stock-analyzer

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Original

English

🇨🇳

Translation

Chinese

A股股票分析

A-share Stock Analysis

概览

Overview

用这个 skill 管理本地 A 股观察池，并通过固定脚本生成结构化交易建议。标准流程不是只看单点实时数据，而是先看持仓，再结合历史行情和实时行情判断趋势与阶段，然后分析板块和板块内最强三只股票，最后输出带过程说明的操作建议。

Use this skill to manage local A-share watchlists and generate structured trading recommendations through fixed scripts. The standard process does not only look at single-point real-time data; instead, it first checks positions, then judges trends and phases by combining historical and real-time market data, analyzes sectors and the top three strongest stocks within the sector, and finally outputs operational recommendations with process explanations.

工作流

Workflow

1. 先判断请求类型

1. First Determine Request Type

把任务归到以下几类之一：

股票池维护：新增、移动、更新、查看或删除本地观察池与持仓池条目。
元数据补全：批量补股票简称或概念板块。
数据抓取：获取最新个股资料、板块龙头或风险事件。
完整分析：结合板块对标和风险提示，输出买入、观察、持有、减仓或卖出的建议。

Categorize the task into one of the following types:

Watchlist Maintenance: Add, move, update, view or delete entries in local watchlists and position pools.
Metadata Completion: Batch fill in stock abbreviations or concept sectors.
Data Fetching: Obtain the latest individual stock information, sector leaders or risk events.
Comprehensive Analysis: Output recommendations of Buy, Watch, Hold, Reduce Position or Sell combined with sector benchmarking and risk prompts.

2. 使用固定脚本

2. Use Fixed Scripts

优先运行本 skill 自带脚本，不要临时重写抓取逻辑：

```
scripts/watchlist_manager.py
```
- 维护
```
watchlist
```
  和
```
positions
```
- 支持
```
--auto-symbol
```
  根据名称反查股票代码
- 支持
```
--auto-name
```
  自动补股票简称
- 支持
```
enrich --auto-symbol --auto-name
```
  批量回填空的代码和名称字段
- 当用户修正概念板块时，直接写回
```
watchlists.json
```
  条目
```
scripts/fetch_stock_profile.py
```
- 抓取个股资料、行情快照、最新基本面、资金流和概念板块
```
scripts/fetch_sector_leaders.py
```
- 解析股票所属概念板块，并排序出最强三只对标股
```
scripts/fetch_risk_events.py
```
- 优先读取当日风险缓存；需要时联网刷新未来业绩披露、近期财报发布、减持公告和负面新闻
```
scripts/refresh_daily_risks.py
```
- 批量刷新股票池的每日风险缓存，并写入本地
```
data/risk_cache/
```
```
scripts/stock_score.py
```
- 汇总其他脚本数据，生成固定输出结构和最终建议

除非脚本确实缺少必需字段，否则不要发明另一套数据路径。脚本失败时，要明确说明失败点和缺失数据。

Prioritize running the scripts built into this skill, do not temporarily rewrite fetching logic:

```
scripts/watchlist_manager.py
```
- Maintain
```
watchlist
```
  and
```
positions
```
- Support
```
--auto-symbol
```
  to reverse-check stock codes based on names
- Support
```
--auto-name
```
  to automatically fill in stock abbreviations
- Support
```
enrich --auto-symbol --auto-name
```
  to batch fill empty code and name fields
- When users correct concept sectors, directly write back to entries in
```
watchlists.json
```
```
scripts/fetch_stock_profile.py
```
- Fetch individual stock information, market snapshots, latest fundamentals, capital flows and concept sectors
```
scripts/fetch_sector_leaders.py
```
- Parse the concept sectors that stocks belong to, and sort out the top three strongest benchmark stocks
```
scripts/fetch_risk_events.py
```
- Prioritize reading the day's risk cache; refresh future performance disclosures, recent financial report releases, share reduction announcements and negative news online when needed
```
scripts/refresh_daily_risks.py
```
- Batch refresh the daily risk cache of stock pools and write to local
```
data/risk_cache/
```
```
scripts/stock_score.py
```
- Summarize data from other scripts, generate fixed output structure and final recommendations

Do not invent another set of data paths unless the scripts are indeed missing required fields. When a script fails, clearly explain the failure point and missing data.

2.1 完整分析的固定执行顺序

2.1 Fixed Execution Sequence for Comprehensive Analysis

做持仓分析时，按下面的顺序执行，不要跳步：

查看当前持仓
查询目标股历史行情
查询目标股实时行情
结合历史行情和实时行情判断趋势、所处阶段、是否出现关键异动
查询所属板块数据，判断板块趋势
查询同板块最强三只个股
分析这三只个股的历史走势和今日表现，作为目标股的横向参考
如果财报刚披露或出现异常大跌，再辅助联网搜索是否存在“不及预期”信号
汇总分析过程，给出每个持仓的操作建议

When conducting position analysis, follow the sequence below without skipping steps:

Check current positions
Query historical market data of target stocks
Query real-time market data of target stocks
Judge trends, current phases and whether key abnormal movements occur by combining historical and real-time market data
Query sector data of the target stocks and judge sector trends
Query the top three strongest stocks in the same sector
Analyze the historical trends and today's performance of these three stocks as horizontal references for the target stocks
If financial reports have just been disclosed or there is an abnormal sharp drop, supplement with online searches to determine if there are signals of "falling short of expectations"
Summarize the analysis process and give operational recommendations for each position

2.2 风险事件的每日刷新

2.2 Daily Refresh of Risk Events

风险提示要尽量发生在事实以前，而不是只在大跌后补解释。

每个交易日至少刷新一次
```
positions
```
的风险缓存
风险缓存默认保存到
```
data/risk_cache/<symbol>.json
```
单票分析时优先读取当天缓存，缺失时再联网刷新
刷新的重点包括：
- 未来几天是否有业绩披露计划
- 近期是否刚发布年报、季报或业绩预告
- 近期是否有减持公告
- 近期是否有明显负面新闻

建议命令（以下路径相对于本 skill 根目录，即

skills/stock-analyzer/

）：

python scripts/refresh_daily_risks.py --bucket positions

python scripts/fetch_risk_events.py 002837 --refresh --pretty

Windows 任务计划程序可直接调用插件根目录下的
```
run_daily_refresh.bat
```
也可以运行插件根目录下的
```
register_daily_refresh_task.ps1
```
自动创建每天
```
08:30
```
/
```
12:30
```
的计划任务

注册命令示例：

powershell -NoProfile -ExecutionPolicy Bypass -File .\register_daily_refresh_task.ps1

如果同名任务已存在：

powershell -NoProfile -ExecutionPolicy Bypass -File .\register_daily_refresh_task.ps1 -Force

其中第 4 步必须显式判断这些内容：

当前趋势：上升、震荡、走弱或破位
所处阶段：启动、主升、加速、分歧、高位震荡、退潮等
关键异动：大涨、大跌、突然放量、放量滞涨、缩量反弹、跌破重要均线、突破阶段高点

如果出现关键异动，要把该信号单独拎出来解释，不能只给结论。

出现以下任一情形时，允许并建议辅助联网搜索：

财报披露日前后 3 个交易日
当日跌幅明显偏大，例如
```
<= -7%
```
放量大跌
用户明确提到“业绩不及预期”或“财报雷”

搜索目的不是替代行情数据，而是判断是否存在这些附加信息：

财报是否低于市场预期
是否出现业绩快报、业绩预告、年报或季报中的负面表述
市场主流解读是否把当日大跌归因于财报不及预期

Risk prompts should be given as early as possible, rather than only providing explanations after a sharp drop.

Refresh the risk cache of
```
positions
```
at least once per trading day
Risk cache is saved to
```
data/risk_cache/<symbol>.json
```
by default
Prioritize reading the day's cache when analyzing individual stocks; refresh online if the cache is missing
Key points of refresh include:
- Whether there is a performance disclosure plan in the next few days
- Whether annual reports, quarterly reports or performance forecasts have just been released recently
- Whether there are share reduction announcements recently
- Whether there are obvious negative news recently

Recommended commands (paths below are relative to the root directory of this skill, i.e.,

skills/stock-analyzer/

python scripts/refresh_daily_risks.py --bucket positions

python scripts/fetch_risk_events.py 002837 --refresh --pretty

Windows Task Scheduler can directly call
```
run_daily_refresh.bat
```
in the plugin root directory
You can also run
```
register_daily_refresh_task.ps1
```
in the plugin root directory to automatically create scheduled tasks at
```
08:30
```
/
```
12:30
```
every day

Example registration commands:

powershell -NoProfile -ExecutionPolicy Bypass -File .\register_daily_refresh_task.ps1

If a task with the same name already exists:

powershell -NoProfile -ExecutionPolicy Bypass -File .\register_daily_refresh_task.ps1 -Force

The following contents must be explicitly judged in Step 4:

Current trend: Upward, oscillating, weakening or breaking below support
Current phase: Initiation, main uptrend, acceleration, divergence, high-level oscillation, retreat, etc.
Key abnormal movements: Sharp rise, sharp drop, sudden volume increase, volume increase with stagnant price, volume decrease with rebound, breaking below important moving averages, breaking through stage highs

If key abnormal movements occur, this signal must be explained separately, not just giving conclusions.

Auxiliary online searches are allowed and recommended in any of the following situations:

Within 3 trading days before or after the financial report disclosure date
Obvious large drop on the day, e.g.,
```
<= -7%
```
Sharp drop with increased volume
Users explicitly mention "performance falling short of expectations" or "financial report thunder"

The purpose of searching is not to replace market data, but to judge whether there is such additional information:

Whether the financial report falls below market expectations
Whether there are negative statements in performance bulletins, performance forecasts, annual reports or quarterly reports
Whether the mainstream market interpretation attributes the day's sharp drop to financial reports falling short of expectations

3. 板块优先使用本地覆盖

3. Prioritize Local Coverage for Sectors

先检查

watchlists.json

条目中的

concept_sector

。如果没有填写，再由

fetch_stock_profile.py

或

fetch_sector_leaders.py

实时解析概念板块。用户显式修正概念板块时，要通过

watchlist_manager.py

直接写回条目本身。

First check the

concept_sector

field in entries of

watchlists.json

. If it is not filled, then parse the concept sector in real-time via

fetch_stock_profile.py

fetch_sector_leaders.py

. When users explicitly correct concept sectors, directly write back to the entries via

watchlist_manager.py

3.1 先补名称，再补板块

3.1 Fill in Names First, Then Sectors

如果用户只给了股票名称，没有给代码，先用

watchlist_manager.py

自动反查代码：

单条新增：
```
add 名称 --auto-symbol
```
批量补全：
```
enrich --auto-symbol
```

如果本地条目的

name

为空，优先用

watchlist_manager.py

自动回填股票简称：

单条新增或更新：
```
add --auto-name
```
或
```
update --auto-name
```
批量补全：
```
enrich --auto-name
```

如果代码、名称或板块实时补全失败：

明确说明失败来源，比如
```
quote_snapshot
```
或
```
stock_info
```
保留已有本地数据，不要用猜测值覆盖

If users only provide stock names without codes, first use

watchlist_manager.py

to automatically reverse-check codes:

Single entry addition:
```
add <name> --auto-symbol
```
Batch completion:
```
enrich --auto-symbol
```

If the

name

field of local entries is empty, prioritize using

watchlist_manager.py

to automatically fill in stock abbreviations:

Single entry addition or update:
```
add --auto-name
```
or
```
update --auto-name
```
Batch completion:
```
enrich --auto-name
```

If real-time completion of codes, names or sectors fails:

Clearly explain the source of failure, such as
```
quote_snapshot
```
or
```
stock_info
```
Retain existing local data, do not overwrite with guessed values

4. 按固定结构输出

4. Output in Fixed Structure

做完整分析时，始终按这个顺序输出：

标的基本信息
历史行情与趋势判断
实时行情与关键异动
所属概念板块与板块趋势
板块最强三只对标表
对标股历史走势与今日表现参考
技术面、基本面、风险面评分
建议动作：
```
买入
```
、
```
观察
```
、
```
持有
```
、
```
减仓
```
、
```
卖出
```
仓位建议
止损和止盈条件
风险事件提示
简短结论

使用

references/对标分析模板.md

中的模板。

When conducting comprehensive analysis, always output in the following order:

Basic information of the target stock
Historical market data and trend judgment
Real-time market data and key abnormal movements
Belonging concept sector and sector trend
Benchmark table of the top three strongest stocks in the sector
Reference of historical trends and today's performance of benchmark stocks
Scores of technical aspects, fundamental aspects and risk aspects
Recommended action:
```
Buy
```
,
```
Watch
```
,
```
Hold
```
,
```
Reduce Position
```
,
```
Sell
```
Position recommendation
Stop-loss and take-profit conditions
Risk event prompts
Brief conclusion

Use the template in

references/对标分析模板.md

5. 保守处理失败场景

5. Conservatively Handle Failure Scenarios

如果实时数据不完整或不可用：

说明是哪个脚本失败、缺了哪些字段。
如果用户已经提供数据，可以用，但要明确标记为用户提供。
板块对标或风险事件不可用时，不要给出强结论。
如果
```
concept_sector
```
未解析成功，必须明确提示用户是否手动补充板块信息后再重跑分析，不能静默跳过板块环节。

If real-time data is incomplete or unavailable:

Explain which script failed and which fields are missing.
If users have provided data, it can be used, but must be clearly marked as user-provided.
Do not give strong conclusions when sector benchmarking or risk events are unavailable.
If
```
concept_sector
```
fails to be parsed, must clearly prompt users whether to manually supplement sector information and re-run the analysis, cannot silently skip the sector link.

数据文件

Data Files

这个 skill 把本地状态存到

data/

下：

```
data/watchlists.json
```
：观察池和持仓池
```
data/risk_cache/
```
：按股票保存的每日风险扫描结果

JSON 结构见

references/股票池结构.md

。

This skill stores local status under

data/

```
data/watchlists.json
```
: Watchlists and position pools
```
data/risk_cache/
```
: Daily risk scan results saved by stock

See

references/股票池结构.md

for JSON structure.

参考资料

Reference Materials

按需加载这些文件：

```
references/数据来源.md
```
- 数据源优先级、实时数据假设、失败回退方式
```
references/股票池结构.md
```
- 本地 JSON 布局和字段说明
```
references/A股评分规则.md
```
- 技术面、基本面、风险面的评分规则
```
references/对标分析模板.md
```
- 固定对标表和结论结构

Load these files as needed:

```
references/数据来源.md
```
- Data source priorities, real-time data assumptions, and fallback methods for failures
```
references/股票池结构.md
```
- Local JSON layout and field descriptions
```
references/A股评分规则.md
```
- Scoring rules for technical aspects, fundamental aspects and risk aspects
```
references/对标分析模板.md
```
- Fixed benchmark table and conclusion structure