This Sophon-3 AI model flow starts with the S&P 500 as the asset universe, then chains together two AI model nodes: Sophon-3 Screener and Sophon-3 Forecast.

The idea is simple: first narrow the universe to more interesting candidates, then forecast their near-term return potential before sending the results into portfolio construction. This creates a practical workflow where screening, forecasting, and position sizing work together instead of as separate steps.

In this template, Sophon-3 Screener uses data types such as Price and P/B Ratio. Price is the market’s current per-share value for the stock, reflecting what participants are willing to pay right now. P/B Ratio (price-to-book) compares the market’s valuation to the company’s book value (net assets on the balance sheet); it’s a quick way to see whether the stock is priced at a premium or discount versus its accounting equity base.

Together, these inputs give the screener both a market pricing lens (what the stock trades at) and a balance-sheet valuation lens (how that price compares to net assets).

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 S&P 500 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.

Chaining the two nodes is meaningful because they solve different parts of the problem. Sophon-3 Screener helps reduce noise by finding better candidates, while Sophon-3 Forecast complements it by analyzing price behavior and short-term timing. Those forecasts then flow into portfolio construction, where stronger conviction can translate into larger position sizes.