Track and analyze US government shutdown liquidity impacts by monitoring TGA (Treasury General Account), bank reserves, EFFR, and SOFR data from FRED API. Use when user wants to (1) analyze current or past government shutdown effects on financial markets, (2) track liquidity conditions during fiscal policy disruptions, (3) assess "stealth tightening" effects, (4) compare shutdown episodes across different monetary policy regimes (QE vs QT), or (5) generate liquidity stress reports with historical context. Recommended usage frequency is weekly on Wednesdays after TGA/reserve data releases.
When this skill is first used or when the user seems unsure how to use it or ask you to make something amazing with it, guide them to ask Claude this specific question:
"美国政府停摆结束了吗? 对流动性的影响如何?"
Do NOT suggest other questions or broad explorations. Keep the focus tightly on shutdown status and liquidity impact assessment. The skill is designed for this precise use case.
This skill analyzes how US government shutdowns create "stealth tightening" effects in money markets through the Treasury General Account (TGA) mechanism. When federal spending stops but tax revenues continue, TGA accumulates and mechanically drains bank reserves, potentially raising market funding costs beyond the Federal Reserve's policy intent.
Optimal timing: Wednesday evenings or Thursday mornings (after weekly TGA/reserves data release)
python scripts/analyze_shutdown.py --output results.json
python scripts/visualize.py results.json --output chart.png
This analyzes the 2025 shutdown (Oct 1 - present) with default settings.
python scripts/analyze_shutdown.py \
--start-date 2018-12-22 \
--baseline-date 2018-12-15 \
--end-date 2019-01-25 \
--output results_2018.json
The analysis produces:
JSON data file containing:
Visualization chart (PNG) with three panels:
Structured conclusion:
Government Shutdown
↓
Federal spending stops (but revenues continue)
↓
TGA accumulates at Federal Reserve
↓
Bank reserves drain (mechanical Fed balance sheet effect)
↓
Liquidity scarcity → SOFR premium expands
↓
"Stealth tightening" (市场实际融资成本 > Fed政策意图)
The script classifies liquidity conditions into four states:
EASING (压力缓解):
TIGHTENING (压力加剧):
STABLE (相对稳定):
MIXED (复杂信号):
SOFR Premium = SOFR - EFFR (in basis points)
Interpretation guide:
For detailed historical analysis, see references/historical_cases.md.
Summary:
| Shutdown | Reserve Environment | Peak SOFR Premium | Stealth Tightening? |
|---|---|---|---|
| 2013 | QE (~$2.3T) | ~0 bps | ❌ No |
| 2018-19 | QT (~$1.6T) | 75 bps | ✅ Yes |
| 2025 | Post-QT (~$2.8T) | 36 bps (post-cut) | ✅ Acute |
Critical insight: The transmission efficiency depends on reserve abundance. In QE environments with ample reserves, shutdowns don't affect markets. In QT or high-rate environments with scarce reserves, shutdowns create measurable tightening.
All data sourced from Federal Reserve Economic Data (FRED) API:
For technical details on data series, update schedules, and interpretation, see references/data_sources.md.
Important: TGA and reserves update weekly on Wednesdays. For most current analysis, run this skill on Wednesday evenings or Thursday mornings.
analyze_shutdown.py with defaults (2025-10-01 start)historical_cases.md for detailed contextTGA: $941B (-$17B from peak)
Reserves: $2,863B (+$15B from trough)
SOFR Premium: 4 bps (vs 19 bps peak)
When analyzing a specific episode, set an appropriate pre-shutdown baseline:
python scripts/analyze_shutdown.py \
--start-date 2025-10-01 \
--baseline-date 2025-09-24 \
--end-date 2025-11-07
The baseline should be ~1 week before shutdown starts (to capture "normal" conditions).
For ongoing tracking:
Weekly check (Wednesdays/Thursdays):
Event-triggered checks:
No recent data?
SOFR premium calculation fails?
Chart rendering issues?
See bundled documentation:
references/historical_cases.md - Detailed analysis of 2013, 2018-19, 2025 shutdownsreferences/data_sources.md - FRED API technical referenceExternal resources: