FRED API

The Federal Reserve Economic Data (FRED) API provides programmatic access to over 800,000 economic time series from 108 sources.

Key Features

Data Coverage

Macroeconomic Indicators: GDP, inflation, unemployment
Financial Data: Interest rates, stock indices, exchange rates
Regional Data: State and MSA-level statistics
International Data: Economic indicators from multiple countries

API Capabilities

Free to use with API key
No rate limits for reasonable use
Historical data going back decades
Regular updates (daily, weekly, monthly)
Multiple data formats (JSON, XML)

Popular Data Series

Key Economic Indicators

GDP: Gross Domestic Product (GDPC1)
Unemployment: Civilian Unemployment Rate (UNRATE)
Inflation: Consumer Price Index (CPIAUCSL)
Interest Rates: Federal Funds Rate (DFF)

Financial Markets

S&P 500: Stock Market Index (SP500)
Treasury Yields: 10-Year Treasury (DGS10)
Exchange Rates: Dollar Index (DTWEXBGS)

Getting Started

1. Get an API Key

2. Make Your First Request

import requests
import pandas as pd

API_KEY = "your-api-key"
BASE_URL = "https://api.stlouisfed.org/fred/series/observations"

params = {
    "series_id": "GDP",
    "api_key": API_KEY,
    "file_type": "json"
}

response = requests.get(BASE_URL, params=params)
data = response.json()

# Convert to DataFrame
df = pd.DataFrame(data['observations'])
df['date'] = pd.to_datetime(df['date'])
df['value'] = pd.to_numeric(df['value'], errors='coerce')

print(df.head())

Using the fredapi Library

from fredapi import Fred

fred = Fred(api_key='your-api-key')

# Get GDP data
gdp = fred.get_series('GDP')

# Get unemployment rate
unemployment = fred.get_series('UNRATE')

# Search for series
results = fred.search('inflation')

Common Use Cases

Economic Research

Analyze business cycles
Study monetary policy impacts
Compare regional economies
Research historical trends

Data Science Projects

Time series forecasting
Economic indicator modeling
Causal impact analysis
Machine learning features

Financial Analysis

Market trend analysis
Risk assessment
Portfolio optimization
Economic scenario planning

Example: Economic Analysis

import matplotlib.pyplot as plt
from fredapi import Fred

fred = Fred(api_key='your-api-key')

# Get data
unemployment = fred.get_series('UNRATE')
gdp_growth = fred.get_series('A191RL1Q225SBEA')

# Plot
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))

unemployment.plot(ax=ax1, title='Unemployment Rate')
ax1.set_ylabel('Percent')

gdp_growth.plot(ax=ax2, title='GDP Growth Rate', color='green')
ax2.set_ylabel('Percent')

plt.tight_layout()
plt.show()

Advanced Features

Data Transformations

Apply transformations directly via API:

Percentage change
Difference
Compounded annual rate
Natural log

params = {
    "series_id": "GDP",
    "api_key": API_KEY,
    "file_type": "json",
    "units": "pch"  # Percent change
}

Multiple Series

Combine multiple series for analysis:

from fredapi import Fred
import pandas as pd

fred = Fred(api_key='your-api-key')

series_ids = ['GDP', 'UNRATE', 'CPIAUCSL']
data = {}

for series_id in series_ids:
    data[series_id] = fred.get_series(series_id)

df = pd.DataFrame(data)
correlation = df.corr()
print(correlation)

Best Practices

Cache Data: Save frequently used series locally
Handle Missing Values: Not all series are complete
Check Frequency: Series have different update frequencies
Read Documentation: Understand what each series measures
Attribution: Credit FRED when publishing results

Integration with Analysis Tools

Causal Impact Analysis

from causalimpact import CausalImpact
from fredapi import Fred

fred = Fred(api_key='your-api-key')

# Get data for causal analysis
y = fred.get_series('RETAIL')
x1 = fred.get_series('UNRATE')
x2 = fred.get_series('CPIAUCSL')

data = pd.DataFrame({'y': y, 'x1': x1, 'x2': x2})

# Run causal impact analysis
ci = CausalImpact(data, pre_period, post_period)
print(ci.summary())

Resources

Limitations

Real-time data may have revisions
Some series require seasonal adjustment
International coverage varies by country
No tick-level financial data