Macro Regime Monitoring Skill v2

Purpose

This skill defines a repeatable macro-regime monitoring method. It fetches fresh macro-market data, builds a multi-asset observation vector, maps the vector into a 4-state macro regime framework, and outputs a concise monitoring report.

It is designed for:

• macro regime identification;
• Markov transition matrix monitoring;
• robust Student-t observation filtering;
• change-point / transition risk detection;
• R1-to-R2 upgrade alerts;
• scenario tree updates;
• risk monitoring.

It is not an automatic trading system and must not be presented as a buy/sell signal.

Regime Framework

Regime	Name	Core Features
R0	High-rate absorption	Treasury yields high but no longer rising aggressively; equities resilient; dollar stable
R1	Bear steepening + dollar pressure	Long-end yields rising; USD stronger; small caps and EM weak
R2	Credit / sovereign stress spillover	Credit spreads widen; sovereign spreads widen; risk assets deleverage together
R3	Rate decline / policy repair	Yields fall; driven by growth shock or policy/liquidity support

Always separate:

1. verified market data;
2. computed indicators;
3. deterministic rule inference;
4. robust statistical diagnostics;
5. judgment calls;
6. monitoring suggestions.

Data Sources

• FRED: US Treasury yields and credit OAS.
• Yahoo Finance: DXY, ETF/index proxies.

Statistical Layer

The Markov chain is not used as the sole model. It is only used as a transition-smoothing prior.

The upgraded statistical layer includes:

1. deterministic rule posterior;
2. Student-t observation filter with Markov transition smoothing;
3. robust trailing-window change-point risk;
4. ensemble probability.

R2 Discipline

Do not upgrade to R2 from rates and equity weakness alone. R2 requires credit-spread stress, sovereign-spread stress, or synchronized deleveraging across equities, EM, credit, and high-duration assets.