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track-agri-hedge-fund-positioning

Analyze COT data to quantify hedge fund flows across agricultural futures groups.

3|1|Updated Jan 12, 2026
Search Tags:#data analysis#signals#COT#hedge funds#agriculture#macro indicators
One-click install
npx skills add https://github.com/fatfingererr/macro-skills --skill track-agri-hedge-fund-positioning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: track-agri-hedge-fund-positioning
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/track-agri-hedge-fund-positioning
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill track-agri-hedge-fund-positioning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests, yfinance, matplotlib, and includes scripts (resource) components.

What problem does it solve?

This Skill helps analysts translate COT data into actionable insights on hedge fund flows in agricultural futures, integrating macro indicators to support narrative-based trading decisions.

Core Features & Use Cases

  • COT-based flow tracking: compute weekly net positions and group-level flows (Grains, Oilseeds, Meats, Softs, Dairy) to reveal capital directions.
  • Firepower & Macro Tailwind: quantify buying firepower and macro wind to judge whether funds have room to add or are near crowded levels.
  • Narrative-ready outputs: generate trading calls with annotations, plus visualizations and report-ready JSON/Markdown.

Quick Start

  1. Install dependencies and run the data fetch steps:
    • python scripts/fetch_cot_data.py --start 2025-01-01 --end 2026-01-21
    • python scripts/fetch_macro_data.py --start 2025-01-01 --end 2026-01-21
  2. Run the analysis: python scripts/analyze_positioning.py --start 2025-01-01 --end 2026-01-21
  3. Inspect outputs in output/ (result.json and agri_fund_positioning_*.png)

Frequently Asked Questions about track-agri-hedge-fund-positioning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track hedge fund positioning in agricultural futures using COT data?▼

Hedge fund positioning is quantified by fetching COT data and calculating weekly net positions to determine capital direction. The analysis applies a contracts map to translate raw COT data into group-level insights, revealing weekly flows across Grains, Oilseeds, Meats, Softs, and Dairy.

How do I calculate hedge fund firepower and macro tailwinds for agriculture trading?▼

Firepower and macro tailwinds are calculated by combining COT net position limits with macro indicators fetched via yfinance. This integration quantifies whether funds have room to add to positions or are near crowded levels, producing annotated trading calls with macro context.

Can I generate visualizations and reports of agricultural COT flows in Python?▼

Yes, visualizations and reports of agricultural COT flows are generated in Python using matplotlib, pandas, and numpy. The analysis outputs annotated PNG charts and report-ready JSON/Markdown files detailing weekly flows and trading calls.

What is the best way to map raw COT data to agricultural commodity groups?▼

The best way to map raw COT data to agricultural commodity groups is by applying a contracts map to translate individual futures contracts into group-level insights. This groups raw COT data into Grains, Oilseeds, Meats, Softs, and Dairy to produce weekly flow aggregates.

Does this COT analysis approach work for all agricultural commodity sectors?▼

Yes, this COT analysis works for all major agricultural sectors including Grains, Oilseeds, Meats, Softs, and Dairy. The contracts map translates raw COT data into group-level insights across these categories to produce comprehensive weekly flows and trading calls.

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