Stage 2 — Realtime Factor Projections

Realtime Factors applies a registered factor definition to the latest approved evidence and reports the resulting security ranks. Timestamps and coverage show how current each input is, helping researchers interpret scores assembled from data that updates at different frequencies.

This walkthrough explains how slower-moving observations are combined with intraday price, range, volatility, relative volume, or flow. Each nowcast retains the timing, freshness, and coverage needed to assess its use in a portfolio decision.

What the animation shows

The left panel separates the T-1 model state from the records arriving in the current session. That distinction prevents an old fundamental observation from appearing to change simply because the market price moved.

The center panel is the realtime factor projection matrix. Each column is one factor; each row is one eligible security. Updates can change many factor projections at once, but each factor column is normalized independently across stocks. The animation then moves to the aggregation and ranking panel, where a declared consensus method produces a single ordered view.

The final object is a versioned snapshot consumed by web, desktop, or mobile clients. Every client displays and sorts the same calculated rows.

Combining the daily state with live evidence

Let \(d_{i,j,t-1}\) be the last complete daily score and let \(v_{i,j,\tau}\) be the current-session state at intraday time \(\tau\). A general nowcast is

\[\widetilde{s}_{i,j,\tau} = \phi_j\left( d_{i,j,t-1}, v_{i,j,\tau}, \operatorname{freshness}_{j,\tau}, \operatorname{coverage}_{j,\tau} \right).\]

The function \(\phi_j\) is part of the factor version. It may define a direct intraday signal, an end-of-day proxy, or a controlled blend with the prior state. The nowcast records the estimate available at \(\tau\); realized returns arrive later.

Ranking stocks inside each factor

At a given timestamp, collect the valid projections into an \(N_\tau\times K_\tau\) matrix \(P_\tau\). Each factor column is then ranked across the eligible securities:

\[u_{i,j,\tau} = \operatorname{rank\_zscore}_i(P_\tau[:,j]).\]

For each factor and timestamp, the calculation compares all eligible stocks across the cross-section. A stock's own price history enters only through the factor inputs.

Looking across factors

For security \(i\), a weighted consensus over its valid factors is

\[c_{i,\tau} = \frac{ \sum_{j\in J_{i,\tau}}a_{j,\tau}u_{i,j,\tau} }{ \sum_{j\in J_{i,\tau}}a_{j,\tau} }.\]

The weights \(a_{j,\tau}\) must have a stated meaning. They may be equal, based on trailing reliability, balanced by factor family, or used in a robust trimmed average. Each approach defines a distinct view and travels with its own label.

Coverage travels with the score:

\[\operatorname{coverage}_{i,\tau} = \frac{|J_{i,\tau}|}{K_{\mathrm{requested}}}.\]

Two securities can have the same consensus value with very different amounts of supporting evidence. Showing factor count, coverage, and the latest input timestamp makes that difference visible.

What the front end receives

The backend sorts \(c_{i,\tau}\) into positive and negative projections and publishes one snapshot. A useful row includes the symbol, score, rank, side, factor count, tail count, coverage, as-of time, latest input time, weighting method, and normalization method.

The leaderboard and heat map are projections of that contract. They make the cross-section readable without changing the calculation that produced it.