Trading Agents 简化版

多智能体股票交易信号分析框架。当用户提供股票代码要求分析投资建议时触发。输入一只股票代码，通过7个SubAgent协作分析（基本面研究员、市场信息研究员、新闻研究员、社交媒体研究员、看涨分析师、看跌分析师、投资组合经理），输出买入/卖出/持有建议及决策依据。触发词："分析这只股票"、"给我投资建议"、"这只票值得...

Security Scan

The skill's code, runtime instructions, and requested environment variables are coherent with a multi-agent stock research tool; nothing in the bundle looks disproportionate or clearly malicious, though report-writing and redaction are a potential operational privacy risk to review before use.

Purpose & Capability

The skill claims to run a multi-agent trading analysis and legitimately requires a Tushare Pro token for market/fundamental data; an optional Brave Search API key for web search is reasonable. The provided Python scripts call Tushare and compute indicators consistent with the described functionality.

Instruction Scope

Runtime instructions restrict credential access to TUSHARE_TOKEN and BRAVE_API_KEY and require redaction of any credentials from subagent outputs. The skill writes complete reports (including all subagent outputs and debate history) to ~/.openclaw/workspace/memory/reports/*.md — this is expected for the stated purpose but creates a privacy surface: if any subagent input inadvertently contains sensitive strings, the flow depends on correct redaction rules. Review and test the redaction logic before using with any sensitive inputs.

Install Mechanism

The software uses standard PyPI packages (tushare, pandas, numpy) which is proportionate. One minor inconsistency: the top metadata stated 'No install spec' while skill.json and SKILL.md include pip install instructions; this is not harmful but should be noted. No downloads from unknown hosts or archive extracts are present.

Credentials

Only TUSHARE_TOKEN is required (BRAVE_API_KEY optional). No unrelated credentials or system paths are requested. The included scripts read TUSHARE_TOKEN from the environment and use it for Tushare API calls — proportional to the stated purpose.

Persistence & Privilege

The skill is not always-enabled and does not request elevated platform privileges. It writes reports to a localized workspace path (documented). Autonomous invocation is allowed by default (platform behavior) but is not combined with any elevated credential requests or 'always: true' — no excessive persistence privileges observed.

Assessment

This skill appears to be what it says: a multi-agent stock research framework that uses Tushare and optional web search and writes a markdown report locally. Before installing or running it: 1) Keep your TUSHARE_TOKEN and BRAVE_API_KEY only in environment variables (do not paste them into chat or reports). 2) Inspect and test the redaction behavior: the skill saves full subagent outputs into a final report, so if any subagent output contains credential-like strings the redaction must reliably remove them — validate with non-production tokens first. 3) Run in an isolated environment or sandbox (virtualenv/container) because it will pip-install packages and make network calls to api.tushare.pro (and web search if configured). 4) Verify the pip install step (tushare, pandas, numpy) matches your platform policy and is acceptable. 5) Understand that the tool fetches external data (Tushare, web search) — using it transmits queries to those services. 6) For privacy/regulatory reasons, avoid feeding any personal or secret information into prompts/subagents. If you need higher assurance, review the SKILL.md and included scripts line-by-line or run them with a dummy Tushare token to observe actual behavior.

TradingAgents - Stock Trading Signal Analysis Assistant

Multi-agent collaborative stock trading signal analysis framework with two-round debate mechanism, inspired by the open-source TradingAgents project.

Environment Requirements

Required Environment Variables

Variable	Required	Description
TUSHARE_TOKEN	✅	Tushare Pro API Token, get at: https://tushare.pro/register
BRAVE_API_KEY	⚪	Brave Search API Key (optional, for news search enhancement)

Python Dependencies

Install required packages before using this skill:

pip install tushare>=1.3.0 pandas>=1.5.0 numpy>=1.21.0

Secure Configuration

Security Warning: Do NOT paste your API tokens in chat messages.

Set Environment Variables

Before running this skill, ensure the following environment variables are set:

export TUSHARE_TOKEN=your_token_here
export BRAVE_API_KEY=your_brave_key_here  # optional

Avoid:

• Do NOT add tokens to ~/.bashrc, ~/.zshrc, or other shell config files
• Do NOT include tokens in reports or SubAgent communications
• Do NOT paste tokens in chat messages

Side Effects

This skill will:

• Write reports to ~/.openclaw/workspace/memory/reports/trading-agents-*.md
• Connect to Tushare Pro API (api.tushare.pro)
• Use web_search for news and sentiment data

No data is transmitted externally beyond these API calls.

Security Rules

Credential Protection

1. Only read TUSHARE_TOKEN and BRAVE_API_KEY from environment variables
2. Never read other configuration files (e.g., openclaw.json, .bashrc, etc.)
3. Never include API keys or tokens in:
   • SubAgent inter-communications
   • Generated reports
   • Any output files

Report Content Rules

All generated reports and SubAgent communications MUST NOT contain:

• API keys (TUSHARE_TOKEN, BRAVE_API_KEY)
• Passwords or secrets
• Any credential values

If a SubAgent receives or generates content containing sensitive credentials, it must redact them before passing to other agents or writing to files.

Workflow (4-Layer Architecture)

Input: Stock Ticker (e.g., 300750.SZ)
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 1: Information Gathering (Parallel)          │
│  ├─ SubAgent 1: Fundamental Analyst                 │
│  ├─ SubAgent 2: Market Analyst                      │
│  ├─ SubAgent 3: News Analyst                        │
│  └─ SubAgent 4: Social Media Analyst                │
└─────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 2: Opinion Formation (Parallel)              │
│  ├─ SubAgent 5: Bull Researcher (Initial Report)    │
│  └─ SubAgent 6: Bear Researcher (Initial Report)    │
└─────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 2.5: Two-Round Debate (Sequential)           │
│  ├─ Round 1:                                        │
│  │   ├─ Bear Researcher → Bull's Arguments          │
│  │   └─ Bull Researcher → Bear's Arguments          │
│  └─ Round 2:                                        │
│      ├─ Bear Researcher → Bull's Rebuttals          │
│      └─ Bull Researcher → Bear's Rebuttals          │
└─────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 3: Final Decision                            │
│  └─ SubAgent 7: Research Manager                    │
│      (Synthesizes all reports + debate history)     │
└─────────────────────────────────────────────────────┘
  ↓
Output: BUY/SELL/HOLD + Investment Plan (Markdown)

Step 7: Export to Markdown Report

Research Manager outputs the complete report to a markdown file:

~/.openclaw/workspace/memory/reports/trading-agents-[stock_code]-[timestamp].md

The markdown file contains:

• Part I: Final Investment Decision
• Part II: Layer 1 Reports (4 reports)
• Part III: Layer 2 Reports (2 initial reports)
• Part IV: Debate History (4 responses)
• Part V: Appendix

---

Execution Steps

Step 1: Parse Stock Ticker

Extract stock ticker from user input, format to standard:

• A-shares: 600519.SH, 300750.SZ
• HK stocks: 00700.HK
• US stocks: AAPL

Step 2: Execute Layer 1 SubAgents in Parallel

Use sessions_spawn to launch in parallel 4 SubAgents:

SubAgent	Task	Output
Fundamental Analyst	Fundamental analysis	Fundamental report
Market Analyst	Market technical analysis	Market analysis report
News Analyst	News analysis	News summary report
Social Media Analyst	Social sentiment analysis	Sentiment report

Step 3: Collect Layer 1 Reports

Use subagents(action=list) to check all SubAgent completion status, then collect report content.

Step 4: Execute Layer 2 SubAgents in Parallel (Initial Reports)

Pass Layer 1 reports to Layer 2, launch in parallel 2 SubAgents:

SubAgent	Task	Input	Output
Bull Researcher	Initial bull report	Layer 1 4 reports	Bull report (initial)
Bear Researcher	Initial bear report	Layer 1 4 reports	Bear report (initial)

Step 5: Two-Round Debate

After Layer 2 completes, initiate two rounds of debate:

Round 1:

1. Bear Researcher receives Bull's initial report → Refutes bull arguments
2. Bull Researcher receives Bear's initial report → Refutes bear arguments

Round 2:

1. Bear Researcher receives Bull's Round 1 rebuttals → Counter-rebuttal
2. Bull Researcher receives Bear's Round 1 rebuttals → Counter-rebuttal

Debate Rules:

• Use specific data and reasoning to refute opponent's arguments
• Cite sources from all available reports
• Apply conversational debate style
• Reflect on past experience and lessons learned
• Each response should directly address opponent's specific points
• Never include API keys or tokens in debate content

Step 6: Execute Final Decision SubAgent

Launch Research Manager with ALL inputs:

• Layer 1: 4 reports (Fundamental, Market, News, Social)
• Layer 2: 2 initial reports (Bull, Bear)
• Layer 2.5: 4 debate responses (Round 1 + Round 2)

Research Manager synthesizes all information and makes final investment recommendation.

---

SubAgent Details

SubAgent 1: Fundamental Analyst

Data Sources:

• Tushare Pro API (financial data, valuation metrics)
• Company annual/quarterly reports

Analysis Dimensions:

1. Financial statement analysis
2. Valuation metrics (PE/PB/PS percentile)
3. Growth metrics
4. Company basic information
5. Shareholder structure

Detailed Prompt: See references/fundamental-analyst.md

---

SubAgent 2: Market Analyst

Data Sources:

• Tushare Pro API (daily data)
• Technical indicator calculations

Analysis Dimensions:

1. Price trend (MA system)
2. Technical indicators (MACD, RSI, KDJ, BOLL)
3. Volume analysis
4. Capital flow
5. Market anomaly signals

Detailed Prompt: See references/market-analyst.md

---

SubAgent 3: News Analyst

Data Sources:

• Web Search (Brave Search API)
• Financial media websites

Analysis Dimensions:

1. Company news
2. Industry news
3. Management dynamics
4. Macro news

Detailed Prompt: See references/news-analyst.md

---

SubAgent 4: Social Media Analyst

Data Sources:

• Web Search
• Xueqiu, Guba, Weibo, Zhihu, etc.

Analysis Dimensions:

1. Sentiment heat
2. Investor sentiment (bullish/bearish ratio)
3. Controversy focus
4. KOL opinions

Detailed Prompt: See references/social-analyst.md

---

SubAgent 5: Bull Researcher

Role: Bull analyst advocating for investment

Tasks:

1. Generate initial bull report from Layer 1 data
2. Refute bear's arguments in debate rounds
3. Provide counter-rebuttals in Round 2

Debate Style:

• Conversational tone addressing opponent directly
• Use specific data to counter opponent's points
• Cite sources from all available reports
• Reflect on past experience and mistakes
• Never include API keys in output

Detailed Prompt: See references/bull-researcher.md

---

SubAgent 6: Bear Researcher

Role: Bear analyst advocating against investment

Tasks:

1. Generate initial bear report from Layer 1 data
2. Refute bull's arguments in debate rounds
3. Provide counter-rebuttals in Round 2

Debate Style:

• Conversational tone addressing opponent directly
• Use specific data to expose weaknesses
• Cite sources from all available reports
• Reflect on past experience and mistakes
• Never include API keys in output

Detailed Prompt: See references/bear-researcher.md

---

SubAgent 7: Research Manager

Role: Portfolio manager making final decision

Input (9 items total):

1. Fundamental Analysis Report
2. Market Analysis Report
3. News Analysis Report
4. Social Media Sentiment Report
5. Bull Initial Report
6. Bear Initial Report
7. Round 1 Debate (2 responses)
8. Round 2 Debate (2 responses)

Responsibilities:

1. Synthesize all reports and debate history
2. Identify most persuasive arguments from both sides
3. Make final decision: BUY/SELL/HOLD
4. Develop detailed investment plan
5. Ensure final report contains NO API keys or tokens

Important: Do not default to "HOLD" - must take a stance based on strongest arguments.

Detailed Prompt: See references/research-manager.md

---

Report Format Requirements

Price Change Color Convention (China Style)

• Red = Up (+)
• Green = Down (-)

Data Citation

• All data must cite sources
• When using Tushare Pro, note Data Source: Tushare Pro
• When using Web Search, cite source URL

Security Requirement

• Reports MUST NOT contain any API keys, tokens, or credentials
• If any credential is found in output, redact immediately

Signature

• Use signature at end of financial reports

---

Implementation Scripts

Get Fundamental Data

python3 scripts/get_fundamentals.py <stock_code>

Get Market Data

python3 scripts/get_market_data.py <stock_code>

---

Output Example

# TradingAgents Investment Decision Report: 宁德时代 (300750.SZ)

Report Generated: YYYY-MM-DD HH:MM
Framework Version: TradingAgents v2.0 with Debate Mechanism

---

# Part I: Final Investment Decision

## 一、决策摘要

### 投资建议

# 🟢 买入 / 🔴 卖出 / 🟡 持有

### 核心理由

[一句话概括核心理由，基于辩论结果]

### 信心指数

| 维度 | 信心度 | 说明 |
|------|:------:|------|
| 基本面 | x/5 | [说明] |
| 市场面 | x/5 | [说明] |
| 消息面 | x/5 | [说明] |
| 综合信心 | x/5 | - |

---

## 二、辩论精华回顾

### 2.1 初始观点对比

| 方面 | 看涨观点 | 看跌观点 |
|------|----------|----------|
| 核心论点 | [论点] | [论点] |
| 支撑数据 | [数据] | [数据] |
| 初始得分 | x/5 | x/5 |

### 2.2 第一轮辩论结果

#### 看跌方对看涨方的挑战

| 看涨论点 | 看跌方反驳 | 反驳有效性 | 幸存状态 |
|----------|------------|:----------:|:--------:|
| [论点1] | [反驳] | 高/中/低 | ✅/❌ |
| [论点2] | [反驳] | 高/中/低 | ✅/❌ |

#### 看涨方对看跌方的挑战

| 看跌论点 | 看涨方反驳 | 反驳有效性 | 幸存状态 |
|----------|------------|:----------:|:--------:|
| [论点1] | [反驳] | 高/中/低 | ✅/❌ |
| [论点2] | [反驳] | 高/中/低 | ✅/❌ |

### 2.3 第二轮辩论结果

| 议题 | 看涨最终立场 | 看跌最终立场 | 辩论胜出方 |
|------|--------------|--------------|:----------:|
| [议题1] | [立场] | [立场] | 🟢/🔴/🟡 |
| [议题2] | [立场] | [立场] | 🟢/🔴/🟡 |

### 2.4 辩论胜负关键

**看涨方胜出理由** (如适用):
1. [理由1]
2. [理由2]

**看跌方胜出理由** (如适用):
1. [理由1]
2. [理由2]

---

## 三、最终判断

### 3.1 为什么选择 [买入/卖出/持有]

[详细解释基于辩论结果做出此决策的原因]

### 3.2 经过辩论验证的核心论点

#### 买入/持有支撑论点 (经辩论验证)

| 优先级 | 论点 | 辩论验证结果 | 来源 |
|:------:|------|--------------|------|
| 1 | [论点] | 看跌方无法有效反驳 | [来源] |
| 2 | [论点] | 看跌方反驳力度不足 | [来源] |

#### 卖出/回避风险论点 (经辩论验证)

| 优先级 | 论点 | 辩论验证结果 | 来源 |
|:------:|------|--------------|------|
| 1 | [论点] | 看涨方无法有效反驳 | [来源] |
| 2 | [论点] | 风险确认 | [来源] |

### 3.3 辩论暴露的关键风险

| 风险 | 暴露程度 | 应对策略 |
|------|:--------:|----------|
| [风险1] | 高/中/低 | [策略] |
| [风险2] | 高/中/低 | [策略] |

---

## 四、投资计划

### a) 投资建议

**[买入/卖出/持有]**

| 配置建议 | 建议 |
|----------|------|
| 建议仓位 | 低配(10-20%) / 标配(20-40%) / 高配(40-60%) |
| 持有期限 | 短线(1-3月) / 中线(3-12月) / 长线(1年+) |
| 信心等级 | 低 / 中 / 高 |

### b) 理由

[详细解释这些论据为何能够得到该结论]

### c) 战略行动

| 步骤 | 具体操作 | 时间节点 |
|:----:|----------|----------|
| 1 | 分批建仓：建议分x批买入 | [时间] |
| 2 | 入场时机：[技术面建议] | [条件] |
| 3 | 止损设置：设定止损位在 xx | [条件] |

---

## 五、后续跟踪要点

### 需要关注的关键指标

| 指标 | 当前值 | 警戒值 | 触发行动 |
|------|--------|--------|----------|
| [指标1] | xx | xx | [行动] |

### 需要关注的关键事件

| 事件 | 预计时间 | 对论点的影响 | 行动预案 |
|------|---------|--------------|----------|
| [事件1] | [时间] | 影响[论点] | [预案] |

---

## 六、免责声明

本报告基于多智能体辩论分析框架生成，综合了看涨和看跌双方观点及其辩论过程。投资决策应基于个人风险承受能力和投资目标。本报告仅供参考，不构成投资建议。投资有风险，入市需谨慎。

---

# Part II: Layer 1 Reports (Information Gathering)

## 1. Fundamental Analysis Report

[Complete fundamental analyst report]

## 2. Market Analysis Report

[Complete market analyst report]

## 3. News Analysis Report

[Complete news analyst report]

## 4. Social Media Sentiment Report

[Complete social media analyst report]

---

# Part III: Layer 2 Reports (Opinion Formation)

## 5. Bull Researcher Initial Report

[Complete bull researcher initial report]

## 6. Bear Researcher Initial Report

[Complete bear researcher initial report]

---

# Part IV: Debate History (Layer 2.5)

## Round 1

### Bear's Rebuttal to Bull's Arguments

[Bear's round 1 response]

### Bull's Rebuttal to Bear's Arguments

[Bull's round 1 response]

## Round 2

### Bear's Counter-Rebuttal

[Bear's round 2 response]

### Bull's Counter-Rebuttal

[Bull's round 2 response]

---

# Part V: Appendix

## Data Sources

- Tushare Pro API
- Web Search (Brave Search)
- Social Media Platforms

## Methodology

This report was generated using the TradingAgents Multi-Agent Debate Framework:
1. **Layer 1**: 4 specialized analysts gather comprehensive information
2. **Layer 2**: Bull and Bear researchers form opposing viewpoints
3. **Layer 2.5**: Two rounds of structured debate
4. **Layer 3**: Portfolio manager synthesizes debate outcomes into final decision

## Disclaimer

本报告基于多智能体辩论分析框架生成，综合了看涨和看跌双方观点及其辩论过程。投资决策应基于个人风险承受能力和投资目标。本报告仅供参考，不构成投资建议。投资有风险，入市需谨慎。

---

🐂 TradingAgents Multi-Agent Debate Analysis Framework

---

Version History

Version	Date	Changes
v2.4.2	2026-03-26	Synchronized SKILL.md Output Example with research-manager.md Output Format (full Part I-V structure)
v2.4.0	2026-03-26	Enhanced security: removed openclaw.json reference, added SubAgent security rules, unified install spec
v2.3.0	2026-03-26	Removed PDF export, simplified dependencies, unified metadata
v2.2.0	2026-03-26	Security hardening: Fixed metadata inconsistencies, improved credential handling
v2.0.0	2026-03-25	Added two-round debate mechanism
v1.0.0	2026-03-20	Initial release with 7 SubAgents

---

Security & Privacy

Credential Handling

• TUSHARE_TOKEN and BRAVE_API_KEY are sensitive credentials
• Read only from environment variables
• Do NOT paste tokens in chat messages
• Do NOT store tokens in shell config files (.bashrc, .zshrc)

Data Handling

• Financial data fetched from Tushare Pro API only
• Reports saved locally, not transmitted externally
• No telemetry or analytics collected

SubAgent Security Rules

• SubAgents MUST NOT read any configuration beyond TUSHARE_TOKEN and BRAVE_API_KEY
• SubAgents MUST NOT pass API keys in inter-agent communications
• Generated reports MUST NOT contain any sensitive credential information

Side Effects

• Writes reports to ~/.openclaw/workspace/memory/reports/
• Connects to external APIs (Tushare, web search)
• Does not modify any other files or configurations