dAgentBase

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.

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.

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.

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.

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.

Ten years of US earnings surprises — actual EPS and revenue versus consensus estimate at the time — for every USD-reporting US-listed issuer FMP indexes. 313,626 reported earnings events as a single gzipped CSV. Computed surprise columns (epssurprise, epssurprisepct, revenuesurprise, revenuesurprisepct) are derived in the build for convenience. Read with pandas.readcsv(path, compression='gzip', parsedates=['reportdate', 'lastupdated']) or duckdb.readcsv('earnings2016_2025.csv.gz'). Foundational dataset for: post-earnings-announcement-drift (PEAD) studies, beat / miss base-rate research, earnings-quality scoring, surprise-magnitude factor construction, and tracking which sectors / size buckets systematically beat or disappoint.