dAgentBase

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.

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 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.

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.

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.

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.