BLOG

Behind the numbers.

Code examples, market analysis, and data quality deep-dives.

How to Get Historical Market Cap Data in Python
How Much of the S&P 500 Survives 20 Years? Index Turnover Analysis in Python
Does Joining the S&P 500 Bring New Institutional Owners? 13F Event Study in Python
Is Volatility Seasonal? Calendar Month Analysis of Realized Volatility in Python
Does Fast Revenue Growth Force Companies to Borrow? Cash Funding Analysis in Python
How Far Back Does SEC EDGAR Data Go?
Are One-Time Charges Really One-Time? Charge Frequency Analysis in Python
Does Buying the Dip Work? Short-Term Reversal by Volatility Regime in Python
Alpha Vantage vs Massive vs xfinlink for Fundamentals
How Long Does a Stock Take to Recover From a 50% Fall? Drawdown Analysis in Python
Do Companies That Shrink Their Share Count Outperform? Net Buyback Yield in Python
How Much Does the Dow's Price Weighting Distort It? Index Weighting Analysis in Python
How to Get SEC Form 4 Insider Trading Data in Python
Can Anything Predict Next Month's Stock Returns? Out-of-Sample R-Squared Testing in Python
How Much of a Stock's Return Comes From Its Sector? Variance Decomposition in Python
Altman Z-Score: Where To Get It in Python
Do Value Screens Agree on Which Stocks Are Cheap? Multiple Overlap Analysis in Python
Annual vs Quarterly Financial Data: Which to Use
Do High Returns on Capital Persist? ROIC Fade Analysis in Python
Does Past Beta Predict Future Beta? Beta Stability Testing in Python
Do Defensive Sectors Actually Defend? Up and Down Capture in Python
How to Choose a Financial Data API
Do Small Caps Actually Beat Large Caps? Size Premium Test in Python
What If You Miss the Market's Best Days? Extreme-Day Analysis in Python
Does Rebalancing Add Return? Fixed-Weight vs Drift Portfolios in Python
Which S&P 500 Companies Are Closest to Default? Merton Distance-to-Default in Python
What Happens to Stocks Removed From the S&P 500? Replacement Pair Analysis in Python
Does the Golden Cross Work? 50/200 Moving Average Crossover Backtest in Python
Financial Data for Academic Finance Research
Does Skipping the Most Recent Month Improve Momentum? S&P 500 Decile Sorts in Python
Does Cointegration Survive Out of Sample? Pairs Trading Validation in Python
Which Dividends Are Not Covered by Cash? Free-Cash-Flow Coverage Screening in Python
GICS vs SIC vs NAICS: Which Industry Classification to Use
How Many Stocks Does It Take to Diversify? Random Portfolio Simulation in Python
How Many Days of Data Does a Volatility Estimate Need? Range-Based Estimators in Python
How Much of S&P 500 Cash Flow Is Stock Compensation? Cross-Sectional Analysis in Python
What Is a 13F Filing? Institutional Holdings Explained
Does Revenue Growth Explain Profit Growth? Cross-Sectional Decomposition in Python
How Much of the Nasdaq 100 Is Already in the S&P 500? Index Overlap Analysis in Python
How Often Does a 99% Value-at-Risk Limit Actually Break? VaR Backtesting in Python
Real-Time vs End-of-Day Market Data: Which Do You Need?
How Concentrated Are S&P 500 Earnings? Point-in-Time Index Analysis in Python
Does Volatility Scale With the Square Root of Time? Variance Ratio Test in Python
Does Goodwill Distort the Price-to-Book Screen? Goodwill-Adjusted Valuation in Python
How Are Shares Outstanding Reported (and Why They Disagree)
Do Low-Volatility Stocks Deliver Better Risk-Adjusted Returns? S&P 500 Quintile Sorts in Python
Does Trend Following Beat Buy and Hold? Time-Series Momentum in Python
Has the Stock-Bond Correlation Flipped? 60/40 Portfolio Risk in Python
What API to Use for a Stock Screener
Which Assets Hedge Inflation Shocks? Macro Factor Betas in Python
Does Covariance Shrinkage Beat the Sample Covariance? Minimum-Variance Portfolios in Python
Do Faster Inventory Turns Mean Thinner Margins? Gross Margin Return on Inventory in Python
SEC EDGAR API vs Fundamentals API: Which to Use
Does Fast Earnings Growth Persist? Rank Correlation Analysis in Python
Split Adjustment Explained: Adjusted Close vs Close
Which Trading Day of the Month Pays Best? Turn-of-the-Month Analysis in Python
Can You Use Yahoo Finance Data Commercially?
How Many Independent Bets Does a Nine-Sector Portfolio Give You? Eigenvalue Analysis in Python
Which Volatility Forecast Wins One Month Ahead? HAR vs EWMA in Python
How Concentrated Are Institutional Equity Portfolios? Form 13F Concentration Analysis in Python
Do Stocks Earn Their Returns Overnight or Intraday? Return Decomposition in Python
When Do Corporate Insiders Actually Trade? Form 4 Timing Analysis in Python
Data Requirements for Backtesting a Trading Strategy
What Is Survivorship Bias in Backtesting?
Do High Dividend Yields Come From Bigger Payouts or Falling Prices? Yield Decomposition in Python
Do Stocks Fall Harder Than They Rise? Downside Beta vs Upside Beta in Python
Does Volatility Targeting Improve Sharpe Ratios? Seven-Asset Backtest in Python
Free Stock Market Data APIs: What You Actually Get
How to Give an LLM Financial Data With an MCP Server
Does Post-Earnings Announcement Drift Survive Real Filing Dates? PEAD Event Study in Python
Do Insider Buying Clusters Predict Returns? Signal Testing in Python
Does the S&P 500 Index Effect Still Exist? Event Study in Python
Are Companies Leaving the S&P 500 Faster Than They Used To? Index Survival Analysis in Python
Does Gross Profitability Predict Stock Returns? Quintile Factor Test in Python
What Growth Rate Is the Market Pricing In? Reverse DCF in Python
Does a Strong Balance Sheet Cushion Drawdowns? Leverage and Downside Risk in Python
Does Ticker Recycling Corrupt a Mean-Reversion Backtest? Entity-Resolved Z-Scores in Python
How Much of a Growth Screen's Backtested Edge Is Survivorship Bias? Point-in-Time Index Testing in Python
Why Do Leveraged ETFs Decay? Measuring Volatility Drag in Python
Are Consumer Staples Margins Shrinking Under Inflation? Gross Margin Trend Analysis in Python
Do Weak Jobs Reports Predict Market Drawdowns? NFP Surprise Event Study in Python
Is the Rotation From Tech to Industrials Backed by Earnings? Relative EPS Growth Analysis in Python
Is the Semiconductor Rally Broadening Beyond NVIDIA? Return Dispersion Analysis in Python
Which Stocks Benefit Most When Oil Prices Fall? Oil Beta Screening in Python
Do Bond Returns Predict Stock Returns? Granger Causality Test in Python
Which Stocks Actually Drive Portfolio Returns? Shapley Value Attribution in Python
Does "Sell in May" Still Work? Calendar Anomaly Backtest in Python
How to Build Complete Price History Through Ticker Changes? Entity Resolution in Python
Are KO and PEP Cointegrated? Pairs Trading Signal Construction in Python
Which Commodities Have the Strongest Momentum? Rotation Backtest in Python
Which Commodity ETFs Have the Worst Tail Risk? Expected Shortfall in Python
Are Gold Miners Leveraged Gold Bets? Rolling Beta Analysis in Python
Does the Base-Metals-to-Gold Ratio Lead Cyclical Stocks? Signal Test in Python
Can Risk Parity Tame Commodity Volatility? Portfolio Optimization in Python
Are Power Stocks Becoming an AI Infrastructure Trade? Momentum Screening in Python
Which AI Chip Stocks Have Margin Momentum? Profitability Trend Analysis in Python
Which AI Stocks Are Cheapest Relative to Growth? Growth-Adjusted Valuation in Python
Does AI Stock Leadership Persist? Momentum Backtest in Python
Which AI Stocks Have the Cleanest Balance Sheets? Net Cash Screening in Python
Can Risk Parity Reduce Mega-Cap Drawdowns? Portfolio Optimization in Python
Which Growth Stocks Are Self-Funding? Cash-Flow Quality Screening in Python
Which Sectors Struggle When the Dollar Rallies? Sector Rotation Analysis in Python
Do Cheap Stocks Hold Up When Bonds Sell Off? Valuation Rotation in Python
Does the Nasdaq 100 Have Better Growth Quality Than the Dow? Index Constituent Analysis in Python
Do Healthcare Cash-Flow Margins Predict Returns? Signal Evaluation in Python
Which Dividend Stocks Survive a Cash-Flow Stress Test? Dividend Screening in Python
Does Heavy Insider Selling Predict Weak Returns? Insider Flow Test in Python
Can Quality Screens Reduce Small-Cap Balance-Sheet Risk? Russell 2000 Test in Python
Which Retailers Have Positive Operating Leverage? Margin Screening in Python
Is MSTR a Leveraged Bitcoin Proxy? Rolling Beta Analysis in Python
Is Micron's Memory Cycle Recovering? Inventory and Margin Forecasting in Python
Which Sectors Work When Bonds Rally? Rate-Sensitive Rotation in Python
Do One-Month Price Extremes Reverse? Signal Evaluation in Python
Do Low-Volatility S&P 500 Stocks Reduce Drawdowns? Factor Test in Python
Is AI Capex Paying Back Fast Enough? Revenue Hurdle Forecasting in Python
Could Shorter AI Asset Lives Hit Earnings? Depreciation Stress Test in Python
How Much AI Capex Risk Can a Portfolio Remove? Constrained Optimization in Python
Is the AI Capex Trade Crowded? Rolling Volatility and Sector Rotation in Python
Did the AI Boom Come From Existing S&P 500 Members? Point-in-Time Momentum Test in Python
Is AI Revenue Circular? Customer-Vendor Capex Loop Analysis in Python
Is the AI Trade Connected to Private Credit? Rolling Correlation Network in Python
Is Apollo More Balance-Sheet Sensitive Than Peers? Leverage Screen in Python
Are AI Earnings Supported by Cash Flow? Accrual and Capex Screen in Python
Can Defensive Stocks Hedge AI Drawdowns? Basket Regime Test in Python
How Fast Does the Market Price In Fed Decisions? FOMC Event Study in Python
How Much Are Options Sellers Overpaid? The Variance Risk Premium in Python
Which Companies Have the Worst Earnings Quality? Sloan Accrual Screen with Geographic Revenue Data in Python
Does the Oil-to-Gold Ratio Signal Recessions? XLE/GLD Backtest in Python
Is AI Spending Crowding Out Free Cash Flow? Capex Sustainability Across the Mag 7 in Python
Does a Long Energy / Short Bonds Portfolio Capture Inflation Surprises? Factor Construction in Python
Can a Hidden Markov Model Detect Oil Market Regimes? HMM Analysis in Python
Do Grain Prices Predict Food Inflation? Granger Causality Test in Python
Does the Corporate Credit Spread Predict Stock Market Crashes? BAA-AAA Spread Analysis in Python
Do Oil Stocks Hedge Inflation? Rolling Beta Analysis in Python
Which Stocks Are Most Rate-Sensitive? Equity Duration via Bond Beta in Python
Which Companies Have the Highest Accrual Ratios? Earnings Quality Screening in Python
Is Alpha Persistent or Decaying? Rolling Sharpe Ratio Analysis in Python
Are Markets Trending or Mean-Reverting? Hurst Exponent Analysis in Python
Is Consumer Discretionary vs Staples a Leading Indicator? XLY/XLP Ratio Analysis in Python
Does Heavy Capex Predict Future Stock Returns? Capital Expenditure Analysis in Python
How to Estimate Cost of Equity Using CAPM in Python
Is Volatility Predictable? Testing for Volatility Clustering in Python
Which Industrials Are Overleveraged? Net Debt to EBITDA Screening in Python
GM Before and After Bankruptcy: Why Entity Resolution Matters for Financial Data
What Is Adjusted Beta? Merrill Lynch Beta Shrinkage in Python
How Good Is a Stock Pick? Information Ratio and Tracking Error in Python
Do Stock Returns Follow a Normal Distribution? Testing for Fat Tails in Python
Which Large Caps Have the Highest Free Cash Flow Yield? FCF Screening in Python
Which Sectors Won Over 5 Years? Sector Rotation Analysis in Python
How to Forecast Stock Volatility with GARCH Models in Python
Are Stock Prices Mean-Reverting? Augmented Dickey-Fuller Test in Python
How to Calculate CAPM Alpha and Beta with Regression in Python
How to Compare Sector Sharpe Ratios and Sortino Ratios in Python
DELL: Why Stitching Historical Price Data Together Is Wrong
How to Analyze Drawdown and Recovery for Bank Stocks in Python
How to Screen SaaS Stocks by Revenue Growth and Cash Flow in Python
How to Screen REITs by Dividend Yield and Valuation in Python
How Correlated Are the Magnificent 7? Intra-Group Correlation in Python
AAPL vs XOM: Do Individual Stocks Have Seasonal Patterns?
How to Rank Large-Cap Stocks by Momentum in Python
How to Build a Multi-Endpoint Financial Dashboard in Python
How to Compare Volatility Across Energy Stocks in Python
How to Screen Healthcare Stocks by Valuation in Python
How to Build a Sector Correlation Matrix for Portfolio Diversification in Python
How to Find Oversold and Overbought Stocks Using Z-Scores in Python
How to Measure Earnings Quality: Cash Flow vs Net Income in Python
How to Build a Multi-Factor Stock Screen in Python (Value + Momentum + Quality)
How to Build a Simple DCF Model for Any Stock in Python
How to Screen Tech Stocks by Revenue Growth in Python
How to Screen Stocks by Balance Sheet Health in Python
Is "Sell in May" Real? SPY Monthly Seasonality Over 10 Years
How to Compare Sector Performance YTD Using Python
How to Screen Dividend Stocks by Yield and Quality in Python
How to Calculate Max Drawdown and Recovery Time for Any Stock in Python
How to Compare Profitability Across Mega-Cap Tech Stocks in Python
Why Ticker Symbols Are Unreliable: The Recycling Problem Every Quant Should Know
How to Calculate and Compare Stock Volatility in Python
How to Screen Blue-Chip Stocks by P/E Ratio in Python
How to Track Companies Through Ticker Changes, Bankruptcies, and Renames in Python
S&P 500 Turnover: How Much the Index Has Changed Since 2010
How to Calculate Stock Beta and Correlation in Python
← All articles

How Far Back Does SEC EDGAR Data Go?

EDGAR’s electronic archive opens in 1993, and that single date hides three different answers. Filings phased in over four years, so the first calendar year with a broad set of annual reports is 1997, not 1993. Keyword search across the text of filings reaches back only to 2001. The structured numeric data behind the data.sec.gov APIs starts in 2009, because 2009 is when the SEC first required XBRL tagging. A fundamentals panel assembled purely from SEC electronic sources therefore begins in 2009 in practice, whatever start date the code asks for. Anything older has to come from a source that did the work of digitising it.

What exactly starts in 1993?

The Commission adopted interim rules requiring electronic filing on 23 February 1993, and the phase-in began on 26 April 1993. Then it stopped. After a statutorily mandated test group finished in December 1993, the SEC held back further phase-in while staff evaluated system performance over a six-month test period running from January to June 1994. Final rules covering all domestic registrants arrived on 19 December 1994, and phase-in restarted on 30 January 1995 (SEC, Overview of the EDGAR rules, read 12 August 2026).

That schedule shows up plainly in the SEC’s own counts. Its published tally of electronic filings by form type, updated to June 2026, records these documents filed under form type 10-K by calendar year:

Calendar year 10-K filings on EDGAR
1993 4
1994 1,844
1995 2,178
1996 4,251
1997 6,540

Four in 1993. Somebody who reads “EDGAR starts in 1993”, sets a start date to match and then builds a cross-section will get a sample that is empty in its first year, thin for the two after it, and representative only from 1997 onward. The filings made before the phase-in went in on paper, and the SEC never went back and loaded them.

Why does EDGAR full-text search only reach 2001?

Full-text search is a separate index layered over the archive rather than part of it, and it covers filings submitted electronically since 2001; the widest option the interface offers is labelled “All (since 2001)” (EDGAR Full Text Search FAQ, read 12 August 2026). Filings from 1993 through 2000 are still in EDGAR and still retrievable by company and form type. They are not searchable by phrase.

The distinction bites on a specific kind of question. “What did this company file in 1998” works. “Which companies first disclosed a going-concern doubt in 1998” does not, because the only way to answer it is to read the text, and the index that would let you read all of it at once starts three years later. For pulling up an actual filing, EDGAR is the source of record and no vendor replaces it. For assembling numbers across many companies and many decades, it is the wrong shape of tool.

Why do the structured SEC APIs start in 2009?

Because they are made of XBRL, and XBRL “was first required by the SEC in 2009” (SEC, EDGAR Application Programming Interfaces, read 12 August 2026). The company-concept, company-facts and frames endpoints return tagged facts, so their coverage cannot start before the tagging obligation did. The Financial Statement Data Sets, which package the face financials into quarterly archives, begin in the same year and are described by the SEC as “extracted from corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL)”.

One more limit is worth knowing before building anything on the submissions endpoint: it returns “at least one year’s of filing or to 1,000 (whichever is more) of the most recent filings”. It answers what a company filed lately, not what it filed in 1996.

What starts when

Verified against each source’s own pages on 12 August 2026.

Source Earliest data What it returns
EDGAR filing archive 1993, thin until 1997 Filed documents, one company at a time
EDGAR full-text search 2001 Phrase search across filing text
data.sec.gov XBRL APIs 2009 Tagged facts per company or per period
SEC Financial Statement Data Sets 2009 Quarterly archives of face-financial numbers
Alpha Vantage daily time series “20+ years of historical data” Daily OHLCV per symbol
xfinlink fundamentals 1950 Annual and quarterly statements as a DataFrame
xfinlink daily prices 1996 Daily bars, split-adjusted close, total return

Alpha Vantage’s depth statement is quoted from its API documentation; the xfinlink rows come from the pricing page, where full history is included on every paid plan and the free tier serves a rolling one-year window.

What breaks when a panel starts in 2009?

2009 is an unhelpful place for a financial history to begin, and not only because it is recent. It begins at the bottom, after the credit crisis had already run. A leverage screen fitted on 2009 onward has seen balance sheets recovering from a contraction and never one walking into it. The inflation of the late 1970s sits outside the window entirely, as does the 2000 unwind and the recession that followed it.

Sample length also decides what a test is allowed to claim. Our data requirements for backtesting note that a ten-year window contains no full credit cycle, and the same reasoning applies harder to fundamentals, where each company contributes one observation per year rather than 250. Fifteen years of annual statements across 500 names is 7,500 company-years, which sounds like plenty until the question involves distress, and distress is concentrated in the years the window excludes.

A second problem arrives with the universe rather than the statements. Pairing a 2009-onward panel with a current index list reintroduces survivorship bias on top of the truncation, and the two errors point the same way.

How do you get statements from before EDGAR?

From a provider that digitised the pre-electronic record and normalised it into one schema. xfinlink serves annual and quarterly statements back to 1950, which is 43 fiscal years before EDGAR accepted its first 10-K.

import xfinlink as xfl

xfl.set_api_key("YOUR_API_KEY")  # free at https://xfinlink.com/signup

tickers = ["GE", "IBM", "KO", "PG", "XOM"]
df = xfl.fundamentals(tickers, period_type="annual",
                      start="1900-01-01", end="2026-12-31",
                      fields=["revenue"])

for t in tickers:
    s = df[df["ticker"] == t]
    print(f"{t}  {s['fiscal_year'].min()}-{s['fiscal_year'].max()}  "
          f"rows={len(s)}  before 2009={(s['fiscal_year'] < 2009).sum()}")

print(df[df["fiscal_year"] == 1950][["ticker", "period_end", "revenue"]]
      .sort_values("ticker").to_string(index=False))

Output:

GE  1950-2025  rows=76  before 2009=59
IBM  1950-2025  rows=76  before 2009=59
KO  1950-2025  rows=76  before 2009=59
PG  1950-2026  rows=77  before 2009=59
XOM  1950-2025  rows=76  before 2009=59

ticker period_end  revenue
    GE 1950-12-31   2232.9
   IBM 1950-12-31    214.9
    KO 1950-12-31    215.2
    PG 1950-06-30    632.9
   XOM 1950-12-31   3134.6

Revenue is in millions of dollars. Of the 76 annual statements each company contributes, 59 predate the XBRL requirement, which is where a panel built from data.sec.gov would have had to start. Procter & Gamble carries 77 because its fiscal year ends in June, so fiscal 2026 has already closed and filed.

The shape of the result matters as much as the depth. One call returns five companies in one DataFrame with fiscal-year labels and period-end dates already aligned, which is the part that takes the longest when the same panel is assembled from raw filings. The trade-offs between the two approaches are set out in our guide to the SEC EDGAR API versus a fundamentals API. Field names and parameters are in the docs.

FAQ

Can I get pre-1993 filings from EDGAR at all?
No. Those filings were submitted on paper and were not loaded into the electronic archive afterwards. Their numbers survive only where somebody transcribed them.

Does EDGAR full-text search cover the 1990s?
No. The index covers filings submitted electronically since 2001. Filings from 1993 to 2000 remain retrievable by company and form type, one document at a time.

Is a history starting in 2009 enough for a backtest?
For anything about growth, margins or capital intensity, often yes. For anything about distress, leverage or how a strategy behaves in a credit contraction, no, because the window begins after the last one ended.

How far back do daily prices go?
xfinlink serves daily bars from 1996 on paid plans, and a rolling one-year window on the free tier.

Built with xfinlink — free financial data API for Python. pip install -U xfinlink

Built with xfinlink — free financial data API for Python. pip install -U xfinlink
← All articles