What makes 持仓诊断分析 notable?

972 downloads. Trust evidence available

How should 持仓诊断分析 be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

持仓诊断分析

实时诊断持仓，结合腾讯行情和技术指标，计算买卖评分及状态，给出操作建议和盈亏情况。 Skill: 持仓诊断分析 Owner: jayden-zhong Summary: 实时诊断持仓，结合腾讯行情和技术指标，计算买卖评分及状态，给出操作建议和盈亏情况。 Tags: latest:1.1.0 Version history: v1.1.0 | 2026-06-02T03:12:47.919Z | user 统一持仓数据源+卖出评分优化+持仓诊断增强 v1.0.0 | 2026-04-22T10:33:45.740Z | user v1.0.0: 持仓诊断系统，集成买入评分(M/A/R/V四维)+卖出评分+PASS/FAIL状态，支持腾讯API实时行情 Archive index: Archive v1.1.0: 16 files, 40439 bytes Files: analyze_holdings.py (5881b), check_holdings_detail.py (1963b), check_hold

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

972

Updated

Oct 11, 2026

Version

1.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 972 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 972 downloadsadoption · observed Oct 11, 2026
Latest release: 1.1.0release · observed Jun 2, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.1.0release
统一持仓数据源+卖出评分优化+持仓诊断增强
Observed Jun 2, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s176spm6d59vka8srp6r8yznrh83xc9z:holdings-analysis

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-jayden-zhong-holdings-analysis/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

12,971 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

# 持仓诊断与分析 (Holdings Analysis)

## 简介

持仓实时诊断工具。读取持仓列表，调用腾讯API获取实时行情，结合技术指标计算买入评分（M/A/R/V四维）和卖出评分，给出PASS/FAIL状态和操作信号。

---

## 核心文件

| 文件 | 用途 |
|------|------|
| `_holdings_std.py` | 持仓数据（成本价、买入日期） |
| `check_holdings_v4.py` | 主脚本：买入评分 + M/A/R/V + 卖出评分 + PASS/FAIL |
| `analyze_holdings.py` | v1版持仓分析 |
| `analyze_holdings_v2.py` | v2版持仓分析 |
| `check_holdings_detail.py` | 持仓详情（含趋势追踪） |
| `check_holdings_trend.py` | 持仓趋势追踪 |
| `run_holdings.py` | 快速运行入口 |

---

## 使用方法

```bash
# 标准持仓诊断
python check_holdings_v4.py

# 持仓趋势追踪
python check_holdings_trend.py
```

---

## 评分体系

### 买入评分（满分100）
- **PASS** 阈值：≥55分，进入趋势合格池
- **FAIL** 阈值：<55分，不符合趋势选股标准

### 卖出评分（满分100）
- 评分越高代表风险越大
- 参考阈值：≥25分考虑分批止盈，≥40分强烈建议离场

### M/A/R/V 四维
- **M**：动能（MACD方向）
- **A**：结构（均线排列）
- **R**：RSI强弱
- **V**：量价配合

---

## 输出格式

```
代码 名称   评分  M/A/R/V   卖出评分  状态  盈亏%
000682 东方电子  59  21/20/18/0  20  PASS  -1.1%
```

---

## 持仓数据维护

编辑 `_holdings_std.py` 中的 `HOLDINGS` 列表添加新持仓：
```python
HOLDINGS = [
    {"code": "000682", "buy_price": 13.20, "buy_date": "2026-03-30"},
    {"code": "300529", "buy_price": 18.60, "buy_date": "2026-04-22"},
]
```

---

## 免责声明

本系统仅供学习研究使用，不构成任何投资建议。

_meta.json

{
  "ownerId": "kn7b1c5p9t2asdhfd0wep064w583w8n4",
  "slug": "holdings-analysis",
  "version": "1.1.0",
  "publishedAt": 1780369967919
}

skill-card.md

## Description:

实时诊断股票持仓，调用腾讯行情数据并结合技术指标计算买入评分、卖出评分、PASS/FAIL 状态、操作信号和盈亏情况。

This skill is for research and development only.

## Publisher:

[jayden-zhong](https://clawhub.ai/user/jayden-zhong)

### License/Terms of Use:

MIT-0

## Use Case:

External users and developers use this skill to analyze a supplied stock holdings list, retrieve market data, compute technical scores, and identify hold, warn, or sell signals for portfolio review. The artifact states that the analysis is for learning and research and does not constitute investment advice.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The security summary says the skill runs code from hardcoded external .qclaw workspace paths and can load replacement modules outside the reviewed package.

Mitigation: Review before installing, run only when every referenced workspace file is trusted, and prefer a packaged version that reads holdings from explicit JSON or CSV inputs.

Risk: Stock analysis output can be mistaken for investment advice.

Mitigation: Use the output as informational portfolio review, confirm market data and decisions independently, and keep the non-advice disclaimer visible to users.

Risk: Live market-data requests can fail or return incomplete data, which may leave prices, scores, or signals missing.

Mitigation: Check generated JSON and text summaries for missing or zero-valued fields before relying on the analysis.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/jayden-zhong/skills/holdings-analysis)

## Skill Output:

**Output Type(s):** [text, JSON, shell commands, configuration, guidance]

**Output Format:** [Plain text and JSON analysis summaries with Python command examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May write local holdings analysis files and depends on live market data availability.]

## Skill Version(s):

1.1.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

holdings_result.json

{
  "date": "2026-05-07",
  "positions": [
    {
      "code": "000682",
      "name": "东方电子",
      "cur_price": 13.52,
      "buy_price": 13.2,
      "profit_pct": 2.42,
      "days": 22,
      "buy_score": 49,
      "macd_score": 11,
      "ma_score": 8,
      "rsi_score": 16,
      "vol_score": 14,
      "sell_score": 0,
      "action": "HOLD",
      "signals": [],
      "rsi": 62.7,
      "overall": "HOLD",
      "status": "FAIL"
    },
    {
      "code": "002202",
      "name": "金风科技",
      "cur_price": 28.44,
      "buy_price": 26.1,
      "profit_pct": 8.97,
      "days": 15,
      "buy_score": 57,
      "macd_score": 23,
      "ma_score": 16,
      "rsi_score": 18,
      "vol_score": 0,
      "sell_score": 0,
      "action": "HOLD",
      "signals": [],
      "rsi": 69.1,
      "overall": "HOLD",
      "status": "PASS"
    },
    {
      "code": "603868",
      "name": "飞科电器",
      "cur_price": 35.3,
      "buy_price": 34.73,
      "profit_pct": 1.64,
      "days": 9,
      "buy_score": 63,
      "macd_score": 17,
      "ma_score": 20,
      "rsi_score": 15,
      "vol_score": 11,
      "sell_score": 12,
      "action": "HOLD",
      "signals": [
        "RSI超买回落"
      ],
      "rsi": 73.9,
      "overall": "HOLD",
      "status": "PASS"
    }
  ],
  "summary": {
    "total_profit": 13.03,
    "avg_buy_score": 56.3,
    "avg_sell_score": 4.0,
    "sell_count": 0,
    "warning_count": 0
  }
}

skill.json

{
  "name": "holdings-analysis",
  "version": "1.0.0",
  "description": "持仓诊断与分析系统 - 买入评分+卖出评分+PASS/FAIL状态，支持持仓趋势追踪和分批止盈止损",
  "author": "ts30",
  "tags": ["股票", "持仓", "技术分析", "风险管理"],
  "main": "check_holdings_v4.py",
  "scripts": {
    "analyze": "python check_holdings_v4.py",
    "detail": "python check_holdings_detail.py"
  },
  "dependencies": {
    "python": ">=3.8",
    "pandas": ">=1.0.0",
    "numpy": ">=1.0.0"
  },
  "config": {
    "buy_threshold": 55,
    "sell_threshold": 25
  }
}

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