Kanchi Dividend Sop

Kanchi Dividend Sop

Overview

Implement Kanchi's 5-step method as a deterministic workflow for US dividend investing. Prioritize safety and repeatability over aggressive yield chasing.

When to Use

Use this skill when the user needs:

• Kanchi-style dividend stock selection adapted for US equities.
• A repeatable screening and pullback-entry process instead of ad-hoc picks.
• One-page underwriting memos with explicit invalidation conditions.
• A handoff package for monitoring and tax/account-location workflows.

Prerequisites

API Key Setup

The entry signal script requires FMP API access:

export FMP_API_KEY=your_api_key_here

Input Sources

Prepare one of the following inputs before running the workflow:

1. Output from skills/value-dividend-screener/scripts/screen_dividend_stocks.py.
2. Output from skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth.py.
3. User-provided ticker list (broker export or manual list).

Expected JSON Input Format

When using --input, provide JSON in one of these formats:

{
  "profile": "balanced",
  "candidates": [
    {"ticker": "JNJ", "bucket": "core"},
    {"ticker": "O", "bucket": "satellite"}
  ]
}

Or simplified:

{
  "tickers": ["JNJ", "PG", "KO"]
}

For deterministic artifact generation, provide tickers to:

python3 skills/kanchi-dividend-sop/scripts/build_sop_plan.py \
  --tickers "JNJ,PG,KO" \
  --output-dir reports/

For Step 5 entry timing artifacts:

python3 skills/kanchi-dividend-sop/scripts/build_entry_signals.py \
  --tickers "JNJ,PG,KO" \
  --alpha-pp 0.5 \
  --output-dir reports/

Workflow

1) Define mandate before screening

Collect and lock the parameters first:

• Objective: current cash income vs dividend growth.
• Max positions and position-size cap.
• Allowed instruments: stock only, or include REIT/BDC/ETF.
• Preferred account type context: taxable vs IRA-like accounts.

Load references/default-thresholds.md and apply baseline settings unless the user overrides.

2) Build the investable universe

Start with a quality-biased universe:

• Core bucket: long dividend growth names (for example, Dividend Aristocrats style quality set).
• Satellite bucket: higher-yield sectors (utilities, telecom, REITs) in a separate risk bucket.

Use explicit source priority for ticker collection:

1. skills/value-dividend-screener/scripts/screen_dividend_stocks.py output (FMP/FINVIZ).
2. skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth_rsi.py output.
3. User-provided broker export or manual ticker list when APIs are unavailable.

Return a ticker list grouped by bucket before moving forward.

3) Apply Kanchi Step 1 (yield filter with trap flag)

Primary rule:

• forward_dividend_yield >= 3.5%

Trap controls:

• Flag extreme yield (>= 8%) as deep-dive-required.
• Flag sudden jump in payout as potential special dividend artifact.

Output:

• PASS or FAIL per ticker.
• deep-dive-required flag for potential yield traps.

4) Apply Kanchi Step 2 (growth and safety)

Require:

• Revenue and EPS trend positive on multi-year horizon.
• Dividend trend non-declining over the review period.

Add safety checks:

• Payout ratio and FCF payout ratio in reasonable range.
• Debt burden and interest coverage not deteriorating.

When trend is mixed but not broken, classify as HOLD-FOR-REVIEW instead of hard reject.

5) Apply Kanchi Step 3 (valuation) with US sector mapping

Use references/valuation-and-one-off-checks.md and apply sector-specific valuation logic:

• Financials: PER x PBR can remain primary.
• REITs: use P/FFO or P/AFFO instead of plain P/E.
• Asset-light sectors: combine forward P/E, P/FCF, and historical range.

Always report which valuation method was used for each ticker.

6) Apply Kanchi Step 4 (one-off event filter)

Reject or downgrade names where recent profits rely on one-time effects:

• Asset sale gains, litigation settlement, tax effect spikes.
• Margin spike unsupported by sales trend.
• Repeated "one-time/non-recurring" adjustments.

Record one-line evidence for each FAIL to keep auditability.

7) Apply Kanchi Step 5 (buy on weakness with rules)

Set entry triggers mechanically:

• Yield trigger: current yield above 5y average yield + alpha (default +0.5pp).
• Valuation trigger: target multiple reached (P/E, P/FFO, or P/FCF).

Execution pattern:

• Split orders: 40% -> 30% -> 30%.
• Require one-sentence sanity check before each add: "thesis intact vs structural break".

8) Produce standardized outputs

Always produce three artifacts:

1. Screening table (PASS, HOLD-FOR-REVIEW, FAIL with evidence).
2. One-page stock memo (use references/stock-note-template.md).
3. Limit-order plan with split sizing and invalidation condition.

Output

Return and/or generate:

1. SOP screening summary in markdown.
2. Underwriting memo set based on

references/stock-note-template.md.

1. Optional plan artifact file generated by

skills/kanchi-dividend-sop/scripts/build_sop_plan.py in reports/.

1. Optional Step 5 entry-signal artifacts generated by

skills/kanchi-dividend-sop/scripts/build_entry_signals.py in reports/.

Cadence

Use this minimum rhythm:

• Weekly (15 min): check dividend and business-news changes only.
• Monthly (30 min): rerun screening and refresh order levels.
• Quarterly (60 min): deep safety review using latest filings/earnings.

Multi-Skill Handoff

Run this skill first, then hand off outputs:

1. To kanchi-dividend-review-monitor for daily/weekly/quarterly anomaly detection.
2. To kanchi-dividend-us-tax-accounting for account-location and tax classification planning.

Guardrails

• Do not issue blind buy calls without Step 4 and safety checks.
• Do not treat high yield as value before validating coverage quality.
• Keep assumptions explicit when data is missing.

Resources

• skills/kanchi-dividend-sop/scripts/build_sop_plan.py: deterministic SOP plan generator.
• skills/kanchi-dividend-sop/scripts/tests/test_build_sop_plan.py: tests for plan generation.
• skills/kanchi-dividend-sop/scripts/build_entry_signals.py: Step 5 target-buy calculator (5y avg yield + alpha).
• skills/kanchi-dividend-sop/scripts/tests/test_build_entry_signals.py: tests for signal calculations.
• references/default-thresholds.md: baseline thresholds and profile tuning.
• references/valuation-and-one-off-checks.md: sector valuation map and one-off checklist.
• references/stock-note-template.md: one-page memo template for each candidate.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: xonevn-ai
• Source: xonevn-ai/xone-trading-skills
• License: MIT
• Homepage: https://xone.vn

Install and usage instructions live in the source repository linked above.