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Financial Dataset

The most complete standardized SEC EDGAR dataset for quantitative research

105M+ financial facts from 12,000+ companies spanning 30+ years. Point-in-time accurate. Zero survivorship bias. Delivered as 8 Parquet tables you can query with DuckDB, Python, or Excel.

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105M+

Financial Facts

12,000+

Companies

30+

Years of History

Parquet Tables

Data Source

Built on the authoritative source for US public company financials

The SEC requires every public company to file structured XBRL financial statements. These filings are published as the EDGAR Financial Statements Data Sets — quarterly ZIP archives containing machine-readable data for every 10-K, 10-Q, 8-K, and 20-F filed with the Commission.

Each quarterly release includes five core files: num.txt (numeric values), sub.txt (submission metadata), tag.txt (XBRL tag definitions), pre.txt (presentation linkbase), and cal.txt (calculation linkbase).

Source Details

PublisherU.S. Securities and Exchange Commission

DatasetEDGAR Financial Statements Data Sets

FormatQuarterly ZIP archives (TSV files)

Update FrequencyQuarterly + amendments filed continuously

CoverageAll SEC-registered entities filing XBRL (2009+, with historical data back to 1994)

Source URLsec.gov/dera/data/financial-statement-data-sets

Processing Pipeline

From raw SEC filings to queryable Parquet in 10 steps

Every financial fact passes through a deterministic pipeline that standardizes, enriches, and validates before export. No manual intervention. No estimation.

SEC EDGAR Ingestion

Quarterly XBRL bulk downloads from SEC EDGAR Financial Statements Data Sets are ingested automatically. Each release contains num.txt, sub.txt, tag.txt, pre.txt, and cal.txt files covering every public filing.

XBRL Submission Parsing

Every XBRL submission in the quarterly dump is parsed. Filing metadata, entity info, tagged numeric values, and calculation linkbases are extracted and validated.

Entity & Security Normalization

CIK numbers are resolved to standardized entity records. SIC codes are mapped to sectors and industries. Exchange information, ticker symbols, and CUSIP identifiers are enriched from multiple sources.

Concept Standardization

12,000+ raw XBRL tags are mapped to approximately 200 standardized financial concepts. Revenue synonyms, debt variants, and custom extensions all resolve to a single canonical concept name.

Point-in-Time Indexing

Every fact receives a knowledge_at timestamp equal to the SEC acceptance date of the filing that introduced it. No backfilling, no estimation. What was known on any date is precisely queryable.

Amendment Reconciliation

10-K/A and 10-Q/A filings (restated financials) are tracked separately. Original values and restated values coexist in the dataset, each with their own knowledge_at timestamp.

Derived Quarterly Values

Q2 and Q3 cash flow statements in 10-Qs report year-to-date totals. The pipeline computes the incremental quarterly figure and stores it as derived_quarterly_value alongside the raw YTD number.

Index Membership Enrichment

S&P500 and Russell 2000 membership is tracked historically with effective and end dates. The references table carries an is_sp500 boolean for instant filtering.

Parquet Export

Column-oriented Parquet files with ZSTD compression are generated for each tier. Optimized for DuckDB, Polars, and Spark. Exported to Cloudflare R2 after every EDGAR quarterly release.

Manifest Update

manifest.json records the snapshot date, last_updated timestamp, and row counts for every table. SDKs and integrations use this to detect fresh data automatically.

Schema

8 Parquet tables, one complete financial universe

Each table is a column-oriented Parquet file with ZSTD compression. Query with DuckDB, Polars, Spark, or any engine that reads Parquet.

entity

~15,000 rows

Every SEC-registered entity that has filed XBRL financial statements. Includes active, delisted, bankrupt, and acquired companies.

ciknamesic_codesectorindustrystate_of_incorporationfiscal_year_endcategory

security

~18,000 rows

Ticker symbols and exchange listings. One entity may have multiple securities. Tracks active status and delisting dates.

entity_idsymbolexchangecusipis_activedelisted_datesecurity_type

filing

~800,000 rows

Every XBRL filing processed: 10-K, 10-Q, 8-K, 20-F, and their amendments. The knowledge_at field is the SEC acceptance timestamp.

accession_identity_idform_typefiling_dateperiod_endknowledge_atis_amendment

fact

105M+ rows

Core Table

The core table. Every standardized financial fact extracted from every filing. Supports point-in-time queries via knowledge_at and quarterly derivation via derived_quarterly_value.

entity_idaccession_idstandard_conceptnumeric_valuederived_quarterly_valueunitdecimalsfiscal_yearfiscal_periodperiod_endknowledge_atform_type

valuation

~500,000 rows

Pre-computed valuation ratios and financial metrics. P/E, P/B, EV/EBITDA, FCF yield, ROE, ROIC, gross margin, and more. Updated with each filing cycle.

entity_idvaluation_datemodel_typeper_share_valuecurrent_pricemargin_of_safetyvaluation_label

taxonomy_guide

~12,000 rows

Maps every raw XBRL tag to its standardized concept. Includes the original tag name, namespace, description, and financial statement category.

raw_tagnamespacestandard_conceptdescriptioncategorystatement_type

index_membership

~25,000 rows

Historical index constituents with effective and end dates. Know exactly which companies were in the S&P500 on any given date.

entity_idindex_nameeffective_dateend_date

references

~18,000 rows

Start Here

Derived flat join of entity + security + index_membership. One row per security. Eliminates 3-table joins for most queries. The starting point for any cross-company analysis.

ciksymbolnamesectorindustryexchangeis_activeis_sp500sic_code

Browse full schema with types and descriptions

Point-in-Time Accuracy

Know exactly what was known, and when

Every fact in the dataset carries a knowledge_at timestamp — the exact date and time the SEC accepted the filing that introduced that fact.

This is critical for backtesting. Without point-in-time data, your 2015 backtest unknowingly uses data that was only available in 2016 (look-ahead bias). The result: inflated returns that evaporate in live trading.

Concrete Example: AAPL Q1 FY2020

April 30, 2020: Apple files 10-Q for Q1 FY2020. Revenue: $58.3B. knowledge_at = 2020-04-30.

June 15, 2020: Apple files 10-Q/A with restated figures. Revised revenue: $58.3B (confirmed). knowledge_at = 2020-06-15.

Your backtest on May 1, 2020: Filtering by knowledge_at <= '2020-05-01' returns only the original filing. The amendment is invisible — exactly as it would have been in real time.

Point-in-time query with the Python SDK

Fetch AAPL revenue as it was known on a specific date

from valuein_sdk import ValueinClient, ValueinError

sql = """
SELECT
  r.symbol,
  f.filing_date,
  f.period_end,
  f.knowledge_at,
  fa.numeric_value / 1e9 AS revenue_billions,
  f.form_type
FROM references r
JOIN filing f   ON f.entity_id = r.cik
JOIN fact fa    ON fa.accession_id = f.accession_id
WHERE r.symbol = 'AAPL'
  AND fa.standard_concept = 'Revenues'
  AND f.form_type IN ('10-Q', '10-Q/A')
  AND f.knowledge_at <= '2020-05-01'
ORDER BY f.period_end DESC, f.knowledge_at DESC
LIMIT 5;
"""

try:
    with ValueinClient() as client:
        df = client.query(sql)
        print(df)
except ValueinError as e:
    print(f"Valuein error: {e}")

Why this matters

Eliminates look-ahead bias in walk-forward backtests
Supports event studies around filing dates
Amendment tracking shows original vs. restated values
Reproduces any historical research state exactly

Zero Survivorship Bias

Every company that ever filed. Including the ones that failed.

Most financial datasets only include companies that are still active today. That means your backtest never considers the Enrons, the Lehmans, the RadioShacks — companies that went bankrupt and dragged portfolios down.

The result? Inflated historical returns that don't replicate in live trading. Academic research estimates survivorship bias overstates annual returns by 1-2 percentage points.

Valuein includes every entity that ever filed XBRL financial statements with the SEC. Delisted, bankrupt, acquired, merged — they are all here, with their complete filing history up to the date they ceased operations.

Universe Composition

Active Companies

~6,000

Delisted / Acquired / Bankrupt

~6,000

~50% of the full universe consists of companies that are no longer actively trading. Excluding them fundamentally distorts any historical analysis.

Notable companies in the full universe

Enron Corp

Lehman Brothers

RadioShack

Toys R Us

Blockbuster

WorldCom

Bear Stearns

Washington Mutual

Kodak

Sears Holdings

All with complete financial statements through their final SEC filing.

Concept Standardization

12,000+ XBRL tags. ~200 standardized concepts.

The XBRL taxonomy is sprawling. Apple reports revenue as RevenueFromContractWithCustomerExcludingAssessedTax. Older filings use SalesRevenueNet. Some companies create custom extensions entirely. Cross-company analysis becomes impossible without standardization.

Valuein maps every raw tag to a canonical standard_concept — while preserving the original tag in the taxonomy_guide table. No black box. Full provenance.

Raw XBRL Tag		Standardized Concept
`us-gaap:RevenueFromContractWithCustomerExcludingAssessedTax`		`Revenues`
`us-gaap:SalesRevenueNet`		`Revenues`
`us-gaap:Revenues`		`Revenues`
`us-gaap:SalesRevenueGoodsNet`		`Revenues`
`us-gaap:RevenueFromContractWithCustomerIncludingAssessedTax`		`Revenues`
`custom:TotalNetRevenues`		`Revenues`

This is just one concept. Revenue alone has 80+ raw XBRL synonyms across the filing universe. The taxonomy_guide table documents every mapping — browse it in the Data Catalog.

Amendment Tracking

Original and restated values, side by side

When a company files a 10-K/A or 10-Q/A, it is restating previously reported financial data. Most datasets silently overwrite the original values. Valuein keeps both.

The original filing and the amendment each have their own knowledge_at timestamp. The is_amendment flag on the filing table distinguishes them. You can query original-only, amended-only, or compare both.

10-K/A: Amended annual report — restated annual financials
10-Q/A: Amended quarterly report — restated quarterly financials
Both original and restated values stored with distinct knowledge_at
is_amendment flag on the filing table for easy filtering

Query both original and restated values

Compare a company's original 10-K with its amendment

from valuein_sdk import ValueinClient, ValueinError

sql = """
SELECT
  r.symbol,
  f.form_type,
  f.filing_date,
  f.knowledge_at,
  f.is_amendment,
  fa.standard_concept,
  fa.numeric_value / 1e9 AS value_billions
FROM references r
JOIN filing f   ON f.entity_id = r.cik
JOIN fact fa    ON fa.accession_id = f.accession_id
WHERE r.symbol = 'XYZ'
  AND fa.standard_concept = 'Revenues'
  AND f.form_type IN ('10-K', '10-K/A')
  AND f.period_end = '2023-12-31'
ORDER BY f.knowledge_at ASC;
"""

try:
    with ValueinClient() as client:
        df = client.query(sql)
        # Row 1: original 10-K
        # Row 2: amended 10-K/A (if filed)
        print(df)
except ValueinError as e:
    print(f"Valuein error: {e}")

Coverage

Coverage at a glance

Filing Types Covered

10-K

Annual report

10-Q

Quarterly report

8-K

Current report (material events)

20-F

Annual report (foreign private issuers)

10-K/A

Annual report amendment

10-Q/A

Quarterly report amendment

Date Range1994 to present

Update FrequencyQuarterly (with continuous amendments)

Standardized Concepts~200

Raw XBRL Tags Mapped12,000+

Delivery FormatParquet with ZSTD compression

Tier Breakdown

Tier	Data Scope	Rate Limit	Price
S&P500	S&P500 · 500+ tickers · 1994–present	10,000 / day	Free
Institutional	Full universe · 12,000+ tickers incl. delisted · 1994–present	Unlimited	$200/mo
Custom	Custom · Negotiated universe · redistribution license	Unlimited + SLA	Contact sales

Compare plans in detail

Start querying 105M+ facts today

Register free to access the full S&P500 universe — no credit card required. Institutional full-universe access at $200/mo.

Get API Access Python SDK Docs

Also available via Excel Power Query and direct Parquet download.