What AI actually does for a discretionary trader

Where models genuinely help, where they quietly hurt, and why backtest-shaped confidence is the real risk.

The honest version, from someone who trades manually and uses software to support it rather than replace it.

Where it genuinely helps

  • Screening and ranking. Reducing thousands of names to a short list.
  • Summarising. Concalls, filings and long documents.
  • Bookkeeping. Journalling, tagging, computing what your rules would have done.

Where it quietly hurts

  • Backtest-shaped confidence. It is trivially easy to find a rule that worked on the past. Out-of-sample discipline is the whole game.
  • Non-stationarity. Markets change regime. A model trained on one regime is confidently wrong in the next.
  • Signal laundering. A model output feels objective. It is still a hypothesis with a number attached.

Your own take goes here — what you actually use, and one thing that didn’t work.

See the trades behind the writing

Every index-options position I hold, live, with running P&L.

Track record