Curvewire: Treasury yield curves + 15 BLS series (build 2026-09-24)

README.md

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+---
+license: cc0-1.0
+pretty_name: "Curvewire: US Treasury yield curve + BLS macro panel (official sources)"
+language:
+- en
+tags:
+- finance
+- economics
+- macroeconomics
+- interest-rates
+- yield-curve
+- treasury
+- inflation
+- cpi
+- jobs
+- unemployment
+- bls
+- time-series
+- tabular
+task_categories:
+- time-series-forecasting
+- tabular-regression
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: treasury_yields
+  default: true
+  data_files:
+  - split: train
+    path: data/treasury_yields.parquet
+- config_name: treasury_real_yields
+  data_files:
+  - split: train
+    path: data/treasury_real_yields.parquet
+- config_name: bls_monthly
+  data_files:
+  - split: train
+    path: data/bls_monthly.parquet
+---
+
+# Curvewire: US Treasury yield curve + BLS macro panel
+
+The US numbers macro models, trading bots and LLM agents ask for most, taken **straight from the agencies that publish them** (US Treasury and the Bureau of Labor Statistics), not scraped from redistributors, and cleaned into three tidy tables:
+
+| config | rows | what |
+|---|---|---|
+| `treasury_yields` (default) | 9,189 | Daily par yield curve, 1 month to 30 years, 1990-01-02 → 2026-09-23, with 10y–2y and 10y–3m spreads |
+| `treasury_real_yields` | 5,936 | Daily real (TIPS) par yield curve, 5 to 30 years, 2003-01-02 → 2026-09-23 |
+| `bls_monthly` | 7,514 | 15 headline monthly series in long format, with 1-month and 12-month changes |
+
+Latest readings in this build (see `meta.json`): 10-year yield **5.11%**, 2-year 4.85% (10y–2y spread +0.26 pt), 3-month 4.19%; August 2026 CPI **+3.35%** year on year (core +2.45%), unemployment rate **4.1%**, payrolls +162k on the month; JOLTS job openings 7.27 million (July).
+
+## BLS series included
+
+| series_id | series | units |
+|---|---|---|
+| CUSR0000SA0 / CUUR0000SA0 | CPI-U, all items (seasonally adjusted / not) | index 1982-84=100 |
+| CUSR0000SA0L1E | Core CPI (less food and energy) | index |
+| CUSR0000SAF1, CUSR0000SA0E, CUSR0000SAH1 | CPI food, energy, shelter | index |
+| CES0000000001 | Total nonfarm payrolls | thousands |
+| CES0500000003, CES0500000002 | Average hourly earnings, average weekly hours (private) | USD, hours |
+| LNS14000000, LNS11300000, LNS12300000 | Unemployment rate, participation rate, employment-population ratio | percent |
+| JTS000000000000000JOL, JTS000000000000000QUR | JOLTS job openings, quits rate | thousands, percent |
+| WPSFD4 | PPI final demand | index |
+
+`bls_monthly` columns: `series_id`, `series_name`, `units`, `seasonally_adjusted`, `date` (first day of the month), `value`, `footnote_codes` (BLS codes, e.g. `P` = preliminary), `change_1m`, `pct_change_1m`, `pct_change_12m` (null for series already in percent), `change_12m`.
+
+`treasury_yields` columns: `date`, `y_1m`, `y_6w` (1.5-month bill, from 2025), `y_2m`, `y_3m`, `y_4m`, `y_6m`, `y_1y`, `y_2y`, `y_3y`, `y_5y`, `y_7y`, `y_10y`, `y_20y`, `y_30y` (percent), `spread_10y_2y`, `spread_10y_3m` (percentage points). Maturities Treasury didn't publish on a date (e.g. 20-year before 1993, 30-year in 2002–2006) are `null`.
+
+## Use it
+
+```python
+from datasets import load_dataset
+curve = load_dataset("shaw276/curvewire-us-macro-panel", "treasury_yields", split="train").to_pandas()
+print(curve[curve.spread_10y_2y < 0].date.agg(["min", "max", "count"]))   # inversion days
+
+bls = load_dataset("shaw276/curvewire-us-macro-panel", "bls_monthly", split="train").to_pandas()
+cpi = bls[bls.series_id == "CUSR0000SA0L1E"].set_index("date")["pct_change_12m"]
+print(cpi.tail(12))   # core CPI inflation, last 12 months
+```
+
+## Refresh and revisions
+
+Rebuilt from the agencies' own files (Treasury's daily CSVs, BLS bulk time-series files); `meta.json` has the build time and each series' latest month. BLS revises recent months (payrolls for two months, CPI seasonal factors each February), and every refresh picks the revisions up, so always use the latest build rather than stitching old ones together.
+
+## Licence
+
+US Treasury and BLS data are works of the US federal government, in the public domain; this compilation is released under **CC0 1.0**. Please cite the agencies as the source. Compiled by **CyberMax**. Not affiliated with or endorsed by the US Treasury or BLS. Not investment advice. Series from redistributors with third-party copyright (e.g. some FRED series) are deliberately not included.
+
+## Also from CyberMax
+
+- Free datasets: [Tickerbell: SEC 8-K events by Item](https://huggingface.co/datasets/shaw276/tickerbell-sec-8k-events) · [Boardroom Buys: SEC Form 4 insider transactions](https://huggingface.co/datasets/shaw276/sec-form4-insider-transactions)
+- Tools for AI agents and data teams (pay per use, on Apify), e.g. [Boardroom Buys: SEC insider trading tracker](https://apify.com/cybermax/sec-insider-tracker) and [Swellmeter: Google Trends API](https://apify.com/cybermax/google-trends): https://apify.com/cybermax

data/bls_monthly.parquet

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+size 134446

data/treasury_real_yields.parquet

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+size 55792

data/treasury_yields.parquet

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+oid sha256:18cebbaf40582ef9bd2075e418cc244c2f902fb56fe33a067e2fec5743dee744
+size 201517

meta.json

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+{
+ "built_at": "2026-09-24T01:33:09Z",
+ "treasury_yields": 9189,
+ "treasury_yields_range": [
+  "1990-01-02",
+  "2026-09-23"
+ ],
+ "treasury_real_yields": 5936,
+ "treasury_real_yields_range": [
+  "2003-01-02",
+  "2026-09-23"
+ ],
+ "bls_monthly": 11026,
+ "bls_series": 15,
+ "bls_latest_period": {
+  "CUSR0000SA0": "2026-08",
+  "CUUR0000SA0": "2026-08",
+  "CUSR0000SA0L1E": "2026-08",
+  "CUSR0000SAF1": "2026-08",
+  "CUSR0000SA0E": "2026-08",
+  "CUSR0000SAH1": "2026-08",
+  "CES0000000001": "2026-08",
+  "CES0500000003": "2026-08",
+  "CES0500000002": "2026-08",
+  "LNS14000000": "2026-08",
+  "LNS11300000": "2026-08",
+  "LNS12300000": "2026-08",
+  "JTS000000000000000JOL": "2026-07",
+  "JTS000000000000000QUR": "2026-07",
+  "WPSFD4": "2026-08"
+ },
+ "latest_curve": {
+  "date": "2026-09-23",
+  "y_1m": 3.99,
+  "y_6w": 4.07,
+  "y_2m": 4.1,
+  "y_3m": 4.19,
+  "y_4m": 4.3,
+  "y_6m": 4.31,
+  "y_1y": 4.49,
+  "y_2y": 4.85,
+  "y_3y": 4.97,
+  "y_5y": 4.99,
+  "y_7y": 5.05,
+  "y_10y": 5.11,
+  "y_20y": 5.45,
+  "y_30y": 5.4,
+  "spread_10y_2y": 0.26,
+  "spread_10y_3m": 0.92
+ }
+}