支持 us stock

backend/app/agent/smart_agent.py

@@ -12,6 +12,7 @@ from app.skills.technical_analysis import TechnicalAnalysisSkill
 from app.skills.fundamental import FundamentalSkill
 from app.skills.visualization import VisualizationSkill
 from app.skills.advanced_data import AdvancedDataSkill
+from app.skills.us_stock_skill import USStockSkill
 from app.services.llm_service import llm_service
 from app.services.tushare_service import tushare_service
 from app.utils.logger import logger
@@ -43,7 +44,8 @@ class SmartStockAgent:
         skill_manager.register(FundamentalSkill())
         skill_manager.register(VisualizationSkill())
         skill_manager.register(AdvancedDataSkill())
-        logger.info("技能注册完成（Tushare Pro高级数据）")
+        skill_manager.register(USStockSkill())
+        logger.info("技能注册完成（Tushare Pro高级数据 + 美股支持）")
 
     async def process_message(
         self,
@@ -1000,6 +1002,7 @@ MA60：{f"{ma['ma60']:.2f}" if ma['ma60'] else '计算中'}
 1. **stock_specific** - 针对特定股票或指数的问题
    例如："贵州茅台怎么样"、"分析一下比亚迪"、"600519的技术指标"、"帮我看看这只股票"
    **重要**：指数查询也属于此类，例如："上证指数怎么样"、"分析大盘"、"A股指数走势"、"深证成指"
+   **美股支持**：美股查询也属于此类，例如："苹果股票怎么样"、"AAPL分析"、"特斯拉走势"、"TSLA技术指标"
 
 2. **macro_finance** - 宏观金融/市场问题（不针对特定股票或指数）
    例如："最近有什么投资机会"、"现在适合买股票吗"、"市场情绪如何"
@@ -1014,6 +1017,7 @@ MA60：{f"{ma['ma60']:.2f}" if ma['ma60'] else '计算中'}
 - 如果用户问题不明确，但可能与金融相关，优先归类为 general_chat，以便引导用户
 - 如果用户提到"这只股票"、"它"等代词，查看对话历史判断是否指特定股票
 - **如果用户提到"大盘"、"上证"、"深证"、"A股指数"等，归类为 stock_specific，并在stock_names中填入对应的指数名称**
+- **如果用户提到美股公司名称（如苹果、特斯拉、微软）或美股代码（如AAPL、TSLA、MSFT），归类为 stock_specific，并在stock_names中填入对应的股票名称或代码**
 - 对于模糊的问题，不要强行归类，使用 general_chat 类型
 
 请以JSON格式返回分析结果：
@@ -1021,7 +1025,8 @@ MA60：{f"{ma['ma60']:.2f}" if ma['ma60'] else '计算中'}
     "type": "问题类型",
     "description": "问题的简要描述（用一句话概括用户想了解什么）",
     "keywords": ["关键词1", "关键词2"],
-    "stock_names": ["股票名称或指数名称"] (仅当type为stock_specific时，如果有的话)
+    "stock_names": ["股票名称或指数名称或美股代码"] (仅当type为stock_specific时，如果有的话),
+    "market": "A股" 或 "美股" (仅当type为stock_specific时，根据股票类型判断)
 }}
 
 只返回JSON，不要有任何其他内容。"""
@@ -1071,6 +1076,7 @@ MA60：{f"{ma['ma60']:.2f}" if ma['ma60'] else '计算中'}
     ) -> Dict[str, Any]:
         """处理针对特定股票或指数的问题"""
         stock_names = intent_analysis.get('stock_names', [])
+        market = intent_analysis.get('market', 'A股')  # 默认A股
 
         if not stock_names:
             return {
@@ -1081,6 +1087,14 @@ MA60：{f"{ma['ma60']:.2f}" if ma['ma60'] else '计算中'}
         # 提取第一个股票或指数
         stock_keyword = stock_names[0]
 
+        # 检测是否为美股
+        is_us_stock = self._is_us_stock(stock_keyword, market)
+
+        if is_us_stock:
+            # 处理美股
+            return await self._handle_us_stock(stock_keyword, message)
+
+        # 处理A股和指数
         # 指数映射表
         index_mapping = {
             "上证指数": "000001.SH",
@@ -1332,6 +1346,285 @@ MA60：{f"{ma['ma60']:.2f}" if ma['ma60'] else '计算中'}
             "metadata": {"type": "chat"}
         }
 
+    def _is_us_stock(self, keyword: str, market: str) -> bool:
+        """
+        判断是否为美股
+
+        Args:
+            keyword: 股票关键词
+            market: 市场类型（从LLM意图分析中获取）
+
+        Returns:
+            是否为美股
+        """
+        # 如果LLM已经判断为美股
+        if market == "美股":
+            return True
+
+        # 检查是否为全大写字母（美股代码特征）
+        if keyword.isupper() and keyword.isalpha() and len(keyword) <= 5:
+            return True
+
+        # 常见美股公司名称映射
+        us_stock_names = {
+            "苹果": "AAPL",
+            "特斯拉": "TSLA",
+            "微软": "MSFT",
+            "谷歌": "GOOGL",
+            "亚马逊": "AMZN",
+            "Meta": "META",
+            "脸书": "META",
+            "英伟达": "NVDA",
+            "奈飞": "NFLX",
+            "网飞": "NFLX",
+            "迪士尼": "DIS",
+            "可口可乐": "KO",
+            "麦当劳": "MCD",
+            "星巴克": "SBUX",
+            "耐克": "NKE",
+            "波音": "BA",
+            "英特尔": "INTC",
+            "AMD": "AMD",
+            "高通": "QCOM",
+            "推特": "TWTR",
+            "优步": "UBER",
+            "Uber": "UBER",
+            "Airbnb": "ABNB",
+            "爱彼迎": "ABNB",
+        }
+
+        if keyword in us_stock_names:
+            return True
+
+        return False
+
+    def _get_us_stock_symbol(self, keyword: str) -> str:
+        """
+        获取美股代码
+
+        Args:
+            keyword: 股票关键词
+
+        Returns:
+            美股代码
+        """
+        # 如果已经是代码格式，直接返回
+        if keyword.isupper() and keyword.isalpha():
+            return keyword
+
+        # 中文名称映射
+        us_stock_names = {
+            "苹果": "AAPL",
+            "特斯拉": "TSLA",
+            "微软": "MSFT",
+            "谷歌": "GOOGL",
+            "亚马逊": "AMZN",
+            "Meta": "META",
+            "脸书": "META",
+            "英伟达": "NVDA",
+            "奈飞": "NFLX",
+            "网飞": "NFLX",
+            "迪士尼": "DIS",
+            "可口可乐": "KO",
+            "麦当劳": "MCD",
+            "星巴克": "SBUX",
+            "耐克": "NKE",
+            "波音": "BA",
+            "英特尔": "INTC",
+            "AMD": "AMD",
+            "高通": "QCOM",
+            "推特": "TWTR",
+            "优步": "UBER",
+            "Uber": "UBER",
+            "Airbnb": "ABNB",
+            "爱彼迎": "ABNB",
+        }
+
+        return us_stock_names.get(keyword, keyword.upper())
+
+    async def _handle_us_stock(self, keyword: str, message: str) -> Dict[str, Any]:
+        """
+        处理美股查询
+
+        Args:
+            keyword: 股票关键词
+            message: 用户消息
+
+        Returns:
+            分析结果
+        """
+        # 获取美股代码
+        symbol = self._get_us_stock_symbol(keyword)
+
+        logger.info(f"处理美股查询: {keyword} -> {symbol}")
+
+        try:
+            # 调用美股分析技能
+            result = await skill_manager.execute_skill(
+                "us_stock_analysis",
+                symbol=symbol,
+                analysis_type="comprehensive"
+            )
+
+            if not result.get("success"):
+                return {
+                    "message": f"抱歉，未找到美股 {symbol}。请确认股票代码是否正确。\n\n提示：美股代码通常为大写字母，如 AAPL（苹果）、TSLA（特斯拉）、MSFT（微软）等。",
+                    "metadata": {"type": "error"}
+                }
+
+            # 使用LLM分析美股数据
+            if self.use_llm:
+                analysis = await self._llm_us_stock_analysis(result["data"], message)
+            else:
+                analysis = self._format_us_stock_data(result["data"])
+
+            return {
+                "message": analysis,
+                "metadata": {
+                    "type": "us_stock_analysis",
+                    "data": result["data"]
+                }
+            }
+
+        except Exception as e:
+            logger.error(f"美股查询失败: {e}")
+            return {
+                "message": f"查询美股 {symbol} 时出错：{str(e)}",
+                "metadata": {"type": "error"}
+            }
+
+    async def _llm_us_stock_analysis(self, data: Dict[str, Any], user_message: str) -> str:
+        """使用LLM分析美股数据"""
+        from datetime import datetime
+        current_time = datetime.now().strftime("%Y-%m-%d %H:%M")
+
+        # 提取关键数据
+        symbol = data.get("symbol", "")
+        name = data.get("name", "")
+        sector = data.get("sector", "")
+        industry = data.get("industry", "")
+        current_price = data.get("current_price", 0)
+        change = data.get("change", 0)
+        change_pct = data.get("change_percent", 0)
+        volume = data.get("volume", 0)
+        market_cap = data.get("market_cap", 0)
+        pe_ratio = data.get("pe_ratio", 0)
+        pb_ratio = data.get("pb_ratio", 0)
+        dividend_yield = data.get("dividend_yield", 0)
+        week_52_high = data.get("52_week_high", 0)
+        week_52_low = data.get("52_week_low", 0)
+        technical = data.get("technical_indicators", {})
+        description = data.get("description", "")
+
+        # 格式化市值
+        market_cap_str = f"${market_cap / 1e9:.2f}B" if market_cap > 1e9 else f"${market_cap / 1e6:.2f}M"
+
+        # 构建分析提示
+        prompt = f"""你是一位专业的美股分析师。请基于以下数据对 {name} ({symbol}) 进行全面分析。
+
+【基本信息】
+股票代码：{symbol}
+公司名称：{name}
+所属行业：{sector} - {industry}
+公司简介：{description[:300] if description else '暂无'}
+
+【实时行情】（数据时间：{current_time}）
+当前价格：${current_price:.2f}
+涨跌额：${change:.2f}
+涨跌幅：{change_pct:.2f}%
+成交量：{volume:,}
+市值：{market_cap_str}
+
+【估值指标】
+市盈率(PE)：{f"{pe_ratio:.2f}" if pe_ratio else '暂无'}
+市净率(PB)：{f"{pb_ratio:.2f}" if pb_ratio else '暂无'}
+股息率：{f"{dividend_yield * 100:.2f}%" if dividend_yield else '暂无'}
+52周最高：${week_52_high:.2f}
+52周最低：${week_52_low:.2f}
+
+【技术指标】
+MA5：{f"${technical.get('ma5'):.2f}" if technical.get('ma5') else '计算中'}
+MA10：{f"${technical.get('ma10'):.2f}" if technical.get('ma10') else '计算中'}
+MA20：{f"${technical.get('ma20'):.2f}" if technical.get('ma20') else '计算中'}
+MA60：{f"${technical.get('ma60'):.2f}" if technical.get('ma60') else '计算中'}
+RSI：{f"{technical.get('rsi'):.2f}" if technical.get('rsi') else '计算中'}
+MACD：{f"{technical.get('macd'):.4f}" if technical.get('macd') else '计算中'}
+
+用户问题：{user_message}
+
+请提供专业的分析报告，包括：
+
+## 📊 行情概览
+简要总结当前股价表现和市场表现（2-3句话）
+
+## 💼 公司基本面
+- 行业地位和竞争优势
+- 估值水平分析（PE、PB是否合理）
+- 盈利能力和成长性
+
+## 📈 技术面分析
+- 当前趋势判断（基于均线系统）
+- 关键支撑位和压力位
+- RSI和MACD信号解读
+
+## 💡 投资建议
+- 短期操作建议（1-2周）
+- 中期投资价值（1-3个月）
+- 风险提示
+
+写作要求：
+1. 语言专业但易懂，避免过度修饰
+2. 分析客观理性，基于数据和事实
+3. 每个部分独立成段，段落间用空行分隔
+4. 控制在500-600字
+5. 最后声明："以上分析仅供参考，不构成投资建议。美股投资有风险，请谨慎决策。"
+"""
+
+        try:
+            analysis = llm_service.chat(
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.7,
+                max_tokens=2000
+            )
+
+            if analysis:
+                return f"【美股分析】{name} ({symbol})\n\n{analysis}"
+            else:
+                return self._format_us_stock_data(data)
+
+        except Exception as e:
+            logger.error(f"LLM美股分析失败: {e}")
+            return self._format_us_stock_data(data)
+
+    def _format_us_stock_data(self, data: Dict[str, Any]) -> str:
+        """格式化美股数据（降级方案）"""
+        symbol = data.get("symbol", "")
+        name = data.get("name", "")
+        current_price = data.get("current_price", 0)
+        change = data.get("change", 0)
+        change_pct = data.get("change_percent", 0)
+        market_cap = data.get("market_cap", 0)
+        pe_ratio = data.get("pe_ratio", 0)
+        technical = data.get("technical_indicators", {})
+
+        market_cap_str = f"${market_cap / 1e9:.2f}B" if market_cap > 1e9 else f"${market_cap / 1e6:.2f}M"
+
+        change_emoji = "📈" if change >= 0 else "📉"
+
+        return f"""【美股行情】{name} ({symbol})
+
+{change_emoji} 当前价格：${current_price:.2f}
+涨跌：${change:.2f} ({change_pct:+.2f}%)
+市值：{market_cap_str}
+市盈率：{pe_ratio:.2f if pe_ratio else '暂无'}
+
+【技术指标】
+MA5：${technical.get('ma5', 0):.2f if technical.get('ma5') else '计算中'}
+MA20：${technical.get('ma20', 0):.2f if technical.get('ma20') else '计算中'}
+RSI：{technical.get('rsi', 0):.2f if technical.get('rsi') else '计算中'}
+
+以上数据仅供参考，不构成投资建议。"""
+
 
 # 创建全局实例
 smart_agent = SmartStockAgent()

backend/app/services/us_stock_service.py

@@ -0,0 +1,282 @@
+"""
+美股数据服务 - 使用 yfinance 获取美股数据
+"""
+from typing import Optional, Dict, Any, List
+import yfinance as yf
+from datetime import datetime, timedelta
+import pandas as pd
+from app.utils.logger import logger
+
+
+class USStockService:
+    """美股数据服务类"""
+
+    def __init__(self):
+        """初始化美股数据服务"""
+        self.cache = {}  # 简单的内存缓存
+
+    def get_stock_info(self, symbol: str) -> Optional[Dict[str, Any]]:
+        """
+        获取美股基本信息
+
+        Args:
+            symbol: 股票代码（如 AAPL, TSLA）
+
+        Returns:
+            股票基本信息字典
+        """
+        try:
+            stock = yf.Ticker(symbol)
+            info = stock.info
+
+            if not info or 'symbol' not in info:
+                logger.warning(f"未找到股票: {symbol}")
+                return None
+
+            # 提取关键信息
+            result = {
+                "symbol": symbol,
+                "name": info.get("longName", info.get("shortName", symbol)),
+                "sector": info.get("sector", "未知"),
+                "industry": info.get("industry", "未知"),
+                "market_cap": info.get("marketCap", 0),
+                "current_price": info.get("currentPrice", info.get("regularMarketPrice", 0)),
+                "previous_close": info.get("previousClose", 0),
+                "open": info.get("open", 0),
+                "day_high": info.get("dayHigh", 0),
+                "day_low": info.get("dayLow", 0),
+                "volume": info.get("volume", 0),
+                "avg_volume": info.get("averageVolume", 0),
+                "pe_ratio": info.get("trailingPE", 0),
+                "forward_pe": info.get("forwardPE", 0),
+                "pb_ratio": info.get("priceToBook", 0),
+                "dividend_yield": info.get("dividendYield", 0),
+                "52_week_high": info.get("fiftyTwoWeekHigh", 0),
+                "52_week_low": info.get("fiftyTwoWeekLow", 0),
+                "50_day_avg": info.get("fiftyDayAverage", 0),
+                "200_day_avg": info.get("twoHundredDayAverage", 0),
+                "beta": info.get("beta", 0),
+                "eps": info.get("trailingEps", 0),
+                "description": info.get("longBusinessSummary", ""),
+            }
+
+            logger.info(f"获取美股信息成功: {symbol}")
+            return result
+
+        except Exception as e:
+            logger.error(f"获取美股信息失败 {symbol}: {e}")
+            return None
+
+    def get_historical_data(
+        self,
+        symbol: str,
+        period: str = "1mo",
+        interval: str = "1d"
+    ) -> Optional[pd.DataFrame]:
+        """
+        获取美股历史K线数据
+
+        Args:
+            symbol: 股票代码
+            period: 时间周期 (1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max)
+            interval: K线间隔 (1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo)
+
+        Returns:
+            包含OHLCV数据的DataFrame
+        """
+        try:
+            stock = yf.Ticker(symbol)
+            hist = stock.history(period=period, interval=interval)
+
+            if hist.empty:
+                logger.warning(f"未找到历史数据: {symbol}")
+                return None
+
+            logger.info(f"获取美股历史数据成功: {symbol}, 周期: {period}")
+            return hist
+
+        except Exception as e:
+            logger.error(f"获取美股历史数据失败 {symbol}: {e}")
+            return None
+
+    def get_financial_data(self, symbol: str) -> Optional[Dict[str, Any]]:
+        """
+        获取美股财务数据
+
+        Args:
+            symbol: 股票代码
+
+        Returns:
+            财务数据字典
+        """
+        try:
+            stock = yf.Ticker(symbol)
+
+            # 获取财务报表
+            financials = stock.financials
+            balance_sheet = stock.balance_sheet
+            cashflow = stock.cashflow
+
+            result = {
+                "symbol": symbol,
+                "income_statement": financials.to_dict() if not financials.empty else {},
+                "balance_sheet": balance_sheet.to_dict() if not balance_sheet.empty else {},
+                "cash_flow": cashflow.to_dict() if not cashflow.empty else {},
+            }
+
+            # 获取关键财务指标
+            info = stock.info
+            result["key_metrics"] = {
+                "revenue": info.get("totalRevenue", 0),
+                "gross_profit": info.get("grossProfits", 0),
+                "ebitda": info.get("ebitda", 0),
+                "net_income": info.get("netIncomeToCommon", 0),
+                "total_assets": info.get("totalAssets", 0),
+                "total_debt": info.get("totalDebt", 0),
+                "total_cash": info.get("totalCash", 0),
+                "operating_cash_flow": info.get("operatingCashflow", 0),
+                "free_cash_flow": info.get("freeCashflow", 0),
+                "roe": info.get("returnOnEquity", 0),
+                "roa": info.get("returnOnAssets", 0),
+                "profit_margin": info.get("profitMargins", 0),
+                "operating_margin": info.get("operatingMargins", 0),
+            }
+
+            logger.info(f"获取美股财务数据成功: {symbol}")
+            return result
+
+        except Exception as e:
+            logger.error(f"获取美股财务数据失败 {symbol}: {e}")
+            return None
+
+    def calculate_technical_indicators(self, hist: pd.DataFrame) -> Dict[str, Any]:
+        """
+        计算技术指标
+
+        Args:
+            hist: 历史数据DataFrame
+
+        Returns:
+            技术指标字典
+        """
+        try:
+            if hist.empty or len(hist) < 20:
+                return {}
+
+            close = hist['Close']
+
+            # 计算移动平均线
+            ma5 = close.rolling(window=5).mean().iloc[-1] if len(close) >= 5 else None
+            ma10 = close.rolling(window=10).mean().iloc[-1] if len(close) >= 10 else None
+            ma20 = close.rolling(window=20).mean().iloc[-1] if len(close) >= 20 else None
+            ma60 = close.rolling(window=60).mean().iloc[-1] if len(close) >= 60 else None
+
+            # 计算RSI
+            delta = close.diff()
+            gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+            loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+            rs = gain / loss
+            rsi = 100 - (100 / (1 + rs))
+            rsi_value = rsi.iloc[-1] if len(rsi) >= 14 else None
+
+            # 计算MACD
+            exp1 = close.ewm(span=12, adjust=False).mean()
+            exp2 = close.ewm(span=26, adjust=False).mean()
+            macd = exp1 - exp2
+            signal = macd.ewm(span=9, adjust=False).mean()
+            macd_value = macd.iloc[-1] if len(macd) >= 26 else None
+            signal_value = signal.iloc[-1] if len(signal) >= 26 else None
+
+            # 计算布林带
+            bb_middle = close.rolling(window=20).mean()
+            bb_std = close.rolling(window=20).std()
+            bb_upper = bb_middle + (bb_std * 2)
+            bb_lower = bb_middle - (bb_std * 2)
+
+            result = {
+                "ma5": float(ma5) if ma5 and not pd.isna(ma5) else None,
+                "ma10": float(ma10) if ma10 and not pd.isna(ma10) else None,
+                "ma20": float(ma20) if ma20 and not pd.isna(ma20) else None,
+                "ma60": float(ma60) if ma60 and not pd.isna(ma60) else None,
+                "rsi": float(rsi_value) if rsi_value and not pd.isna(rsi_value) else None,
+                "macd": float(macd_value) if macd_value and not pd.isna(macd_value) else None,
+                "macd_signal": float(signal_value) if signal_value and not pd.isna(signal_value) else None,
+                "bb_upper": float(bb_upper.iloc[-1]) if len(bb_upper) >= 20 and not pd.isna(bb_upper.iloc[-1]) else None,
+                "bb_middle": float(bb_middle.iloc[-1]) if len(bb_middle) >= 20 and not pd.isna(bb_middle.iloc[-1]) else None,
+                "bb_lower": float(bb_lower.iloc[-1]) if len(bb_lower) >= 20 and not pd.isna(bb_lower.iloc[-1]) else None,
+            }
+
+            return result
+
+        except Exception as e:
+            logger.error(f"计算技术指标失败: {e}")
+            return {}
+
+    def get_comprehensive_analysis(self, symbol: str) -> Optional[Dict[str, Any]]:
+        """
+        获取美股综合分析数据
+
+        Args:
+            symbol: 股票代码
+
+        Returns:
+            综合分析数据字典
+        """
+        try:
+            # 获取基本信息
+            info = self.get_stock_info(symbol)
+            if not info:
+                return None
+
+            # 获取历史数据
+            hist = self.get_historical_data(symbol, period="6mo", interval="1d")
+            if hist is None or hist.empty:
+                return {
+                    "success": False,
+                    "error": "无法获取历史数据"
+                }
+
+            # 计算技术指标
+            technical = self.calculate_technical_indicators(hist)
+
+            # 获取最近的价格数据
+            latest = hist.iloc[-1]
+            prev = hist.iloc[-2] if len(hist) > 1 else latest
+
+            # 计算涨跌幅
+            change = latest['Close'] - prev['Close']
+            change_pct = (change / prev['Close'] * 100) if prev['Close'] != 0 else 0
+
+            result = {
+                "success": True,
+                "symbol": symbol,
+                "name": info["name"],
+                "sector": info["sector"],
+                "industry": info["industry"],
+                "current_price": float(latest['Close']),
+                "change": float(change),
+                "change_percent": float(change_pct),
+                "volume": int(latest['Volume']),
+                "market_cap": info["market_cap"],
+                "pe_ratio": info["pe_ratio"],
+                "pb_ratio": info["pb_ratio"],
+                "dividend_yield": info["dividend_yield"],
+                "52_week_high": info["52_week_high"],
+                "52_week_low": info["52_week_low"],
+                "technical_indicators": technical,
+                "description": info["description"][:500] if info["description"] else "",
+            }
+
+            logger.info(f"获取美股综合分析成功: {symbol}")
+            return result
+
+        except Exception as e:
+            logger.error(f"获取美股综合分析失败 {symbol}: {e}")
+            return {
+                "success": False,
+                "error": str(e)
+            }
+
+
+# 创建全局实例
+us_stock_service = USStockService()

backend/app/skills/us_stock_skill.py

@@ -0,0 +1,118 @@
+"""
+美股分析技能
+"""
+from typing import Dict, Any
+from app.skills.base import BaseSkill, SkillParameter
+from app.services.us_stock_service import us_stock_service
+from app.utils.logger import logger
+
+
+class USStockSkill(BaseSkill):
+    """美股分析技能"""
+
+    def __init__(self):
+        super().__init__()
+        self.name = "us_stock_analysis"
+        self.description = "分析美股（如 AAPL, TSLA, MSFT 等），获取实时行情、技术指标、基本面数据"
+        self.parameters = [
+            SkillParameter(
+                name="symbol",
+                type="string",
+                description="美股代码（如 AAPL, TSLA, MSFT）",
+                required=True
+            ),
+            SkillParameter(
+                name="analysis_type",
+                type="string",
+                description="分析类型：basic（基本信息）、technical（技术分析）、fundamental（基本面）、comprehensive（综合分析）",
+                required=False,
+                default="comprehensive"
+            )
+        ]
+
+    async def execute(self, **kwargs) -> Dict[str, Any]:
+        """
+        执行美股分析
+
+        Args:
+            symbol: 美股代码
+            analysis_type: 分析类型
+
+        Returns:
+            分析结果字典
+        """
+        try:
+            symbol = kwargs.get("symbol", "").upper()
+            analysis_type = kwargs.get("analysis_type", "comprehensive")
+
+            if not symbol:
+                return {
+                    "success": False,
+                    "error": "请提供美股代码"
+                }
+
+            logger.info(f"开始分析美股: {symbol}, 类型: {analysis_type}")
+
+            if analysis_type == "basic":
+                # 基本信息
+                info = us_stock_service.get_stock_info(symbol)
+                if not info:
+                    return {
+                        "success": False,
+                        "error": f"未找到股票 {symbol}"
+                    }
+                return {
+                    "success": True,
+                    "data": info
+                }
+
+            elif analysis_type == "technical":
+                # 技术分析
+                hist = us_stock_service.get_historical_data(symbol, period="6mo")
+                if hist is None or hist.empty:
+                    return {
+                        "success": False,
+                        "error": "无法获取历史数据"
+                    }
+
+                technical = us_stock_service.calculate_technical_indicators(hist)
+                latest = hist.iloc[-1]
+
+                return {
+                    "success": True,
+                    "data": {
+                        "symbol": symbol,
+                        "current_price": float(latest['Close']),
+                        "volume": int(latest['Volume']),
+                        "technical_indicators": technical
+                    }
+                }
+
+            elif analysis_type == "fundamental":
+                # 基本面分析
+                financial = us_stock_service.get_financial_data(symbol)
+                if not financial:
+                    return {
+                        "success": False,
+                        "error": "无法获取财务数据"
+                    }
+                return {
+                    "success": True,
+                    "data": financial
+                }
+
+            else:
+                # 综合分析（默认）
+                result = us_stock_service.get_comprehensive_analysis(symbol)
+                return result
+
+        except Exception as e:
+            logger.error(f"美股分析失败: {e}")
+            return {
+                "success": False,
+                "error": str(e)
+            }
+
+
+# 创建全局实例
+us_stock_skill = USStockSkill()

backend/requirements.txt

@@ -10,8 +10,9 @@ pydantic==2.5.3
 pydantic-settings==2.1.0
 python-dotenv==1.0.0
 slowapi==0.1.9
-websockets==12.0
+websockets>=13.0
 pandas>=2.2.0
 numpy>=1.26.0
 python-multipart==0.0.6
 aiohttp==3.9.1
+yfinance>=0.2.36

frontend/css/style.css

@@ -153,11 +153,11 @@ html, body {
     align-items: center;
     justify-content: center;
     text-align: center;
-    padding: 40px;
+    padding: 40px 20px;
 }
 
 .welcome-icon {
-    margin-bottom: 32px;
+    margin-bottom: 20px;
     opacity: 0.3;
 }
 
@@ -169,14 +169,56 @@ html, body {
     font-size: 28px;
     font-weight: 300;
     letter-spacing: 1px;
-    margin-bottom: 12px;
+    margin-bottom: 8px;
     color: var(--text-primary);
 }
 
-.welcome p {
+.welcome-subtitle {
     font-size: 14px;
     color: var(--text-secondary);
     letter-spacing: 0.5px;
+    margin-bottom: 40px;
+}
+
+.guide-section {
+    width: 100%;
+    max-width: 700px;
+    margin-bottom: 24px;
+}
+
+.example-queries {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 10px;
+    justify-content: center;
+}
+
+.example-btn {
+    padding: 10px 16px;
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid rgba(255, 255, 255, 0.1);
+    border-radius: 6px;
+    color: var(--text-secondary);
+    font-size: 13px;
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.example-btn:hover {
+    background: rgba(255, 255, 255, 0.05);
+    border-color: var(--accent);
+    color: var(--accent);
+    transform: translateY(-1px);
+}
+
+.welcome-footer {
+    margin-top: 20px;
+}
+
+.welcome-footer p {
+    font-size: 13px;
+    color: var(--text-secondary);
+    letter-spacing: 0.5px;
 }
 
 /* Messages */

frontend/index.html

@@ -53,12 +53,27 @@
                 <!-- Welcome Screen -->
                 <div v-if="messages.length === 0" class="welcome">
                     <div class="welcome-icon">
-                        <svg width="80" height="80" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1">
+                        <svg width="60" height="60" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1">
                             <path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/>
                         </svg>
                     </div>
-                    <h1>开始对话</h1>
+                    <h1>AI 金融智能体</h1>
-                    <p>输入股票代码或名称，获取实时分析</p>
+                    <p class="welcome-subtitle">支持 A股 + 美股双市场分析</p>
+
+                    <div class="guide-section">
+                        <div class="example-queries">
+                            <button class="example-btn" @click="sendExample('分析贵州茅台')">分析贵州茅台</button>
+                            <button class="example-btn" @click="sendExample('比亚迪怎么样')">比亚迪怎么样</button>
+                            <button class="example-btn" @click="sendExample('上证指数走势')">上证指数走势</button>
+                            <button class="example-btn" @click="sendExample('分析特斯拉')">分析特斯拉</button>
+                            <button class="example-btn" @click="sendExample('苹果股票')">苹果股票</button>
+                            <button class="example-btn" @click="sendExample('NVDA基本面')">NVDA基本面</button>
+                        </div>
+                    </div>
+
+                    <div class="welcome-footer">
+                        <p>💬 输入股票名称或代码开始分析</p>
+                    </div>
                 </div>
 
                 <!-- Messages -->
@@ -116,7 +131,7 @@
                     <textarea
                         v-model="userInput"
                         @keydown.enter.exact.prevent="sendMessage"
-                        placeholder="输入消息..."
+                        placeholder="输入股票名称或代码..."
                         rows="1"
                         :disabled="loading"
                         ref="textarea"

frontend/js/app.js

@@ -94,6 +94,12 @@ createApp({
             }
         },
 
+        sendExample(exampleText) {
+            // Set the example text to input and send
+            this.userInput = exampleText;
+            this.sendMessage();
+        },
+
         renderMarkdown(content) {
             if (!content) return '';