Curious about LLM prediction performance, I built this tool to create an auditable, transparent record of LLM (GPT-Models) stock forecasts. I know LLMs aren’t designed for predictions, but I think a huge amount of data can give more insights into their capabilities.
The core idea is methodology transparency:
Baseline: The app ignores the price at the request time. It uses the Actual Closing Price (T_0) as the non-negotiable baseline for all subsequent sequential trend checks.
Tracking: Accuracy is measured against both the Overall Trend (start to finish) and sequential Micro-Trends (step-by-step). Test a tracking run and critique the methodology:
https://glassballai.sumotrainer.com/main
Seeking Feedback: Does using the Closing Price as the sequential baseline feel robust for this type of analysis?
Any other key input parameter (like specific news volume or market sentiment) I should be tracking?
(Note: This is an MVP on a temporary URL and is not financial advice.)
submitted by /u/aufgeblobt
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