stock-trading-agents-light（股票交易 Agent 光）

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

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:

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:

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

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