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Financial Dataset

The most complete standardized SEC EDGAR dataset for quantitative research

Name: Valuein SEC EDGAR Financial Dataset
Creator: Valuein
License: https://valuein.biz/data-license

105M+ financial facts from ~18,000 entities spanning 30+ years. Point-in-time accurate. Zero survivorship bias. Delivered as 11 Parquet tables (8 core + 3 derived) you can query with DuckDB, Python, or via MCP Server.

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105M+

Financial Facts

18,000+

Entities

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

11,966 raw XBRL tags are mapped to ~150 canonical standard_concept values (95% coverage). Revenue synonyms, debt variants, and custom extensions all resolve to a single canonical concept name.

Point-in-Time Indexing

Every fact receives a accepted_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 accepted_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, Russell 1000/2000/3000, NASDAQ100, and Wilshire 5000 membership is tracked historically in index_membership with effective_date / removal_date and [) interval semantics. JOIN references on cik = cik for any membership question (current or historical) — there is no is_sp500 flag.

Parquet Export

Column-oriented Parquet files with ZSTD compression are generated for each tier. Optimized for DuckDB, Polars, and Spark. Exported to distributed object storage 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

11 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

~18,000 rows

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

ciknamesic_codesic_descriptionsectorindustrystate_of_incorporationfiscal_year_endbusiness_addressmailing_addressformer_namesis_foreignflagscategory

security

~18,000 rows

Ticker symbols and exchange listings (SCD Type 2 with valid_from / valid_to). One entity may have multiple securities — filter is_primary_ticker = TRUE for one row per CIK.

identity_idsymbolexchangemicvalid_fromvalid_tois_activeis_primary_tickerfigicomposite_figishare_class_figisecurity_typemarket_sector

filing

~12M rows

Every XBRL filing processed: 10-K, 10-Q, 8-K, 20-F, and their amendments since 1994. accepted_at is the SEC acceptance timestamp; superseded_by chains the amendment lineage.

accession_identity_idform_typecore_typefiling_datereport_dateaccepted_atis_amendmentamendment_nosuperseded_byis_xbrlis_inline_xbrlis_xbrl_numericis_auditedprimary_documentsizefile_numberact

fact

105M+ rows

Core Table

The core table. Every standardized financial fact extracted from every filing. Supports point-in-time queries via accepted_at, quarterly derivation via derived_quarterly_value, and Bloomberg Option-C view (value_current vs. value_as_filed) for restated values.

fact_identity_idaccession_idconceptstandard_conceptnumeric_valuederived_quarterly_valuevalue_currentvalue_as_filedfirst_filed_atrestatedunitreporting_currencyfiscal_yearfiscal_periodperiod_endperiod_span_daysis_cumulativeaccepted_at

valuation

~500,000 rows

Pre-computed intrinsic value estimates per entity. Multiple model_type rows coexist per (entity_id, valuation_date): 'dcf', 'dcf_fcf', 'ddm'. Recomputed each pipeline run; not point-in-time.

entity_idvaluation_datemodel_typeper_share_valuecurrent_pricemargin_of_safetyvaluation_labeldiscount_rategrowth_rateterminal_rate

taxonomy_guide

~150 rows

Definitions for every standard_concept used in the fact table — human_name, definition, unit_type, balance_type, and source_reference (US-GAAP taxonomy reference).

standard_concepthuman_namedefinitionunit_typebalance_typesource_reference

index_membership

~50,000 rows

Historical index constituents (SP500, NASDAQ100, RUSSELL3000, WILSHIRE5000). Keyed on cik (since migration 0015). [) interval semantics. JOIN references on cik = cik to attach company metadata.

cikindex_nameeffective_dateremoval_dateannouncement_dateremoval_announcement_dateremoval_reasonsuccessor_ciksourceconfidence

references

~18,000 rows

Start Here

Derived flat join of entity + security. One row per security. Eliminates 2-table joins for company metadata. The starting point for any cross-company analysis. For index membership (current or historical), JOIN with index_membership on cik = cik.

ciksymbolnamesectorindustryexchangemicis_activevalid_fromvalid_tosic_codeentity_typefigicomposite_figi

ratio

~1.2M rows

Pipeline-computed financial ratios per entity per fiscal period. Recomputed every pipeline run (ON CONFLICT DO UPDATE). Filter by category for grouped screens.

entity_idratio_namecategoryvalueunitperiod_endfiscal_yearfiscal_periodis_ttmcomputed_at

factor_scores

~18,000 rows

Cross-sectional factor scores and percentile ranks computed from the latest two 10-K filings. 10 factors matching the SDK AlphaEngine built-in set. composite_rank is the equal-weight average of all non-null factor _rank columns.

entity_idperiod_endfiscal_yearaccepted_atroegross_marginoperating_marginrevenue_growth_yoyfcf_to_assetsdebt_to_equitypiotroski_f_scorecomposite_rank

earnings_signals

~250,000 rows

Trend-based earnings expectations and surprise metrics. eps_trend_est is a trailing 4-quarter average of EPSDiluted; eps_surprise_pct is (actual - trend) / |trend|.

entity_idperiod_endfiscal_yearfiscal_periodaccepted_ateps_actualeps_trend_esteps_surprise_pctrevenue_actualrevenue_yoy_pct

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 accepted_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. accepted_at = 2020-04-30.

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

Your backtest on May 1, 2020: Filtering by accepted_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.accepted_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.accepted_at <= '2020-05-01'
ORDER BY f.period_end DESC, f.accepted_at DESC
LIMIT 5;
"""

try:
    with ValueinClient() as client:
        df = client.run_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 Entities

~8,000

Delisted / Acquired / Bankrupt

~8,000

~50% of the 18,000-entity 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

11,966 XBRL tags. ~150 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 accepted_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 accepted_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.accepted_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.accepted_at ASC;
"""

try:
    with ValueinClient() as client:
        df = client.run_query(sql)
        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~150

Raw XBRL Tags Mapped11,966 (95% coverage)

Delivery FormatParquet with ZSTD compression

Tier Breakdown

Tier	Data Scope	Rate Limit	Price
S&P500	S&P500 · 500+ tickers · 1994–present	60 req/min · 1,000 req/hr	Free
Pro	Active + delisted US universe · ~18,000 entities · 30-year history (1995→present)	100 req/min · 3,000 req/hr	$49/mo
Institutional	Two datasets: fundamentals (~18,000 entities, 1990–present) + smart-money (Forms 3/4/5/144 + 13F/13D/13G)	300 req/min · 10,000 req/hr	$499/mo

Compare plans in detail

Start querying 105M+ facts today

Register free to access the full S&P500 universe — no credit card required. Pro full-universe + 30-year history at $49/mo. Institutional with smart-money data (insider + institutional ownership) + webhooks + redistribution at $499/mo.

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Also available via direct Parquet download.