This template uses the datatype pair Dividend Yield and Total Current Liabilities. It expresses stronger investment views through a focused portfolio structure with fewer effective bets. Historically this archetype aligns with higher concentration metrics (for example, stronger top-position influence and higher concentration indices) and lower breadth than diversified profiles. Because exposures are more focused, outcome dispersion is typically more sensitive to security-selection accuracy.
Screener-N3 continually learns which combinations improve selection quality over time.
The screened assets are then passed into Forecast-N3, which estimates near-term return direction and magnitude.
Forecast output is then passed through a second Screener-N3 as a post-model gate, so only names that satisfy forecast-aware rules proceed to portfolio construction.
Portfolio construction assigns larger weights to stronger forecasts within that filtered set.
The result is a more selective, higher-conviction structure that can improve signal purity but may reduce breadth.
That makes node chaining meaningful: first screening reduces noise, forecasting adds timing, second screening enforces quality control, and portfolio construction turns all stages into actionable allocations.
