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Agent2026

Conviction Collapse Detector

Exit before the crash. Detects when social hype diverges from price.

Live & ShippingStack: Python · Pandas · NumPy · Matplotlib
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What is the Conviction Collapse Detector?

The Conviction Collapse Detector is an AI signal detection tool that identifies when social sentiment around a token is declining while its price remains elevated — signaling an impending sentiment-driven collapse 1 to 7 days in advance.

The Problem It Solves

In crypto memecoin markets, price often decouples from social sentiment before a crash. The price stays high while the community's conviction quietly erodes. Traders who only watch price charts miss this divergence entirely. The Conviction Collapse Detector quantifies this hidden signal and alerts traders before the inevitable correction.

Key Features

AI-powered sentiment analysis that tracks social conversation trends. Divergence detection that identifies when price and sentiment are moving in opposite directions. Backtested with a 67.6% win rate, 2.29 Sharpe ratio, and 37.9% total return over 71 trades on 100 micro-cap tokens. Signals arrive 1 to 7 days before sentiment-driven price collapses.

Tech Stack

Built with Python for data analysis and machine learning. Pandas for data processing and manipulation. NumPy for numerical computations. Matplotlib for visualization of sentiment-price divergence charts. The model was backtested on 100 micro-cap tokens with 71 trade signals.

Frequently asked questions

What is conviction collapse in crypto?
Conviction collapse is when social sentiment and community belief in a token decline while the price remains artificially high, often preceding a significant price crash.
How accurate is the Conviction Collapse Detector?
Backtested with a 67.6% win rate, 2.29 Sharpe ratio, and 37.9% total return over 71 trades on a universe of 100 micro-cap tokens.
How early does the detector signal an exit?
The detector identifies conviction collapse 1 to 7 days before sentiment-driven price collapses, giving traders time to exit positions.

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