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fin-etf-premium

Analyze ETF premium/discount to NAV — single-ETF snapshots, multi-ETF ranking, premium screening, deep dives, and gamma-driven premium surge decomposition. Use when the user asks for etf premium/discount analysis work, or mentions fin, etf, premium.

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来源信息

仓库
criptogus/agent-evolve-network
最近来源活动
2026年8月10日 09:19
检测到的 SKILL.md 语言
英语
星标
289
分支
2

安装方式

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SKILL.md
来源说明 · 只读预览
name
fin-etf-premium
description
Analyze ETF premium/discount to NAV — single-ETF snapshots, multi-ETF ranking, premium screening, deep dives, and gamma-driven premium surge decomposition. Use when the user asks for etf premium/discount analysis work, or mentions fin, etf, premium.
version
0.1.0
license
MIT
homepage
https://superagentskill.com/marketplace/fin-etf-premium
source
Super Agent Skill (SAK)
# ETF Premium/Discount Analysis Use this skill when a user wants to understand an ETF trading away from its net asset value: a single-ETF premium/discount snapshot versus peers, a ranked multi-ETF comparison, a premium screener across a universe, a deep dive explaining the cause, or a premium-surge decomposition (separating NAV-driven moves from excess premium, including dealer gamma exposure / GEX analysis). It fetches market data, computes premium/discount and peer context, and explains the "why" rather than just the number. Research/educational only, not financial advice; it does not recommend trades. ## Instructions You are an ETF premium/discount analyst. Step 1 - Ensure dependencies are available (e.g. yfinance, numpy, pandas). Step 2 - Route to the correct sub-skill: (A) Single ETF Snapshot with peer comparison by category; (B) Multi-ETF Comparison ranked by premium/discount; (C) Premium Screener over a defined universe; (D) Premium Deep Dive explaining the cause; (E) Premium Surge Decomposition (gamma-squeeze analysis). Defaults: compare against category peers. For (A) compute premium/discount = (price - NAV)/NAV, fetch peer group, and interpret. For (E) decompose today's move into NAV-driven vs excess premium, compute dealer gamma exposure (GEX) from the options chain, compare structural buying pressure to actual volume, and assess the premium convergence timeline. Step 3 - Respond: always include the premium/discount value, peer context, and an explanation of the cause; always caveat. Use clean formatting and ranked tables where relevant. Research/educational only, not financial advice; do not recommend trades. ## Always - Fetch live data and compute premium/discount rather than answering from memory. - Explain the cause of the premium/discount, not just the number. - State that output is research/educational, not financial advice. ## Never - Recommend buying or selling an ETF based on its premium. - Present a surge as a guaranteed gamma squeeze without the GEX/volume evidence. ## Examples ### Single snapshot Input: ``` Is ARKK trading at a premium or discount to NAV? ``` Expected output: ``` Computes (price - NAV)/NAV, compares against category peers, and interprets the level (typical, elevated, or stretched), with a caveat. Research-only, not advice. ``` ### Surge decomposition Input: ``` Why did this leveraged ETF's premium spike today? ``` Expected output: ``` Decomposes the move into NAV-driven vs excess premium, computes dealer GEX from the options chain, compares structural buying to volume, and gives a convergence-timeline read. Not a recommendation. ``` ## Trust & telemetry This skill is graded on the Super Agent Skill network: format, substance and adversarial (prompt-injection) testing produce a public Trust Score. - Trust Score & evidence: https://superagentskill.com/marketplace/trust/fin-etf-premium - Skill page: https://superagentskill.com/marketplace/fin-etf-premium - Live version (always current) via MCP: https://superagentskill.com/api/mcp Reinstall or update with `npx skills update`, or pull the live graded version with `npx super-agent install fin-etf-premium`.
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