stockbee-setup-fluency-trainer

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Original

English

🇨🇳

Translation

Chinese

Stockbee Setup Fluency Trainer

Stockbee交易模型熟练度训练工具

Build and maintain a model book for Stockbee-style Momentum Burst setups. This skill turns daily screener candidates into structured study records, updates them after the 3-day and 5-day windows mature, and summarizes which setup features are working or failing.

构建并维护Stockbee风格动量突破交易模型的手册。本工具可将每日筛选器候选标的转化为结构化的学习记录，在3日和5日周期成熟后更新记录，并总结哪些交易模型特征有效或失效。

When to Use

使用场景

User wants to study Stockbee Momentum Burst setups systematically
User asks to build a model book from
```
stockbee-momentum-burst-screener
```
output
User wants to review failed candidates, missed trades, or A/B setup quality
User wants 3-day / 5-day forward returns, MFE, MAE, and stop-hit outcomes
User wants to improve setup recognition before increasing position size
User asks which Stockbee tags should be promoted, downgraded, or filtered

用户想要系统性研究Stockbee动量突破交易模型
用户要求从
```
stockbee-momentum-burst-screener
```
输出结果构建交易模型手册
用户想要复盘失败候选标的、错过的交易或A/B类交易模型质量
用户需要3日/5日远期收益、MFE、MAE以及止损触发结果
用户想要在增加仓位前提升交易模型识别能力
用户询问哪些Stockbee标签应被优先使用、降级或过滤

Prerequisites

前置条件

Python 3.10+
A
```
stockbee-momentum-burst-screener
```
JSON report, or compatible candidate JSON
Optional: FMP API key for outcome updates when offline OHLCV JSON is not supplied
Recommended local state path:
```
state/stockbee/model_book.jsonl
```

Python 3.10+
```
stockbee-momentum-burst-screener
```
生成的JSON报告，或兼容的候选标的JSON文件
可选：当未提供离线OHLCV JSON文件时，用于更新结果的FMP API密钥
推荐本地状态路径：
```
state/stockbee/model_book.jsonl
```

Workflow

工作流程

Step 1: Ingest Momentum Burst Candidates

步骤1：导入动量突破候选标的

Run after the Stockbee Momentum Burst screener has produced a JSON report.

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py ingest \
  --screener-json reports/stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json \
  --model-book state/stockbee/model_book.jsonl \
  --output-dir reports/

Use

--include-rejects

when intentionally building a negative-example set. Otherwise rejected candidates are skipped.

在Stockbee动量突破筛选器生成JSON报告后运行。

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py ingest \
  --screener-json reports/stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json \
  --model-book state/stockbee/model_book.jsonl \
  --output-dir reports/

当有意构建负样本集时，使用

--include-rejects

参数。否则会跳过被拒绝的候选标的。

Step 2: Update 3-Day and 5-Day Outcomes

步骤2：更新3日和5日结果

Use FMP:

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
  --model-book state/stockbee/model_book.jsonl \
  --horizons 3,5 \
  --output-dir reports/

Use offline OHLCV JSON:

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
  --model-book state/stockbee/model_book.jsonl \
  --prices-json data/daily_ohlcv.json \
  --horizons 3,5 \
  --output-dir reports/

The update step records:

Forward close return for each horizon
MFE and MAE over each horizon
Stop-hit status and first stop-hit date

Outcome tags such as

STRONG_WINNER

WORKED

FAILED_STOP

FAILED_FADE

CHOPPY_FAILURE

, or

NEUTRAL

使用FMP：

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
  --model-book state/stockbee/model_book.jsonl \
  --horizons 3,5 \
  --output-dir reports/

使用离线OHLCV JSON文件：

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
  --model-book state/stockbee/model_book.jsonl \
  --prices-json data/daily_ohlcv.json \
  --horizons 3,5 \
  --output-dir reports/

更新步骤会记录：

每个周期的远期收盘收益
每个周期内的MFE和MAE
止损触发状态及首次触发日期

结果标签，例如

STRONG_WINNER

、

WORKED

、

FAILED_STOP

、

FAILED_FADE

、

CHOPPY_FAILURE

或

NEUTRAL

Step 3: Summarize Cohorts

步骤3：总结群组数据

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py summarize \
  --model-book state/stockbee/model_book.jsonl \
  --group-by rating,primary_trigger,setup_tags \
  --min-sample 5 \
  --output-dir reports/

Review the generated Markdown and JSON reports. Treat

rule_candidates

as evidence prompts, not automatic rule changes.

bash

python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py summarize \
  --model-book state/stockbee/model_book.jsonl \
  --group-by rating,primary_trigger,setup_tags \
  --min-sample 5 \
  --output-dir reports/

查看生成的Markdown和JSON报告。将

rule_candidates

视为证据提示，而非自动规则变更。

Step 4: Convert Evidence Into Practice

步骤4：将证据转化为实践

For cohorts with enough examples:

Promote tags with high win rate, positive 5-day expectancy, and acceptable average MAE
Downgrade or filter tags with weak 5-day expectancy, frequent stop hits, or repeated fade failures
Inspect representative charts manually before changing trade rules
Log accepted lessons in
```
trader-memory-core
```
or the monthly review process

对于拥有足够样本的群组：

优先使用高胜率、5日预期收益为正且平均MAE可接受的标签
降级或过滤5日预期收益不佳、频繁触发止损或反复出现回调失败的标签
在修改交易规则前手动检查代表性图表
在
```
trader-memory-core
```
或月度复盘流程中记录已采纳的经验

Model Book Fields

模型手册字段

Each JSONL record includes:

record_id

symbol

setup_date

primary_trigger

```
rating
```
,
```
setup_score
```
,
```
setup_tags
```

entry_reference

stop_reference

risk_pct_to_stop

```
human_label
```
,
```
human_decision
```
,
```
human_notes
```
```
outcomes.3d
```
and
```
outcomes.5d
```
```
overall_outcome
```
,
```
matured
```
,
```
raw_candidate
```

每条JSONL记录包含：

record_id

、

symbol

、

setup_date

、

primary_trigger

```
rating
```
、
```
setup_score
```
、
```
setup_tags
```

entry_reference

、

stop_reference

、

risk_pct_to_stop

```
human_label
```
、
```
human_decision
```
、
```
human_notes
```
```
outcomes.3d
```
和
```
outcomes.5d
```
```
overall_outcome
```
、
```
matured
```
、
```
raw_candidate
```

Interpretation Rules

解读规则

```
STRONG_WINNER
```
: 5-day close return >= 8% or MFE >= 12%, with no stop hit
```
WORKED
```
: 5-day close return >= 4% or MFE >= 6%, with no stop hit
```
FAILED_STOP
```
: Stop was touched within the horizon
```
FAILED_FADE
```
: Forward return <= -2% without a recorded stop hit
```
CHOPPY_FAILURE
```
: Adverse excursion was large and forward progress was poor
```
NEUTRAL
```
: No decisive follow-through or failure
```
PENDING
```
: Not enough future bars yet

```
STRONG_WINNER
```
：5日收盘收益≥8%或MFE≥12%，且未触发止损
```
WORKED
```
：5日收盘收益≥4%或MFE≥6%，且未触发止损
```
FAILED_STOP
```
：在周期内触发止损
```
FAILED_FADE
```
：远期收益≤-2%且未记录止损触发
```
CHOPPY_FAILURE
```
：反向波动幅度大且远期进展不佳
```
NEUTRAL
```
：无明确的后续上涨或下跌
```
PENDING
```
：未来K线数据不足

Output

输出内容

```
state/stockbee/model_book.jsonl
```
- Durable setup model book

stockbee_setup_fluency_ingest_YYYY-MM-DD_HHMMSS.json/md

stockbee_setup_fluency_update_YYYY-MM-DD_HHMMSS.json/md

stockbee_setup_fluency_summary_YYYY-MM-DD_HHMMSS.json/md

```
state/stockbee/model_book.jsonl
```
- 持久化的交易模型手册

stockbee_setup_fluency_ingest_YYYY-MM-DD_HHMMSS.json/md

stockbee_setup_fluency_update_YYYY-MM-DD_HHMMSS.json/md

stockbee_setup_fluency_summary_YYYY-MM-DD_HHMMSS.json/md

Resources

参考资源

```
references/model_book_schema.md
```
- JSONL schema and lifecycle states
```
references/outcome_tags.md
```
- Outcome classification and tag definitions
```
references/review_workflow.md
```
- Daily, 3-day, 5-day, and monthly review routine

```
references/model_book_schema.md
```
- JSONL schema和生命周期状态
```
references/outcome_tags.md
```
- 结果分类及标签定义
```
references/review_workflow.md
```
- 每日、3日、5日及月度复盘流程