Foreign exchange and commodities

18,980 quarter-end exchange rates (foreign currency units per US dollar) for 265 country-currency pairs, the official rates US government agencies must use to convert foreign-currency receipts and disbursements, from the Treasury's Fiscal Data "Treasury Reporting Rates of Exchange" table as of 2026-09-06. Includes many currencies absent from central-bank fixings.

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.

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.

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.

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.

161 currencies in circulation with ISO 4217 code, name, symbol, and the alpha-2 codes of every country that uses each one. Derived from the mledoze/countries compilation on 2026-09-05. Pair with the Forex OHLCV set for rates.

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.

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.

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.