Stock prices and market data

Twenty years of daily OHLCV for 36 major global equity indices, volatility benchmarks, and US Treasury-yield series — 185,681 index-day rows in a single gzipped CSV. Coverage spans US broad-market (S&P 500/400/600, NASDAQ Composite, NASDAQ-100, Dow, Russell 2000, NYSE), volatility (VIX, VXN), Treasury yields (5y/10y/30y), European majors (FTSE 100, DAX, CAC 40, Euro Stoxx 50, IBEX, FTSE MIB, SMI, AEX, OMX), Asia-Pacific (Nikkei 225, TOPIX, Hang Seng, Shanghai/Shenzhen, KOSPI, TAIEX, SENSEX, NIFTY 50, ASX 200, NZX 50), and Americas ex-US (TSX, Bovespa, IPC, Merval). Pairs naturally with the existing Index Constituent History listing for survivorship-bias-free benchmark studies. Each row tags symbol, human-readable name, country (ISO), and quote currency. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: benchmark backtesting, beta computation, regime detection (VIX/yields), cross-asset correlation studies, and as the reference series for relative-strength signals.

Twenty years of daily open/high/low/close/volume bars for every current S&P 500 constituent (503 tickers including dual-class shares like BRK.A/BRK.B and GOOG/GOOGL). Date range: 2006-01-03 through 2026-09-05. Sourced live from Financial Modeling Prep and packaged as a single gzipped CSV — one row per (symbol, date), sorted by symbol then date ascending. Pandas, DuckDB, R, and most data tools read .csv.gz natively. Bedrock dataset for backtesting, factor research, event studies, and ML training. Note: prices reflect FMP's reported values at fetch time and are dividend/split adjusted as provided by FMP.

Ten years of daily open/high/low/close/volume bars for the top 3,000 US-listed stocks by market cap (NVIDIA, GOOGL/GOOG, AAPL, MSFT, … through ~$1B mid-caps). 6,148,886 rows packaged as 11 per-year gzipped CSVs. Universe spans NASDAQ, NYSE, and AMEX listings only (filtered out foreign exchanges and ADRs with dot-suffixes). Read with pandas.readcsv(path, compression='gzip', parsedates=['date']) or duckdb.readcsv('usstocksohlcv*.csv.gz') to load all years at once. Pair this with the Index Constituent Membership History listing for survivorship-bias-free Russell-3000-style backtesting. Bedrock dataset for full-universe quant research, factor model construction, and ML training across small/mid/large-cap regimes.

Ten years of daily OHLCV across all 40 commodity contracts indexed by FMP — 109,218 rows in a single gzipped CSV. Covers energy (WTI/Brent crude, natural gas, gasoline, heating oil), precious & base metals (gold, silver, platinum, palladium, copper, aluminum), grains & oilseeds (wheat, corn, soybeans, rice, oats, soybean meal & oil), softs (sugar, coffee, cocoa, cotton, orange juice, lumber), livestock (live cattle, lean hogs, feeder cattle, class III milk), Treasury futures (2y, 5y, 10y, 30y), fed funds, US Dollar Index, and index futures (E-mini S&P, NASDAQ-100, mini Dow, Russell 2000). Each row tags a category field for fast filtering. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: commodity-curve research, inflation-hedge backtests, cross-asset correlations, macro-regime detection, and rates/futures basis analysis.

Ten years of daily OHLCV across 92 liquid forex pairs and precious-metal spot rates — 264,813 pair-day rows. Covers USD majors (EUR/USD, GBP/USD, USD/JPY, USD/CHF, USD/CAD, AUD/USD, NZD/USD), G10 vs USD, Scandinavian crosses, all major EM currencies (CNY/CNH, INR, KRW, BRL, MXN, TRY, ZAR, RUB, IDR, THB, MYR, PHP), principal non-USD crosses (EUR/GBP, EUR/JPY, GBP/JPY, AUD/JPY, etc.), and precious-metal spots (XAU, XAG, XPT, XPD) denominated in USD plus EUR/GBP/JPY/AUD/CHF. Single gzipped CSV sorted (symbol, date asc). Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: FX carry strategies, currency-hedged backtests, regime detection (USD index proxy), EM correlation studies, and cross-rate arbitrage research.

Five years of daily OHLCV bars for the 250 largest cryptocurrencies by market cap as of build date — Bitcoin, Ethereum, Tether, BNB, XRP, Solana, USDC and 243 others. 426,989 daily bars as a single gzipped CSV — one row per (symbol, date), sorted by symbol then date ascending. Date range covers the 2020 bull, 2022 bear, 2024 BTC ETF approval, 2025 cycle peak. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']) or duckdb.readcsv('cryptoohlcv_5y.csv.gz'). Bedrock dataset for: crypto strategy backtesting, regime detection, BTC dominance studies, alt-season identification, vol-of-vol analysis, training ML models on cycles 2020-2025.

Two-file companion dataset for survivorship-bias-free backtesting. (1) Current constituents (635 rows): every member of the S&P 500, NASDAQ-100, and Dow Jones Industrial Average as of build date, with sector, sub-sector, headquarters, founding year, CIK, and date first added to the index. (2) Historical changes (2055 rows): every add/drop event for these three indices, stretching back to 1957 (S&P 500), 1985 (NASDAQ-100), and 1994 (Dow Jones), with the symbol added, the symbol replaced, the date, and the reason given by S&P/NASDAQ/Dow. Why this matters: every realistic backtest of an index strategy on point-in-time membership requires knowing who was IN the index at each historical date — without this, you suffer survivorship bias (only seeing winners that survived to today). Reconstruct membership at any past date by starting with current and replaying changes backwards. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']).

Five years of ticker-tagged news articles for the top 1,500 US-listed issuers by market cap, covering 1,453,949 articles across 1,453 companies. Each row carries publication timestamp, publisher, site domain, headline, article body snippet, canonical URL, and image URL — all linked back to the underlying ticker symbol. Split into one CSV.GZ file per calendar quarter (21 files) for partial-history loading. Read with pandas.readcsv(path, compression='gzip', parsedates=['publishedDate']). Useful for: training news-sentiment models, event-study backtests around news catalysts, building ticker-mention frequency time series, fine-tuning LLMs on financial-news prose, and reconstructing the news flow around earnings, M&A, regulatory events, and macro shocks.

Four-file macro toolkit covering: (1) Treasury Yields: full daily yield curve since 1990 — 12 maturities (1mo through 30y) for 63 trading days. (2) Economic Indicators: 113 rows across 15 series (GDP, CPI, inflation rate, unemployment, federal funds, consumer sentiment, retail sales, industrial production, mortgage rates, jobless claims, nonfarm payrolls, durable goods, recession probabilities) since 1990 in long format. (3) Commodities OHLCV: 192,095 daily bars across 40 commodity contracts (E-Mini S&P, gold, oil, natural gas, grains, metals, etc.) 2006-present. (4) Forex OHLCV: 140,000 daily bars across 28 major + minor currency pairs (EURUSD, USDJPY, GBPUSD, AUDJPY, …) 2006-present. Read each with pandas.readcsv(path, compression='gzip', parsedates=['date']). Pair with the equity/crypto/fundamentals listings to build risk-on/risk-off regime models, macro-overlay strategies, currency-hedged backtests, or commodity-aware sector rotation.

Every Senate and House Periodic Transaction Report (PTR) filed under the STOCK Act, as indexed by FMP. 10,100 Senate trades + 10,100 House trades = 20,200 transactions in 2 gzipped CSVs. Each row has the politician's name, party district, transaction date, disclosure date (often weeks/months later — that lag is itself a signal), transaction type (Purchase / Sale / Exchange), asset symbol, asset description, asset type (Stock, Option, Bond, Mutual Fund), amount range (STOCK Act bins like '$1,001 – $15,000'), spouse/dependent owner flag, comment, and a direct URL to the official PDF/HTML disclosure. Read with pandas.readcsv(path, compression='gzip', parsedates=['transactionDate', 'disclosureDate']). Polymarket-trader catnip — track Pelosi, Crapo, Tuberville, et al. in near-real-time. Useful for: lawmaker-replication strategies, sector-rotation signals from committee members trading regulated industries, and detecting unusual transaction clusters around legislation.

Five years of sell-side analyst activity for the top 3,000 US-listed issuers, delivered as three complementary files: 85,777 individual upgrade/downgrade events, 73,147 individual price target changes, and 144,410 monthly aggregate strong-buy / buy / hold / sell / strong-sell counts. Each event row carries the analyst firm, previous and new grade or target, the stock price at the time of the call, and a link back to the originating news item. Read with pandas.readcsv(path, compression='gzip', parsedates=['publishedDate']). Useful for: analyst-momentum factors, upgrade/downgrade event studies, calibrating analyst accuracy by firm, building target-change feature stacks, and reconstructing consensus drift around earnings.

Ten years of sell-side consensus quarterly estimates for 8,480 US-listed issuers, covering 175,834 quarter-periods. Each row carries low / avg / high consensus values across six fundamental lines — revenue, EBITDA, EBIT, net income, EPS, SG&A — plus analyst-coverage counts. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: pairing with the Earnings Surprises listing for analyst-accuracy / drift research, tracking estimate revisions over time, building consensus-bias factors, training models on the dispersion (high − low) as a proxy for fundamental uncertainty.

Current holdings for the top 500 US-listed ETFs by AUM — 623,253 ETF-position rows covering 434 ETFs. Each row carries the parent ETF's symbol, name, AUM (market cap), sector and exchange, alongside the held asset's ticker, full name, ISIN, CUSIP, share count, weight percentage, and market value. Rows pre-sorted by ETF symbol then weight descending, so top holdings per fund appear first. Pairs naturally with the existing ETF Master List for fund-level metadata. Read with pandas.readcsv(path, compression='gzip', parsedates=['updatedAt']). Useful for: flow analysis (who's buying / dumping a stock at the ETF level), factor-exposure decomposition (roll up positions across multiple ETFs), copycat strategies for active ETFs, sector/factor overlap analysis, and identifying liquidity sources for specific names.

Five years of SEC Form 4 insider transaction filings for the top 918 US-listed companies by reporting history, covering 2021-01-04 through 2026-09-04. 601,965 transactions as a single gzipped CSV — one row per filed transaction. Columns include the filing date, transaction date, reporting insider name and role, transaction type (open-market buy/sell, option exercise, RSU vest, gift, etc.), shares transacted, price per share, total shares owned after the transaction, and a direct link to the EDGAR filing. Sourced from Financial Modeling Prep (Form 4 mirror of EDGAR). Read with pandas.readcsv(path, compression='gzip', parsedates=['filingDate', 'transactionDate']) or duckdb.read_csv('file.csv.gz'). Useful for: tracking insider sentiment, building cluster-buying signals, identifying executives unloading positions ahead of weakness, screen for management vs. board behavior divergence.

200,021 observations across 36 core US macro series from FRED: the Treasury curve (1-month to 30-year) and curve spreads, fed funds and SOFR, TIPS and breakeven inflation, high-yield and investment-grade OAS, the broad dollar index and major FX crosses, WTI and Henry Hub, CPI, core CPI and PCE, unemployment, payrolls, initial claims, industrial production, retail sales, housing starts, consumer sentiment, nominal and real GDP, M2, the Fed balance sheet, vehicle sales and real disposable income. Long format (series_id, date, value) with series name, frequency and unit on every row, full history to 2026-09-05.

Five years of SEC Form 13F-HR institutional holdings for the top 500 US funds by AUM (combined AUM at Q4 2025 sample: $60.1T). 14,006,161 (manager × quarter × position) rows packaged as 20 per-quarter gzipped CSVs. Universe spans BlackRock, Vanguard / Geode, State Street, Fidelity (FMR), Berkshire Hathaway, Norges Bank, Bridgewater, Citadel, Renaissance, Two Sigma, Millennium, Coatue, Tiger Global, Pershing Square, Elliott, and ~485 more. Each row is one position held by one manager at quarter-end, with shares, USD value, filing date, and CUSIP. Read with pandas.readcsv(path, compression='gzip', parsedates=['periodenddate', 'filingdate']) or duckdb.readcsv('holdings_*.csv.gz'). Standard inputs for: hedge fund replication / cloning strategies, smart-money cluster signals, tracking famous-investor stake changes, sector rotation analysis, ownership-overlap network research, 13F-derived factor construction (popularity, conviction, concentration).

10,412 ticker-to-CIK mappings for every SEC registrant with a listed ticker, straight from the SEC's company_tickers.json as of 2026-09-05. The join key between market data (tickers) and EDGAR filings (CIKs).

Two-file dataset of every dividend payment and every stock split for every USD-reporting US-listed issuer FMP indexes (~17,500). 410,299 dividend rows + 17,982 split rows. Dividend rows include declaration / record / payment dates, the actual dividend amount, the split-adjusted dividend, the yield at announcement, and frequency (Quarterly, Monthly, Annual, Special). Split rows include the date and the split ratio (numerator / denominator). Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Required for: building total-return series from price-only OHLCV (combine with the S&P 500 OHLCV dataset), dividend-growth screening, post-split adjustment of historical prices, special-dividend event studies.

Ten years of pre-computed quarterly financial ratios and metrics for the top 3,000 US-listed issuers, covering 103,013 ratio-period rows across 2,973 companies and 102,983 key-metric rows across 2,973 companies. Two complementary files in one listing: (1) keyratiosquarterly — profitability margins, turnover ratios, liquidity, solvency, leverage, valuation multiples (P/E, P/B, P/S, P/FCF, EV/EBITDA), per-share book values, dividend metrics, and tax/interest burdens. (2) keymetricsquarterly — market cap, enterprise value, EV multiples, return-on-capital family (ROA, ROE, ROIC, ROCE), working-capital cycles (DSO, DPO, DIO, cash conversion cycle), and capex/R&D/SBC intensity. Saves buyers weeks of feature engineering on top of raw IS/BS/CFS statements. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: fundamental factor models, multi-factor backtests, screening by financial health, training ML models on pre-computed financial features.

Ten years of weekly Commitment of Traders (COT) reports from the CFTC for 65 futures contracts (E-Mini S&P, Nasdaq 100, gold, oil, natural gas, grains, metals, currency futures, Treasury futures, VIX, and more). 31,976 weekly rows in a single gzipped CSV with ~128 columns covering: long/short positions for commercial hedgers, non-commercial speculators (managed money), other reportables, and non-reportables (small specs/retail), plus net positioning, open interest, and percentage breakdowns. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). The classic positioning dataset for futures traders — useful for sentiment extremes, contrarian setups (commercial vs spec divergence), and macro-overlay strategies. Pair with the Macro Bundle's commodities OHLCV to combine price action with positioning shifts.

Five years of full-text earnings call transcripts from the top ~1,000 US public companies by reporting frequency (plus Tesla and Meta, which IPO'd more recently), covering Q1 2021 through Q4 2025. 19,677 transcripts across 1002 symbols, packaged as 10 gzipped JSONL files — one file per half-year (H1 = Q1+Q2, H2 = Q3+Q4) to stay under platform per-file size limits. Each line is one transcript with fields: symbol, fiscalyear, fiscalquarter, date, content (full prepared remarks + Q&A, speaker labels preserved). Sourced from Financial Modeling Prep. Read natively with pandas.readjson(path, lines=True, compression='gzip') or duckdb.readjson('file.jsonl.gz', lines=true) — concatenate all files for the full 5-year corpus. Industry-grade NLP corpus: ~25 KB per transcript average. Ideal for sentiment models, topic modeling, executive language change-detection, earnings-drift research keyed off transcript embedding similarity, and LLM fine-tuning on real corporate disclosure language.

119,109 observations across 23 US external-sector series from FRED: the monthly goods, services and total trade balance with exports and imports on a balance-of-payments basis (Census Bureau and BEA), the quarterly current-account and goods balances from the BEA international transactions accounts, quarterly NIPA exports, imports and net exports, daily Federal Reserve H.10 spot rates for the Chinese yuan, Canadian dollar, Mexican peso, Japanese yen, South Korean won, Indian rupee, Brazilian real, Swiss franc, British pound and euro, and the monthly nominal broad, advanced-foreign-economies and emerging-market-economies dollar indices. Long format with series name, frequency and unit on every row, full history from FRED as of 2026-09-06.

264,827 daily reference rates for 44 currencies against the euro, published by the European Central Bank since January 1999, from the ECB Data Portal as of 2026-09-05. The canonical daily fixing used in European settlement and reporting.

Environmental, Social, and Governance scores for the top 1,000 US-listed issuers by market cap (approximating the Russell 1000). Three complementary files: (1) esgdisclosures — 68,097 per-filing E/S/G/composite scores across 978 companies, linked back to the originating SEC form. (2) esgratings — 18,099 per-fiscal-year ESG risk-rating letters (A–F scale) plus industry rank, across 967 companies. (3) esgsectorbenchmark — 6,640 sector-level annual averages for benchmarking. ESG data of this quality is typically paywalled by MSCI / Sustainalytics; this dataset gives you an open, reproducible alternative. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: ESG-tilted portfolio construction, sustainability research, regulatory disclosure analysis, and training models that need ESG features.

27,407 country-month observations of two European Commission survey indicators, the consumer confidence indicator (BS-CSMCI) and the industrial confidence indicator (BS-ICI), as seasonally adjusted percentage balances for 35 geographies (EU and euro-area aggregates, every EU member state and candidate countries), 1980 to 2026, from the Eurostat dissemination API as of 2026-09-06. Long format with an indicator column; monthly periods are dated to the first of the month.