scanner-pmcc

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English

🇨🇳

Translation

Chinese

PMCC Scanner

PMCC扫描器

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

通过对期权链质量进行评分，找到最优的PMCC配置方案。

What is PMCC?

什么是PMCC？

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

买入深度ITM LEAPS看涨期权（delta ~0.80）+ 对应卖出短期OTM看涨期权（delta ~0.20），是备兑看涨期权的低成本替代方案。

Instructions

使用说明

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

bash

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

注意： 如果未安装
uv
或找不到
pyproject.toml
，请将下方所有命令中的
uv run python
替换为
python
。

bash

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

Arguments

参数说明

```
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

```
SYMBOLS
```
- 逗号分隔的股票代码，或来自看涨扫描器的JSON文件路径
```
--min-leaps-days
```
- LEAPS的最小到期天数（默认：270 = 9个月）
```
--leaps-delta
```
- 目标LEAPS delta值（默认：0.80）
```
--short-delta
```
- 目标卖出看涨期权delta值（默认：0.20）
```
--output
```
- 将结果保存到JSON文件

Scoring System (max ~11 points)

评分系统（满分约11分）

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

分类	条件	得分
Delta准确度	LEAPS在目标值±0.05范围内	+2
	LEAPS在目标值±0.10范围内	+1
	卖出期权在目标值±0.05范围内	+1
	卖出期权在目标值±0.10范围内	+0.5
流动性	LEAPS成交量+未平仓合约> 100	+1
	LEAPS成交量+未平仓合约> 20	+0.5
	卖出期权成交量+未平仓合约> 500	+1
	卖出期权成交量+未平仓合约> 100	+0.5
价差	LEAPS买卖价差 < 5%	+1
	LEAPS买卖价差 < 10%	+0.5
	卖出期权买卖价差 < 10%	+1
	卖出期权买卖价差 < 20%	+0.5
IV水平	25-50%（理想区间）	+2
	20-60%	+1
收益率	年化收益率> 50%	+2
	年化收益率> 30%	+1

Output

输出结果

Returns JSON with:

```
criteria
```
- Scan parameters used
```
results
```
- Array sorted by score:
- ```
symbol
```
  ,
```
price
```
  ,
```
iv_pct
```
  ,
```
pmcc_score
```
- ```
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
```
errors
```
- Symbols that failed (no options, insufficient data)

返回JSON格式数据，包含：

```
criteria
```
- 本次扫描使用的参数
```
results
```
- 按得分排序的结果数组：
- ```
symbol
```
  ,
```
price
```
  ,
```
iv_pct
```
  ,
```
pmcc_score
```
- ```
leaps
```
  - 到期日、行权价、delta、买价/卖价、价差百分比、成交量、未平仓合约
- ```
short
```
  - 到期日、行权价、delta、买价/卖价、价差百分比、成交量、未平仓合约
- ```
metrics
```
  - 净支出、短期收益率、年化收益率、所需资金
```
errors
```
- 扫描失败的股票代码（无期权数据、数据不足）

Examples

示例

bash

undefined

bash

undefined

Scan specific symbols

扫描指定股票代码

uv run python scripts/scan.py AAPL,MSFT,GOOGL,NVDA

Use output from bullish scanner

使用看涨扫描器的输出结果作为输入

uv run python scripts/scan.py bullish_results.json

Custom delta targets

自定义delta目标

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

Longer LEAPS (1 year minimum)

更长期限的LEAPS（最少1年到期）

uv run python scripts/scan.py AAPL,MSFT --min-leaps-days 365

Save results

保存结果

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

undefined

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

undefined

Key Constraints

核心限制

Short strike must be above LEAPS strike
Options with bid = 0 (illiquid) are skipped
Moderate IV (25-50%) scores highest

卖出期权行权价必须高于 LEAPS行权价
买价为0的期权（流动性不足）会被跳过
中等IV（25-50%）得分最高

Interpretation

结果解读

Score > 9: Excellent candidate
Score 7-9: Good candidate
Score 5-7: Acceptable with caveats
Score < 5: Poor liquidity or structure

得分> 9：极佳候选标的
得分7-9：良好候选标的
得分5-7：可接受，但存在限制
得分< 5：流动性差或结构不合理

Dependencies

依赖

```
numpy
```
```
pandas
```
```
scipy
```
```
yfinance
```

```
numpy
```
```
pandas
```
```
scipy
```
```
yfinance
```