The Alpha Factory Illusion: Why Your Factor Mining Agent Only Looks Like It Is Learning

Everyone in the quant community is talking about factor mining skills right now. You hand the agent a research direction, walk away, and a few days later a batch of factor expressions is waiting for you. It sounds like the most restful workflow ever invented.

Then you actually run one. After three or four days of continuous iteration, something strange shows up in the logs. The type of error keeps changing. The total volume of errors barely moves.

In the early rounds the failures are directional. Momentum, reversal, volatility, the whole canon of classical paradigms marches through one after another, backtest IC means hover near the floor, and the pass rate stays miserable. In the middle rounds the failures become operational. Field names are wrong, data types do not match, and the backtest engine simply throws and returns. In the late rounds the system starts making strategic adjustments on its own, running a correlation self check before submission and voluntarily discarding any candidate that sits too close to the existing pool.

Read that arc quickly and it looks like maturation. Rookie mistakes, then mechanical competence, then strategic self discipline. That is roughly the career path of a human researcher, compressed into seventy two hours.

The analogy is seductive. The systems we have actually run give a different answer.