AI is already extraordinarily efficient. When artificial intelligence captures real-time signals at millisecond speed and overwhelms subjective analysis with massive factor data, where do individual investors fit in the quant wave? We build a trading methodology for the AI era along three dimensions: factor mining — using AI to discover, backtest, and refine genuinely effective, high-quality factors, letting machines handle data breadth while human thinking controls logical depth; domain-partitioned management — different markets, assets, and cycles call for different factor systems, with per-domain modeling, validation, and attribution; strategy fusion — organically combining quantitative signals with subjective judgment, where AI automation handles execution and risk control while macro-logic variables set direction and timing, forming a human-machine collaborative decision loop.
AI-Guided Trading: Finding a Middle Way in Quant
KeynoteAbstract
A trading methodology for the AI era built on three pillars — factor mining, domain-partitioned management, and strategy fusion — combining quantitative signals with human judgment into a human-machine decision loop.