sector-analyst

Sector rotation analysis fetches sector uptrend data from a public CSV, computes risk regime scores, and estimates market cycle phases without API keys. You run a local Python script to generate a structured report covering current situation and scenario planning.

Sector rotation analysis tracks cyclical versus defensive sector positioning to estimate market cycle phases. You need it when validating market-cycle positioning, identifying overbought or oversold signals, and consolidating sector-level data into a structured narrative for timing decisions.

No API key is required to fetch sector uptrend data for market cycle analysis. The process retrieves data directly from public CSV sources and runs locally using a Python script, eliminating external authentication barriers for sector rotation computations.

Yes, you can supply optional chart images to augment CSV-based sector rotation insights. The analysis integrates these visual inputs to provide supplementary industry-level context alongside computed rankings and cycle phase estimates.

The best way to estimate market cycle phases is computing risk regime scores and cyclical versus defensive positioning from public CSV sector data. This approach produces structured scenario planning and executive summaries without external API dependencies.

Using public CSV data for sector rotation analysis requires a local Python environment and depends on the CSV's data freshness and coverage. It does not support real-time streaming or automated API updates, meaning overbought and oversold signals rely on static snapshots.