Thanks for your great question! . In the current MVP, users select the stocks manually, but the optimization does update dynamically if you change the analysis period — so the portfolio adapts to the data for the selected range. In the full product, users will simply choose an investment universe, and the system will automatically fetch relevant assets and update the optimization in real time or historical time based on user input. The quantum approach allows us to explore many combinations simultaneously, which makes the optimization faster and more reliable than traditional methods.

Sorry I cannot share code publicly.

Sorry I could not share the code public ally :).

Thanks for your interest! I’m happy to explain how it works: in the current MVP, users select stocks manually, and the system optimizes the portfolio dynamically based on the selected period. In the full product, users will choose an investment universe, and the system will automatically fetch assets and update the optimization in real-time.

The core quantum approach allows exploring many portfolio combinations simultaneously—as shown in the table of asset combinations in our platform—which makes the optimization faster and more reliable than classical methods.

I’m keeping the code private for now, as it’s part of the core product. For context, our startup is incorporated in Switzerland and fully compliant, so you can be assured this is a serious, trustable project. I’m happy to answer technical questions about the approach or share high-level algorithm insights.