name: usfiscaldata description: Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics. license: MIT allowed-tools: Read Write Edit Bash metadata: version: "1.1" skill-author: K-Dense Inc.

U.S. Treasury Fiscal Data API

Free, open REST API from the U.S. Department of the Treasury for federal financial data. No API key or registration required.

Base URL: https://api.fiscaldata.treasury.gov/services/api/fiscal_service

Browse 54 datasets and 179 data tables via the dataset search. Verify endpoint paths on each dataset's API Quick Guide — paths change over time.

Installation

uv pip install requests pandas

Quick Start

import requests
import pandas as pd

BASE_URL = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service"

# Get the current national debt (Debt to the Penny)
resp = requests.get(f"{BASE_URL}/v2/accounting/od/debt_to_penny", params={
    "sort": "-record_date",
    "page[size]": 1
})
data = resp.json()["data"][0]
print(f"Total public debt as of {data['record_date']}: ${float(data['tot_pub_debt_out_amt']):,.0f}")

# Get Treasury exchange rates for recent quarters
resp = requests.get(f"{BASE_URL}/v1/accounting/od/rates_of_exchange", params={
    "fields": "country_currency_desc,exchange_rate,record_date",
    "filter": "record_date:gte:2024-01-01",
    "sort": "-record_date",
    "page[size]": 100
})
df = pd.DataFrame(resp.json()["data"])

Authentication

None required. The API is fully open and free.

Core Parameters

| Parameter | Example | Description | |-----------|---------|-------------| | fields= | fields=record_date,tot_pub_debt_out_amt | Select specific columns | | filter= | filter=record_date:gte:2024-01-01 | Filter records | | sort= | sort=-record_date | Sort (prefix - for descending) | | format= | format=json | Output format: json, csv, xml | | page[size]= | page[size]=100 | Records per page (default 100) | | page[number]= | page[number]=2 | Page index (starts at 1) |

Filter operators: lt, lte, gt, gte, eq, in

# Multiple filters separated by comma
"filter=country_currency_desc:in:(Canada-Dollar,Mexico-Peso),record_date:gte:2024-01-01"

Key Datasets & Endpoints

Debt

| Dataset | Endpoint | Frequency | |---------|----------|-----------| | Debt to the Penny | /v2/accounting/od/debt_to_penny | Daily | | Historical Debt Outstanding | /v2/accounting/od/debt_outstanding | Annual | | Schedules of Federal Debt | /v1/accounting/od/schedules_fed_debt | Monthly |

Daily & Monthly Statements

| Dataset | Endpoint | Frequency | |---------|----------|-----------| | DTS Operating Cash Balance | /v1/accounting/dts/operating_cash_balance | Daily | | DTS Deposits & Withdrawals | /v1/accounting/dts/deposits_withdrawals_operating_cash | Daily | | Monthly Treasury Statement (MTS) | /v1/accounting/mts/mts_table_1 (18 tables — see datasets-fiscal.md) | Monthly |

Interest Rates & Exchange

| Dataset | Endpoint | Frequency | |---------|----------|-----------| | Average Interest Rates on Treasury Securities | /v2/accounting/od/avg_interest_rates | Monthly | | Treasury Reporting Rates of Exchange | /v1/accounting/od/rates_of_exchange | Quarterly | | Interest Expense on Public Debt | /v2/accounting/od/interest_expense | Monthly |

Securities & Auctions

| Dataset | Endpoint | Frequency | |---------|----------|-----------| | Treasury Securities Auctions Data | /v1/accounting/od/auctions_query | As Needed | | Treasury Securities Upcoming Auctions | /v1/accounting/od/upcoming_auctions | As Needed | | Treasury Securities Buybacks | /v1/accounting/od/buybacks_operations | As Needed |

Savings Bonds

| Dataset | Endpoint | Frequency | |---------|----------|-----------| | I Bonds Interest Rates | /v1/accounting/od/i_bonds_interest_rates | Semi-Annual | | Savings Bonds Issues, Redemptions & Maturities | /v1/accounting/od/savings_bonds_report | Monthly |

Response Structure

{
  "data": [...],
  "meta": {
    "count": 100,
    "total-count": 3790,
    "total-pages": 38,
    "labels": {"field_name": "Human Readable Label"},
    "dataTypes": {"field_name": "STRING|NUMBER|DATE|CURRENCY"},
    "dataFormats": {"field_name": "String|10.2|YYYY-MM-DD"}
  },
  "links": {"self": "...", "first": "...", "prev": null, "next": "...", "last": "..."}
}

Note: All values are returned as strings. Convert as needed (e.g., float(), pd.to_datetime()). Null values appear as the string "null".

Common Patterns

Load all pages into a DataFrame

Use the bounded fetch_all() helper in parameters.md. For small result sets, a single request with page[size]=10000 may suffice when meta.total-pages is 1.

# Single-page fetch when total-pages == 1
params = {"sort": "-record_date", "page[size]": 10000}
resp = requests.get(f"{BASE_URL}/v2/accounting/od/debt_outstanding", params=params)
result = resp.json()
if result["meta"]["total-pages"] > 1:
    raise ValueError("Use fetch_all() from parameters.md for multi-page results")
df = pd.DataFrame(result["data"])

Aggregation (automatic sum)

Omitting grouping fields triggers automatic aggregation:

# Sum all deposits/withdrawals by record_date and transaction type
resp = requests.get(f"{BASE_URL}/v1/accounting/dts/deposits_withdrawals_operating_cash", params={
    "fields": "record_date,transaction_type,transaction_today_amt"
})

Reference Files

api-basics.md — URL structure, HTTP methods, versioning, data types
parameters.md — All parameters with detailed examples and edge cases
datasets-debt.md — Debt datasets: Debt to the Penny, Historical Debt, Schedules of Federal Debt, TROR
datasets-fiscal.md — Daily Treasury Statement, Monthly Treasury Statement, revenue, spending
datasets-interest-rates.md — Average interest rates, exchange rates, TIPS/CPI, certified interest rates
datasets-securities.md — Treasury auctions, savings bonds, SLGS, buybacks
response-format.md — Response objects, error handling, pagination, response codes
examples.md — Python, R, and pandas code examples for common use cases

usfiscaldata