Which agricultural products are covered?

It covers a comprehensive range including Grains (Corn, Wheat), Oilseeds (Soybeans, Canola), Meats (Live Cattle, Hogs), Softs (Coffee, Sugar, Cocoa), Fiber, and Dairy.

How does it handle the gap between COT reports?

Since COT data is lagged (published Friday for the previous Tuesday), the skill uses a mid-week validation framework tracking price momentum, open interest, and macro resonance from Wednesday to Friday.

What is the 'Buying Firepower' metric?

Firepower measures how much room funds have to add to their positions. A high score (>0.6) suggests positions are at historical lows with room to buy, while a low score (<0.3) indicates a crowded trade at historical highs.

Where does the positioning data come from?

The skill fetches real-time data directly from the CFTC Socrata API, focusing on the Non-Commercial (Speculative) trader classification.

Agri Hedge Fund Positioning Tracker

Name: Agri Hedge Fund Positioning Tracker
Author: fatfingererr

byfatfingererr

数据科学与机器学习

Quantifies hedge fund capital flows in agricultural commodity markets using CFTC COT data and macro sentiment indicators.

关于

This skill provides a sophisticated framework for tracking hedge fund positioning across agricultural futures by processing CFTC Commitments of Traders (COT) reports. It calculates 'Buying Firepower'—a metric determining if funds have room to expand positions based on historical percentiles—and integrates macro tailwinds from the US Dollar, Crude Oil, and Metals. Designed for traders and analysts, it transforms raw data into actionable narratives, identifying overcrowded trades and potential market reversals across Grains, Oilseeds, Softs, and Meats using real-time API integrations.

主要功能

Multi-group aggregation across Grains, Oilseeds, Meats, Softs, Fiber, and Dairy
Bloomberg-style visualization of fund flows and positioning time series
Automated COT data retrieval via CFTC Socrata API for non-commercial positions
0 GitHub stars
Buying Firepower quantification using N-week historical percentile ranking
Macro tailwind scoring based on DXY, WTI, and industrial metals correlation

使用场景

Correlating macro risk-on/risk-off sentiment with agricultural capital flows
Identifying market exhaustion points in corn or soybean futures through firepower metrics
Validating mid-week price action using open interest changes and macro proxies

关于

主要功能

Multi-group aggregation across Grains, Oilseeds, Meats, Softs, Fiber, and Dairy
Bloomberg-style visualization of fund flows and positioning time series
Automated COT data retrieval via CFTC Socrata API for non-commercial positions
0 GitHub stars
Buying Firepower quantification using N-week historical percentile ranking
Macro tailwind scoring based on DXY, WTI, and industrial metals correlation

使用场景

Correlating macro risk-on/risk-off sentiment with agricultural capital flows
Identifying market exhaustion points in corn or soybean futures through firepower metrics
Validating mid-week price action using open interest changes and macro proxies