090°Open data
Open datasets, fully documented — searchable here, and readable by any LLM.
18 datasets
U.S. Federal Reserve and Bank of Canada
Scheduled policy-rate decision events of the U.S. Federal Reserve (FOMC) and the Bank of Canada, 2026-01-01 to 2027-12-31 (32 rows: 16 FOMC + 16 BoC). Transcribed 2026-09-30 from the keyless official schedule pages (Federal Reserve 'Meeting calendars and information', last updated 2026-09-16; Bank of Canada 'Policy interest rate' fixed announcement dates for 2026 and 2027). Each row carries the decision date, the meeting window (FOMC two-day meetings; BoC single-day announcements), a projection-meeting flag (FOMC SEP + Chair press conference / BoC Monetary Policy Report), the bank's published standard statement release time (Fed 14:00 ET, BoC 09:45 ET), and the FOMC minutes release date with its status (published for the five past 2026 meetings; estimated by the Fed's three-weeks-after-decision rule for later meetings — the rule reproduces all five published dates exactly). Method: official schedules transcribed into a closed bank registry; two-day meetings normalized with the decision on the end date; event_ids assigned chronologically per bank; deterministic row hashes; fail-loud duplicate and bounds gates. Caveats: event timing only — no rate forecasts, market data, or outcomes; unscheduled inter-meeting decisions are absent by construction; the 2028 FOMC calendar was only tentative and is excluded; the BoC publishes no minutes. Meeting dates are published government facts; the panel is an original factual compilation (CC-BY-4.0, commercial use allowed with attribution).
North America gateway import pressure (derived)
Monthly North America gateway import pressure: loaded import TEU for the Port of Los Angeles (USA) and the Port of Vancouver (CAN), 2019-01 -> each port's latest published month, with deterministic derived features — MoM %/YoY %/3-month-annualized import change, trailing-5-year same-month seasonal baseline and deviation from it, a documented 0-100 import_pressure_score = 100*(0.45*mm(YoY) + 0.35*mm(vs_seasonal) + 0.20*mm(3m_ann)) with min-maxed clipped inputs, fixed-cut pressure_tier (surge/elevated/moderate/soft/slack), and a frontload_flag for June-August months running >10% above seasonal baseline. Keyless official port statistics (POLA container-statistics pages; Vancouver Fraser Port Authority container statistics report). One row per year_month x gateway; join keys: date, gateway_code, country_code. Who joins this: an online shop joins monthly gateway import-pressure scores to inventory and order data on year_month to anticipate retail inventory availability and freight-cost pressure 4-8 weeks ahead; a sales team joins it to territory pipeline on year_month.
Annual calendar of US federal (IRS) and Canada federal (CRA) tax deadlines for calendar-year filers: income-tax filing and payment dates, quarterly estimated payments / instalments, corporate, trust and exempt-organization returns, RRSP and IRA/HSA contribution deadlines, FBAR, and employer slip issuance (W-2/1099-NEC, T4/T5). Rule-derived from the published IRS and CRA deadline schedules with the official weekend/holiday next-business-day adjustments applied (including DC Emancipation Day for US Tax Day). Regenerated each run for a rolling window of tax years.
US Home-Price & Rate-Lock Pulse (derived)
Monthly US home-price & rate-lock pulse: S&P/Case-Shiller national home price index (FRED CSUSHPISA; MoM %/YoY %, trailing-12m z-score of the monthly change, 12-month acceleration in pp, trailing-10y percentile rank) and the 30-year fixed mortgage rate (Freddie Mac PMMS via FRED MORTGAGE30US, weekly observations averaged to calendar months; MoM/YoY point changes, trailing-12m z-score, trailing-10y percentile rank), plus a deterministic affordability_pressure_score = 0.5*price_hist_pct_rank + 0.5*rate_hist_pct_rank (0-100; high = expensive homes at expensive rates) and a rule-based market_regime (frozen / rate_shocked / thawing / overheating / cooling / balanced). Panel 1987-01 -> latest published month (Case-Shiller lags ~2 months). Upstream: FRED keyless fredgraph.csv (no API key). One row per year_month, country_code=USA. Who joins this: a subscription business joins monthly home-price momentum to customer cohorts on year_month to model housing-wealth effects on retention and upgrades; an online shop joins it to home-category demand forecasts on year_month.
US Household Spending (BEA via FRED, keyless)
Monthly US household-spending panel, 1959-01 onward: personal consumption expenditures nominal (BEA, SAAR $B), the PCE price index (2017=100) as deflator, and resident population, plus real PCE in 2017 dollars, real per-capita PCE, 12-month and 3-month-annualized real growth, and a documented 0-100 spending_momentum_score with m1..m4 tiers. The spending side of the household balance sheet — complements (no column overlap) the live household_financial_pressure dataset, which carries the saving rate, debt-service ratio and real disposable income. Upstream: U.S. Bureau of Economic Analysis via FRED keyless fredgraph.csv (PCE, PCEPI, POPTHM). One row per month x USA; in-window nulls fail loudly, never imputed. Who joins this: online shops join revenue on year_month, subscription businesses join MRR and churn cohorts, sales teams read momentum_tier as the demand backdrop.
US Metro-Area CPI Inflation (BLS via FRED, keyless)
Monthly US metro-area CPI inflation panel, 1914-12 onward, for the 10 Core-Based Statistical Areas whose CPI the St. Louis Fed redistributes live (New York, Philadelphia, Boston, Chicago, Detroit, Dallas, Atlanta, Miami, San Francisco, Seattle): CPI-U all items, not seasonally adjusted, 1982-84=100, with 12-month percent change (defined for bimonthly metros on same-parity months), 1-month change for the two monthly metros, per-month inflation ranks and i1..i4 heat tiers. Bimonthly metros publish every other month — off-months are honest nulls, never imputed. Upstream: U.S. Bureau of Labor Statistics via FRED keyless fredgraph.csv (CUURA101/102/103/207/208/316/319/320/422/423SA0). One row per metro x month; join keys: metro_code, year_month, country_code=USA. Who joins this: online shops join demand on metro + month, subscription businesses join regional churn, sales teams weight pipeline by local cost pressure.
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