stockbee-exhaustion-hammer-screener

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Stockbee Exhaustion Hammer Screener

Stockbee抛售衰竭锤子形态筛选工具

Screen US equities for Stockbee-style selling-exhaustion hammer 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风格的美股抛售衰竭锤子形态候选股。本skill是一个候选股生成及形态质量评估工作流，而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 exhaustion setup screening
User wants near-close hammer / long lower-wick reversal candidates
User wants to scan strong, liquid stocks that pulled back and may be seeing selling exhaustion
User wants undercut/reclaim candidates before the close or after the close
User provides a symbol list, universe file, or historical / provisional OHLCV JSON for screening

User wants candidate outputs to feed into

technical-analyst

position-sizer

trader-memory-core

, or

stockbee-setup-fluency-trainer

用户要求筛选Stockbee / Pradeep Bonde风格的衰竭形态
用户寻找临近收盘的锤子线/长下影线反转候选股
用户希望扫描回调后可能出现抛售衰竭的强势、高流动性股票
用户寻找收盘前或收盘后的下探回升（undercut/reclaim）候选股
用户提供了用于筛选的股票代码列表、股票池文件或历史/临时OHLCV JSON数据

用户希望将候选股输出结果导入

technical-analyst

、

position-sizer

、

trader-memory-core

或

stockbee-setup-fluency-trainer

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. For the intended near-close use case, the latest bar should be a provisional current-day bar captured near the close.

Optional

--profiles-json

can add quality metadata such as

marketCap

mutualFundHolders

institutionalHolders

, or

institutionalOwnershipPct

Run only after the market-regime workflow allows new swing risk, or mark output as manual-review-only.

用于实时股票池和历史OHLCV筛选的FMP API密钥：
bash
```
export FMP_API_KEY=your_api_key_here
```
可选无API路径：提供包含各股票每日OHLCK线数据的
```
--prices-json
```
文件。针对临近收盘的使用场景，最新K线应为临近收盘时捕获的当日临时K线。
可选的
```
--profiles-json
```
可添加质量元数据，如
```
marketCap
```
（市值）、
```
mutualFundHolders
```
（共同基金持仓）、
```
institutionalHolders
```
（机构持仓）或
```
institutionalOwnershipPct
```
（机构持仓比例）。
仅当市场环境工作流允许新的波段风险时运行，或标记输出为仅手动审核。

Workflow

工作流

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-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --fmp-universe \
  --max-symbols 300 \
  --market-gate allowed \
  --output-dir reports/

Mode B: Explicit symbols

bash

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --symbols APP ENPH NVDA TSLA \
  --market-gate allowed \
  --output-dir reports/

Mode C: Offline / near-close OHLCV JSON

bash

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --prices-json data/near_close_daily_ohlcv.json \
  --profiles-json data/quality_profiles.json \
  --market-gate allowed \
  --output-dir reports/

For a best-effort FMP near-close run, use quote override. This costs one additional quote call per symbol and depends on provider freshness:

bash

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --fmp-universe \
  --use-quote-latest \
  --max-api-calls 700 \
  --market-gate allowed \
  --output-dir reports/

使用以下三种模式之一：

模式A：FMP股票池扫描

bash

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --fmp-universe \
  --max-symbols 300 \
  --market-gate allowed \
  --output-dir reports/

模式B：指定股票代码

bash

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --symbols APP ENPH NVDA TSLA \
  --market-gate allowed \
  --output-dir reports/

模式C：离线/临近收盘OHLCV JSON

bash

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --prices-json data/near_close_daily_ohlcv.json \
  --profiles-json data/quality_profiles.json \
  --market-gate allowed \
  --output-dir reports/

如需通过FMP进行最佳效果的临近收盘扫描，可使用报价覆盖功能。这会为每个股票额外调用一次报价接口，且依赖于供应商的数据新鲜度：

bash

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --fmp-universe \
  --use-quote-latest \
  --max-api-calls 700 \
  --market-gate allowed \
  --output-dir reports/

Step 2: Run the Screening Pass

步骤2：运行筛选

The script detects these setup families:

Selling exhaustion hammer: long lower wick, small body, strong close-location, and recovery from the day low
Undercut/reclaim hammer: current low undercuts the prior short-term low and the near-close price reclaims that level
Prior momentum pullback: recent high formed within the configured lookback, followed by a controlled pullback rather than a long-term downtrend
High-quality / liquid context: price, volume, 20-day average dollar volume, market-cap metadata, and optional holder metadata

It then scores setup quality using:

Quality / liquidity
Prior momentum
Pullback and selling-exhaustion context
Hammer candle geometry
Risk distance to the day low plus buffer
Market gate alignment

脚本会检测以下形态类别：

抛售衰竭锤子线：长下影线、小实体、收盘位置强势、从当日低点回升
下探回升锤子线：当日低点跌破近期短期低点，且临近收盘价格收复该水平
前期动量回调：在设定的回溯期内形成近期高点，随后出现可控回调而非长期下跌趋势
高优质/高流动性背景：价格、成交量、20日平均成交额、市值元数据及可选的持仓元数据

随后通过以下维度对形态质量进行评分：

质量/流动性
前期动量
回调及抛售衰竭背景
锤子线K线形态
至当日低点加缓冲的风险距离
市场环境匹配度

Step 3: Review Output

步骤3：审核输出结果

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

Trigger type and all matched tags
Pullback depth from recent high and days since that high
Undercut/reclaim status and short-term prior low
Hammer geometry: lower wick, body, upper wick, close location, recovery from low
Volume ratios, average dollar volume, and quality metadata
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和Markdown报告。每个候选股会展示：

触发类型及所有匹配标签
从近期高点的回调幅度及距高点的天数
下探回升状态及近期短期低点
锤子线形态：下影线、实体、上影线、收盘位置、从低点的回升幅度
成交量比率、平均成交额及质量元数据
入场参考、止损参考及止损风险百分比
形态评分、评级、状态及拒绝原因
建议的后续操作

Step 4: Send Survivors to Trade Planning

步骤4：将合格候选股送入交易规划

Use the output conservatively:

A / A- candidates: validate chart manually, check earnings/news risk, then send to
```
position-sizer
```
B candidates: manual review or next-day hammer-high confirmation
Watch candidates: keep on watchlist / model book; wait for follow-through or tighter risk
Rejected candidates: retain for post-analysis, not for execution

谨慎使用输出结果：

A / A-级候选股：手动验证图表，检查财报/新闻风险，然后送入
```
position-sizer
```
B级候选股：手动审核或次日确认锤子线高点突破
观察候选股：加入观察列表/模拟账户；等待后续走势或更紧凑的风险区间
被拒绝候选股：保留用于事后分析，不用于实际交易

Output

输出

```
stockbee_exhaustion_hammer_YYYY-MM-DD_HHMMSS.json
```
- Structured candidate list, metadata, thresholds, score components, and rejects

stockbee_exhaustion_hammer_YYYY-MM-DD_HHMMSS.md

- Human-readable report grouped by rating/state

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

stockbee_exhaustion_hammer_YYYY-MM-DD_HHMMSS.md

- 按评级/状态分组的人类可读报告

Resources

资源

```
references/exhaustion_hammer_methodology.md
```
- Stockbee-style method summary and implementation boundaries
```
references/scoring_system.md
```
- Component weights, state thresholds, and failure filters
```
references/near_close_operations.md
```
- Near-close operational checklist and scheduling notes

references/exhaustion_hammer_methodology.md

- Stockbee风格方法总结及实现边界

```
references/scoring_system.md
```
- 组件权重、状态阈值及失败筛选规则
```
references/near_close_operations.md
```
- 临近收盘操作清单及调度说明