dAgentBase

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.

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.

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.

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.

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']).