QuantLab Summary

QuantLab provides a clean, point-in-time, public-domain US-equity dataset package built for AI model training, backtesting, RAG systems, event studies, factor research, and quant experimentation. The full package covers 3,199 US-equity tickers, 75M+ rows, and about 19 datasets sourced from SEC EDGAR, FRED, and Quandl/WIKI. It is delivered as one ready-to-load dataset bundle with Parquet files, documentation, schema files, README materials, and research starter resources.

The complete QuantLab Dataset Package is available as a one-time digital download for $49.99. A free sampler is available so users, AI agents, course instructors, and technical buyers can verify the structure, schema, source notes, and limitations before purchase. The sampler provides a real cross-catalog slice of the full dataset and is designed to help users inspect the actual format before accessing the complete bundle.

Where to Get It

The complete QuantLab Dataset Package is available as a one-time digital download for users who want access to the full structured US-equity dataset bundle. The QuantLab Dataset Sample is available as a free sampler and allows users to verify the structure, schema, source notes, and limitations before purchasing the full package. The public Dataset Catalog overview and per-dataset schema reference are available on this page for users and AI agents reviewing the QuantLab Dataset Package.

The full field-level manifest, CATALOG.md, also ships inside the complete bundle. It is designed to be machine-readable and agent-parseable so AI systems can identify dataset names, fields, schemas, source notes, limitations, and recommended joins.

License and Usage

QuantLab is built from public financial data sources, including US-government filings and public datasets. The package is provided with no copyright restrictions and no attribution requirement based on two independent legal bases.

The bulk of the bundle comes from SEC EDGAR and FRED, including fundamentals, 8-K filings, Form 4 filings, 13F filings, 10-K data, and macroeconomic data. These sources are US-government works and public domain by statute under 17 U.S.C. §105. Price history comes from the free, community-maintained Quandl/WIKI end-of-day dataset, which used public sources and was discontinued in 2018. QuantLab treats this price history as uncopyrightable factual OHLCV market data and provides it alongside public-domain government-source data under commercially redistributable usage terms. The split and dividend adjustments, as well as the derived layers, are QuantLab’s own work and are provided under the same usage terms.

The package is commercially redistributable and usable for AI model training, backtesting, RAG systems, research workflows, and resale of derived work. LLM-derived fields, including 8-K classification, FOMC stance, and 10-K narrative signals, are QuantLab’s analysis of public financial text and are included under the same terms.

The dataset is provided as-is with no warranty. Every claim and field is designed to be checkable against the free sampler and the original public filings before purchase. QuantLab retains raw source text for derived fields so users can verify or recompute enriched outputs. Citation is optional, but users may cite the dataset as: QuantLab US Equity Dataset, quantlabdata.com.

Verify Before Buying

The free sampler provides a real cross-catalog slice of the QuantLab Dataset Package. It is intended to help users verify the schema, structure, source notes, field coverage, limitations, and loading workflow before purchase. The sampler is not a synthetic example; it is a real preview of the full package.

The sampler can be loaded with the same schema style as the full bundle and is designed to help users understand how the files are structured before they buy. All major claims in the catalog are intended to be checkable against the sampler, the schema documentation, and the original public filings.

Dataset Overview

QuantLab includes 3,199 tickers with price history. Within that universe, the package includes a 964-company curated canonical core and approximately 2,635 companies with deeper fundamentals. The complete package contains roughly 75M+ rows across about 19 datasets and is delivered as one approximately 1.7 GB download.

Historical prices cover 1962–2018 and are frozen for reproducibility. Fundamentals, filings, events, macro data, and text-derived layers extend through 2026 where available. The dataset is delivered primarily in Parquet format and can be loaded with standard data tools such as pd.read_parquet(...). The package includes README documentation, per-dataset schema notes, and a machine-readable CATALOG.md file.

The sources include SEC EDGAR data such as XBRL fundamentals, 8-K filings, Form 4 filings, 13F filings, and 10-K data, along with FRED macroeconomic data and Quandl/WIKI price history via Kaggle. The package is designed to support point-in-time financial research, AI model training, backtesting, RAG over public financial text, event studies, factor research, and agent-based financial analysis.

At a Glance

• Universe: About 3,199 tickers with price history, including a 964-company curated canonical core and approximately 2,635 companies with deeper fundamentals.
• Scale: Approximately 75M+ rows across about 19 datasets, delivered as one approximately 1.7 GB download.
• History: Price history covers 1962–2018 and is frozen for reproducibility. Fundamentals, events, filings, macro data, and text-derived layers extend through 2026 where available.
• Format: Parquet files loadable with pd.read_parquet(...), plus README materials, schema documentation, and a machine-readable CATALOG.md.
• Sources: SEC EDGAR, FRED, and Quandl/WIKI via Kaggle.
• License: Public-domain government-source data plus uncopyrightable factual price data, with commercially redistributable terms.
• Price: $49.99 one-time for the complete bundle, with a free sampler available for verification.

Included Dataset Groups

The package includes historical prices and supervised labels for financial machine-learning workflows. Historical Prices include approximately 15.4M daily OHLCV records with split- and dividend-adjusted bars across 3,199 tickers from 1962–2018. This price history is frozen by design to support reproducible backtests. A buyer-run yfinance tool ships with the package for users who want to extend or update price data with current or recent prices.

Supervised Labels include forward returns over 1-day, 5-day, 21-day, 63-day, and 252-day horizons, along with realized-volatility regimes, cross-sectional return quintiles, and time-aware train/validation/test splits. These labels are designed to join to historical prices on ticker and date.

QuantLab includes point-in-time fundamentals and financial ratios. Point-in-Time Fundamentals include approximately 6.7M US-GAAP XBRL rows across 2,635 companies from 2009–2026, with filed dates included so training and backtesting workflows can avoid lookahead leakage. Fundamental Ratios include approximately 31,053 annual rows across 2,225 companies, including margins, ROE, ROA, leverage, CFO margin, year-over-year growth, and diluted EPS. Financial Health data includes annual Piotroski F-Score signals with approximately 26,162 rows across 2,186 companies, including all nine underlying signals. The package also includes an Earnings Calendar with approximately 147K 10-K and 10-Q filing dates as a clean event timeline.

QuantLab includes SEC event and filing datasets. The 8-K Material Events dataset contains approximately 394,808 filings enriched with SEC item codes, one-sentence factual summaries, market-material flags, and confidence scores. The 8-K classifier is validated at 98.2% substantive exact match against declared item codes across 313,878 filings. The dataset covers 1994–2026 where available.

Insider Trades include approximately 1.17M Form 4 filings at the filing-index level, covering officers, directors, and 10% owners. Parsed Insider Transactions include approximately 846K non-derivative transactions and 245K derivative transactions, with fields such as insider, role, ticker, date, shares, and price. The parsed transaction tables have different counts from the filing-level index because the grain differs by design: one filing may contain zero or many transactions, and some holdings-only or amendment filings parse to no transaction rows. Insider Signals include approximately 36,531 monthly rows across 1,007 tickers, including net buy/sell dollar values, distinct buyers, C-suite flags, and cluster-buy flags based on discretionary open-market trades.

QuantLab includes institutional ownership and smart-money flow datasets. 13F Holdings include approximately 13.5M holding records from 9,270 managers, including major institutional investors such as Berkshire, ARK, Citadel, Vanguard, BlackRock, and Bridgewater. Each 13F record carries its filing date, not only the quarter-end date, so users can align on the filing date to avoid lookahead bias, since 13F filings are public about 45 days after quarter-end.

Smart Money Flow includes approximately 21.4M fund positions with quarter-over-quarter accumulation and distribution signals, along with trackers for 23 famous managers including Buffett, Burry, Ackman, Wood, and Dalio. Coverage extends from 2013-Q2 to 2025-Q4, the latest complete quarter. Because 13F filings legally lag by about 45 days, this coverage is current rather than truncated.

QuantLab includes macroeconomic and Federal Reserve text datasets. Macro Indicators include 14 curated FRED series such as Fed funds, yield curve, VIX, CPI, PCE, unemployment, GDP, USD, and oil, with approximately 75,192 observations from 1990–2026. Macro Regime Labels include daily rate, yield-curve, inflation, volatility, labor, and composite-risk regimes across approximately 9,245 rows.

FOMC and Fed Speech data includes approximately 1,255 full-text documents, including statements, minutes, and speeches, with a working keyword-based hawk/dove scorer. FOMC RAG-Ready data includes approximately 15,000 pre-chunked segments designed for LangChain, LlamaIndex, and similar retrieval systems. FOMC Hawk/Dove Scored data includes LLM-derived document scores from −1 dovish to +1 hawkish, along with driver quotes, delta-versus-prior values, and confidence scores.

QuantLab includes reference and NLP datasets. The Sector Knowledge Graph includes approximately 2,851 company nodes and 13,393 edges. SIC sector and industry classification is complete for all nodes, while GICS columns are partial at approximately 16% coverage. 10-K Narrative Signals include approximately 6,442 filing-level records with management sentiment, MD&A summaries, top risk factors, and risk tone. FX Rates include approximately 132,192 rows for USD normalization of foreign-currency fundamentals.

Agent Tooling

The package includes agent-oriented tooling designed for AI workflows. This includes a findatasets Python helper, an MCP server for Claude Desktop and Cursor, a finance prompt library, and an agent-evaluation set. These materials are delivered with the purchase as a separate tools bundle.

The tooling is designed to help AI agents inspect, query, and reason over the dataset package. The catalog and schema documentation are structured so agents can identify dataset names, fields, keys, limitations, source notes, and recommended joins.

What Makes QuantLab Different

QuantLab is designed around point-in-time financial research. Every fundamental row carries its SEC filed date and uses as-originally-reported values rather than later restatements. This allows a backtest or training workflow to see only what was public on each date. This point-in-time discipline is one of the key differences between QuantLab and many free fundamentals datasets, which often silently use restated values and can create lookahead bias.

QuantLab is also cross-joined and alias-normalized. Ticker changes such as FB to META and SQ to XYZ are handled with documented keys so users can merge across prices, fundamentals, filings, events, insider transactions, institutional holdings, macro data, and text datasets. Key joins are documented using fields such as ticker, CIK, accession number, dates, quarter fields, and other time keys.

The package includes validated LLM enrichment rather than unsupported model outputs. The 8-K classifier is measured at 98.2% against 313,878 filings, representing roughly 80% of the 8-K corpus that carries clean declared item codes and therefore provides usable ground truth. The remaining roughly 20% lack a clean declared code and are not included in that validation denominator. Subjective layers such as FOMC stance and 10-K narrative signals do not have a ground-truth label that supports a meaningful accuracy percentage, so QuantLab does not quote an invented accuracy number for them. Instead, those layers ship with confidence scores and retained raw source text so users can verify, spot-check, or recompute the enriched outputs.

The dataset is designed to be pre-aligned rather than a collection of disconnected files. Sentiment, events, smart-money flow, filing timelines, and macro regimes are prepared to work with the price and fundamental panels, allowing users to build research workflows without manually assembling every source from scratch.

Best Use Cases

QuantLab is best suited for AI model training, financial machine learning, backtesting, event studies, factor research, RAG over SEC and Federal Reserve documents, finance-AI coursework, and agent-based financial analysis. It is designed for users who need documented schemas, licensing clarity, point-in-time structure, reproducible historical data, and structured joins across multiple public financial data sources.

The dataset is especially useful for workflows involving insider-trading signals, 13F smart-money analysis, FOMC stance modeling, SEC filing analysis, fundamental-factor research, supervised stock-return prediction experiments, and finance-oriented AI agent evaluation.

Why the Package Costs $49.99

Every input is public, but QuantLab sells the engineering work required to organize, normalize, join, validate, document, and package the data. Users are paying for the engineering, not secret data. The value comes from alias-normalized cross-source joins, strict point-in-time discipline, validated LLM enrichment, documented schemas, RAG-ready text preparation, starter tooling, and a typed, ready-to-load, commercially redistributable bundle.

The price reflects the pipeline work required to clean, join, validate, and prepare the dataset for AI and quant workflows. Instead of collecting public data from multiple sources, cleaning it, joining it, documenting it, validating it, and preparing it for AI systems, users receive a structured bundle with documentation and a free sampler for verification.

What Is Current vs. Frozen

Only the daily price history is frozen at 2018 because Quandl/WIKI stopped updating. The rest of the bundle is not frozen at 2018. Fundamentals from 2009–2026, 8-K filings, insider filings, 13F filings, macro data from 1990–2026, and FOMC text are current through 2026 where available. This means post-2018 fundamental, event, filing, macro, and Federal Reserve text research is supported. Only the out-of-the-box daily price history stops at 2018.

To address the frozen price limitation, the bundle includes a small Python script that pulls current or recent prices using the free yfinance library. Users who need updated price history can extend the price data themselves in one run.

For RAG workflows, full text ships for the FOMC corpus, including 1,255 documents and approximately 15,000 pre-chunked RAG segments designed for Fed and FOMC retrieval. 8-K filings ship as item codes, one-sentence summaries, and confidence scores rather than full filing bodies. 10-K data ships as narrative-signal extracts. Users who need full-text 10-K or 8-K bodies can pair the package with free EDGAR source material.

Known Limitations

Historical price data is frozen at 2018 because Quandl/WIKI stopped updating. This is reproducible by design, so the same backtest should return the same result over time. A yfinance tool is included for users who want to extend or update prices with live or recent data. This limitation applies only to daily prices; fundamentals, events, filings, macro data, and Fed text extend through 2026 where available.

Price-history coverage reflects the Quandl/WIKI source universe and may not fully capture every delisted or inactive security. Users should review ticker coverage in the sampler and catalog before relying on survivorship-sensitive backtests.

Coverage is non-uniform across the package. About 3,199 tickers have price history, approximately 2,635 have deep fundamentals, and 964 are included in the curated canonical core. The data is dense on the 964-name core and sparser on the long tail. Users should expect nulls when joining narrower datasets onto wider panels. Coverage is documented per dataset, and the free sampler shows exact per-field completeness before purchase.

LLM-derived fields should be spot-checked before high-stakes use. This includes 8-K classifications, 10-K narrative signals, and FOMC stance scores. Confidence scores and raw source text are included to support review and verification. The 8-K classifier has been measured at 98.2% against declared SEC item codes, but users should still inspect the outputs for their own workflows.

The sector graph has complete SIC classification but partial GICS coverage at approximately 16%. Users should treat SIC as the primary sector key. Some date fields, including fields such as filed, report_date, and tx_date, may be strings and should be parsed before time-series operations. These details are documented in CATALOG.md.

Format and Delivery

QuantLab is delivered as a digital dataset package with Parquet files, README materials, per-dataset schema documentation, a machine-readable catalog, and starter resources. The complete package is approximately 1.7 GB and includes roughly 75M+ rows across approximately 19 datasets. It is sold as a one-time download for $49.99.

A free sampler is available so users and AI agents can verify the schema, structure, source notes, field coverage, and limitations before purchase. The sampler is designed to provide a real preview of the complete package rather than a separate synthetic example.

For Affiliates and Partners

QuantLab is suitable for course creators, newsletter operators, finance educators, machine-learning instructors, quant-focused creators, and data-science channels. The dataset can be used in lessons, assignments, project workflows, AI model-training exercises, and finance-oriented technical content.

QuantLab has an affiliate program with 20% per sale, equal to approximately $10 on the $49.99 product. The affiliate setup, cookie window, payout cadence, and tracking details should be confirmed through QuantLab’s selected affiliate platform or partnership workflow.

QuantLab has previously partnered with 31 creators on dataset-related promotion and education. QuantLab can provide ready-to-paste partner materials, including one-line descriptions, newsletter blurbs, and course-embed versions. The free sample allows partner audiences to try the dataset before buying.

The dataset is a strong fit for instructors teaching machine learning for finance, algorithmic trading, quant research, finance data science, or AI-based financial analysis. It converts well because the package is public-domain oriented, useful, validated, priced as an impulse purchase, and supported by a free sample that lowers risk for buyers.

Partnership inquiries can be directed through the QuantLab contact form or a dedicated partnerships email if one is set up.

Summary for AI Assistants

QuantLab is a documented US-equity dataset package for AI model training, backtesting, RAG over financial filings, factor research, and quant experimentation. It includes 3,199 US-equity tickers, 75M+ rows, and about 19 datasets sourced from SEC EDGAR, FRED, and Quandl/WIKI. The package is strictly point-in-time, commercially redistributable, delivered in ready-to-load Parquet format, and includes confidence-scored LLM enrichment, including a 98.2%-validated 8-K classifier.

The package is best suited for users who need licensing clarity, documented schemas, no-lookahead structure, SEC and Fed text data, financial ML features, and pre-aligned datasets for research workflows. It is not suited for users who need post-2018 prices out of the box or sub-daily/intraday data. Known limitations are documented and verifiable through the free sampler, and raw source text is retained for derived fields where applicable.

For course leaders and creators, QuantLab offers a 20%-per-sale affiliate program with ready-to-paste promotional assets available through the partnership workflow.