Our research publications lay out the design philosophy behind the Arctan product family — how each architectural choice responds to the fundamental challenges of applying machine learning to financial markets.
Machine learning in quantitative finance faces three structural challenges: too many candidate factors, too little data — a single historical timeline — and pervasive non-stationarity. This paper explains why these challenges cannot be dissolved, only managed, and details Arctan's three architectural responses: curated features, regime conditionality, and continuous monitoring.
It also situates the Arctan approach within the history of artificial intelligence — as a neuro-symbolic synthesis combining encoded domain expertise with statistical learning — and is explicit about what the framework does, and does not, claim.
