Alphaear Signal Tracker

This is a signal tracking pattern for monitoring how investment theses evolve as new market data comes in. It's built around an agentic workflow where you research a signal, analyze it to generate an initial investment thesis, then continuously update it as facts change. The core logic compares new information against your existing thesis to decide if confidence should go up, down, or if the signal is outright falsified. Right now it's not a standalone tool but rather a pattern extracted from a larger FinAgent implementation, so you'll be looking at the fin_agent.py source to see how it actually works. Useful if you're building systematic trading systems that need to maintain living, breathing investment theses rather than one-off analysis.