Hypothesis
  hfmarketdata.io advertises 1min→1day bars across stock/etf/futures/index/
  fx/crypto plus daily options chains. We believe the advertised granularity
  is real, but that session semantics, timestamp conventions, adjustment
  behavior, missing-data patterns and response limits are NOT documented
  precisely enough to design honest experiments — they must be measured.

Falsification criterion
  Not a statistical hypothesis: this experiment FAILS if any downstream-
  critical property (granularity, timestamp basis, adjustment arithmetic,
  row caps, completeness) cannot be pinned down empirically, or if the
  advertised 1-minute granularity turns out to be resampled/absent.

Artifact null(s)
  None (no anomaly is claimed). This experiment EXISTS to seed the artifact
  taxonomy that later experiments must beat.

Method
  Eight probe families (A1–A8) through the single cached client:
  inventory; history bounds per class (representative tickers AAPL, SPY, ES,
  SPX, EURUSD, BTC); intraday session structure incl. a sparse ticker (AIZN);
  daily-vs-1min aggregate comparison; AAPL 2020 4:1 split across all three
  adjustment series; row-cap + pagination check on one year of SPY 1min;
  latency profile; options coverage. All responses cached; manifest indexed.

Result
  See analysis.md and results/expA_data_reality/20260812T054515Z/results.json.
  54 network requests, 274 571 rows, 0 retries; re-run = 55 cache hits,
  0 network requests.

Interpretation
  Confidence n/a (no anomaly claim). Dataset is fit for Q1–Q3 research with
  five load-bearing caveats (see data_source_profile.md §9).

Next experiment
  expB_artifact_baselines: build the measured null distributions for
  bid-ask bounce, stale prices, and non-synchronous lead-lag on THIS data.