This Sophon-3 AI model flow starts with the DIJA-30 as the asset universe, then chains together three AI model nodes: Sophon-3 Screener, Sophon-3 Forecast, and a second Sophon-3 Screener before portfolio construction.
The idea is simple: first narrow the universe to more interesting candidates, then forecast their near-term return potential, then select the Top N forecasts to optimize against a benchmark before sending the results into portfolio construction. This creates a practical workflow where screening, forecasting, selection, and position sizing work together instead of as separate steps.
In this template, the first Sophon-3 Screener uses data types including Total Equity and Total Liabilities. Total Equity represents the shareholders’ residual claim on the company (assets minus liabilities); it helps describe the balance-sheet cushion supporting the business. Total Liabilities is the company’s total obligations (short-term and long-term) and helps describe how much the business owes across debt and operating liabilities.
Together, these inputs give the screener a balance-sheet structure lens: how much net value supports the company versus how much it owes.
Sophon-3 Screener is an AI model node that continually learns from new market dynamics instead of relying on fixed screening rules. Its role is to filter the DIJA-30 toward stocks with stronger potential versus the benchmark.
The selected names are then passed to Sophon-3 Forecast, another AI model node, built for continual deep learning forecasting. Sophon-3 Forecast focuses more directly on price dynamics and estimates near-term return direction and magnitude.
After forecasting, the second Sophon-3 Screener ranks and selects the Top N forecasts to create a tighter candidate set for benchmark-aware optimization in portfolio construction.
Chaining the nodes is meaningful because they solve different parts of the problem. The first Sophon-3 Screener reduces noise by finding better candidates, Sophon-3 Forecast adds timing by estimating near-term return potential, and the second Sophon-3 Screener concentrates the signal by selecting the strongest forecasts before optimization and position sizing.
