economic data api
Economic Data API with Point-in-Time Macro Series
FinzData provides an economic data API that delivers US macro series with every vintage (ALFRED-style as_of), money supply M1/M2/M3, Fed net liquidity, credit spreads, and Quantitative Model regime labels through a Python client that mirrors yfinance.
Overview of Economic Data Coverage
FinzData's economic data API provides access to 18,000+ US macro series with every historical revision, enabling point-in-time analysis for backtesting and research. The coverage includes FRED/ALFRED vintages, money supply metrics (M1, M2, M3), Federal Reserve net liquidity measures, credit spreads, and Quantitative Model regime labels. All data is available as-of specific dates to eliminate look-ahead bias in economic analysis.
Users can retrieve macroeconomic indicators with the same simplicity as stock data, using familiar Python syntax. The API returns pandas DataFrames with timestamps and vintage information, making it easy to merge macro data with financial time series for regime analysis or predictive modeling.
Accessing Vintage Macro Data
import finzdata as yf
c = yf.Client()
df = c.macro("CPIAUCSL", as_of="2020-06-30")
print(df.head())Money Supply and Liquidity Measures
The economic data API includes Federal Reserve money supply series (M1, M2, M3) with full revision history, allowing analysts to study monetary policy effects with point-in-time accuracy. Fed net liquidity measures are also available, providing insights into financial conditions that traditional money supply metrics may miss.
These series are essential for understanding how changes in central bank balance sheets propagate through financial markets. Users can access both the raw monetary aggregates and derived liquidity indicators that help explain asset price movements across different market regimes.
Credit Spreads and Financial Conditions
Credit spread data through the economic data API captures the risk premiums embedded in corporate bond yields relative to government treasuries. This includes investment-grade and high-yield spreads that serve as leading indicators of economic stress and financial market turbulence.
By accessing credit spreads with point-in-time vintages, researchers can accurately backtest strategies that depend on financial conditions without introducing look-ahead bias. The data helps identify turning points in credit cycles that often precede broader economic downturns.
Quantitative Model Regime Labels
FinzData's Quantitative Model regime labels are available through the economic data API, providing monthly classifications of economic and market environments based on macroeconomic factors. These labels help investors and analysts adapt strategies to different regimes such as expansion, slowdown, contraction, and recovery.
The regime labels combine multiple indicators including growth, inflation, monetary policy, and financial conditions to create a comprehensive view of the economic landscape. Users can access these labels historically to test how different factors performed across various market environments.
Integration with Financial Data
The economic data API seamlessly integrates with FinzData's other datasets, allowing users to combine macroeconomic indicators with stock prices, fundamentals, COT data, and alternative data. This enables comprehensive analysis of how economic regimes affect different asset classes and sectors.
For example, users can analyze how Quantitative Model regimes correlate with sector performance, or how money supply changes affect different factors in equity markets. The consistent API design makes it easy to merge macro data with financial time series using pandas.
Frequently asked questions
What is point-in-time macroeconomic data?
Point-in-time macroeconomic data provides economic indicators as they were available on specific historical dates, eliminating look-ahead bias in backtesting by using only information that would have been known at that time.
How do I access FRED data vintages in Python?
Use the FinzData Python client with c.macro(series_id, as_of='YYYY-MM-DD') to retrieve any FRED/ALFRED series as it existed on a specific date, returning a pandas DataFrame with vintage information.
What money supply data is available through the API?
The economic data API provides M1, M2, and M3 money supply series with full revision history, plus Federal Reserve net liquidity measures, all available as-of specific dates for point-in-time analysis.
Can I get credit spreads with historical revisions?
Yes, the API includes credit spread data (investment-grade and high-yield) with full ALFRED-style vintages, allowing accurate backtesting of strategies that depend on financial conditions without look-ahead bias.
How do Quantitative Model regime labels work?
The regime labels provide monthly classifications of economic environments based on macro factors including growth, inflation, monetary policy, and financial conditions, helping users adapt strategies to different market regimes.
Is the economic data API free to use?
The free tier provides last 12 months of all macro series with 1,000 API calls daily. Paid look-back bundles start at $195 for 5 years of history, with institutional plans at $975 plus monthly updates.