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Glossary

factrix names its dispatch axes after the structure of the data (Scope × Signal) and the regression layout (Mode). The literature this borrows from uses overlapping but not identical vocabulary — this glossary maps the factrix terms to their nearest industry equivalents and flags collisions where the same word means different things in factrix vs Alphalens / Barra / standard panel-econometrics.

Scope axis — INDIVIDUAL vs COMMON

INDIVIDUAL

Per-asset factor values: each (date, asset_id) pair has its own factor reading. The cross-section is the unit of inference (per-date information coefficient (IC), FM λ, CAAR per event date). This is the regime most equity factor research operates in.

Industry equivalents:

  • Alphalens: "characteristic" / "factor". Alphalens panels are always per-asset signals, so the term is implicit.
  • Standard cross-sectional asset pricing: "stock characteristic" (size, book-to-market, momentum). Same regime; factrix routes these through Individual × Continuous.
  • MSCI / Axioma: "factor exposure" of asset i to factor k.

COMMON

A factor that does not vary across assets: on any given date every asset_id carries the same factor value. The panel layout is unchanged (still long-format (date, asset_id) rows) — scope is a factor attribute, not a data shape. Macro factors (VIX, USD index, term spread, monetary-policy shocks) and broadcast event dummies (FOMC announcement days) live here.

Industry equivalents and collisions:

  • Macro / global factor literature: "common factor" = a single series that drives multiple assets. Matches factrix COMMON.
  • Barra "common factor": a shared risk factor estimated from cross-sectional regressions on a panel of asset characteristics (Barra style / size / value / momentum factors). Estimation-driven, per-asset latent loadings. Collides with factrix COMMON, which is a given broadcast series with no estimation step on the factor itself.
  • Alphalens: no direct equivalent. Closest is "group-neutral" / benchmark series, but Alphalens does not natively dispatch to a COMMON cell.

When porting Barra terminology in: a Barra "common factor" with raw cross-sectional loadings is a factrix INDIVIDUAL × CONTINUOUS problem (the loadings are the per-asset characteristic). A Barra "common factor" treated as a single series shared across assets is a factrix COMMON × CONTINUOUS problem.

Signal axis — CONTINUOUS vs SPARSE

CONTINUOUS

Factor takes a real-valued reading on every observation in the panel. Most equity characteristics; macro factors; ML predictor scores.

SPARSE

Factor is non-zero only on event dates and zero elsewhere; the panel encodes a discrete event arrival process. The general schema is {0, R} where R is any real value (positive, negative, or any magnitude); the simplest form is {0, 1} for a pure event flag. See compute_caar's input-form table for the resulting estimator distinction.

Industry equivalents:

  • MacKinlay (1997) event-study vocabulary: factrix's SPARSE cell is the event-study cell; the factor column is the announcement indicator. factrix's factor != 0 filter is the event-window selector.
  • Alphalens: no direct equivalent — Alphalens assumes continuous characteristics. Event-study workflows live outside Alphalens.

Mode axis — PANEL vs TIMESERIES

Mode is derived from data at evaluate-time, not configured:

  • N ≥ 2 → PANEL.
  • N == 1 → TIMESERIES.

PANEL

Multi-asset panel; cross-section participates in inference (per-date aggregation, cross-asset SE).

Industry equivalents: panel data; longitudinal data; pooled cross-section / time-series. The econometrics literature usually says "panel" with no further qualification.

TIMESERIES

Single-asset series. Time axis is the only sample axis; cross-section is degenerate.

Industry equivalents: time-series regression; single-asset ordinary least squares (OLS). Note that factrix TIMESERIES mode does not mean Alphalens' "time-series of cross-sectional means" (that is per-date aggregation of a PANEL); the two collide on the word time-series and disagree on what is aggregated. See Timeseries-mode conventions for the specific contracts that apply when factrix routes here.

Other terms

the metric evaluate() runs

Every (scope, signal, metric, mode) dispatch cell maps to exactly one metric callable that evaluate() runs internally — ic, fama_macbeth, caar, or ts_beta — and that metric's p-value becomes FactorProfile.primary_p. Defined by the factrix._registry._DISPATCH_REGISTRY. Other metrics applicable to the same cell are called separately via list_metrics and the per-metric callable; they do not enter the FactorProfile.

The cell-keyed reverse-index lives at Metric applicability § Cell to evaluate-metric.

Legacy synonyms (pre-#144 docs and source comments): procedure-canonical, cell-canonical, primary metric, canonical metric. New content uses the behavioral phrasing above; legacy occurrences are phased out via the ratchet test in tests/test_docs_terminology_ratchet.py so the count can only decrease.

factor

The signal column. Same role as Alphalens "factor", Barra "exposure", or generic "alpha score" / "predictor". The procedure-level INPUT_SCHEMA consumes a column literally named factor; pass evaluate(..., factor_col="alpha") to use a differently-named column without renaming the panel first (Panel schema § factor_col=).

forward_return

The forward return column consumed by every dispatch cell. factrix's compute_forward_return(df, forward_periods=N) produces a per-period return with t+1 entry:

forward_return[t] = (price[t+1+N] / price[t+1] − 1) / N

Two non-textbook choices to internalize:

  • t+1 entry, not t: signal at t is computed from data up to and including price[t], so trading at price[t] would assume same-bar execution. Entry at t+1 enforces a causal boundary and cleanly separates the return window from the BMP estimation window.
  • Divided by N: result is expressed per period rather than as the cumulative N-period return, so factor evaluations at different forward_periods are directly comparable. If you need the cumulative N-period number, multiply by N.

Only simple returns are implemented; no log-return option. factrix takes forward_return as input rather than computing it inside evaluate() — attach it with compute_forward_return before dispatch so the horizon N is explicit and aligned with AnalysisConfig.forward_periods.

Distinct from "spot return" (contemporaneous one-period return) and "realised return" (ex-post return for risk attribution).

signal_horizon (datasets only)

A property of the synthetic signal embedded in make_cs_panel / make_event_panel — the horizon at which the generator's nominal IC is realised. Aligning AnalysisConfig.forward_periods with signal_horizon is the regime where the synthetic IC / drift is calibrated; mismatched horizons induce IC decay (synthetic) and bias (in TIMESERIES mode — see Timeseries-mode conventions § Non-overlap).

non-overlapping returns

Sampling every h-th date so consecutive observations are independent under the h-period forecasting null. factrix's Individual × Continuous cell defaults to this for ic and caar; Newey-West (NW) heteroskedasticity-and-autocorrelation-consistent (HAC) is the alternative on the full overlapping series. See Statistical methods § HAC SE.

estimation_window

Per-asset pre-event sample used to fit the abnormal-return baseline for bmp_test and corrado_rank_test. See Metric applicability § estimation_window.

Multiple testing

FDR — False Discovery Rate

The expected proportion of false positives among rejected nulls, in contrast to FWER (Family-Wise Error Rate, the probability of any false positive). For a screening rule that rejects R of m hypotheses and produces V false positives, FDR ≡ E[V / max(R, 1)]. factrix's primary screening primitive is BHY (Benjamini-Hochberg-Yekutieli), which controls FDR at a user-chosen q under arbitrary dependence. See Statistical methods and bhy.

BHY — Benjamini-Hochberg-Yekutieli

Step-up FDR-controlling procedure that allows arbitrary dependence between p-values — including violations of the positive regression dependence on a subset (PRDS) condition required by Benjamini-Hochberg 1995, at the cost of a log(m) factor. Mathematically implements Benjamini & Yekutieli (2001). factrix's default multiplicity-correction method because financial p-values are typically not independent. For the BH / BY / BHY naming convention, see Statistical methods § Multiple-testing under dependence.

family

The set of hypotheses jointly entered into a single FDR / FWER controlling procedure. Once a hypothesis joins a family, the procedure's FDR claim only holds within that family. Family choice is a contract decision: re-running BHY on a filtered subset of survivors does not preserve FDR ≤ q. See Cross-function reference § expand_over for the sample-restriction vs hypothesis-dimension split.

Survivors

Result type from the screening functions (bhy, partial_conjunction, bhy_hierarchical). Carries the post-correction adjusted q-value (adj_q) per identity plus a boolean survivor[i] ↔ adj_q[i] ≤ q duality so downstream functions (compare(survivors)) can render leaderboards without re-applying the threshold.