Datasets:
series_id large_stringclasses 271
values | date date32 | value float64 -22,822 30.9M |
|---|---|---|
AGSEBMPTCMAHDFS | 2026-01-01 | 3,231,348 |
AGSEBMPTCMAHDFS | 2026-04-01 | 3,281,934 |
ASCMA | 2026-01-01 | 3,987,500 |
ASCMA | 2026-04-01 | 4,011,464 |
ASHMA | 2026-01-01 | 14,823,299 |
ASHMA | 2026-04-01 | 14,983,477 |
ASMRMA | 2026-01-01 | 2,472,965 |
ASMRMA | 2026-04-01 | 2,493,366 |
ASTMA | 2026-01-01 | 21,669,428 |
ASTMA | 2026-04-01 | 21,878,384 |
BOGZ1FL263061130Q | 2026-01-01 | 3,902,230 |
BOGZ1FL263061130Q | 2026-04-01 | 3,806,810 |
BOGZ1FL263061145Q | 2026-01-01 | 5,446,430 |
BOGZ1FL263061145Q | 2026-04-01 | 5,462,410 |
BOGZ1FL313161110Q | 2026-01-01 | 6,819,224 |
BOGZ1FL313161110Q | 2026-04-01 | 6,690,117 |
BOGZ1FL633061105Q | 2026-01-01 | 3,426,437 |
BOGZ1FL633061105Q | 2026-04-01 | 3,281,560 |
CBLBSNNCB | 2026-01-01 | 7,668,270 |
CBLBSNNCB | 2026-04-01 | 7,751,572 |
COLIRLTLT01STM | 2025-11-01 | 12.52 |
COLIRLTLT01STM | 2025-12-01 | 12.81 |
COLIRLTLT01STM | 2026-01-01 | 12.73 |
COLIRLTLT01STM | 2026-02-01 | 13.21 |
COLIRLTLT01STM | 2026-03-01 | 13.56 |
COLIRLTLT01STM | 2026-04-01 | 13.16 |
COLIRLTLT01STM | 2026-05-01 | 14.01 |
COLIRLTLT01STM | 2026-06-01 | 12.32 |
COLIRLTLT01STM | 2026-07-01 | 12.04 |
COLIRLTLT01STM | 2026-08-01 | 12.18 |
COLIRLTLT01STM | 2026-09-01 | 12.73 |
CORSFRMACBS | 2026-01-01 | 0 |
CORSFRMACBS | 2026-04-01 | 0 |
CPF3M | 2025-11-01 | 3.86 |
CPF3M | 2025-12-01 | 3.74 |
CPF3M | 2026-01-01 | 3.63 |
CPF3M | 2026-02-01 | 3.66 |
CPF3M | 2026-03-01 | 3.69 |
CPF3M | 2026-04-01 | 3.77 |
CPF3M | 2026-05-01 | 3.72 |
CPF3M | 2026-06-01 | 3.77 |
CPF3M | 2026-07-01 | 3.82 |
CPF3M | 2026-08-01 | 3.82 |
CPF3M | 2026-09-01 | 3.94 |
CPFF | 2025-11-03 | -0.01 |
CPFF | 2025-11-06 | -0.02 |
CPFF | 2025-11-07 | -0.01 |
CPFF | 2025-11-13 | -0.06 |
CPFF | 2025-11-17 | 0 |
CPFF | 2025-11-18 | 0.02 |
CPFF | 2025-11-19 | 0.03 |
CPFF | 2025-11-20 | -0.05 |
CPFF | 2025-11-21 | 0 |
CPFF | 2025-11-24 | -0.08 |
CPFF | 2025-11-25 | 0.02 |
CPFF | 2025-11-26 | -0.03 |
CPFF | 2025-12-02 | 0 |
CPFF | 2025-12-03 | -0.05 |
CPFF | 2025-12-04 | -0.1 |
CPFF | 2025-12-05 | -0.17 |
CPFF | 2025-12-08 | -0.14 |
CPFF | 2025-12-09 | -0.21 |
CPFF | 2025-12-10 | -0.17 |
CPFF | 2025-12-16 | 0.17 |
CPFF | 2025-12-17 | -0.05 |
CPFF | 2025-12-18 | 0.06 |
CPFF | 2025-12-23 | 0.02 |
CPFF | 2025-12-30 | 0.06 |
CPFF | 2026-01-06 | 0.02 |
CPFF | 2026-01-07 | 0 |
CPFF | 2026-01-13 | -0.06 |
CPFF | 2026-01-15 | 0.03 |
CPFF | 2026-01-21 | -0.06 |
CPFF | 2026-01-22 | 0.03 |
CPFF | 2026-01-26 | -0.07 |
CPFF | 2026-01-28 | 0.03 |
CPFF | 2026-02-04 | -0.02 |
CPFF | 2026-02-05 | 0.06 |
CPFF | 2026-02-06 | -0.01 |
CPFF | 2026-02-09 | 0.09 |
CPFF | 2026-02-10 | 0 |
CPFF | 2026-02-12 | -0.02 |
CPFF | 2026-02-13 | 0.02 |
CPFF | 2026-02-25 | 0.02 |
CPFF | 2026-02-27 | 0.05 |
CPFF | 2026-03-02 | 0.04 |
CPFF | 2026-03-03 | 0.03 |
CPFF | 2026-03-04 | 0.02 |
CPFF | 2026-03-05 | -0.01 |
CPFF | 2026-03-09 | 0.04 |
CPFF | 2026-03-11 | 0.04 |
CPFF | 2026-03-16 | 0.11 |
CPFF | 2026-03-17 | 0.09 |
CPFF | 2026-03-18 | 0.02 |
CPFF | 2026-03-24 | 0.13 |
CPFF | 2026-04-01 | 0.26 |
CPFF | 2026-04-02 | 0.09 |
CPFF | 2026-04-07 | 0.1 |
CPFF | 2026-04-08 | 0.1 |
CPFF | 2026-04-09 | 0.09 |
US macro data: Rates (271 series)
271 rates series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-10-26 on) of each series' latest values. It is refreshed weekly.
import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-rates/data/long.parquet") # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-rates/data/wide_daily.parquet") # one column per series
or datasets.load_dataset("FinzData/macro-rates"). Wide tables by frequency: wide_daily (73 series), wide_weekly (37 series), wide_monthly (115 series), wide_quarterly (46 series). series.csv lists every series
with its title, units, frequency and how far back the full history goes.
The full history, point in time, through one API
The FinzData API serves every series back to its first observation, every revision (what was known on any past date, so backtests do not peek at later revisions), and daily updates, in the same format:
import finzdata as fz # pip install finzdata
c = fz.Client() # free API key: https://api.finzdata.com/
c.macro("DGS10") # full history, latest values
c.macro("DGS10", vintage="first") # as first released
c.macro("DGS10", as_of="2020-03-01") # exactly what was known on that date
c.macro_series(category="rates") # the catalogue for this category
Most-watched series in this package
| ID | Title | Units | Frequency | Full history from |
|---|---|---|---|---|
DGS10 |
Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 1962 |
T10Y2Y |
10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity | Percent | Daily | 1976 |
FEDFUNDS |
Federal Funds Effective Rate | Percent | Monthly | 1954 |
T10Y3M |
10-Year Treasury Constant Maturity Minus 3-Month Treasury Constant Maturity | Percent | Daily | 1982 |
SOFR |
Secured Overnight Financing Rate | Percent | Daily | 2018 |
DGS30 |
Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 1977 |
DFF |
Federal Funds Effective Rate | Percent | Daily, 7-Day | 1954 |
DGS2 |
Market Yield on U.S. Treasury Securities at 2-Year Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 1976 |
DGS5 |
Market Yield on U.S. Treasury Securities at 5-Year Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 1962 |
DGS1 |
Market Yield on U.S. Treasury Securities at 1-Year Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 1962 |
DFEDTARU |
Federal Funds Target Range - Upper Limit | Percent | Daily, 7-Day | 2008 |
REAINTRATREARAT10Y |
10-Year Real Interest Rate | Percent | Monthly | 1982 |
EFFR |
Effective Federal Funds Rate | Percent | Daily | 2000 |
IORB |
Interest Rate on Reserve Balances (IORB Rate) | Percent | Daily, 7-Day | 2021 |
SOFR30DAYAVG |
30-Day Average SOFR | Percent | Daily | 2018 |
DGS3MO |
Market Yield on U.S. Treasury Securities at 3-Month Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 1981 |
DGS20 |
Market Yield on U.S. Treasury Securities at 20-Year Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 1962 |
RPONTSYD |
Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations | Billions of US Dollars | Daily | 2000 |
DTB3 |
3-Month Treasury Bill Secondary Market Rate, Discount Basis | Percent | Daily | 1954 |
TB3MS |
3-Month Treasury Bill Secondary Market Rate, Discount Basis | Percent | Monthly | 1934 |
GS10 |
Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis | Percent | Monthly | 1953 |
THREEFYTP10 |
Term Premium on a 10 Year Zero Coupon Bond | Percent | Daily | 1990 |
DGS1MO |
Market Yield on U.S. Treasury Securities at 1-Month Constant Maturity, Quoted on an Investment Basis | Percent | Daily | 2001 |
DRSFRMACBS |
Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks | Percent | Quarterly, End of Period | 1991 |
WDTGAL |
Liabilities and Capital: Liabilities: Deposits with F.R. Banks, Other Than Reserve Balances: U.S. Treasury, General Account: Wednesday Level | Millions of U.S. Dollars | Weekly, As of Wednesday | 2002 |
All 271 series: series.csv. Browse them at https://finzdata.com/macro/category/rates.
More from FinzData
- Every macro category: https://huggingface.co/FinzData · index on GitHub: https://github.com/FinzData/macro-data
- Survivorship-free US stock prices since 2000, COT positioning, 13F holdings, insider trades, short interest
- Pricing (history bundles from $195 once): https://finzdata.com/pricing
- Python client (open source): https://github.com/FinzData/finzdata-python
Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData. Data for research, not investment advice.
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