Coriva

One of the core tasks in quantitative investing is mining alpha factors — finding signals that predict asset returns. The traditional approach relies on researchers manually constructing factor expressions, or using automated search methods like Genetic Programming (GP) to brute-force combinations in the operator space. The former depends on human experience and intuition — low efficiency but high interpretability. The latter is efficient but produces deeply nested operator expressions that are nearly impossible for researchers to interpret. AlphaGPT (paper) brings large language models into the factor mining pipeline, using an LLM as the factor “generator.” The follow-up work, AlphaGPT 2.0 (paper), further introduces a human-in-the-loop closed cycle. ...

The worst part about quantitative trading isn’t having a bad strategy. It’s not knowing whether your strategy is good or bad. A strategy with 30% annualized returns sounds great, until you realize the max drawdown was 60% — you’d never have held through it. A Sharpe ratio of 2.0 looks impressive, but if it’s propped up by a few windfall trades in extreme market conditions, the Sortino ratio will tell a very different story. Metrics aren’t decorations for backtesting reports. They’re the tools that help you decide whether a strategy is worth putting real money behind. ...