pod-thesis-validate

Thesis validation works by extracting falsifiable assumptions from your existing investment thesis and checking each against current market data. It assigns per-assumption verdicts of intact, broken, unknown, or N/A to identify what still holds.

You check trading assumptions by re-running validation against real-time signals to detect aged-out assumptions, moved price targets, or passed catalysts. It generates a dated validation document with per-assumption verdicts to show what broke and what remains intact.

Yes, assumption checking can be automated using Alpaca for live market data and Plaid for holdings. The tool uses these live data pulls to verify named signals within your thesis and optionally performs web checks for catalyst confirmation.

Yes, web checks for catalyst confirmation require explicit opt-in per assumption. Live market data pulls via Alpaca and Plaid proceed automatically, but external web verification for catalysts demands individual user permission for each checked claim.

You maintain audit trails by generating a dated validation document under the thesis folder and updating the thesis README with the latest validation summary. The system logs timeline events and learnings to inform future session preambles.

Limitations include returning unknown verdicts when data is unavailable and requiring explicit opt-in for web-based catalyst confirmation. The system relies on Alpaca and Plaid data coverage, meaning assumptions lacking named signals cannot be automatically verified.