us-gov-shutdown-tracker

Track government shutdown liquidity stress by fetching TGA, bank reserves, EFFR, and SOFR data from FRED to evaluate stealth tightening conditions. This process computes the SOFR premium, identifies liquidity stress, and classifies the overall status as EASING, TIGHTENING, STABLE, or MIXED for quick assessment.

Compare liquidity conditions across shutdown episodes by running Python scripts that fetch historical FRED data and perform baseline analysis. The skill supports historical comparisons for the 2013, 2018-19, and 2025 shutdown periods to identify changes in TGA, bank reserves, EFFR, and SOFR metrics.

Stealth tightening in money markets is measured by monitoring shifts in TGA balances, bank reserves, EFFR, and SOFR via FRED data. The skill calculates the SOFR premium and evaluates these liquidity stress signals to classify whether the market is experiencing easing, tightening, or stable conditions.

Generate visualization charts for TGA and SOFR liquidity health checks by running the skill's automated Python data-fetching scripts. The analysis outputs a JSON data payload and automatic chart generation to provide concise reports and support quick liquidity decision-making.

Fetching EFFR and TGA data for shutdown analysis requires accessing the FRED database via Python requests and pandas dependencies. The skill automates this data collection process to compute baselines, identify liquidity stress, and generate comparative reports for ongoing monitoring.

Using pandas and requests for money market liquidity tracking limits analysis to FRED data availability for TGA, bank reserves, EFFR, and SOFR metrics. The skill relies on Python scripts for data-fetching and baseline comparison, meaning accuracy depends on the continuity and update frequency of the FRED data source.