stockbee-momentum-burst-screener

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Original

English

🇨🇳

Translation

Chinese

Stockbee Momentum Burst Screener

Stockbee动量突破筛选工具

Screen US equities for Stockbee-style short-term Momentum Burst candidates. The skill is a candidate-generation and setup-quality workflow, not a signal service or an auto-execution system.

筛选符合Stockbee风格的美股短期动量突破候选股。本工具用于生成候选股及评估形态质量，并非信号服务或自动执行系统。

When to Use

使用场景

User asks for Stockbee / Pradeep Bonde style Momentum Burst screening
User wants 4% breakout, dollar breakout, or range expansion candidates
User asks for short-term 3-5 day swing momentum setups
User wants to review whether a daily breakout has A/B/C setup quality
User provides a symbol list, universe file, or historical OHLCV JSON for screening

User wants candidate outputs to feed into

technical-analyst

position-sizer

, or

trader-memory-core

用户要求进行Stockbee / Pradeep Bonde风格的动量突破筛选
用户需要4%突破、美元突破或区间扩张的候选股
用户询问短期3-5日波段动量形态
用户希望复盘日线突破是否具备A/B/C级形态质量
用户提供标的列表、股票池文件或历史OHLCV JSON用于筛选

用户希望将候选股输出结果导入

technical-analyst

、

position-sizer

或

trader-memory-core

Prerequisites

前置条件

FMP API key for live universe and historical OHLCV screening:
bash
```
export FMP_API_KEY=your_api_key_here
```
Optional no-API path: provide
```
--prices-json
```
containing daily OHLCV bars by symbol.
Run only after the market-regime workflow allows new swing risk, or mark output as manual-review-only.

用于实时股票池及历史OHLCV筛选的FMP API密钥：
bash
```
export FMP_API_KEY=your_api_key_here
```
可选无API路径：提供包含各标的日线OHLCV数据的
```
--prices-json
```
文件
仅在市场状态流程允许新波段风险时运行，或标记输出为仅手动复盘

Workflow

工作流程

Step 1: Choose Input Mode

步骤1：选择输入模式

Use one of three modes:

Mode A: FMP universe scan

bash

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --fmp-universe \
  --max-symbols 300 \
  --output-dir reports/

Mode B: Explicit symbols

bash

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --symbols NVDA SMCI PLTR TSLA \
  --output-dir reports/

Mode C: Offline OHLCV JSON

bash

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --prices-json data/daily_ohlcv.json \
  --output-dir reports/

可使用以下三种模式之一：

模式A：FMP股票池扫描

bash

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --fmp-universe \
  --max-symbols 300 \
  --output-dir reports/

模式B：指定标的

bash

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --symbols NVDA SMCI PLTR TSLA \
  --output-dir reports/

模式C：离线OHLCV JSON

bash

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --prices-json data/daily_ohlcv.json \
  --output-dir reports/

Step 2: Run the Screening Pass

步骤2：运行筛选

The script detects these trigger families:

4% Breakout:
```
close / previous_close >= 1.04
```
, volume above previous day, and volume above the liquidity floor
Dollar Breakout:
```
close - open >= 0.90
```
, volume above the liquidity floor
Range Expansion: current daily range exceeds the prior three daily ranges while the prior day was not already extended

It then scores setup quality using:

Trigger strength
Volume expansion
Prior base / range contraction quality
Close location near the high of day
Risk distance to the trigger-day low
Failure filters such as prior 3-day run-up or recent 4% breakdown
Market gate alignment

脚本会识别以下触发类型：

4%突破：
```
close / previous_close >= 1.04
```
，成交量高于前一日且达到流动性阈值
美元突破：
```
close - open >= 0.90
```
，成交量达到流动性阈值
区间扩张： 当前日线区间超过前三个交易日的区间，且前一日未出现区间扩张

随后通过以下维度评估形态质量：

触发强度
成交量放大情况
前期底部/区间收缩质量
收盘位置接近当日高点的程度
到触发日低点的风险距离
失败过滤条件（如前3日上涨或近期4%破位）
市场状态匹配度

Step 3: Review Output

步骤3：查看输出结果

Read the generated JSON and Markdown reports. For each candidate, present:

Trigger type and all matched trigger tags
Day gain, dollar gain, volume ratio, and close-location percentage
Prior base length and base width
Entry reference, stop reference, and risk percentage to stop
Setup score, rating, state, and reject reasons
Suggested downstream action

阅读生成的JSON及Markdown报告。每份候选股报告包含：

触发类型及所有匹配的触发标签
当日涨幅、美元涨幅、成交量比率及收盘位置百分比
前期底部长度及宽度
入场参考价、止损参考价及止损风险百分比
形态评分、评级、状态及拒绝原因
建议后续操作

Step 4: Send Survivors to Trade Planning

步骤4：将合格标的送入交易规划环节

Use the output conservatively:

A / A- candidates: send to
```
technical-analyst
```
for manual chart validation, then
```
position-sizer
```
B candidates: watchlist or smaller-risk review only
Watch-only candidates: keep in model book; do not plan a trade unless chart review upgrades the setup
Rejected candidates: retain for post-analysis, not for execution

谨慎使用输出结果：

A / A-级候选股： 送入
```
technical-analyst
```
进行手动图表验证，再导入
```
position-sizer
```
B级候选股： 仅加入观察列表或进行低风险复盘
仅观察候选股： 保留在模型库中，除非图表复盘升级形态质量，否则不规划交易
被拒绝候选股： 保留用于事后分析，不用于交易执行

Output

输出文件

```
stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json
```
- Structured candidate list, metadata, thresholds, score components, and rejects
```
stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.md
```
- Human-readable report grouped by rating/state

```
stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json
```
- 结构化候选股列表、元数据、阈值、评分组件及被拒绝标的

stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.md

- 按评级/状态分组的易读性报告

Resources

参考资源

```
references/momentum_burst_methodology.md
```
- Stockbee-style method summary and implementation boundaries
```
references/scoring_system.md
```
- Component weights, state thresholds, and failure filters
```
references/entry_exit_rules.md
```
- Entry reference, stop, sizing handoff, and exit template

```
references/momentum_burst_methodology.md
```
- Stockbee风格方法总结及实现边界
```
references/scoring_system.md
```
- 组件权重、状态阈值及失败过滤条件
```
references/entry_exit_rules.md
```
- 入场参考价、止损、仓位分配及离场模板