Stage 3 — Walk-forward FMP validation

A convincing backtest has to reproduce the decision that could have been made on each historical date. StrategyNet rebuilds the signal, risk model, universe, and FMP from the information available then. The positions are frozen before the next return appears. Once observed, that return becomes one entry in the validation record.

This reconstruction is more involved than applying today's model to yesterday's data. It measures how the process behaved using only the information available at each decision time.

What the animation shows

The registered factor portfolios enter on the left with their versions and current status. The walk-forward engine advances through rebalance dates in the center. At each step it constructs the FMP, locks the weights, observes the next-period return, and accounts for turnover and costs.

The evidence panel keeps ranking statistics separate from portfolio-return statistics. Rank IC and ICIR describe whether the signal orders securities consistently. Net Sharpe and drawdown describe the return path of the constructed FMP. Coverage, exposure leakage, and correlation with the existing book complete the review from operational and portfolio perspectives.

Finally, explicit gates remove candidates that fail the minimum standard. The survivors are ordered for research review, with capital allocation reserved for the next stage.

Freeze first, measure second

For FMP \(j\) constructed at time \(t\), the next gross return is

\[f_{j,t+1}^{\mathrm{gross}} = h_{j,t}^{\mathsf T}r_{t+1}.\]

One-way turnover is

\[\operatorname{TO}_{j,t} = \frac{1}{2}\lVert h_{j,t}-h_{j,t-1}\rVert_1,\]

so a simple net-return calculation is

\[f_{j,t+1}^{\mathrm{net}} = f_{j,t+1}^{\mathrm{gross}} -\kappa_t\operatorname{TO}_{j,t}.\]

Production cost models can be more detailed. In every case, high-turnover signals are evaluated on returns after estimated trading frictions.

Separate ranking and return evidence

The daily rank information coefficient compares the signal or construction weights with subsequent security returns:

\[\operatorname{IC}_{j,t} = \operatorname{corr}_{\mathrm{rank},i} \left(x_{i,j,t},r_{i,t+1}\right).\]

Over a trailing window, the annualized ICIR is

\[\operatorname{ICIR}_{j,t} = \sqrt{252}\, \frac{\overline{\operatorname{IC}}_{j,t}} {s_{IC,j,t}}.\]

ICIR measures whether the ranking relationship is positive and stable. The FMP's Sharpe ratio measures its realized return path. Portfolio construction, covariance, concentration, turnover, and costs can cause the two measures to disagree.

Acceptance rules before ordering

Candidate selection starts with requirements. A deployment may require positive out-of-sample IC and net return, minimum coverage, bounded drawdown and turnover, acceptable exposure leakage, and low enough redundancy with factor portfolios already selected. Ordering begins after those rules are applied.

After those checks, a transparent ordering score can combine standardized evidence:

\[q_j = \theta_1 Z(\operatorname{ICIR}_j) +\theta_2 Z(\operatorname{Sharpe}^{\mathrm{net}}_j) -\theta_3 Z(|\operatorname{MDD}_j|) -\theta_4 Z(\operatorname{Turnover}_j) -\theta_5 Z(\operatorname{Redundancy}_j).\]

The coefficients are configurable and should be shown when they are used. The score \(q_j\) orders research candidates for promotion. The portfolio allocator receives a separately calibrated expected-return estimate.

What leaves this stage

The output is an ordered set of FMPs with their evidence attached: return history, IC history, turnover, costs, drawdown, coverage, correlations, and acceptance results. Stage four can select from that set while still seeing why an FMP passed and what risks it adds.