Quantustik inverts each ticker's historical price distribution through the time-independent Schrödinger equation into a market "potential", which then weights the Feynman path ensemble that produces the forecast.
The fitted potential is reconstructed from where a ticker's own historical price has actually spent time — a density estimate over past price levels — while separate parameters calibrated from volatility, autocorrelation, and market-conditions context shape how the distribution diffuses and drifts within it. A stock whose history shows heavy trading at two distinct price areas can produce a two-humped, multi-modal distribution instead of one average line splitting the difference.
Live example: AAPL's forecast currently assigns roughly 91% probability to price being higher over the next 3 months — a direct readout of the model's full probability distribution (the weighted path ensemble), not a separate guess bolted on afterward. See the full AAPL forecast for the complete distribution.
Because the forecast is a full ensemble of price paths rather than a point estimate, every forecast ships with a mean path plus calibrated quantile bands — the honest shape of any real market forecast is a range of outcomes with associated probabilities.
No. The model borrows the mathematics of the Schrödinger equation as a forecasting technique; it is not a physical claim about quantum effects in markets.
The potential is reconstructed from where the ticker's own historical price has actually spent time, while separate parameters calibrated from volatility and market-conditions context shape how the distribution diffuses and drifts, recalibrated as new data arrives.
It naturally produces a full, honestly calibrated probability distribution — including genuinely multi-modal outcomes — rather than a single line with an ad hoc error band.
AAPL analysis shows this metric in context, or browse all S&P 500 tickers.
Educational research only — not investment advice.