stockbee-20pct-study

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English

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Stockbee 20% Study

Stockbee 20%异动研究

Build a daily event study of US equities that moved +20% or -20% over a defined window. Convert large movers into structured study records, classify the catalyst and chart context, update forward outcomes, and summarize recurring patterns for research.

This skill is a research, model-book, and setup-fluency workflow. It does not generate buy/sell signals, place orders, or output broker execution instructions.

针对在指定时间窗口内涨跌幅达+20%或-20%的美股，构建每日事件研究。将大幅异动个股转化为结构化研究记录，对催化剂和图表背景进行分类，更新后续走势结果，并总结重复模式用于研究。

本技能是一套研究、模型库及行情熟悉度工作流，不生成买卖信号、下达订单或输出券商执行指令。

When to Use

使用场景

User wants to run a Stockbee-style daily 20% mover study
User asks which stocks moved +20% or -20% today, this week, or over a configurable lookback window
User wants to backfill historical 20% movers and study what happened next
User wants to identify continuation, reversal, exhaustion, or theme-cluster patterns
User wants to build a model book of explosive winners, major failures, and failed low-quality pops
User wants edge hints for downstream strategy research rather than immediate trade signals

用户希望运行Stockbee风格的每日20%异动研究
用户询问今日、本周或可配置回溯窗口内哪些个股涨跌幅达+20%或-20%
用户希望回填历史20%异动个股并研究后续走势
用户希望识别延续、反转、衰竭或主题集群模式
用户希望构建暴涨牛股、重大失败案例及劣质冲高回落案例的模型库
用户希望为下游策略研究获取优势提示，而非即时交易信号

Prerequisites

前置条件

Python 3.9+
FMP API key for live US universe scans, or offline OHLCV JSON via
```
--prices-json
```
Optional structured news/catalyst JSON for higher-quality catalyst classification
Recommended market regime artifact from
```
market-regime-daily
```
Recommended local state path:
```
state/stockbee/20pct_study_events.jsonl
```

Python 3.9+
用于实时美股全市场扫描的FMP API密钥，或通过
```
--prices-json
```
传入的离线OHLCV JSON文件
可选：结构化新闻/催化剂JSON文件，用于提升催化剂分类质量
推荐：来自
```
market-regime-daily
```
的市场状态文件
推荐本地状态路径：
```
state/stockbee/20pct_study_events.jsonl
```

Workflow

工作流

Step 1: Scan for 20% Movers

步骤1：扫描20%异动个股

Run after the US market close, or against the latest complete daily bar in an offline OHLCV file.

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
  --fmp-universe \
  --max-symbols 300 \
  --as-of 2026-06-28 \
  --lookback-days 5 \
  --min-abs-return-pct 20 \
  --min-price 5 \
  --min-dollar-volume 20000000 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

Use offline data instead of FMP:

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
  --prices-json data/us_daily_ohlcv.json \
  --as-of 2026-06-28 \
  --lookback-days 5 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

在美国股市收盘后运行，或针对离线OHLCV文件中的最新完整日K线运行。

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
  --fmp-universe \
  --max-symbols 300 \
  --as-of 2026-06-28 \
  --lookback-days 5 \
  --min-abs-return-pct 20 \
  --min-price 5 \
  --min-dollar-volume 20000000 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

使用离线数据替代FMP：

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
  --prices-json data/us_daily_ohlcv.json \
  --as-of 2026-06-28 \
  --lookback-days 5 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

Step 2: Enrich and Classify Events

步骤2：丰富并分类事件

Use structured catalyst data when available. The enrichment step is best-effort: if no news record is found, the event remains a price-only

NO_CLEAR_NEWS

study record.

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py enrich \
  --events-json reports/stockbee_20pct_events_YYYY-MM-DD_HHMMSS.json \
  --news-json data/catalysts_YYYY-MM-DD.json \
  --market-regime reports/market_regime_latest.json \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

如有结构化催化剂数据请使用。 enrichment步骤为尽力而为：若未找到新闻记录，该事件将仅保留价格数据，标记为

NO_CLEAR_NEWS

研究记录。

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py enrich \
  --events-json reports/stockbee_20pct_events_YYYY-MM-DD_HHMMSS.json \
  --news-json data/catalysts_YYYY-MM-DD.json \
  --market-regime reports/market_regime_latest.json \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

Step 3: Update Matured Forward Outcomes

步骤3：更新成熟的后续走势结果

Update 1-day, 3-day, 5-day, 10-day, and 20-day forward outcomes after enough future bars exist.

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py update-outcomes \
  --prices-json data/us_daily_ohlcv.json \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --horizons 1,3,5,10,20 \
  --output-dir reports/

The update records close return, MFE, MAE, direction-adjusted continuation return, and outcome tags.

在有足够未来K线数据后，更新1日、3日、5日、10日及20日的后续走势结果。

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py update-outcomes \
  --prices-json data/us_daily_ohlcv.json \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --horizons 1,3,5,10,20 \
  --output-dir reports/

更新记录包含收盘回报、MFE、MAE、方向调整后的延续回报及结果标签。

Step 4: Summarize Cohorts

步骤4：总结群组

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py summarize \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --group-by direction,catalyst.label,technical_context.pattern_label,technical_context.close_quality \
  --min-sample 10 \
  --output-dir reports/

Treat

rule_candidates

and exported edge hints as research prompts. Require representative chart review, sample-size thresholds, and out-of-sample validation before changing trade rules.

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py summarize \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --group-by direction,catalyst.label,technical_context.pattern_label,technical_context.close_quality \
  --min-sample 10 \
  --output-dir reports/

将

rule_candidates

和导出的优势提示视为研究线索。在更改交易规则前，需进行代表性图表审查、样本量阈值验证及样本外验证。

Step 5: Historical Backfill

步骤5：历史回填

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py backfill \
  --from 2020-01-01 \
  --to 2026-06-28 \
  --prices-json data/us_daily_ohlcv.json \
  --min-abs-return-pct 20 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

Backfill records are marked

CURRENT_UNIVERSE_BACKFILL_SURVIVORSHIP_BIAS

by default. Add

--survivorship-complete

only when the supplied OHLCV includes delisted symbols and historical universe coverage.

bash

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py backfill \
  --from 2020-01-01 \
  --to 2026-06-28 \
  --prices-json data/us_daily_ohlcv.json \
  --min-abs-return-pct 20 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

默认情况下，回填记录标记为

CURRENT_UNIVERSE_BACKFILL_SURVIVORSHIP_BIAS

。仅当提供的OHLCV包含已退市个股及历史全市场覆盖数据时，才可添加

--survivorship-complete

参数。

Output Format

输出格式

stockbee_20pct_events_YYYY-MM-DD_HHMMSS.json

— scan metadata and event records

stockbee_20pct_daily_report_YYYY-MM-DD_HHMMSS.md

— human-readable daily 20% study report

stockbee_20pct_enriched_YYYY-MM-DD_HHMMSS.json

— enriched event records

stockbee_20pct_outcome_update_YYYY-MM-DD_HHMMSS.json/md

— matured forward outcome update

stockbee_20pct_cohort_summary_YYYY-MM-DD_HHMMSS.json/md

— cohort statistics and rule candidates

```
stockbee_20pct_edge_hints_YYYY-MM-DD_HHMMSS.yaml
```
— edge-hint export for downstream research skills
```
state/stockbee/20pct_study_events.jsonl
```
— durable 20% mover model book

stockbee_20pct_events_YYYY-MM-DD_HHMMSS.json

— 扫描元数据及事件记录

stockbee_20pct_daily_report_YYYY-MM-DD_HHMMSS.md

— 人类可读的每日20%异动研究报告

stockbee_20pct_enriched_YYYY-MM-DD_HHMMSS.json

— 丰富后的事件记录

stockbee_20pct_outcome_update_YYYY-MM-DD_HHMMSS.json/md

— 成熟的后续走势结果更新

stockbee_20pct_cohort_summary_YYYY-MM-DD_HHMMSS.json/md

— 群组统计数据及规则候选

```
stockbee_20pct_edge_hints_YYYY-MM-DD_HHMMSS.yaml
```
— 用于下游研究技能的优势提示导出文件
```
state/stockbee/20pct_study_events.jsonl
```
— 持久化的20%异动个股模型库

Resources

资源

```
references/methodology.md
```
— 20% study methodology and review checklist
```
references/event_schema.md
```
— JSONL event record schema
```
references/catalyst_taxonomy.md
```
— catalyst and risk label definitions
```
references/scoring_system.md
```
— event quality and study priority scoring
```
references/cohort_mining_rules.md
```
— overfitting controls and sample-size rules
```
scripts/run_20pct_study.py
```
— CLI for scan, enrich, update-outcomes, summarize, and backfill

```
references/methodology.md
```
— 20%异动研究方法论及审查清单
```
references/event_schema.md
```
— JSONL事件记录 schema
```
references/catalyst_taxonomy.md
```
— 催化剂及风险标签定义
```
references/scoring_system.md
```
— 事件质量及研究优先级评分体系
```
references/cohort_mining_rules.md
```
— 过拟合控制及样本量规则
```
scripts/run_20pct_study.py
```
— 用于扫描、丰富、更新结果、总结及回填的CLI工具