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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
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Alpha Vantage vs Massive vs xfinlink for Fundamentals

Three vendors, one job: turn SEC filings into a table of revenue and net income that compares cleanly across companies. All three do that job. The separation shows up elsewhere, in how far back the statements reach, how many companies a single request covers, what the licence permits, and what the monthly bill comes to. Checked against each vendor’s own pages on 11 August 2026, a request for IBM returns 20 annual income statements from Alpha Vantage, records dating to 29 March 2009 from Massive, and 66 annual periods beginning in 1960 from xfinlink.

How far back does each one go?

Alpha Vantage documents INCOME_STATEMENT as returning “the annual and quarterly income statements for the company of interest, with normalized fields mapped to GAAP and IFRS taxonomies of the SEC”, refreshed “generally... on the same day a company reports its latest earnings and financials”. The demo call published in that documentation, run on 11 August 2026, returned 20 annual reports covering fiscal 2006 through fiscal 2025 and 81 quarterly reports from June 2006 to June 2026. Each annual record carried 26 keys, two of them the period end date and the reporting currency.

Massive is where polygon.io now points; the old domain answered with a 301 redirect to massive.com on 11 August 2026. It splits the statements into one endpoint each. The income statements page states that “Records date back to March 29, 2009” and accepts a timeframe of quarterly, annual or trailing twelve months. Its plan table marks the endpoint as not included on Basic, Starter or Developer, and available with all history on Stocks Advanced at $199 per month or on the Financials & Ratios Expansion at $29 per month.

xfinlink serves statements back to 1950 on paid plans, with the income statement, balance sheet and cash flow of a period arriving in the same row. IBM comes back with 66 annual periods:

import xfinlink as xfl

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

df = xfl.fundamentals("IBM", period_type="annual", start="1960-01-01", end="2025-12-31",
                      fields=["revenue", "net_income"])
print(len(df), "annual periods")
print(df[["fiscal_year", "period_end", "revenue", "net_income"]].head(3).to_string(index=False))
print(df[["fiscal_year", "period_end", "revenue", "net_income"]].tail(2).to_string(index=False))

Output:

66 annual periods
 fiscal_year period_end  revenue  net_income
        1960 1960-12-31 1436.053     168.181
        1961 1961-12-31 1694.296     207.228
        1962 1962-12-31 1925.221     241.387
 fiscal_year period_end  revenue  net_income
        2024 2024-12-31  62753.0      6023.0
        2025 2025-12-31  67535.0     10593.0

IBM’s 1960 revenue of $1.44 billion sits in the same column, on the same scale, as its 2025 revenue of $67.5 billion. Values are in millions of dollars throughout.

Why does 2009 keep appearing as a floor?

Because that is roughly where the structured public record starts. The SEC’s Financial Statement Data Sets, which publish the numeric face-financial data extracted from filings through XBRL, cover January 2009 to March 2026 and update quarterly, and the SEC states plainly that it “cannot guarantee the accuracy of the data sets”. A pipeline that begins with XBRL inherits that starting date. Alpha Vantage reaches past it for IBM, back to fiscal 2006, so the floor is not identical everywhere, but it is close enough to be the first thing to check.

Whether that matters depends entirely on the study window. A factor test that starts in 1995 and a recession comparison that needs 2001 and 2008 in the same table both stop at the wall. Older filings are still public, but they exist as documents rather than as columns, and converting them is work that has to happen once per company and once per line item. That gap between a filing and a usable table is the subject of a separate note on the SEC EDGAR API versus a fundamentals API.

How many companies does one request cover?

Alpha Vantage takes one symbol per call, with function and symbol both required. Its premium page describes the standard usage limit as “25 API requests per day”, while its support FAQ describes the free service as “25 API requests per minute and unlimited API requests for verified open-source or educational projects”. The two pages disagree as of 11 August 2026, so confirm the number before sizing a job around it. Paid plans start at 75 requests per minute for $49.99 per month and reach 1,200 per minute for $249.99 per month.

Massive filters its statement endpoints by ticker or CIK and controls volume through rows rather than calls: the limit parameter “Defaults to ‘100’ if not specified. The maximum allowed limit is ‘50000’”. The Stocks Starter plan at $29 per month advertises unlimited API calls, though statements are not part of that tier.

xfinlink caps tickers per call by plan, at 1 on Free, 100 on Pro, 500 on Max and 5,000 on Redistribution. A small panel is a single call:

panel = xfl.fundamentals(["IBM", "KO", "GE"], period_type="annual",
                         start="2023-01-01", end="2024-12-31",
                         fields=["revenue", "net_income"])
print(panel[["ticker", "fiscal_year", "revenue", "net_income"]].to_string(index=False))

Output:

ticker  fiscal_year  revenue  net_income
    GE         2023    35348        9482
    GE         2024    38702        6556
   IBM         2023    61860        7502
   IBM         2024    62753        6023
    KO         2023    45754       10714
    KO         2024    47061       10631

Three companies, one schema, each fiscal calendar preserved in its own period_end. Which period type belongs in which analysis is covered in the note on annual versus quarterly data.

Side by side

Alpha Vantage Massive xfinlink
Statement history 20 annual periods for IBM, fiscal 2006 to 2025, measured 11 Aug 2026 “Records date back to March 29, 2009” To 1950 on paid plans; 66 annual periods for IBM
Companies per request One symbol Filter by ticker or CIK, up to 50,000 rows 1 Free / 100 Pro / 500 Max / 5,000 Redistribution
Entry price for statements Free key, then $49.99/month $29/month Financials & Ratios, or $199/month Stocks Advanced Free tier on a 1-year window, then $29/month Pro
Licence Terms published as a PDF $29 and $199 plans marked “Individual use only”; business plans quoted separately Max $79/month company-wide internal; Redistribution $249/month adds end-user display
Rest of the platform Options, forex, crypto, news sentiment, earnings estimates, listing and delisting status Real-time and delayed market data; the financials expansion sells without a stocks subscription Prices, computed metrics, index membership, insider filings, 13F holdings

What does the licence allow?

Massive labels both individual fundamentals routes “Individual use only” on its pricing page, at $29 per month for Financials & Ratios and $199 per month for Stocks Advanced, and quotes business plans separately. Alpha Vantage publishes its terms as a PDF rather than a web page, which is worth reading in full before anything ships. xfinlink splits the two cases on the pricing page: Max at $79 per month carries a company-wide internal licence, and Redistribution at $249 per month adds the right to display the data to end users.

yfinance deserves an honest mention here, because it costs nothing and is perfectly adequate for a weekend script. Its own documentation states that the project “is not affiliated, endorsed, or vetted by Yahoo, Inc.” and that “the Yahoo! finance API is intended for personal use only”, which answers the question for anything with paying customers attached to it.

Which one fits which job?

Alpha Vantage covers the widest surface outside the statements themselves. One key also reaches options, forex, crypto, news sentiment, earnings estimates and a listing status endpoint that the documentation positions for survivorship research. For a single-company lookup, or a dashboard that wants several asset classes from one vendor, it is the shortest route.

Massive is the sensible pick when market data is the main purchase and fundamentals ride along, or when a served trailing-twelve-month timeframe saves the trouble of summing quarters. Buying the financials expansion without a stocks subscription is possible, at the same $29 as the entry stocks plan.

Everything else in fundamentals work points the other way: panels across hundreds of companies, windows that open before 2009, and statements that need to sit beside prices and index membership under one key. That is the shape xfinlink is built around, and the free tier reaches all of it on a one-year window before any decision about paying. The docs list the full field set per statement.

FAQ

Can I get pre-2009 statements without paying anyone?
Not as structured data from the SEC. The Financial Statement Data Sets begin in January 2009; earlier filings are public as documents, so a pre-2009 panel means either a parsing project or a vendor that has already finished one.

Which is cheapest to start with?
Alpha Vantage’s free key and xfinlink’s free tier both cost nothing, the latter giving 100 requests a day on a one-year history window. Massive’s statement endpoints start at $29 per month.

Does any of them serve trailing twelve months directly?
Massive accepts timeframe=trailing_twelve_months on its statement endpoints. xfinlink returns a TTM snapshot through xfl.metrics(ticker, period_type="ttm"). Alpha Vantage returns annual and quarterly reports, leaving the summation to the caller.

How current is each one after an earnings release?
Alpha Vantage states that its statement data is “generally refreshed on the same day a company reports”. Massive documents its statement endpoints as end-of-day, updated daily.

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
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