Stage 1 — From signals to a pure FMP

A convincing factor result can be manufactured accidentally by using data that was not yet available or by letting a sector bet masquerade as stock-selection skill. The first stage prevents both mistakes: it fixes what was knowable at the decision time, makes the observations comparable, and isolates the intended signal from common risks.

The animation follows that work from raw prices, fundamentals, estimates, news, and derivatives to a pure factor-mimicking portfolio. The result is not the final client portfolio; it is a controlled way to measure whether one signal produced an investable return.

What the animation shows

Read the three panels from left to right.

1. Point-in-time features. Timestamped observations pass an availability check and assemble into a matrix with securities in rows and features in columns. Values with different units are ranked across the current universe and placed on a common scale.
2. AI-assisted composite. Registered inputs can be combined into a proposed factor recipe. The signs, transformations, weights, universe, and forecast horizon remain visible and editable. The proposal becomes a versioned research object only after review.
3. Active risk model and FMP solve. The approved signal enters beside the market, sector, style, and covariance estimates. The optimizer removes the unwanted risk exposures while retaining exposure to the signal. The output is a long/short vector of security weights: the pure FMP.

AI assists in the middle panel by finding registered inputs, suggesting a composition, and explaining overlap with existing factors. The workflow keeps future returns outside its information set, routes every proposal through review, and sends approved recipes into the validation stages that follow.

From raw values to one factor score

Let \(\mathcal{D}_t\) contain the records known by time \(t\). A feature for security \(i\) is constructed only from that information set:

\[x_{i,m,t}=g_m(\mathcal{D}_t,i).\]

Cross-sectional ranking makes unlike measurements comparable. If \(z_{i,m,t}\) is the normalized value of feature \(m\), a composite candidate \(j\) can be written as

\[s_{i,j,t}=\sum_{m=1}^{M_j} b_{m,j,t}z_{i,m,t}.\]

StrategyNet ranks the blend again to obtain the final signal exposure,

\[X_{A,t}[:,j]=\operatorname{rank\_zscore}(s_{:,j,t}).\]

The final ranking puts the blended features on a shared scale. Its positive and negative values describe relative standing within the eligible universe.

Purifying the signal with the risk model

Let \(X_{R,t}\) contain common risk exposures and let the asset covariance model be

\[Q_t=X_{R,t}\Sigma_{R,t}X_{R,t}^{\mathsf T}+D_t.\]

For signal \(j\), the pure FMP \(h_{j,t}\) is the minimum-risk portfolio that is neutral to the declared common risks and has unit exposure to the chosen signal:

\[\begin{aligned} \underset{h}{\operatorname{minimize}}\quad & h^{\mathsf T}Q_t h \\ \text{subject to}\quad & X_{R,t}^{\mathsf T}h=0, \\ & X_{A,t}^{\mathsf T}h=e_j. \end{aligned}\]

The first constraint neutralizes the market, sector, and style exposures that could otherwise obscure the idea. The second preserves the selected signal and suppresses the other alpha signals in the construction set. In matrix form, the covariance-aware solution is

\[H_t=Q_t^{-1}C_t \left(C_t^{\mathsf T}Q_t^{-1}C_t\right)^{+}G,\]

where the columns of \(H_t\) are the pure FMP security weights.

What leaves this stage

This stage produces a dated, versioned set of holdings built from a dated signal and risk model. When the next period's asset returns become observable, those frozen holdings produce the realized FMP return

\[f_{t+1}=H_t^{\mathsf T}r_{t+1}.\]

Repeating that construction through time creates the FMP return history used in walk-forward validation. Before that history exists, the factor remains a research hypothesis expressed as a controlled portfolio.