git clone https://github.com/AlataChan/tradingagents-mcp.git
cd tradingagents-mcp

conda create -n tradingagents python=3.8
conda activate tradingagents

pip install -r requirements_hybrid.txt

cp env.template .env
# 编辑 .env 文件，填入您的API密钥

# OpenAI API (必需)
OPENAI_API_KEY="sk-..."

# Alpha Vantage API (免费，推荐)
ALPHA_VANTAGE_KEY="..."

# FinnHub API (可选，付费备用)
FINNHUB_API_KEY="..."

# 分析单只股票
python main_hybrid.py AAPL

# 分析多只股票
python main_hybrid.py AAPL MSFT GOOGL

graph TD
    A[用户请求] --> B[混合配置管理器]
    B --> C[数据获取层]
    C --> D[Yahoo Finance]
    C --> E[AkShare]
    C --> F[Alpha Vantage]
    D --> G[数据融合]
    E --> G
    F --> G
    G --> H[分析引擎]
    H --> I[决策输出]

graph LR
    A[Yahoo Finance] -->|失败| B[AkShare]
    B -->|失败| C[Alpha Vantage]
    C -->|失败| D[缓存数据]
    A -->|成功| E[数据融合]
    B -->|成功| E
    C -->|成功| E
    D -->|成功| E

from tradingagents.hybrid_config import HybridConfig

config = HybridConfig(
    mode="balanced",
    cache_ttl_hours=6,
    max_retries=3,
    concurrent_requests=3,
    enable_akshare=True,
    enable_alpha_vantage=True
)

from tradingagents.hybrid_config import HybridConfig
from main_hybrid import HybridTradingEngine

# 初始化引擎
config = HybridConfig(mode="balanced")
engine = HybridTradingEngine(config)

# 分析股票
result = engine.analyze_stock("AAPL")
print(f"决策: {result['decision']}")
print(f"置信度: {result['confidence']}")
print(f"数据源: {result['data_sources']}")

# 启动Web界面
streamlit run web_streamlit_app.py

# 批量分析多只股票
symbols = ["AAPL", "MSFT", "GOOGL", "AMZN"]
results = engine.batch_analyze(symbols)

for symbol, result in results.items():
    print(f"{symbol}: {result['decision']} ({result['confidence']}%)")

# 查看详细日志
tail -f logs/trading_analysis.log

# 错误日志过滤
grep "ERROR" logs/trading_analysis.log

# 安装开发依赖
pip install -r requirements_dev.txt

# 运行测试
pytest tests/

# 代码格式化
black tradingagents/