |
| res_min | int | Native resolution (weekly) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: derived (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("agri-positioning-matrix_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 9 zones
Barley · Corn · Cotton · Oats · Peanuts · Rice · Sorghum · Soybeans · Wheat
Related datasets
EU CAP Subsidy Flows
Common Agricultural Policy expenditure, cleaned to analysis-ready Parquet: EUR paid per EU member state x CAP instrument (direct payments, market measures, rural development) x year, 2003-2022. Fully aggregated (member-state level, no beneficiary individuals) - the GDPR-safe view of where CAP money goes. Source: DG AGRI 'Financing the CAP' indicators.
European Agricultural Output Prices
The real output-price index of Europe's major crops - cereals, wheat, barley, grain maize, industrial crops and oilseeds - deflated to 2020=100, per country, annual. Cleaned from Eurostat's agricultural price statistics into one tidy panel: the farm-gate price signal, in real terms, that agri-commodity traders, food manufacturers and ag-lenders track. Distinct from our global crop-production volumes - this is PRICE, not tonnage.
European Agriculture
The European agriculture picture in one clean annual panel, per country: crop OUTPUT PRICES (real index, 2020=100 - cereals, wheat, barley, maize, industrial crops, oilseeds), MEAT production (bovine, pig, sheep & goat, thousand tonnes), MILK & DAIRY (cows', ewes', goats' milk + cream) and FISHERIES catches (by species group). Assembled from Eurostat's agricultural statistics into one tidy long file - the whole EU farm-and-sea supply-and-price signal that agri-commodity traders, food manufacturers and ag-lenders track, without stitching a dozen cubes. The `indicator` column selects the series.