bankregreports 0.2.0

Python client for the BankRegReports Data API — regulatory data on every US bank, credit union, and holding company.

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

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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 →

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Installation

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

Requires: Python 3.8+ · requests>=2.25

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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")

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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()

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Coverage — 101 datasets across 16 domains

Domain	What you get
Banks — snapshot & trends	Latest metrics, 20-quarter time series, full Call Report/UBPR detail
Banks — scores & peers	CAMELS-style scores, percentile ranks, peer benchmarks
Banks — sub-books	Deposits, securities (HTM/AFS/unrealized G/L), loans, HMDA
Banks — structure	Parent chain, subsidiaries, NIC events, FDIC history, FRY-15
Branches	FDIC SoD + NIC combined, rollup stats, map-ready lat/lon
Risk & compliance	ML failure prediction + feature attribution, enforcement actions, CFPB complaints
Screeners	Full-universe filter, M&A target scoring, CRE concentration, growth
SEC / EDGAR	10-K/10-Q/8-K, XBRL facts, insider Form-4, exec comp, 13D/G, 13F
Industry & macro	Aggregates, rate environment, credit spreads, VIX
Rates & yield curve	Mortgage, T-bill, real yield, consumer, FDIC national deposit rates
Failures & events	FDIC failure feed, M&A/charter events, enforcement feed
Credit unions (NCUA)	5300 Call Report, trends, branches, ATMs, peer benchmarks
Holding companies	Y-9C BHC snapshot and quarterly trends
CECL & analytics	Macro-conditioned lifetime ECL, DFAST scenarios
Watchlist & alerts	Track institutions, threshold alerts on any metric
Reference & catalog	MDRM definitions, metric catalog with formulas

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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/

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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))

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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")

Exception	HTTP	Meaning
AuthenticationError	401	Missing or revoked API key
UpgradeRequiredError	403	Plan doesn't cover this dataset
NotFoundError	404	Institution or resource not found
ValidationError	422	Invalid parameters
RateLimitError	429	Throttled — check e.retry_after
ServerError	5xx	Server error (auto-retried)

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Configuration

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

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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.

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Resources

Platform	bankregreports.com
API sign-up	bankregreports.com/api
Swagger / REST docs	api.bankregreports.com/api/v1/docs/
SDK docs	www.bankregreports.com/api/
Changelog	CHANGELOG.md
Support	[e-mail]

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License

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