What is the Q-PI + ML (default) model on the Fear & Greed forecast?

The Q-PI + ML chip is the Fear & Greed forecast's default selection. It combines the two strongest ideas in the switcher: the quantum path-integral sampler's multi-well dynamics (which can produce two-scenario, bimodal forecasts) and the machine-learning drift trained on the index's own sub-indicators.

Why it's the default — stated honestly

It is the default because it ranked best on MAPE and RMSE in the walk-forward backtest at the time it was made default — not because we assert it is permanently the best. That distinction matters: a walk-forward ranking can shift as new data arrives. So this choice is presented as something you can verify or refute yourself, not a claim to take on trust. The "Model performance" table under the Fear & Greed forecast re-runs that exact backtest on current data and shows every model's coverage, MAPE, RMSE, Brier and hit-rate side by side. If another model is beating it today, the table will show you.

What 'combining' actually buys you

The path-integral half gives the forecast a realistic shape — including the ability to split into two scenarios when the market faces a fork — while the ML half lets the sub-indicators tilt the drift when they turn early. Neither guarantees accuracy; combining them is a hypothesis the backtest table is there to test. When in doubt, the plain OU baseline is the honest yardstick to compare against.

Frequently asked questions

Why is Q-PI + ML the default?

Because it ranked best on MAPE and RMSE in the walk-forward backtest at the time it was made default — not because we claim it is permanently best. You can verify or refute that in the 'Model performance' table under the chart.

Could another model be better today?

Possibly — a walk-forward ranking can shift as new data arrives. That's exactly why the live 'Model performance' table re-runs the backtest on current data and shows every model side by side, so you can check rather than assume.

What does combining Q-PI and ML actually add?

The path-integral half gives the forecast a realistic shape (including two-scenario bimodal forecasts); the ML half lets the sub-indicators tilt the drift when they turn early. Combining them is a hypothesis the backtest table is there to test.

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Related terms

Educational research only — not investment advice.