bankregreports — BankRegAPI Python SDK

Python client for the BankRegReports Data API — clean, validated, cross-source regulatory data on every US bank, credit union, and holding company, sourced from FFIEC Call Reports, UBPR, FDIC, Federal Reserve, NCUA, and SEC EDGAR.

Full SDK docs  ·  Swagger / OpenAPI  ·  Get API key  ·  bankregreports.com

Why BankRegReports

Raw US bank regulatory data is public but arrives as pipe-delimited bulk files with cryptic MDRM codes, four competing unit conventions, silent schema changes between quarters, and no linkage between FDIC, FFIEC, Federal Reserve, and SEC identifiers. Cleaning it to a usable state takes months. Enterprise data platforms solve this — at $20K–$200K+/year with multi-year contracts.

BankRegReports is the middle ground: the same cleaned, validated, cross-source-linked data through a modern REST API with a Python SDK, at a fraction of the cost. Built by practitioners who have been in ALCO, been through regulatory exams, and needed this data to actually exist.

Read the full background: Why We Built BankRegReports →

Installation

pip install bankregreports
# with pandas (recommended)
pip install "bankregreports[pandas]"

Requires: Python 3.8+ · requests>=2.25

Quick start

import bankregreports

# Set BANKREG_API_TOKEN env var, or pass the token directly
brr = bankregreports.BankReg("brr_xxx")

# Screen every US bank — returns a pandas DataFrame
df = brr.screener(state="GA", min_assets=500_000)
print(df[["legal_name", "total_assets", "cet1_ratio", "roa"]].head(10))

Works as a context manager:

with bankregreports.BankReg("brr_xxx") as brr:
    df_banks = brr.screener(state="TX")
    df_rates = brr.rates("mortgage")

Authentication

Get a free API key at bankregreports.com/api. All keys begin with brr_.

# Explicit token
brr = bankregreports.BankReg("brr_xxx")

# Environment variable (recommended)
# export BANKREG_API_TOKEN=brr_xxx
brr = bankregreports.BankReg()

Coverage — 101 datasets across 16 domains

API reference (selected methods)

# ── Banks ──────────────────────────────────────────────────────────────────
brr.bank(852218)                                # latest snapshot
brr.bank_trends(852218, quarters=12)            # quarterly time series
brr.bank_deep_dive(852218)                      # full Call Report/UBPR detail (~150 fields)
brr.bank_profile(852218)                        # snapshot + scorecard + ranks + ML prediction
brr.bank_compare([852218, 480228, 37])          # side-by-side comparison
brr.screener(state="GA", min_assets=500_000)    # filter the full ~4,400-bank universe
brr.scorecard(852218)                           # CAMELS-style letter grade + component scores
brr.peer_comparison(852218)                     # bank vs. peer benchmarks

# ── Sub-books ──────────────────────────────────────────────────────────────
brr.bank_deposits(852218, trends=True)          # deposit composition trends
brr.bank_securities(852218)                     # HTM/AFS, unrealized G/L, OTTI
brr.loan_portfolio(852218)                      # CRE, C&I, consumer, ag concentrations
brr.bank_hmda(852218)                           # HMDA mortgage lending

# ── Risk & analytics ───────────────────────────────────────────────────────
brr.prediction(852218, detail=True)             # ML failure probability + feature attribution
brr.ma_screener()                               # M&A target scoring + valuation
brr.enforcement_actions(852218)                 # FDIC/OCC/FRB/CFPB enforcement history

# ── SEC / EDGAR ────────────────────────────────────────────────────────────
brr.sec_filings(852218)                         # 10-K, 10-Q, 8-K, proxy index
brr.sec_insider_txns(852218)                    # Form-4 insider buy/sell
brr.sec_13f(852218)                             # institutional holdings
brr.executive_comp(852218)                      # NEO compensation from proxy

# ── Credit unions (NCUA) ───────────────────────────────────────────────────
brr.credit_union(5536, trends=True)             # 5300 Call Report snapshot + trends
brr.cu_peer_benchmarks()                        # CU peer benchmarks by asset tier

# ── Rates & macro ──────────────────────────────────────────────────────────
brr.rates("mortgage")                           # Freddie Mac 30yr/15yr series
brr.yield_curve(latest=True)                    # current yield curve snapshot
brr.industry(trends=True, quarters=20)          # 20-quarter industry aggregate trend

# ── Generic accessor ───────────────────────────────────────────────────────
brr.list_datasets()                             # all 101 datasets: name, path, tier
brr.dataset("screener", state="GA")             # forward-compatible generic accessor

Full reference: www.bankregreports.com/api/  ·  Interactive REST docs: api.bankregreports.com/api/v1/docs/

Pagination

import pandas as pd

# Iterate pages, 200 rows at a time
for page in brr.pages("screener", page_size=200, state="CA"):
    process(page)

# Collect all pages into one DataFrame
all_banks = pd.concat(brr.pages("screener", page_size=500))

Error handling

All errors subclass BankRegError. GET requests auto-retry with exponential backoff on 429 and 5xx (default 3 retries).

from bankregreports import (
    BankReg, BankRegError,
    AuthenticationError, UpgradeRequiredError, NotFoundError,
    RateLimitError, ValidationError, ServerError,
)

try:
    df = brr.ma_screener()
except UpgradeRequiredError:
    print("Upgrade at bankregreports.com/api/")
except RateLimitError as e:
    time.sleep(e.retry_after)
except NotFoundError:
    print("Institution not found")

Configuration

brr = bankregreports.BankReg(
    token="brr_xxx",               # or BANKREG_API_TOKEN env var
    base_url="http://localhost:8000",
    timeout=30,
    max_retries=3,
)

Response format

All list-returning methods return a pandas.DataFrame by default (requires pandas extra). Date columns are coerced to datetime64. The server response envelope is available on df.attrs["meta"].

Pass as_dataframe=False to any method for raw decoded JSON with no pandas dependency.

Resources

License

Copyright © 2024–2026 BankRegReports LLC. Proprietary — see LICENSE.