series_id large_stringclasses 39
values | date date32 | value float64 -0.99 81.9k |
|---|---|---|
ANFCI | 2025-11-07 | -0.571 |
ANFCI | 2025-11-14 | -0.568 |
ANFCI | 2025-11-21 | -0.563 |
ANFCI | 2025-11-28 | -0.559 |
ANFCI | 2025-12-05 | -0.553 |
ANFCI | 2025-12-12 | -0.548 |
ANFCI | 2025-12-19 | -0.551 |
ANFCI | 2025-12-26 | -0.559 |
ANFCI | 2026-01-02 | -0.571 |
ANFCI | 2026-01-09 | -0.58 |
ANFCI | 2026-01-16 | -0.586 |
ANFCI | 2026-01-23 | -0.584 |
ANFCI | 2026-01-30 | -0.576 |
ANFCI | 2026-02-06 | -0.564 |
ANFCI | 2026-02-13 | -0.549 |
ANFCI | 2026-02-20 | -0.531 |
ANFCI | 2026-02-27 | -0.509 |
ANFCI | 2026-03-06 | -0.483 |
ANFCI | 2026-03-13 | -0.461 |
ANFCI | 2026-03-20 | -0.447 |
ANFCI | 2026-03-27 | -0.442 |
ANFCI | 2026-04-03 | -0.452 |
ANFCI | 2026-04-10 | -0.47 |
ANFCI | 2026-04-17 | -0.487 |
ANFCI | 2026-04-24 | -0.497 |
ANFCI | 2026-05-01 | -0.5 |
ANFCI | 2026-05-08 | -0.498 |
ANFCI | 2026-05-15 | -0.497 |
ANFCI | 2026-05-22 | -0.501 |
ANFCI | 2026-05-29 | -0.513 |
ANFCI | 2026-06-05 | -0.53 |
ANFCI | 2026-06-12 | -0.544 |
ANFCI | 2026-06-19 | -0.555 |
ANFCI | 2026-06-26 | -0.562 |
ANFCI | 2026-07-03 | -0.566 |
ANFCI | 2026-07-10 | -0.569 |
ANFCI | 2026-07-17 | -0.573 |
ANFCI | 2026-07-24 | -0.577 |
ANFCI | 2026-07-31 | -0.58 |
ANFCI | 2026-08-07 | -0.582 |
ANFCI | 2026-08-14 | -0.582 |
ANFCI | 2026-08-21 | -0.58 |
ANFCI | 2026-08-28 | -0.577 |
ANFCI | 2026-09-04 | -0.572 |
ANFCI | 2026-09-11 | -0.564 |
ANFCI | 2026-09-18 | -0.555 |
ANFCI | 2026-09-25 | -0.543 |
ANFCI | 2026-10-02 | -0.53 |
ANFCI | 2026-10-09 | -0.519 |
ANFCI | 2026-10-16 | -0.509 |
ANFCI | 2026-10-23 | -0.501 |
CANEPUINDXM | 2025-11-01 | 928.26126 |
CANEPUINDXM | 2025-12-01 | 643.34304 |
CANEPUINDXM | 2026-01-01 | 1,141.55122 |
CANEPUINDXM | 2026-02-01 | 784.74533 |
CANEPUINDXM | 2026-03-01 | 704.32912 |
CANEPUINDXM | 2026-04-01 | 761.61444 |
CANEPUINDXM | 2026-05-01 | 765.70682 |
CANEPUINDXM | 2026-06-01 | 719.33839 |
CANEPUINDXM | 2026-07-01 | 757.55692 |
CANEPUINDXM | 2026-08-01 | 576.83991 |
CANEPUINDXM | 2026-09-01 | 1,010.68316 |
CANEPUINDXM | 2026-10-01 | 642.73089 |
CHNMAINLANDEPU | 2025-11-01 | 454.235919 |
CHNMAINLANDEPU | 2025-12-01 | 420.053392 |
CHNMAINLANDEPU | 2026-01-01 | 375.822356 |
CHNMAINLANDEPU | 2026-02-01 | 337.587589 |
CHNMAINLANDEPU | 2026-03-01 | 570.774152 |
CHNMAINLANDEPU | 2026-04-01 | 330.639711 |
CHNMAINLANDEPU | 2026-05-01 | 267.232197 |
CHNMAINLANDEPU | 2026-06-01 | 231.453474 |
CHNMAINLANDEPU | 2026-07-01 | 334.801991 |
CHNMAINLANDEPU | 2026-08-01 | 286.185018 |
CHNMAINLANDTPU | 2025-11-01 | 756.666738 |
CHNMAINLANDTPU | 2025-12-01 | 515.549605 |
CHNMAINLANDTPU | 2026-01-01 | 372.001635 |
CHNMAINLANDTPU | 2026-02-01 | 323.893609 |
CHNMAINLANDTPU | 2026-03-01 | 515.576753 |
CHNMAINLANDTPU | 2026-04-01 | 223.058585 |
CHNMAINLANDTPU | 2026-05-01 | 104.51372 |
CHNMAINLANDTPU | 2026-06-01 | 149.146995 |
CHNMAINLANDTPU | 2026-07-01 | 244.591058 |
CHNMAINLANDTPU | 2026-08-01 | 182.258151 |
DEEPUINDXM | 2025-11-01 | 848.50852 |
DEEPUINDXM | 2025-12-01 | 736.13961 |
DEEPUINDXM | 2026-01-01 | 942.66527 |
DEEPUINDXM | 2026-02-01 | 859.85045 |
DEEPUINDXM | 2026-03-01 | 1,213.87227 |
DEEPUINDXM | 2026-04-01 | 1,067.49118 |
DEEPUINDXM | 2026-05-01 | 901.62879 |
DEEPUINDXM | 2026-06-01 | 940.25245 |
DEEPUINDXM | 2026-07-01 | 789.61293 |
DEEPUINDXM | 2026-08-01 | 787.04885 |
DEEPUINDXM | 2026-09-01 | 805.01782 |
DEEPUINDXM | 2026-10-01 | 749.14584 |
EMVMACROBUS | 2025-11-01 | 0 |
EMVMACROBUS | 2025-12-01 | 0 |
EMVMACROBUS | 2026-01-01 | 0 |
EMVMACROBUS | 2026-02-01 | 0.45643 |
EMVMACROBUS | 2026-03-01 | 0 |
US macro data: Financial Conditions (39 series)
39 financial conditions series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-11-01 on) of each series' latest values. It is refreshed weekly.
import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-financial-conditions/data/long.parquet") # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-financial-conditions/data/wide_daily.parquet") # one column per series
or datasets.load_dataset("FinzData/macro-financial-conditions"). Wide tables by frequency: wide_daily (4 series), wide_weekly (7 series), wide_monthly (18 series), wide_quarterly (10 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("NFCI") # full history, latest values
c.macro("NFCI", vintage="first") # as first released
c.macro("NFCI", as_of="2020-03-01") # exactly what was known on that date
c.macro_series(category="financial_conditions") # the catalogue for this category
Most-watched series in this package
| ID | Title | Units | Frequency | Full history from |
|---|---|---|---|---|
NFCI |
Chicago Fed National Financial Conditions Index | Index | Weekly, Ending Friday | 1971 |
STLFSI4 |
St. Louis Fed Financial Stress Index | Index | Weekly, Ending Friday | 1993 |
USEPUINDXD |
Economic Policy Uncertainty Index for United States | Index | Daily, 7-Day | 1985 |
ANFCI |
Chicago Fed Adjusted National Financial Conditions Index | Index | Weekly, Ending Friday | 1971 |
WUIGLOBALWEIGHTAVG |
World Uncertainty Index: Global: GDP Weighted Average | Index | Quarterly | 1960 |
USEPUINDXM |
Economic Policy Uncertainty Index for United States | Index | Monthly | 1985 |
EPUTRADE |
Economic Policy Uncertainty Index: Categorical Index: Trade policy | Index | Monthly | 1985 |
NFCILEVERAGE |
Chicago Fed National Financial Conditions Leverage Subindex | Index | Weekly, Ending Friday | 1971 |
KCFSI |
Kansas City Financial Stress Index | Index | Monthly | 1990 |
NFCICREDIT |
Chicago Fed National Financial Conditions Credit Subindex | Index | Weekly, Ending Friday | 1971 |
CHNMAINLANDEPU |
Economic Policy Uncertainty Index: Mainland Papers for China | Index | Monthly | 1949 |
EMVOVERALLEMV |
Equity Market Volatility Tracker: Overall | Index | Monthly | 1985 |
WUIIDN |
World Uncertainty Index for Indonesia | Index | Quarterly | 1952 |
NFCIRISK |
Chicago Fed National Financial Conditions Risk Subindex | Index | Weekly, Ending Friday | 1971 |
WLEMUINDXD |
Equity Market-related Economic Uncertainty Index | Index | Daily, 7-Day | 1985 |
EUEPUINDXM |
Economic Policy Uncertainty Index for Europe | Index | Monthly | 1987 |
KCPRU |
Kansas City Fed's Measure of Policy Rate Uncertainty (KCPRU) | Percentage Points | Daily | 1989 |
WUIGLOBALSMPAVG |
World Uncertainty Index: Global: Simple Average | Index | Quarterly | 1960 |
NFCINONFINLEVERAGE |
Chicago Fed National Financial Conditions Index Nonfinancial Leveral Subindex | Index | Weekly, Ending Friday | 1971 |
WUIVNM |
World Uncertainty Index for Vietnam | Index | Quarterly | 1956 |
EPUMONETARY |
Economic Policy Uncertainty Index: Categorical Index: Monetary policy | Index | Monthly | 1985 |
CHNMAINLANDTPU |
Trade Policy Uncertainty Index: Mainland Papers for China | Index | Monthly | 2000 |
EMVMACROBUS |
Equity Market Volatility Tracker: Macroeconomic News and Outlook: Business Investment And Sentiment | Index | Monthly | 1985 |
WUIUSA |
World Uncertainty Index for United States | Index | Quarterly | 1952 |
INDEPUINDXM |
Economic Policy Uncertainty Index for India | Index | Monthly | 2003 |
All 39 series: series.csv. Browse them at https://finzdata.com/macro/category/financial-conditions.
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.
- Downloads last month
- 324