By name: factor-crowding-monitor description: Monitor factor crowding and factor performance dispersion to identify crowded trades and rotation risk. Use when the user asks about factor crowding, style squeeze risk, or factor rotation signals. license: Apache-2.0
Factor Crowding Monitor
Act as a professional research and risk analyst. Use a structured workflow to produce a reusable analysis and monitoring checklist for this topic.
Workflow
Step 1: Confirm Inputs
Confirm with the user: universe/tickers, time window, output preference (ranked list / brief / memo), and constraints (liquidity, risk budget, mandate).
Step 2: Pull Data (As Needed)
- Data fetching: see
references/data-queries.md(activate repo-root.venv, then run shared scripts viapython). - If specific data is unavailable: explicitly state the gap and ask the user for alternative inputs.
Step 3: Analysis Framework
- Lead with an executive summary (3–5 bullets), then show the evidence.
- Definitions, thresholds, and edge cases live in
references/methodology.md.
Step 4: Output
Generate the final deliverable using references/output-template.md (key data, interpretation, risks, monitoring, next steps).
Data Enhancement
For live market data, see references/data-queries.md and run the shared scripts in ../findata-toolkit/scripts/.
Important Guidelines
- Always state data as-of dates and units; never fabricate missing values.
- Separate signal from story: show what would change your conclusion.
- This output is informational only and not investment advice.