tradingagents-analysis

Original：🇨🇳 Chinese

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A-share multi-agent AI investment research and analysis tool - 15 AI analysts collaborate to complete technical analysis, fundamental analysis, market sentiment judgment, capital flow tracking (northbound capital/main capital), macroeconomic analysis and game theory deduction, and output structured trading suggestions and risk assessment. Supports Shanghai and Shenzhen A-share stock codes and Chinese names. Multi-agent AI stock analysis for China A-shares. 15 specialized analysts collaborate across technical analysis, fundamental analysis, sentiment analysis, smart money flow tracking, macro economics, and game theory to deliver structured buy/sell/hold recommendations with risk assessment.

2installs

Sourcekylinmountain/tradingagents-ashare

Added on2026-04-04

NPX Install

npx skill4agent add kylinmountain/tradingagents-ashare tradingagents-analysis

SKILL.md Content (Chinese)

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TradingAgents Multi-Agent A-Share Investment Research and Analysis

Use the TradingAgents API to let 15 specialized AI analysts conduct in-depth five-stage collaborative research and judgment on A-share stocks, and output structured investment recommendations.

🎯 Quick Start

Just tell me directly:

"Help me analyze Kweichow Moutai"
"Is CATL worth buying?"
"Analyze the technical aspect of 600519"
"How is BYD's recent capital flow?"

I will call 15 AI analysts to conduct in-depth analysis from five dimensions: market, technology, fundamentals, sentiment and capital, and provide you with professional investment advice.

🤖 System Architecture: 5 Stages · 15 Agents

Stage	Agents	Responsibility
1. Analyst Team	Market / News / Sentiment / Fundamentals / Macro / Smart Money	Multi-dimensional raw data interpretation
2. Game Theory Referee	Game Theory Manager	Analysis of expectation gap between main force and retail investors
3. Bull/Bear Debate	Bull/Bear Researchers + Referee	Intensive game of opposing views
4. Execution Decision	Trader	Generate operation suggestions through comprehensive research and judgment
5. Risk Management and Control	Aggressive/Neutral/Conservative Analysts + Portfolio Manager	Multi-dimensional risk control review

TradingAgents Multi-Agent Investment Research

Use the TradingAgents API to let 15 specialized AI analysts conduct deep, five-stage collaborative research on A-Share stocks, delivering structured trading recommendations.

🤖 System Architecture: 5 Stages · 15 Agents

Stage	Agents	Role
1. Analyst Team	Market / News / Sentiment / Fundamentals / Macro / Smart Money	Multi-dimensional raw data analysis
2. Game Theory	Game Theory Manager	Main-force vs. retail expectation gap
3. Bull/Bear Debate	Bull & Bear Researchers + Judge	Adversarial viewpoint debate
4. Trade Execution	Trader	Synthesize research into actionable decision
5. Risk Control	Aggressive / Neutral / Conservative + Portfolio Manager	Multi-layer risk review

📋 Applicable Scenarios

✅ Suitable for:

In-depth individual stock analysis (technical + fundamental)
Investment decision reference
Post-market review analysis
Risk assessment of held positions
Capital flow and market sentiment research and judgment

❌ Not suitable for:

Real-time intraday monitoring (analysis takes 1-5 minutes)
Ultra-short-term trading (minute-level decision making)
Non-A-share markets such as cryptocurrency and US stocks

🔒 Privacy and Security

Sending scope: This skill only extracts parameters such as stock name/code, analysis date, analysis perspective from the conversation, and sends them to the backend API as
```
symbol
```
/
```
trade_date
```
/
```
horizons
```
fields. Do not send the original conversation text, do not read local files, and do not upload any other private data.
Token security:
```
TRADINGAGENTS_TOKEN
```
(format
```
ta-sk-*
```
) is the only credential to access the backend. Please use the minimum privilege token. If you suspect leakage, please immediately revoke and regenerate it at app.510168.xyz.
Sensitive content prompt: Please do not paste personal account information, real positions or other sensitive content into the analysis request. This skill cannot prevent users from actively submitting these contents.
Self-hosting: If you need full control over the data flow, you can refer to GitHub Documentation to deploy the backend by yourself, and point
```
TRADINGAGENTS_API_URL
```
to your self-built server.

About credential metadata: The frontmatter of this skill declares
TRADINGAGENTS_TOKEN
as
primaryEnv
in
metadata.openclaw
and lists it in
requires.env
.

🔒 Privacy & Data Transmission

What is sent: Only the extracted stock symbol, trade date, and analysis parameters (
```
symbol
```
,
```
trade_date
```
,
```
horizons
```
) are transmitted to the backend. The raw conversation text is never forwarded.
Token:
```
TRADINGAGENTS_TOKEN
```
(pattern
```
ta-sk-*
```
) is the sole credential. Use a minimal-privilege token and rotate it immediately if compromised.
Sensitive content: Do not paste personal account data, real positions, or other sensitive information into analysis requests.
Self-hosting: For full data sovereignty, deploy the backend yourself and set
```
TRADINGAGENTS_API_URL
```
to your server. See the GitHub repo.

Credential metadata: This skill's frontmatter declares
TRADINGAGENTS_TOKEN
as
primaryEnv
under
metadata.openclaw.requires.env
.

⚙️ Quick Configuration

Method 1: Use official hosting service (zero deployment, out of the box)

Log in to https://app.510168.xyz
Go to Settings → API Tokens to create a token
Configure environment variables:

bash

export TRADINGAGENTS_TOKEN="ta-sk-your_key_here"

Method 2: Private deployment (data is completely independent and controllable)

If you have requirements for data privacy, you can deploy the backend by yourself, and all analysis data is only processed on your own server:

bash

# 1. Deploy the backend, refer to https://github.com/KylinMountain/TradingAgents-AShare
# 2. Point the API address to the self-built service
export TRADINGAGENTS_API_URL="http://your-server:8000"
export TRADINGAGENTS_TOKEN="ta-sk-your_key_here"

🚀 Common Operations

Recommended method: Use integrated script (automatic submission → polling → get results)

bash

# Script path (relative to skill directory)
bash scripts/analyze.sh <symbol[,symbol2,...]> [trade_date] [horizons]

# Single analysis
bash scripts/analyze.sh 贵州茅台
bash scripts/analyze.sh 600519.SH 2026-03-22
bash scripts/analyze.sh 600519.SH 2026-03-22 medium

# Batch analysis (comma separated, parallel submission, unified waiting)
bash scripts/analyze.sh 贵州茅台,比亚迪,宁德时代
bash scripts/analyze.sh 600519.SH,002594.SZ,300750.SZ 2026-03-22

The script will automatically complete: submit task → poll status every 15 seconds → output JSON results after completion. In batch mode, all tasks are submitted in parallel, polled uniformly, and finally aggregated and output. The default timeout is 600 seconds.

You can adjust the behavior through environment variables:

```
POLL_INTERVAL
```
— polling interval in seconds (default 15)
```
POLL_TIMEOUT
```
— maximum waiting seconds (default 600)

Manual step-by-step operation (if you need to call a step separately)

All requests use

$TRADINGAGENTS_TOKEN

as the Bearer token.

Submit analysis task

bash

curl -X POST "${TRADINGAGENTS_API_URL:-https://api.510168.xyz}/v1/analyze" \
  -H "Authorization: Bearer $TRADINGAGENTS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"symbol": "贵州茅台"}'

Query task status

bash

curl "${TRADINGAGENTS_API_URL:-https://api.510168.xyz}/v1/jobs/{job_id}" \
  -H "Authorization: Bearer $TRADINGAGENTS_TOKEN"

Get complete analysis results (after the task is completed)

bash

curl "${TRADINGAGENTS_API_URL:-https://api.510168.xyz}/v1/jobs/{job_id}/result" \
  -H "Authorization: Bearer $TRADINGAGENTS_TOKEN"

📊 Sample Output

json

{
  "decision": "BUY",
  "direction": "看多",
  "confidence": 78,
  "target_price": 1850.0,
  "stop_loss_price": 1680.0,
  "risk_items": [
    {"name": "估值偏高", "level": "medium", "description": "当前 PE 处于历史 75 分位"},
    {"name": "外资流出", "level": "low",    "description": "近 5 日北向资金小幅净流出"}
  ],
  "key_metrics": [
    {"name": "PE",   "value": "32.5x",  "status": "neutral"},
    {"name": "ROE",  "value": "31.2%",  "status": "good"},
    {"name": "毛利率", "value": "91.5%", "status": "good"}
  ],
  "final_trade_decision": "综合技术面突破与基本面支撑，建议逢低分批建仓..."
}

🔄 Task Execution Process

In-depth analysis usually takes 1 to 5 minutes:

Identify the target: Only extract the stock name or code (and optional date/perspective) from the conversation, and do not send the original conversation text
Notify the user: Feedback that the task is about to be submitted, and it is expected to take 1-5 minutes
Execute the script: Use the Bash tool to run
```
bash scripts/analyze.sh <symbol> [date] [horizons]
```
(set
```
run_in_background: true
```
), and the script automatically completes submission, polling and result acquisition
Summarize the conclusion: After the script output is completed, parse the JSON results, and show the user the decision, direction, target price, and risk points

Important: Do not manually write curl polling loops, directly use the
scripts/analyze.sh
script.

📌 Supported Target Range

Shanghai and Shenzhen A-shares: Chinese name (such as "BYD", "CATL") or code (
```
002594.SZ
```
,
```
601012.SH
```
)

💡 Notes

Polling frequency: The interval between each polling is not less than 15 seconds
Data robustness: If some data sources are missing, the system will conduct spillover analysis based on macro and industry logic
Short-term mode: When entering "Analyze XX short-term", the system automatically switches to 14-day technical analysis, skips financial report data, and is faster

tradingagents-analysis

NPX Install

Tags

SKILL.md Content (Chinese)

TradingAgents Multi-Agent A-Share Investment Research and Analysis

🎯 Quick Start

🤖 System Architecture: 5 Stages · 15 Agents

TradingAgents Multi-Agent Investment Research

🤖 System Architecture: 5 Stages · 15 Agents

📋 Applicable Scenarios

🔒 Privacy and Security

🔒 Privacy & Data Transmission

⚙️ Quick Configuration

🚀 Common Operations

📊 Sample Output

🔄 Task Execution Process

📌 Supported Target Range

💡 Notes