A-Share MCP Server

""" Analysis tools for MCP server. Contains tools for generating stock analysis reports. """ import logging from datetime import datetime, timedelta from mcp.server.fastmcp import FastMCP from src.data_source_interface import FinancialDataSource from src.formatting.markdown_formatter import format_df_to_markdown logger = logging.getLogger(__name__) def register_analysis_tools(app: FastMCP, active_data_source: FinancialDataSource): """ Register analysis tools with the MCP app. Args: app: The FastMCP app instance active_data_source: The active financial data source """ @app.tool() def get_stock_analysis(code: str, analysis_type: str = "fundamental") -> str: """ 提供基于数据的股票分析报告，而非投资建议。 Args: code: 股票代码，如'sh.600000' analysis_type: 分析类型，可选'fundamental'(基本面)、'technical'(技术面)或'comprehensive'(综合) Returns: 数据驱动的分析报告，包含关键财务指标、历史表现和同行业比较 """ logger.info( f"Tool 'get_stock_analysis' called for {code}, type={analysis_type}") # 收集多个维度的实际数据 try: # 获取基本信息 basic_info = active_data_source.get_stock_basic_info(code=code) # 根据分析类型获取不同数据 if analysis_type in ["fundamental", "comprehensive"]: # 获取最近一个季度财务数据 recent_year = datetime.now().strftime("%Y") recent_quarter = (datetime.now().month - 1) // 3 + 1 if recent_quarter < 1: # 处理年初可能出现的边界情况 recent_year = str(int(recent_year) - 1) recent_quarter = 4 profit_data = active_data_source.get_profit_data( code=code, year=recent_year, quarter=recent_quarter) growth_data = active_data_source.get_growth_data( code=code, year=recent_year, quarter=recent_quarter) balance_data = active_data_source.get_balance_data( code=code, year=recent_year, quarter=recent_quarter) dupont_data = active_data_source.get_dupont_data( code=code, year=recent_year, quarter=recent_quarter) if analysis_type in ["technical", "comprehensive"]: # 获取历史价格 end_date = datetime.now().strftime("%Y-%m-%d") start_date = (datetime.now() - timedelta(days=180) ).strftime("%Y-%m-%d") price_data = active_data_source.get_historical_k_data( code=code, start_date=start_date, end_date=end_date ) # 构建客观的数据分析报告 report = f"# {basic_info['code_name'].values[0] if not basic_info.empty else code} 数据分析报告\n\n" report += "## 免责声明\n本报告基于公开数据生成，仅供参考，不构成投资建议。投资决策需基于个人风险承受能力和研究。\n\n" # 添加行业信息 if not basic_info.empty: report += f"## 公司基本信息\n" report += f"- 股票代码: {code}\n" report += f"- 股票名称: {basic_info['code_name'].values[0]}\n" report += f"- 所属行业: {basic_info['industry'].values[0] if 'industry' in basic_info.columns else '未知'}\n" report += f"- 上市日期: {basic_info['ipoDate'].values[0] if 'ipoDate' in basic_info.columns else '未知'}\n\n" # 添加基本面分析 if analysis_type in ["fundamental", "comprehensive"] and not profit_data.empty: report += f"## 基本面指标分析 ({recent_year}年第{recent_quarter}季度)\n\n" # 盈利能力 report += "### 盈利能力指标\n" if not profit_data.empty and 'roeAvg' in profit_data.columns: roe = profit_data['roeAvg'].values[0] report += f"- ROE(净资产收益率): {roe}%\n" if not profit_data.empty and 'npMargin' in profit_data.columns: npm = profit_data['npMargin'].values[0] report += f"- 销售净利率: {npm}%\n" # 成长能力 if not growth_data.empty: report += "\n### 成长能力指标\n" if 'YOYEquity' in growth_data.columns: equity_growth = growth_data['YOYEquity'].values[0] report += f"- 净资产同比增长: {equity_growth}%\n" if 'YOYAsset' in growth_data.columns: asset_growth = growth_data['YOYAsset'].values[0] report += f"- 总资产同比增长: {asset_growth}%\n" if 'YOYNI' in growth_data.columns: ni_growth = growth_data['YOYNI'].values[0] report += f"- 净利润同比增长: {ni_growth}%\n" # 偿债能力 if not balance_data.empty: report += "\n### 偿债能力指标\n" if 'currentRatio' in balance_data.columns: current_ratio = balance_data['currentRatio'].values[0] report += f"- 流动比率: {current_ratio}\n" if 'assetLiabRatio' in balance_data.columns: debt_ratio = balance_data['assetLiabRatio'].values[0] report += f"- 资产负债率: {debt_ratio}%\n" # 添加技术面分析 if analysis_type in ["technical", "comprehensive"] and not price_data.empty: report += "## 技术面分析\n\n" # 计算简单的技术指标 # 假设price_data已经按日期排序 if 'close' in price_data.columns and len(price_data) > 1: latest_price = price_data['close'].iloc[-1] start_price = price_data['close'].iloc[0] price_change = ( (float(latest_price) / float(start_price)) - 1) * 100 report += f"- 最新收盘价: {latest_price}\n" report += f"- 6个月价格变动: {price_change:.2f}%\n" # 计算简单的均线 if len(price_data) >= 20: ma20 = price_data['close'].astype( float).tail(20).mean() report += f"- 20日均价: {ma20:.2f}\n" if float(latest_price) > ma20: report += f" (当前价格高于20日均线 {((float(latest_price)/ma20)-1)*100:.2f}%)\n" else: report += f" (当前价格低于20日均线 {((ma20/float(latest_price))-1)*100:.2f}%)\n" # 添加行业比较分析 try: if not basic_info.empty and 'industry' in basic_info.columns: industry = basic_info['industry'].values[0] industry_stocks = active_data_source.get_stock_industry( date=None) if not industry_stocks.empty: same_industry = industry_stocks[industry_stocks['industry'] == industry] report += f"\n## 行业比较 ({industry})\n" report += f"- 同行业股票数量: {len(same_industry)}\n" # 这里可以添加更多行业比较数据 except Exception as e: logger.warning(f"获取行业比较数据失败: {e}") report += "\n## 数据解读建议\n" report += "- 以上数据仅供参考，建议结合公司公告、行业趋势和宏观环境进行综合分析\n" report += "- 个股表现受多种因素影响，历史数据不代表未来表现\n" report += "- 投资决策应基于个人风险承受能力和投资目标\n" logger.info(f"成功生成{code}的分析报告") return report except Exception as e: logger.exception(f"分析生成失败 for {code}: {e}") return f"分析生成失败: {e}"