# Data dictionary (WP46) ## Daily panel (`/panels/.parquet`, one row per session; built by `scripts/build_panels.py`) | column | unit | definition | |---|---|---| | session | date | session identifier (ET date for equities/futures/FX, UTC date for crypto; see D-02) | | close, open | price | session close (last 1-minute bar close in the window) and open; equities: vendor split+dividend-adjusted daily bars | | r | percent | 100·log(close_t / close_{t−1}) — the modelled daily return | | r_on | percent | 100·log(open_t / close_{t−1}) (overnight; 0 for FX/crypto, break return for futures) | | r_oc | percent | 100·log(close_t / open_t) | | rv_1m | percent² | Σ squared 1-minute log returns within the window | | rv_5m | percent² | Σ squared 5-minute calendar returns (grid anchored at the window start, previous tick) | | rv_5m_ss | percent² | average of the five 5-minute RV estimates with 1-minute grid offsets (primary intraday measure) | | bv_5m | percent² | (π/2)·Σ|r_j||r_{j−1}| on the 5-minute grid | | rsv_pos_5m, rsv_neg_5m | percent² | realized semivariances on the 5-minute grid | | medrv_5m | percent² | median realized variance on the 5-minute grid | | n_bars, n_5m | count | 1-minute bars used; 5-minute returns | | expected_bars | count | full-session bar count (391/211 equities; modal count otherwise) | | valid_rm | bool | realized measures usable (D-05, D-06) | ## Forecast table (`results/forecasts///.parquet`, one row per origin) origin (date of the information set), fit_id, status ∈ {ok, stale_fit, failed, forecast_failed, no_fit, missing_input}, method ∈ {analytic, simulation, direct}, h1/h5/h22 (percent², cumulative variance forecasts of the 1/5/22-day returns), sigma2_t, mu (one-step mean, percent), dp1/dp2 (distribution parameters), time_fc_s, admissible, plus the keys symbol, model_id, dist, spec_id, tier, window, refit_every, expanding. ## Fits table (`.fits.parquet`, one row per refit attempt) fit_id, origin, status ∈ {ok, inadmissible, failed}, error, loglik, n_iter, n_restarts, time_s, n_obs, admissible, params, param_names. ## Loss table (`results/metrics/losses/.parquet`) origin, spec_id, model_id, dist, tier, h, proxy, loss, value, status, forecast, target. ## Keys `spec_id = ____w_r` (or `exp` for expanding). Run manifests (`_runs.jsonl`) carry git SHA, host, package versions, wall/CPU time and peak memory per job; dataset versions and SHA-256 of every exported file are in `results/manifests/ingest_manifest.json`.