Quantustik turns S&P 500 market data into calibrated probability distributions using a quantum-mechanics-based model (Schrödinger equation + Feynman path integrals). You get a clean forecast for every S&P 500 ticker: a price path with a 90% confidence band, a market-conditions-aware BUY / WAIT / AVOID / EXIT verdict, and a risk-first entry, stop, and take-profit plan — served both as a human dashboard and as clean JSON for agents, notebooks, and trading bots via a REST API and an MCP server.
On the committed TOP-20 backtest (FMP close data), how often the 90% confidence band contained the realized price depends heavily on when you started. Across 9 quarterly start dates from 31 Mar 2024 to 31 Mar 2026, the band held between 78% and 95% at 3 months, between 71% and 92% at 6 months and between 86% and 96% at 1 year, against a 90% target. We publish every start date rather than one average, because averaging windows that overlap would claim a precision this measurement does not have. That is band calibration, not a directional hit-rate. Individual windows vary far more than the table does (worst at 1 year: NVDA started 31 Dec 2024 (2%)), and every failure is shown on the public calibration and track-record pages — we publish the misses, not just the hits.
Asymmetric conviction (BUY is rare and earned), calibrated confidence instead of vibes, explicit entry/exit timing with volatility-based stops, and non-consensus signals you will not find on a standard charting site. The model output, confidence, and calibration are free and universal; paid plans add history, every forecast horizon, alerts, and exports. See how the hyped AI/quant topics people search for map to what we actually ship in the AI Lab.
Educational research only — not investment advice.