Methodology overview
Our analysis framework is designed to reduce noise and improve decision-quality context for readers.
Core inputs we track
- Price structure: trend, volatility regimes, and support/resistance behavior.
- Market positioning: leverage, liquidations, and sentiment shifts where reliable data is available.
- Macro context: rates, inflation expectations, energy shocks, and risk-on/risk-off conditions.
- Policy and regulation: official updates from relevant agencies and policy bodies.
- On-chain and ecosystem signals: only when the metric clearly maps to a reader-relevant takeaway.
How we turn data into analysis
- Collect inputs from public sources and market datasets.
- Cross-check major claims across more than one source when possible.
- Separate facts, interpretation, and uncertainty.
- Write clear conclusions with practical implications for readers.
What we avoid
- Unsupported price predictions framed as certainties.
- Sensational headlines that overstate weak evidence.
- Copying public summaries without adding useful interpretation.
- Technical indicators presented without market context.
Reader-first interpretation principles
Every page should help readers answer at least one of these questions:
- What changed?
- Why does it matter now?
- What should I monitor next?
Limitations
Some crypto datasets are incomplete, delayed, or inconsistent across providers. Where reliability is uncertain, we note the limitation and avoid false precision.