Foundational research, analytical frameworks, and physics-inspired methodologies developed at Student One.
1) A Physics-First Taxonomy for Surfacing Recurring Events in Financial Time Series
Abstract
We present a physics-inspired framework for surfacing recurring events in financial time series. Rather than forecasting, the system observes, measures, and ranks historically recurring phenomena using three paradigms: (i) Signal Processing (price as a noisy signal), (ii) Complex Systems (markets as non-linear, phase-changing systems), and (iii) Hidden-State Estimation (observed price as a noisy measurement of an underlying state).
Events are defined as crossings between two mathematically constructed time series (e.g., an observation and a transform; a metric and a threshold). We quantify only historical properties: the number of events N, their day coverage (events observed on at least half the days in the window), and their excursions (historical movement following an event, measured deterministically between one crossing and the next). Our engine is indicator-agnostic, asset-agnostic within aligned markets, and non-advisory by design.
View / Download Whitepaper below đź”—
2) A Technical White Paper on Zero-Retention, Zero-Trust, and Stateless Vendor Architecture in Institutional Finance. Physics inspired compliance.
This white paper defines the compliance architecture underlying Student One’s statistical
computation platform. It describes the structural, technical, and operational principles that govern
data handling, system behavior, and retention boundaries within the environment.
The document is designed for evaluation by regulated financial institutions, vendor-management
divisions, operational risk committees, cybersecurity teams, and external auditors. It outlines the
invariant architectural constraints—rather than changeable operational procedures—that
determine Student One’s security posture and compliance guarantees.