Thriving 
HT JU Conditional Probability of Exceedance (CPE): a nonparametric, live-deployed cross-asset tail co-movement signal framework across 161 instruments, plus predictability-limit, AI/ML and climate-finance research (17 papers).
https://zenodo.org/records/21373459 ↗// readme
This repository contains the full code, notebooks, and experiments for an independent quantitative research program built around the Conditional Probability of Exceedance (CPE) framework — a live, production-deployed nonparametric signal system covering 161 instruments across equities, fixed income, gold, cryptocurrencies, commodities, volatility, and FX — plus further research threads that test complementary angles on the same underlying question: what is predictable in financial markets, and over what horizon. The latest, Paper 17, turns the framework’s own claim on weather and climate: does a rare, extreme conditioning event give a better signal than a frequent, everyday one, and does anything in weather pay? Paper 18 then applies the same discipline to a local question: is there a measurable right time to buy a car or a home in Singapore?
Author: Arun Ramanathan, PhD (Independent Researcher | Singapore)
The work here spans five lines of research, documented as 18 numbered papers and two standalone research notes: (a) the CPE framework and its live dashboards (Papers 1–5); (b)…