kanchi-dividend-review-monitor

To monitor a dividend portfolio for risk triggers and anomalies, this skill evaluates holdings using T1-T5 forced-review triggers across daily, weekly, and quarterly intervals to detect abnormal risk signals. It ensures human review before any action.

Dividend risk anomalies detectable with Python and pandas include dividend cuts, coverage deterioration, credit stress proxies, governance alerts, accounting alerts, and structural decline signals. These anomalies are categorized into OK, WARN, and REVIEW portfolio states.

Yes, dividend risk monitoring prevents automatic sell decisions by generating human review tickets for high-risk anomalies. It forces manual assessment of T1-T5 risk triggers before any portfolio action is taken.

You run a risk assessment workflow for high-dividend yielding stocks by executing review scripts and data validation rules against your portfolio data. The skill handles daily, weekly, and quarterly monitoring intervals to generate comprehensive risk reports.

The best way to manage portfolio holdings in OK, WARN, and REVIEW states is through automated state management scripts that track anomaly detection results. This categorization ensures high-risk holdings trigger immediate human review tickets.

Yes, you need numpy, json, and pandas for data validation to detect dividend cut signals accurately. The skill requires structured rule evaluation scripts to process portfolio data and identify coverage deterioration or structural decline.