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Scanner Pmcc

skill-staskh-trading-skills-scanner-pmcc · by staskh

Scan stocks for Poor Man's Covered Call (PMCC) suitability. Analyzes LEAPS and short call options for delta, liquidity, spread, IV, yield, trend direction, and earnings proximity. Use when user asks about PMCC candidates, diagonal spreads, or LEAPS strategies.

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Install

$ agentstack add skill-staskh-trading-skills-scanner-pmcc

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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1mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

PMCC Scanner

Finds optimal Poor Man's Covered Call setups by scoring symbols on option chain quality.

What is PMCC?

Buy deep ITM LEAPS call (delta ~0.80) + Sell short-term OTM call (delta ~0.20) against it. Cheaper alternative to covered calls.

Instructions

> Note: If uv is not installed or pyproject.toml is not found, replace uv run python with python in all commands below.

uv run python scripts/scan.py SYMBOLS [options]

Arguments

  • SYMBOLS - Comma-separated tickers or path to JSON file from bullish scanner
  • --min-leaps-days - Minimum LEAPS expiration in days (default: 270 = 9 months)
  • --leaps-delta - Target LEAPS delta (default: 0.80)
  • --short-delta - Target short call delta (default: 0.20)
  • --output - Save results to JSON file (use this; Claude generates the report from the JSON)
  • --report - Save auto-generated markdown to file (programmatic fallback only — prefer Claude-generated reports)

Scoring System (max possible: 14, range: -4 to 14)

| Category | Condition | Points | |----------|-----------|--------| | Delta Accuracy | LEAPS within ±0.05 | +2 | | | LEAPS within ±0.10 | +1 | | | Short within ±0.05 | +1 | | | Short within ±0.10 | +0.5 | | Liquidity | LEAPS vol+OI > 100 | +1 | | | LEAPS vol+OI > 20 | +0.5 | | | Short vol+OI > 500 | +1 | | | Short vol+OI > 100 | +0.5 | | Spread | LEAPS spread 50% | +2 | | | Annual > 30% | +1 | | Trend | Price > SMA50 | +1 / -1 | | | RSI > 50 | +0.5 / -0.5 | | | MACD > signal | +0.5 / -0.5 | | Earnings | Next earnings > 45 days | +1.0 | | | Earnings within 45 days | -1.0 | | | Earnings within short expiry | -2.0 |

Output

Returns JSON with:

  • criteria - Scan parameters used
  • results - Array sorted by score:
  • symbol, price, iv_pct, pmcc_score, max_possible_score (always 14)
  • leaps - expiry, strike, delta, iv (calculated from bid/ask), last_price, bid/ask, spread%, volume, OI
  • short - expiry, strike, delta, iv (calculated from bid/ask), last_price, bid/ask, spread%, volume, OI
  • earnings_date - next earnings date (YYYY-MM-DD) or null
  • metrics - netdebit, shortyield%, annualyield%, capitalrequired
  • score_breakdown - every scoring component as a _delta (float) + `` (explanation string) pair:
  • Base: leaps_delta, short_delta, leaps_liquidity, short_liquidity, leaps_spread, short_spread, iv, yield
  • Trend: trend_delta, trend (per-indicator dict)
  • Earnings: earnings_delta, earnings
  • All _delta values sum to pmcc_score
  • errors - Symbols that failed (no options, insufficient data)

Report Generation

When the user asks for a report, a written analysis, or a saved document:

  1. Run the scanner with --output to capture JSON data:

``bash uv run python scripts/scan.py SYMBOLS --output sandbox/PMCC_Scan_YYYY-MM-DD_HHmm.json ``

  1. Read the JSON output.
  1. Generate the markdown report yourself using the template defined in templates/markdown-template.md. Do not use the --report flag — that produces mechanical string output. Claude-generated reports include real analysis, contextual warnings, and trader-relevant narrative.
  1. Save the generated markdown to sandbox/PMCC_Scan_YYYY-MM-DD_HHmm.md (match the JSON timestamp).
  1. Display the full report to the user.

Examples

# Scan specific symbols
uv run python scripts/scan.py AAPL,MSFT,GOOGL,NVDA

# Scan and save JSON for report generation
uv run python scripts/scan.py AAPL,MSFT,GOOGL --output sandbox/PMCC_Scan_2026-01-15_1430.json

# Use output from bullish scanner
uv run python scripts/scan.py bullish_results.json

# Custom delta targets
uv run python scripts/scan.py AAPL,MSFT --leaps-delta 0.70 --short-delta 0.15

# Longer LEAPS (1 year minimum)
uv run python scripts/scan.py AAPL,MSFT --min-leaps-days 365

IV Calculation

IV is always computed from market price data via Black-Scholes, never taken from Yahoo Finance's impliedVolatility column:

  • During trading hours: IV derived from bid/ask mid price
  • Off-hours (bid=ask=0): IV derived from last price, using the option's last trade timestamp as the pricing moment (not current wall-clock time)

This applies to both compute_atm_iv (used for scanner baseline IV) and per-option delta calculations.

Key Constraints

  • Short strike must be above LEAPS strike
  • Options with bid = 0 and no last price are skipped
  • Moderate IV (25-50%) scores highest

Interpretation

  • Score > 12: Excellent candidate (strong structure + bullish trend + clear earnings runway)
  • Score 10-12: Good candidate
  • Score 6-10: Acceptable with caveats
  • Score < 6: Poor structure, bearish trend, or earnings risk
  • max_possible_score is always 14 — use pmcc_score / max_possible_score to gauge how close a candidate is to perfect

Dependencies

  • numpy
  • pandas
  • scipy
  • yfinance

Timezone

All timestamps and time-based calculations must use the America/New_York timezone. All JSON output must include generated_at (NY time string) and data_delay fields.

Source & license

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

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

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Versions

  • v0.1.0 Imported from the upstream source.