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us-etf-flow

Analyze U.S. ETF flow data to identify institutional capital movements.

Updated Apr 12, 2026
Search Tags:#flow#etf#rotation#market#sentiment#sector
One-click install
npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill us-etf-flow-daddyelonmusk69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: us-etf-flow
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/finance/us-etf-flow
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill us-etf-flow-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides analysts with a systematic method to interpret US ETF creation and redemption data, giving real‑time insight into institutional capital allocation, sector rotation, and overall market risk appetite without relying on lagging 13F filings.

Core Features & Use Cases

  • Flow Mechanics: explains creation/redemption processes and how sustained inflows or outflows signal institutional demand or liquidation.
  • Broad & Sector ETF Categories: lists major ETFs and defines signal rules for risk‑on, risk‑off, and quality‑flight scenarios.
  • Breadth & Cyclical/Defensive Ratios: calculates sector breadth and risk ratios to classify market conditions.
  • Thematic & Factor Analysis: tracks theme‑specific ETF flows (e.g., AI, clean energy) and factor exposures such as growth, value, momentum, and quality.
  • Signal Generation: provides Python‑style functions to create flow‑based trading signals and a composite outlook for portfolio positioning.

Quick Start

Ask the skill to generate a weekly US ETF flow report highlighting sector breadth and risk signals.

Frequently Asked Questions about us-etf-flow

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

FAQPage Schema
How do ETF flows indicate institutional market sentiment and sector rotation?▼

ETF flows indicate institutional market sentiment by tracking creation and redemption data, where sustained inflows signal capital allocation into sectors and outflows show liquidation. This method bypasses lagging 13F filings to provide real-time risk appetite insights.

What is the best way to analyze US ETF flow data for trading signals?▼

To analyze US ETF flow data for trading signals, use Python libraries like yfinance and external flow data sources to compute flow percentages and breadth. This generates Python-style functions for risk-on, risk-off, and quality-flight portfolio positioning signals.

Can I use yfinance to compute sector breadth and cyclical defensive ratios?▼

Yes, you can use yfinance alongside external flow data sources to compute sector breadth and cyclical defensive ratios. These metrics classify market conditions and help assess overall risk appetite by comparing inflows across major sector ETFs.

How do I generate a weekly US ETF flow report highlighting risk signals?▼

Generate a weekly US ETF flow report by aggregating creation and redemption data to compute sector breadth, cyclical defensive ratios, and flow percentages. This outputs a composite outlook and risk signals for portfolio positioning.

Does ETF flow analysis track thematic and factor exposures like AI and clean energy?▼

Yes, ETF flow analysis tracks thematic and factor exposures like AI and clean energy by monitoring theme-specific ETF flows. It evaluates factor exposures including growth, value, momentum, and quality to assess institutional demand for specific investment themes.

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