vcp-screener

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

VCP Screener - Minervini Volatility Contraction Pattern

VCP筛选工具 - Minervini波动率收缩模式

Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP), identifying Stage 2 uptrend stocks with contracting volatility near breakout pivot points.

筛选标普500指数中符合Mark Minervini波动率收缩模式（VCP）的股票，识别处于第二阶段上涨趋势、在突破枢轴点附近波动率收缩的股票。

When to Use

使用场景

User asks for VCP screening or Minervini-style setups
User wants to find tight base / volatility contraction patterns
User requests Stage 2 momentum stock scanning
User asks for breakout candidates with defined risk

用户要求进行VCP筛选或Minervini风格交易形态分析
用户想要寻找窄幅整理/波动率收缩形态
用户需要第二阶段动量股扫描
用户询问具有明确风险的突破候选股

Prerequisites

前置条件

FMP API key (set
```
FMP_API_KEY
```
environment variable or pass
```
--api-key
```
)
Free tier (250 calls/day) is sufficient for default screening (top 100 candidates)
Paid tier recommended for full S&P 500 screening (
```
--full-sp500
```
)

FMP API密钥（设置
```
FMP_API_KEY
```
环境变量或传递
```
--api-key
```
参数）
免费 tier（每日250次调用）足以满足默认筛选需求（前100个候选股）
若要进行完整的标普500筛选，推荐使用付费 tier（
```
--full-sp500
```
参数）

Workflow

工作流程

Step 1: Prepare and Execute Screening

步骤1：准备并执行筛选

Run the VCP screener script:

bash

undefined

运行VCP筛选脚本：

bash

undefined

Default: S&P 500, top 100 candidates

默认：标普500指数，前100个候选股

python3 skills/vcp-screener/scripts/screen_vcp.py --output-dir skills/vcp-screener/scripts

Custom universe

自定义股票池

python3 skills/vcp-screener/scripts/screen_vcp.py --universe AAPL NVDA MSFT AMZN META --output-dir skills/vcp-screener/scripts

Full S&P 500 (paid API tier)

完整标普500筛选（需付费API tier）

python3 skills/vcp-screener/scripts/screen_vcp.py --full-sp500 --output-dir skills/vcp-screener/scripts

undefined

python3 skills/vcp-screener/scripts/screen_vcp.py --full-sp500 --output-dir skills/vcp-screener/scripts

undefined

Advanced Tuning (for backtesting)

高级调优（用于回测）

Adjust VCP detection parameters for research and backtesting:

bash

python3 skills/vcp-screener/scripts/screen_vcp.py \
  --min-contractions 3 \
  --t1-depth-min 12.0 \
  --breakout-volume-ratio 2.0 \
  --trend-min-score 90 \
  --atr-multiplier 1.5 \
  --output-dir reports/

Parameter	Default	Range	Effect
`--min-contractions`	2	2-4	Higher = fewer but higher-quality patterns
`--t1-depth-min`	8.0%	1-50	Higher = excludes shallow first corrections
`--breakout-volume-ratio`	1.5x	0.5-10	Higher = stricter volume confirmation
`--trend-min-score`	85	0-100	Higher = stricter Stage 2 filter
`--atr-multiplier`	1.5	0.5-5	Lower = more sensitive swing detection
`--contraction-ratio`	0.75	0.1-1	Lower = requires tighter contractions
`--min-contraction-days`	5	1-30	Higher = longer minimum contraction
`--lookback-days`	120	30-365	Longer = finds older patterns

调整VCP检测参数以用于研究和回测：

bash

python3 skills/vcp-screener/scripts/screen_vcp.py \
  --min-contractions 3 \
  --t1-depth-min 12.0 \
  --breakout-volume-ratio 2.0 \
  --trend-min-score 90 \
  --atr-multiplier 1.5 \
  --output-dir reports/

参数	默认值	范围	作用
`--min-contractions`	2	2-4	值越高，筛选出的模式越少但质量越高
`--t1-depth-min`	8.0%	1-50	值越高，排除回调幅度较浅的初始整理
`--breakout-volume-ratio`	1.5x	0.5-10	值越高，对成交量确认的要求越严格
`--trend-min-score`	85	0-100	值越高，对第二阶段趋势的过滤越严格
`--atr-multiplier`	1.5	0.5-5	值越低，对摆动的检测越敏感
`--contraction-ratio`	0.75	0.1-1	值越低，要求整理的幅度越窄
`--min-contraction-days`	5	1-30	值越高，要求整理的最短时长越长
`--lookback-days`	120	30-365	值越长，可发现更早形成的模式

Step 2: Review Results

步骤2：查看结果

Read the generated JSON and Markdown reports
Load
```
references/vcp_methodology.md
```
for pattern interpretation context
Load
```
references/scoring_system.md
```
for score threshold guidance

阅读生成的JSON和Markdown报告
查看
```
references/vcp_methodology.md
```
获取形态解读的相关背景
查看
```
references/scoring_system.md
```
获取评分阈值指导

Step 3: Present Analysis

步骤3：展示分析结果

For each top candidate, present:

VCP composite score and rating
Contraction details (T1/T2/T3 depths and ratios)
Trade setup: pivot price, stop-loss, risk percentage
Volume dry-up ratio
Relative strength rank

对于每个优质候选股，展示：

VCP综合评分及评级
收缩细节（T1/T2/T3回调幅度及比率）
交易设置：枢轴价格、止损位、风险百分比
成交量枯竭比率
相对强度排名

Step 4: Provide Actionable Guidance

步骤4：提供可执行的指导建议

Based on ratings:

Textbook VCP (90+): Buy at pivot with aggressive sizing
Strong VCP (80-89): Buy at pivot with standard sizing
Good VCP (70-79): Buy on volume confirmation above pivot
Developing (60-69): Add to watchlist, wait for tighter contraction
Weak/No VCP (<60): Monitor only or skip

根据评级：

标准VCP（90分及以上）： 在枢轴价格买入，可加大仓位
优质VCP（80-89分）： 在枢轴价格买入，标准仓位
良好VCP（70-79分）： 在成交量确认突破枢轴价格后买入
形成中（60-69分）： 加入观察列表，等待更窄的整理
较弱/非VCP（60分以下）： 仅观察或跳过

3-Phase Pipeline

三阶段流程

Pre-Filter - Quote-based screening (price, volume, 52w position) ~101 API calls
Trend Template - 7-point Stage 2 filter with 260-day histories ~100 API calls
VCP Detection - Pattern analysis, scoring, report generation (no additional API calls)

预筛选 - 基于报价的筛选（价格、成交量、52周价格位置）~101次API调用
趋势模板 - 7项指标的第二阶段过滤，使用260天历史数据 ~100次API调用
VCP检测 - 形态分析、评分、报告生成（无需额外API调用）

Output

输出结果

```
vcp_screener_YYYY-MM-DD_HHMMSS.json
```
- Structured results
```
vcp_screener_YYYY-MM-DD_HHMMSS.md
```
- Human-readable report

```
vcp_screener_YYYY-MM-DD_HHMMSS.json
```
- 结构化结果
```
vcp_screener_YYYY-MM-DD_HHMMSS.md
```
- 易读的报告

Resources

参考资源

```
references/vcp_methodology.md
```
- VCP theory and Trend Template explanation
```
references/scoring_system.md
```
- Scoring thresholds and component weights
```
references/fmp_api_endpoints.md
```
- API endpoints and rate limits

```
references/vcp_methodology.md
```
- VCP理论及趋势模板说明
```
references/scoring_system.md
```
- 评分阈值及各指标权重
```
references/fmp_api_endpoints.md
```
- API端点及调用限制