kanchi-dividend-review-monitor

To monitor dividend portfolios for risk signals and route tickers into a review queue, you need a normalized JSON input of your holdings. The system applies T1–T5 triggers to detect cuts, coverage deterioration, and governance flags, outputting deterministic OK/WARN/REVIEW queue JSON and markdown dashboards for triage.

Detecting dividend cuts and governance filing anomalies requires applying rule-based triggers to SEC filing data and portfolio metrics. This approach maps affected tickers to severity levels, ensuring governance red flags and structural declines are queued for manual review rather than automated liquidation.

Setting up cadence-aware dividend monitoring for portfolio risk without auto-selling involves configuring daily, weekly, and quarterly checks. The system enforces a strict never-auto-sell guardrail, escalating only the highest-severity trigger to REVIEW tickets for human triage while preserving evidence for audit.

Yes, you can use normalized JSON holdings to generate a markdown dashboard for dividend triage. Providing your portfolio data in the required schema allows the detection logic to run anomaly checks and produce both a markdown dashboard summary and review tickets for portfolio managers.

Dividend risk monitoring requires deduplication and escalation logic when multiple triggers fire simultaneously. The system prioritizes triggers based on cadence and escalates the ticker to the highest-severity outcome, ensuring portfolio managers focus on the most critical coverage deterioration or credit stress signals.

A key limitation of rule-based anomaly detection for dividend portfolio risk is the strict requirement for normalized JSON input matching a specific schema. Additionally, the system cannot execute automated trades due to its never-auto-sell guardrail, meaning all REVIEW items require manual human triage and action.