valuein-sdk 0.5.5

Official Python SDK for the Valuein US Core Fundamentals dataset — SEC EDGAR financials via API.

Point-in-Time accurate. Survivorship-bias free. 12M+ filings with 108M+ facts from 1990 to present.

US fundamental data from SEC EDGAR — 10-K, 10-Q, 8-K, 20-F and amendments — covering 10,000+ active and delisted entities. Cleaned, standardized, and queryable via SQL with zero local downloads.

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What You Can Do With This Repository

Use Case	Who	Where to Start
Query financial data via Python	Quants, data engineers	Quickstart
Run 39 pre-built financial signals	Analysts, quants	SQL Templates
Learn with interactive notebooks	Students, new users	Examples & Notebooks
Pull data into Excel	Financial analysts	Excel Integration
Prove data quality to stakeholders	Institutional buyers, compliance	Research & Quality Proofs
Read methodology and compliance docs	Due diligence, enterprise	Documentation
Contribute templates, examples, research	Open-source contributors	Contributing

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Quickstart

1. Install

pip install valuein-sdk

2. Get your API token

A. Get a free API token** (S&P500 coverage, Full history, Active and Inactive companies) → Register at valuein.biz OR B. Get your API token** (Full Universe of US stocks, US Core Fundamentals, PIT and Survivorship-bias free) → $200/month → $1920/year

3. Set your key and run

export VALUEIN_API_KEY="your_token"

from valuein_sdk import ValueinClient

client = ValueinClient()
me = client.me()

print(me["plan"], me["email"])
print(client.manifest())

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Data Plans

Plan	Entities	Coverage	Price
S&P500	~605 current + historical members, full history	Full history	Free — Register
US Core Fundamentals	10,000+ active and delisted	Full universe 1990–present, PIT, restatements	Subscription — Buy now

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Use Case 1 — Python SDK

Install valuein-sdk, authenticate once, run any SQL against the data lake. No downloads. No local database. DuckDB executes your queries in-process against Parquet files on Cloudflare R2.

Ticker lookup

from valuein_sdk import ValueinClient

client = ValueinClient(tables=["entity", "security"])

df = client.query("""
    SELECT e.cik, e.name, e.sector, e.status,
           s.symbol, s.exchange
    FROM   security s
    JOIN   entity   e ON s.entity_id = e.cik
    WHERE  s.symbol = 'AAPL' AND s.is_active = TRUE
""")
print(df)

Revenue trends

from valuein_sdk import ValueinClient

client = ValueinClient(tables=["entity", "security", "filing", "fact"])

df = client.query("""
    SELECT fa.fiscal_year,
           round(fa.numeric_value / 1e9, 2) AS revenue_bn
    FROM   fact     fa
    JOIN   filing   f  ON fa.accession_id = f.accession_id
    JOIN   security s  ON f.entity_id     = s.entity_id
    WHERE  s.symbol            = 'MSFT'
      AND  s.is_active         = TRUE
      AND  fa.standard_concept = 'Revenues'
      AND  f.form_type         = '10-K'
    ORDER  BY fa.fiscal_year DESC
    LIMIT  10
""")
print(df)

Point-in-Time backtest (the most important pattern)

Filter by filing_date, not report_date. Apple's Q3 2023 ended Sep 30 but was filed Nov 3 — using report_date leaks 34 days of future data into your backtest.

from valuein_sdk import ValueinClient

client = ValueinClient(tables=["security", "filing", "fact"])

TRADE_DATE = "2024-01-15"

df = client.query(f"""
    SELECT fa.standard_concept, fa.fiscal_year,
           f.filing_date,
           round(fa.numeric_value / 1e9, 2) AS value_bn
    FROM   fact     fa
    JOIN   filing   f  ON fa.accession_id = f.accession_id
    JOIN   security s  ON f.entity_id     = s.entity_id
    WHERE  s.symbol            = 'NVDA'
      AND  s.is_active         = TRUE
      AND  fa.standard_concept IN ('Revenues', 'NetIncomeLoss')
      AND  f.form_type         = '10-K'
      AND  f.filing_date      <= '{TRADE_DATE}'   -- PIT gate
    ORDER  BY f.filing_date DESC
""")
print(df)

Date columns reference

Column	Table	Use for
report_date / period_end	filing / fact	Aligning to fiscal calendar
filing_date	filing	PIT backtest filter — when the SEC received the filing
knowledge_at	fact	Millisecond-precision PIT for intraday signal research

API reference

from valuein_sdk import ValueinClient

client = ValueinClient(
    api_key="...",       # or read from VALUEIN_API_KEY env var
    gateway_url="...",   # override for local dev only
    tables=["entity"],   # load specific tables; omit to load all
)

client.me()                           # dict: plan, status, email, createdAt
client.manifest()                     # dict: snapshot, last_updated, tables
client.health()                       # dict: ok, worker, env (no auth required)
client.query(sql)                     # DuckDB SQL → pandas DataFrame
client.get(table)                     # Download full table → pandas DataFrame
client.run_template(name, **kwargs)   # Named SQL template → pandas DataFrame
client.tables()                       # List loaded table names

Error handling

from valuein_sdk import (
    ValueinAuthError,      # HTTP 401/403 — invalid or expired token
    ValueinPlanError,      # HTTP 403 — endpoint requires a higher plan
    ValueinRateLimitError, # HTTP 429 — includes .retry_after (seconds)
    ValueinAPIError,       # HTTP 5xx — includes .status_code
    ValueinClient
)

try:
    client = ValueinClient()
    df = client.query("SELECT * FROM fact LIMIT 1000000")
except ValueinRateLimitError as e:
    print(f"Rate limited. Retry in {e.retry_after}s")
except ValueinPlanError:
    print("Upgrade to Full plan for this data")

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Use Case 2 — SQL Templates

39 production-ready SQL templates bundled with the SDK. Run complex financial signals in one line — no SQL required.

from valuein_sdk import ValueinClient

client = ValueinClient()
# DuPont decomposition for MSFT
df = client.run_template("16_dupont_analysis_inputs", ticker="MSFT")
print("Result: ", df)

# Piotroski F-Score screen across S&P500
df = client.run_template("18_piotroski_f_score", tickers="'AAPL', 'MSFT', 'NVDA'")
print("Result: ", df)

# Altman Z-Score (bankruptcy probability)
df = client.run_template("19_altman_z_score", ticker="TSLA")
print("Result: ", df)

# Trailing-twelve-months revenue
df = client.run_template("06_trailing_twelve_months_ttm", ticker="AMZN")
print("Result: ", df)

Template categories

Range	Category	Examples
01–04	Data Access	Fundamentals by ticker, FIGI lookup, peer comparison, survivorship-bias-free screen
05–09	Income Statement	YoY revenue growth, TTM, margin analysis, FCF, R&D intensity
10–15	Balance Sheet	Liquidity, solvency, interest coverage, cash conversion, capex ratios
16–20	Investment Scores	DuPont, Piotroski F-Score, Altman Z-Score, accruals anomaly
21–26	Valuation & Screening	Sector aggregates, peer ranking, dilution, arbitrage signals
27–33	Short Signals	Late filers, restatements, 8-K events, ghost companies
34–39	Advanced Analytics	PIT backtest engine, Z-score outliers, seasonality, XBRL audit

See valuein_sdk/queries/SQL_CHEATSHEET.md for the full template reference.

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Use Case 3 — Examples & Notebooks

Six standalone Python scripts and four Jupyter notebooks, designed to go from install to insight in under 3 minutes.

Python scripts (examples/python/)

Script	Level	What it demonstrates
getting_started.py	Beginner	Auth check, first query, entity counts by sector
usage.py	Reference	Every public SDK method demonstrated
entity_screening.py	Beginner	Screen by sector, SIC code, active vs inactive status
financial_analysis.py	Intermediate	Revenue trends, margins, concept normalization, peer comparison
pit_backtest.py	Intermediate	Correct PIT discipline, restatement impact, filing_date vs report_date
survivorship_bias.py	Intermediate	Delisted/bankrupt companies, index_membership, bias quantification

Run any script standalone:

VALUEIN_API_KEY=your_token python examples/python/getting_started.py

Jupyter notebooks (examples/notebooks/)

Notebook	Open in Colab
Quickstart
Fundamental Analysis
PIT Backtest
Survivorship Bias

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Use Case 4 — Excel Integration

Pull live SEC fundamental data into Microsoft Excel via Power Query. No Python required.

Requirements: Microsoft 365 (build 16.0.17531 or later)

1. Download excel/valuein-fundamentals.xlsx
2. Open the workbook and enter your API token in the Connectivity Guide sheet
3. Click Refresh All — data streams directly from Parquet files on Cloudflare R2

The workbook includes 8 pre-configured sheets: Income Statement, Balance Sheet, Cash Flow, Entities, Securities, Filings, Index Membership, and a Data Dictionary.

For DIY Power Query connections, the M-language source files are in excel/power-query/.

Full setup walkthrough: docs/excel-guide.md

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Use Case 5 — Research & Quality Proofs

16 runnable research modules that prove every data quality claim with code. Designed for institutional due diligence and quantitative research.

# Install research dependencies
uv sync --group research

# Run a proof
python research/quantitative/pit_correctness_proof.py
python research/quality_proof/balance_sheet_check.py

Research modules

research/fundamental/ — Financial statement analysis workflows

• Income statement, balance sheet, cash flow, DuPont decomposition, Altman Z-Score

research/quantitative/ — Factor model and strategy research

• PIT correctness proof, survivorship bias quantification, restatement tracking as short signal, sector rotation

research/data_engineering/ — XBRL normalization and pipeline analysis

• Concept mapping explorer, taxonomy coverage, filing timeline, data freshness by sector

research/quality_proof/ — Automated data quality validation

• Zero PIT violations, balance sheet equation check (Assets = Liabilities + Equity within 1%), coverage report, SEC cross-reference spot-check

See research/README.md for a full breakdown of what each module proves and the key metric it validates.

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Data Schema

Six tables, 90+ columns, fully documented in docs/DATA_CATALOG.xlsx and docs/schema.json.

Table	Primary Key	Description
entity	cik	Legal company — name, sector, SIC code, fiscal year end, status
security	id	Ticker symbols with SCD Type 2 date ranges (valid_from, valid_to, is_active)
filing	accession_id	SEC filing metadata — form type, filing_date, report_date, accepted_at
fact	(entity_id, accession_id, concept, period_end, unit)	Every financial fact from every filing, with knowledge_at PIT timestamp
taxonomy_guide	standard_concept	Definitions for 150 canonical concept names
index_membership	—	Historical index constituent records (S&P 500 entry and exit dates)

Key joins

security.entity_id        →  entity.cik
filing.entity_id          →  entity.cik
fact.entity_id            →  entity.cik
fact.accession_id         →  filing.accession_id
index_membership.security_id  →  security.id

Standard concept names

Raw XBRL tags (15,000+) are normalized to canonical standard_concept values. Use these exact strings:

Concept	standard_concept
Revenue / Sales	'Revenues'
Net Income	'NetIncomeLoss'
Total Assets	'Assets'
Gross Profit	'GrossProfit'
Operating Income	'OperatingIncomeLoss'

Both the raw concept tag and the normalized standard_concept are on the fact table — no join to a separate mapping table needed.

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Why Valuein

Point-in-Time (PIT)	Every fact carries filing_date (SEC receipt) and knowledge_at (millisecond precision). Filter filing_date <= trade_date to eliminate look-ahead bias. Most providers silently overwrite restated numbers — we append every revision.
Survivorship-bias free	10,000+ entities including every delisted, bankrupt, and acquired company in the SEC record back to 1990. A backtest on survivors only is not a real backtest.
Standardized concepts	15,000+ raw XBRL tags mapped to ~150 canonical standard_concept values. One concept name works across every filer, regardless of what tag they filed.
DuckDB SQL	In-process DuckDB with authenticated Parquet streaming. Queries run in milliseconds — no local downloads required.
39 SQL templates	Production-ready queries for Altman Z-Score, DuPont, Piotroski F-Score, TTM, FCF, sector screening, restatement signals, PIT backtest engine, and more.

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Documentation

Document	Description
docs/METHODOLOGY.md	Data sourcing, PIT architecture, restatement handling, XBRL normalization logic
docs/COMPLIANCE_AND_DDQ.md	Data provenance, MNPI policy, PIT integrity, security, SLA summary
docs/SLA.md	Uptime targets, data freshness SLAs, support response times, SLA credits
docs/excel-guide.md	Full Excel / Power Query setup walkthrough
docs/DATA_CATALOG.xlsx	All columns, types, definitions, sample values
docs/schema.json	Machine-readable JSON schema
CHANGELOG.md	Full release history

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Contributing

Contributions are welcome — new SQL templates, example scripts, research modules, and documentation improvements.

See CONTRIBUTING.md for code standards, naming conventions, and the PR process.

Disclosure: This repository is for research and educational purposes only. Not financial advice.

Apache-2.0 License — see LICENSE.