GPT Alpha

A new research series on language-model-native alpha generation. The focus is not "ask ChatGPT for stock picks." The focus is building disciplined pipelines that convert unstructured information into ranked, risk-budgeted trading signals.

What Makes This Series Different

  • LLM-native research: Transcript parsing, event extraction, and narrative comparison instead of raw chatbot opinions
  • Portfolio-first design: Each article includes ranking logic, confidence scoring, and risk controls
  • Human-in-the-loop: Models generate candidate signals, humans approve deployments and kill-switches
  • Retail-realistic: Daily and weekly horizons, public filings, earnings calls, macro releases, and news you can actually access

Risk Disclosure

LLMs do not create edge by themselves. They hallucinate, over-compress nuance, and can produce unstable outputs if prompts or data feeds change. Treat them as research infrastructure, not autonomous PMs.

Any live strategy needs version control, replayable prompts, hard risk limits, transaction cost assumptions, and manual oversight.