Methodology

BullScore is designed to organize messy market information into a repeatable research frame.

The workflow uses public-source research, market context, and model-generated summaries to help users understand why a market feels bullish, bearish, or neutral. The output is meant to speed up orientation, not eliminate uncertainty.

01Public sources and narrative synthesis

02Structured factor thinking instead of one-line hot takes

03Explicit limits around timing, data quality, and model error

What BullScore looks at

BullScore looks at price context, business momentum, narrative pressure, positioning, and macro backdrop together. Different assets emphasize different components, but the workflow always tries to connect story, market structure, and risk.

Valuation or on-chain structure when the market is debating price support
Fundamentals or ecosystem health when business quality matters most
Sentiment and positioning when crowding or narrative extremes are driving the tape
Macro context when rates, liquidity, or policy dominate attention

How the output should be read

BullScore is best used as a map, not as an oracle. The useful question is not 'should I blindly buy or sell?' but 'what is the market rewarding, what is it fading, and what deserves deeper work right now?'

Known limitations

Market data can be delayed, source quality can vary, and model-generated summaries can miss context or overweight the loudest narrative. BullScore should always be paired with your own verification and risk management.

Why citations matter

A useful research tool should make it easier to inspect the underlying sources, not harder. BullScore aims to point users toward the evidence trail so they can verify the reasoning before acting.

See the workflow on real public pages

Start with public stock or crypto pages to see how BullScore turns framework into concrete market explanations.

Open stock research pages Contact support