Trading_skills scanner-pmcc

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.

install

source · Clone the upstream repo

git clone https://github.com/staskh/trading_skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/staskh/trading_skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/scanner-pmcc" ~/.claude/skills/staskh-trading-skills-scanner-pmcc && rm -rf "$T"

manifest: .claude/skills/scanner-pmcc/SKILL.md

source content

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

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 < 5%	+1
	LEAPS spread < 10%	+0.5
	Short spread < 10%	+1
	Short spread < 20%	+0.5
IV Level	25-50% (ideal)	+2
	20-60%	+1
Yield	Annual > 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, bid/ask, spread%, volume, OI
- ```
short
```
  - expiry, strike, delta, bid/ask, spread%, volume, OI
- ```
metrics
```
  - net_debit, short_yield%, annual_yield%, capital_required
- ```
score_breakdown
```
  - every scoring component as a
```
<name>_delta
```
  (float) +
```
<name>
```
  (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)

Examples

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

# 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

# Save results
uv run python scripts/scan.py AAPL,MSFT,GOOGL --output pmcc_results.json

Key Constraints

Short strike must be above LEAPS strike
Options with bid = 0 (illiquid) 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
```