Quantitative trading has a credibility problem, and almost nobody in finance wants to name it out loud. Strategies that print money in backtests turn into cash incinerators the moment they touch live capital. Deep learning models with elegant architectures collapse when volatility shifts. Even the recent wave of LLM-powered trading agents, those theatrical “AI committees” of analysts and risk managers debating each trade, tend to deliver more drama than alpha. A new study introducing a system called