Merge pull request #5 from Cassianvale/dev-1

vue3重构版本

.env

@@ -3,3 +3,4 @@ API_URL=
 API_MODEL=
 ANNOUNCEMENT_TEXT=
 API_TIMEOUT=60
+LOGIN_PASSWORD=

.env.example

@@ -0,0 +1,9 @@
+# API配置
+API_KEY=
+API_URL=
+API_MODEL=
+API_TIMEOUT=60
+# 公告文本
+ANNOUNCEMENT_TEXT=欢迎使用！
+# 登录配置（为空时不需要登录，否则需要经过登录接口验证）
+LOGIN_PASSWORD=

.github/workflows/docker-image.yml

@@ -1,38 +1,190 @@
-name: Docker Build and Push
+name: Docker Build and Deploy
 
 on:
   push:
-    branches: [ main ]  # 只在 main 分支推送时触发
+    branches: [ main ]
+    paths-ignore:
+      - '**.md'
+      - 'docs/**'
+  workflow_dispatch:
+    inputs:
+      deploy:
+        description: '是否部署'
+        type: boolean
+        default: true
+
+env:
+  DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_USERNAME }}
+  TIME: ${{ github.run_number }}-${{ github.run_attempt }}
 
 jobs:
-  build:
+  prepare:
     runs-on: ubuntu-latest
+    outputs:
+      version: ${{ steps.version.outputs.version }}
+    steps:
+      - name: Generate version
+        id: version
+        run: |
+          echo "version=$(date +'%Y%m%d%H%M')" >> $GITHUB_OUTPUT
+
+  build:
+    needs: prepare
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        service: [backend, frontend]
+        include:
+          - service: backend
+            context: .
+          - service: frontend
+            context: ./frontend
+      # 允许其中一个任务失败时，其他任务仍继续执行
+      fail-fast: false
     
     steps:
-    - uses: actions/checkout@v2
+    - name: Checkout code
+      uses: actions/checkout@v4
     
     - name: Set up QEMU
-      uses: docker/setup-qemu-action@v2
+      uses: docker/setup-qemu-action@v3
-    
-    - name: Get current time
-      id: time
-      run: echo "TIME=$(date +'%Y%m%d%H%M')" >> $GITHUB_ENV
     
     - name: Login to Docker Hub
-      uses: docker/login-action@v1
+      uses: docker/login-action@v3
       with:
         username: ${{ secrets.DOCKERHUB_USERNAME }}
         password: ${{ secrets.DOCKERHUB_TOKEN }}
     
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v1
+      uses: docker/setup-buildx-action@v3
+      with:
+        platforms: linux/amd64,linux/arm64
+    
+    - name: Docker meta
+      id: meta
+      uses: docker/metadata-action@v5
+      with:
+        images: ${{ secrets.DOCKERHUB_USERNAME }}/stock-scanner-${{ matrix.service }}
+        tags: |
+          type=raw,value=latest
+          type=raw,value=${{ needs.prepare.outputs.version }}
     
     - name: Build and push
-      uses: docker/build-push-action@v2
+      uses: docker/build-push-action@v5
       with:
-        context: .
+        context: ${{ matrix.context }}
         platforms: linux/amd64,linux/arm64
         push: true
-        tags: |
+        tags: ${{ steps.meta.outputs.tags }}
-          ${{ secrets.DOCKERHUB_USERNAME }}/stock-scanner:latest
+        cache-from: type=gha,scope=${{ matrix.service }}
-          ${{ secrets.DOCKERHUB_USERNAME }}/stock-scanner:${{ env.TIME }}
+        cache-to: type=gha,scope=${{ matrix.service }},mode=max
+
+  deploy:
+    needs: [prepare, build]
+    runs-on: ubuntu-latest
+    if: |
+      success() && 
+      (github.ref == 'refs/heads/main' && github.event_name == 'push') || 
+      (github.event_name == 'workflow_dispatch' && inputs.deploy)
+    
+    steps:
+    - name: Checkout code for prod compose file
+      uses: actions/checkout@v4
+      with:
+        sparse-checkout: |
+          docker-compose.prod.yml
+    
+    - name: Create .env file for deployment
+      run: |
+        cat > .env << EOL
+        DOCKERHUB_USERNAME=${{ secrets.DOCKERHUB_USERNAME }}
+        TAG=${{ needs.prepare.outputs.version }}
+        API_KEY=${{ secrets.API_KEY }}
+        API_URL=${{ secrets.API_URL }}
+        API_MODEL=${{ secrets.API_MODEL }}
+        API_TIMEOUT=${{ secrets.API_TIMEOUT }}
+        LOGIN_PASSWORD=${{ secrets.LOGIN_PASSWORD }}
+        ANNOUNCEMENT_TEXT=${{ secrets.ANNOUNCEMENT_TEXT }}
+        EOL
+    
+    - name: Deploy to server
+      uses: appleboy/ssh-action@master
+      with:
+        host: ${{ secrets.SERVER_HOST }}
+        username: ${{ secrets.SERVER_USERNAME }}
+        key: ${{ secrets.SSH_PRIVATE_KEY }}
+        script_stop: true # 遇到错误时停止执行
+        envs: DEPLOY_PATH
+        script: |
+          # 创建备份目录（如果不存在）
+          mkdir -p ${DEPLOY_PATH}/backups
+          
+          # 如果存在旧容器，备份当前的配置和数据
+          if [ -f ${DEPLOY_PATH}/docker-compose.prod.yml ]; then
+            cp ${DEPLOY_PATH}/docker-compose.prod.yml ${DEPLOY_PATH}/backups/docker-compose.prod.$(date +%Y%m%d%H%M%S).yml
+            if [ -f ${DEPLOY_PATH}/.env ]; then
+              cp ${DEPLOY_PATH}/.env ${DEPLOY_PATH}/backups/.env.$(date +%Y%m%d%H%M%S)
+            fi
+          fi
+    
+    - name: Copy files to server
+      uses: appleboy/scp-action@master
+      with:
+        host: ${{ secrets.SERVER_HOST }}
+        username: ${{ secrets.SERVER_USERNAME }}
+        key: ${{ secrets.SSH_PRIVATE_KEY }}
+        source: "docker-compose.prod.yml,.env"
+        target: ${{ secrets.DEPLOY_PATH }}
+        overwrite: true
+    
+    - name: Start services
+      uses: appleboy/ssh-action@master
+      with:
+        host: ${{ secrets.SERVER_HOST }}
+        username: ${{ secrets.SERVER_USERNAME }}
+        key: ${{ secrets.SSH_PRIVATE_KEY }}
+        script_stop: true
+        script: |
+          cd ${{ secrets.DEPLOY_PATH }}
+          
+          # 拉取最新镜像并启动服务
+          docker compose -f docker-compose.prod.yml pull
+          docker compose -f docker-compose.prod.yml up -d
+          
+          # 等待服务启动完成
+          echo "等待服务启动..."
+          sleep 10
+          
+          # 验证服务是否正常运行
+          if ! curl -s http://localhost:80 > /dev/null; then
+            echo "前端服务未正常运行！"
+            exit 1
+          fi
+          
+          if ! curl -s http://localhost:8888/config > /dev/null; then
+            echo "后端服务未正常运行！"
+            exit 1
+          fi
+          
+          # 清理未使用的镜像和容器
+          docker system prune -af --volumes
+          
+          echo "部署完成并验证成功！"
+  
+  notify:
+    needs: [prepare, build, deploy]
+    runs-on: ubuntu-latest
+    if: always()
+    steps:
+      - name: Notify deployment status
+        uses: rtCamp/action-slack-notify@v2
+        env:
+          SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
+          SLACK_CHANNEL: deployments
+          SLACK_COLOR: ${{ needs.deploy.result == 'success' && 'good' || needs.deploy.result == 'skipped' && 'warning' || 'danger' }}
+          SLACK_TITLE: Stock Scanner Deployment Status
+          SLACK_MESSAGE: |
+            Build: ${{ needs.build.result == 'success' && '✅' || '❌' }}
+            Deploy: ${{ needs.deploy.result == 'success' && '✅' || needs.deploy.result == 'skipped' && '⏭️' || '❌' }}
+            Version: ${{ needs.prepare.outputs.version }}
+          SLACK_FOOTER: GitHub Actions

.gitignore

@@ -21,6 +21,23 @@ build_upload.log
 *.spec
 *.zip
 
+# frontend
+node_modules/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+dist/
+dist-ssr
+*.local
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

Dockerfile

@@ -1,26 +1,52 @@
 # 使用 Python 3.10 作为基础镜像
+FROM python:3.10-slim as builder
+
+# 设置工作目录
+WORKDIR /app
+
+# 安装系统依赖和构建依赖
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    ca-certificates \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制项目文件
+COPY requirements.txt /app/
+
+# 安装 Python 依赖
+RUN pip install --no-cache-dir --user -r requirements.txt
+
+# 第二阶段：运行阶段
 FROM python:3.10-slim
 
 # 设置工作目录
 WORKDIR /app
 
-# 安装系统依赖
+# 安装运行时依赖
 RUN apt-get update && apt-get install -y \
     libgl1-mesa-glx \
-    ca-certificates \ 
+    ca-certificates \
     && rm -rf /var/lib/apt/lists/*
 
-# 复制项目文件
+# 从构建阶段复制Python依赖
-COPY . /app/
+COPY --from=builder /root/.local /root/.local
 
-# 安装 Python 依赖
+# 确保脚本路径在PATH中
-RUN pip install --no-cache-dir -r requirements.txt
+ENV PATH=/root/.local/bin:$PATH
 
 # 设置环境变量
 ENV PYTHONPATH=/app
 
-# 暴露端口（如果需要）
+# 复制应用代码
+COPY . /app/
+
+# 暴露端口
 EXPOSE 8888
 
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+  CMD curl -f http://localhost:8888/config || exit 1
+
 # 启动命令
 CMD ["python", "web_server.py"]

README.md

@@ -9,6 +9,8 @@
 3. 完善Dockerfile、GitHub Actions 支持docker一键部署使用。
 4. 支持x86_64 和 ARM64架构镜像
 5. 支持流式输出，支持前端传入Key(仅作为本地用户使用，日志等内容不会输出) 感谢@Cassianvale
+6. 重构为Vue3+Vite+TS+Naive UI，支持响应式布局
+7. 支持GitHub Actions 一键部署
 
 ## docker一键部署
 ```
@@ -19,9 +21,11 @@ docker run -d \
   -e API_URL=替换为你的api地址 \
   -e API_MODEL=替换为你的模型 \
   -e API_TIMEOUT=60 \
+  -e LOGIN_PASSWORD=替换为你的密码 \
   lanzhihong/stock-scanner:latest
 
 API_TIMEOUT=60   202503040712版本开始 (AI分析发生错误，查看日志是否有timed out类似错误，需要增加你的API超时时间)
+LOGIN_PASSWORD 为空时，表示不需要登录，否则需要经过登录接口验证
 
 注意⚠️： 环境变量名变更，更新版本后需要调整！！！
 
@@ -44,6 +48,18 @@ API_URL 处理逻辑说明：
 ```
 默认8888端口，部署完成后访问  http://127.0.0.1:8888 即可使用。
 
+## Github Actions 部署
+
+| 环境变量 | 说明 |
+| --- | --- |
+| DOCKERHUB_USERNAME | Docker Hub用户名 |
+| DOCKERHUB_TOKEN | Docker Hub访问令牌 |
+| SERVER_HOST | 部署服务器地址 |
+| SERVER_USERNAME | 服务器用户名 |
+| SSH_PRIVATE_KEY | SSH私钥 |
+| DEPLOY_PATH | 部署路径 |
+| SLACK_WEBHOOK | Slack通知Webhook（可选） |
+
 
 ## 注意事项 (Notes)
 - 股票分析仅供参考，不构成投资建议

docker-compose.prod.yml

@@ -0,0 +1,48 @@
+version: '3.8'
+
+services:
+  backend:
+    image: ${DOCKERHUB_USERNAME}/stock-scanner-backend:${TAG:-latest}
+    container_name: stock-scanner-backend
+    ports:
+      - "8888:8888"
+    environment:
+      - API_KEY=${API_KEY}
+      - API_URL=${API_URL}
+      - API_MODEL=${API_MODEL}
+      - API_TIMEOUT=${API_TIMEOUT}
+      - LOGIN_PASSWORD=${LOGIN_PASSWORD}
+      - ANNOUNCEMENT_TEXT=${ANNOUNCEMENT_TEXT}
+    volumes:
+      - ./logs:/app/logs
+      - ./.env:/app/.env
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8888/config"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 5s
+    networks:
+      - stock-scanner-network
+
+  frontend:
+    image: ${DOCKERHUB_USERNAME}/stock-scanner-frontend:${TAG:-latest}
+    container_name: stock-scanner-frontend
+    ports:
+      - "80:80"
+    depends_on:
+      - backend
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:80"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 5s
+    networks:
+      - stock-scanner-network
+
+networks:
+  stock-scanner-network:
+    driver: bridge 

docker-compose.yml

@@ -1,8 +1,11 @@
 version: '3.8'
 
 services:
-  stock-analyzer:
+  backend:
-    build: .
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: stock-scanner-backend
     ports:
       - "8888:8888"
     environment:
@@ -10,6 +13,39 @@ services:
       - API_URL=${API_URL}
       - API_MODEL=${API_MODEL}
       - API_TIMEOUT=${API_TIMEOUT}
+      - LOGIN_PASSWORD=${LOGIN_PASSWORD}
+      - ANNOUNCEMENT_TEXT=${ANNOUNCEMENT_TEXT}
     volumes:
-      - .:/app
+      - ./logs:/app/logs
     restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8888/config"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 5s
+    networks:
+      - stock-scanner-network
+
+  frontend:
+    build:
+      context: ./frontend
+      dockerfile: Dockerfile
+    container_name: stock-scanner-frontend
+    ports:
+      - "80:80"
+    depends_on:
+      - backend
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:80"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 5s
+    networks:
+      - stock-scanner-network
+
+networks:
+  stock-scanner-network:
+    driver: bridge

frontend/Dockerfile

@@ -0,0 +1,36 @@
+# 构建阶段
+FROM node:18-alpine as build-stage
+
+# 设置工作目录
+WORKDIR /app
+
+# 复制 package.json 和 package-lock.json（如果有）
+COPY package*.json ./
+
+# 安装依赖
+RUN npm ci
+
+# 复制项目文件
+COPY . .
+
+# 构建应用
+RUN npm run build
+
+# 生产阶段
+FROM nginx:stable-alpine as production-stage
+
+# 复制自定义nginx配置
+COPY nginx.conf /etc/nginx/conf.d/default.conf
+
+# 从构建阶段复制构建结果到nginx服务目录
+COPY --from=build-stage /app/dist /usr/share/nginx/html
+
+# 暴露80端口
+EXPOSE 80
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+  CMD wget --quiet --tries=1 --spider http://localhost:80 || exit 1
+
+# 设定启动命令
+CMD ["nginx", "-g", "daemon off;"] 

frontend/README.md

@@ -0,0 +1,5 @@
+# Vue 3 + TypeScript + Vite
+
+This template should help get you started developing with Vue 3 and TypeScript in Vite. The template uses Vue 3 `<script setup>` SFCs, check out the [script setup docs](https://v3.vuejs.org/api/sfc-script-setup.html#sfc-script-setup) to learn more.
+
+Learn more about the recommended Project Setup and IDE Support in the [Vue Docs TypeScript Guide](https://vuejs.org/guide/typescript/overview.html#project-setup).

frontend/index.html

@@ -0,0 +1,14 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>股票AI分析系统</title>
+    <link rel="icon" href="/favicon.ico">
+    <meta name="description" content="股票AI分析系统 - 基于Vue 3 + TypeScript + Naive UI">
+  </head>
+  <body>
+    <div id="app"></div>
+    <script type="module" src="/src/main.ts"></script>
+  </body>
+</html>

frontend/nginx.conf

@@ -0,0 +1,85 @@
+server {
+    listen       80;
+    server_name  localhost;
+
+    #access_log  /var/log/nginx/host.access.log  main;
+    
+    root   /usr/share/nginx/html;
+    index  index.html;
+
+    # 缓存静态资源
+    location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
+        expires 1y;
+        add_header Cache-Control "public, max-age=31536000, immutable";
+    }
+
+    # API请求代理到后端服务 - 使用相对路径
+    location /api/ {
+        proxy_pass http://backend:8888/api/;
+        proxy_http_version 1.1;
+        proxy_set_header Upgrade $http_upgrade;
+        proxy_set_header Connection 'upgrade';
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_cache_bypass $http_upgrade;
+        proxy_read_timeout 300s;
+    }
+
+    location /login {
+        proxy_pass http://backend:8888/login;
+        proxy_http_version 1.1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_cache_bypass $http_upgrade;
+    }
+
+    location /check_auth {
+        proxy_pass http://backend:8888/check_auth;
+        proxy_http_version 1.1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_cache_bypass $http_upgrade;
+    }
+
+    location /need_login {
+        proxy_pass http://backend:8888/need_login;
+        proxy_http_version 1.1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_cache_bypass $http_upgrade;
+    }
+
+    location /config {
+        proxy_pass http://backend:8888/config;
+        proxy_http_version 1.1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_cache_bypass $http_upgrade;
+    }
+
+    location /analyze {
+        proxy_pass http://backend:8888/analyze;
+        proxy_http_version 1.1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_cache_bypass $http_upgrade;
+        proxy_read_timeout 300s;
+    }
+
+    # 所有其他路由返回index.html（SPA应用需要）
+    location / {
+        try_files $uri $uri/ /index.html;
+    }
+
+    # 错误页面
+    error_page   500 502 503 504  /50x.html;
+    location = /50x.html {
+        root   /usr/share/nginx/html;
+    }
+} 

frontend/package.json

@@ -0,0 +1,30 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vue-tsc -b && vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@types/node": "^22.13.9",
+    "@vicons/ionicons5": "^0.13.0",
+    "@vueuse/core": "^12.8.2",
+    "axios": "^1.8.1",
+    "marked": "^15.0.7",
+    "naive-ui": "^2.41.0",
+    "npm": "^10.8.2",
+    "vue": "^3.5.13",
+    "vue-router": "^4.5.0"
+  },
+  "devDependencies": {
+    "@types/marked": "^5.0.2",
+    "@vitejs/plugin-vue": "^5.2.1",
+    "@vue/tsconfig": "^0.7.0",
+    "typescript": "~5.7.2",
+    "vite": "^6.2.0",
+    "vue-tsc": "^2.2.4"
+  }
+}

frontend/public/vite.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

frontend/src/App.vue

@@ -0,0 +1,40 @@
+<template>
+  <n-config-provider :theme="theme">
+    <n-message-provider>
+      <n-loading-bar-provider>
+        <n-dialog-provider>
+          <n-notification-provider>
+            <router-view />
+          </n-notification-provider>
+        </n-dialog-provider>
+      </n-loading-bar-provider>
+    </n-message-provider>
+  </n-config-provider>
+</template>
+
+<script setup lang="ts">
+import { ref } from 'vue'
+import { 
+  NConfigProvider, 
+  NMessageProvider, 
+  NLoadingBarProvider, 
+  NDialogProvider, 
+  NNotificationProvider, 
+} from 'naive-ui'
+
+// 主题设置 (默认使用亮色主题)
+const theme = ref<any>(null) // 可以切换为 darkTheme 以启用暗色模式
+</script>
+
+<style>
+body {
+  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, 
+    Cantarell, 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  margin: 0;
+  padding: 0;
+  min-height: 100vh;
+  background-color: #f6f6f6;
+}
+</style>

frontend/src/assets/vue.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="37.07" height="36" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 198"><path fill="#41B883" d="M204.8 0H256L128 220.8L0 0h97.92L128 51.2L157.44 0h47.36Z"></path><path fill="#41B883" d="m0 0l128 220.8L256 0h-51.2L128 132.48L50.56 0H0Z"></path><path fill="#35495E" d="M50.56 0L128 133.12L204.8 0h-47.36L128 51.2L97.92 0H50.56Z"></path></svg>

frontend/src/components/AnnouncementBanner.vue

@@ -0,0 +1,128 @@
+<template>
+  <div v-if="showAnnouncement" class="announcement-container">
+    <n-card class="announcement-card">
+      <template #header>
+        <div class="announcement-header">
+          <n-icon size="18" :component="InformationCircleIcon" class="info-icon" />
+          <span>系统公告</span>
+        </div>
+      </template>
+      <div class="announcement-content" v-html="processedContent"></div>
+      <div class="announcement-timer">{{ remainingTimeText }}</div>
+      <template #action>
+        <n-button quaternary circle size="small" @click="closeAnnouncement">
+          <template #icon>
+            <n-icon :component="CloseIcon" />
+          </template>
+        </n-button>
+      </template>
+    </n-card>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref, computed, onMounted, onBeforeUnmount } from 'vue';
+import { NCard, NIcon, NButton } from 'naive-ui';
+import { InformationCircleOutline as InformationCircleIcon } from '@vicons/ionicons5';
+import { Close as CloseIcon } from '@vicons/ionicons5';
+
+const props = defineProps<{
+  content: string;
+  autoCloseTime?: number;
+}>();
+
+const showAnnouncement = ref(true);
+const remainingTime = ref(props.autoCloseTime || 5);
+const timer = ref<number | null>(null);
+
+const remainingTimeText = computed(() => {
+  return `${remainingTime.value}秒后自动关闭`;
+});
+
+const processedContent = computed(() => {
+  // 处理文本中的URL
+  const urlRegex = /(https?:\/\/[^\s]+)/g;
+  return props.content.replace(
+    urlRegex, 
+    '<a href="$1" target="_blank" class="announcement-link">$1</a>'
+  );
+});
+
+function closeAnnouncement() {
+  showAnnouncement.value = false;
+  if (timer.value !== null) {
+    window.clearInterval(timer.value);
+    timer.value = null;
+  }
+}
+
+function updateTimer() {
+  if (remainingTime.value <= 1) {
+    closeAnnouncement();
+  } else {
+    remainingTime.value--;
+  }
+}
+
+onMounted(() => {
+  timer.value = window.setInterval(updateTimer, 1000);
+});
+
+onBeforeUnmount(() => {
+  if (timer.value !== null) {
+    window.clearInterval(timer.value);
+  }
+});
+</script>
+
+<style scoped>
+.announcement-container {
+  position: fixed;
+  top: 1rem;
+  right: 1rem;
+  max-width: 24rem;
+  z-index: 50;
+  animation: fadeInDown 0.3s ease-out;
+}
+
+.announcement-card {
+  border-left: 4px solid var(--n-primary-color);
+}
+
+.announcement-header {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  font-weight: 500;
+}
+
+.info-icon {
+  color: var(--n-primary-color);
+}
+
+.announcement-content {
+  margin-bottom: 0.5rem;
+  white-space: pre-line;
+}
+
+.announcement-timer {
+  font-size: 0.75rem;
+  color: var(--n-text-color-disabled);
+}
+
+.announcement-link {
+  color: var(--n-primary-color);
+  text-decoration: underline;
+}
+
+@keyframes fadeInDown {
+  from {
+    opacity: 0;
+    transform: translateY(-10px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+</style>

frontend/src/components/ApiConfigPanel.vue

@@ -0,0 +1,642 @@
+<template>
+  <div class="api-config-section">
+    <n-button
+      class="toggle-button"
+      size="small"
+      @click="toggleConfig"
+      :quaternary="true"
+      :type="expanded ? 'primary' : 'default'"
+    >
+      <template #icon>
+        <n-icon :component="expanded ? ChevronUpIcon : ChevronDownIcon" />
+      </template>
+      <span class="toggle-text">API配置 {{ expanded ? '收起' : '展开' }}</span>
+    </n-button>
+    
+    <n-collapse-transition :show="expanded">
+      <n-card class="api-config-card" :bordered="false">
+        <n-alert title="OpenAI API配置" type="info" v-if="isApiInfoVisible" class="api-info-alert">
+          <template #icon>
+            <n-icon :component="InformationCircleIcon" />
+          </template>
+          <p>您可以配置自己的API，也可以使用系统默认配置。API密钥仅在您的浏览器中使用，不会发送到服务器存储。</p>
+          <div class="alert-actions">
+            <n-button text @click="isApiInfoVisible = false">
+              <template #icon>
+                <n-icon :component="CloseIcon" />
+              </template>
+            </n-button>
+          </div>
+        </n-alert>
+
+        <n-grid :cols="24" :x-gap="16" :y-gap="16">
+          <n-grid-item :span="24" :lg-span="14">
+            <n-form-item label="API URL" path="apiUrl">
+              <n-input 
+                v-model:value="apiConfig.apiUrl" 
+                placeholder="https://api.openai.com/v1/chat/completions"
+                @update:value="handleConfigChange"
+                round
+              >
+                <template #prefix>
+                  <n-icon :component="GlobeIcon" />
+                </template>
+              </n-input>
+              <template #feedback>
+                <div class="url-feedback">
+                  <span class="formatted-url">实际请求地址: {{ formattedUrl }}</span>
+                  <div class="url-tips">
+                    <div>提示: URL以/结尾将忽略v1路径</div>
+                    <div>URL以#结尾将使用原始地址</div>
+                  </div>
+                </div>
+              </template>
+            </n-form-item>
+          </n-grid-item>
+
+          <n-grid-item :span="24" :lg-span="10">
+            <n-form-item label="API Key" path="apiKey">
+              <n-input 
+                v-model:value="apiConfig.apiKey" 
+                type="password" 
+                placeholder="sk-..."
+                show-password-on="click"
+                @update:value="handleConfigChange"
+                round
+              >
+                <template #prefix>
+                  <n-icon :component="KeyIcon" />
+                </template>
+              </n-input>
+            </n-form-item>
+          </n-grid-item>
+
+          <n-grid-item :span="12" :lg-span="12">
+            <n-form-item label="模型" path="apiModel">
+              <n-input
+                v-model:value="apiConfig.apiModel"
+                placeholder="输入或选择模型名称"
+                @update:value="handleConfigChange"
+                round
+              >
+                <template #prefix>
+                  <n-icon :component="CodeIcon" />
+                </template>
+                <template #suffix>
+                  <n-dropdown
+                    trigger="click"
+                    :options="modelOptions"
+                    @select="selectModel"
+                    placement="bottom-end"
+                  >
+                    <n-button quaternary circle size="small" class="model-dropdown-btn">
+                      <template #icon>
+                        <n-icon :component="ChevronDownIcon" />
+                      </template>
+                    </n-button>
+                  </n-dropdown>
+                </template>
+              </n-input>
+              <template #feedback>
+                <div class="model-suggestions">
+                  <div class="model-tip">您可以直接输入模型名称，或点击右侧按钮从下拉菜单选择</div>
+                  <span>常用模型:</span>
+                  <div class="model-chips">
+                    <n-tag 
+                      v-for="model in commonModels" 
+                      :key="model.key"
+                      size="small"
+                      round
+                      clickable
+                      @click="selectModel(model.key)"
+                    >
+                      {{ model.label }}
+                    </n-tag>
+                  </div>
+                </div>
+              </template>
+            </n-form-item>
+          </n-grid-item>
+
+          <n-grid-item :span="12" :lg-span="12">
+            <n-form-item label="超时时间(秒)" path="apiTimeout">
+              <n-input-number 
+                v-model:value="apiTimeout" 
+                placeholder="60"
+                :min="1"
+                :max="300"
+                @update:value="handleTimeoutChange"
+                :show-button="false"
+                class="timeout-input"
+              >
+                <template #suffix>
+                  <div class="timeout-controls">
+                    <n-button size="tiny" quaternary @click="decreaseTimeout">
+                      <template #icon><n-icon :component="RemoveIcon" /></template>
+                    </n-button>
+                    <n-button size="tiny" quaternary @click="increaseTimeout">
+                      <template #icon><n-icon :component="AddIcon" /></template>
+                    </n-button>
+                  </div>
+                </template>
+              </n-input-number>
+            </n-form-item>
+          </n-grid-item>
+        </n-grid>
+
+        <div class="api-actions">
+          <div class="api-save-option">
+            <n-switch 
+              v-model:value="apiConfig.saveApiConfig"
+              @update:value="handleConfigChange"
+            />
+            <span class="save-label">保存配置到本地</span>
+          </div>
+          
+          <div class="api-buttons">
+            <n-button 
+              type="primary" 
+              :loading="testingConnection" 
+              :disabled="!isConfigValid"
+              @click="testConnection"
+              round
+            >
+              <template #icon>
+                <n-icon :component="CheckmarkIcon" />
+              </template>
+              测试连接
+            </n-button>
+
+            <n-button @click="resetConfig" round>
+              <template #icon>
+                <n-icon :component="RefreshIcon" />
+              </template>
+              重置
+            </n-button>
+          </div>
+        </div>
+        
+        <n-divider v-if="connectionStatus" style="margin: 16px 0 12px" />
+        
+        <div v-if="connectionStatus" class="connection-status" :class="connectionStatus.type">
+          <n-icon :component="connectionStatus.icon" class="status-icon" />
+          <span class="status-message">{{ connectionStatus.message }}</span>
+        </div>
+      </n-card>
+    </n-collapse-transition>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref, computed, onMounted } from 'vue';
+import { 
+  NButton, 
+  NIcon, 
+  NCard, 
+  NCollapseTransition, 
+  NGrid, 
+  NGridItem, 
+  NFormItem, 
+  NInput,
+  NInputNumber,
+  NSwitch,
+  NAlert,
+  NDivider,
+  NDropdown,
+  NTag,
+  useMessage
+} from 'naive-ui';
+import { 
+  ChevronDown as ChevronDownIcon, 
+  ChevronUp as ChevronUpIcon,
+  InformationCircleOutline as InformationCircleIcon,
+  Close as CloseIcon,
+  Globe as GlobeIcon,
+  Key as KeyIcon,
+  CheckmarkCircleOutline as CheckmarkIcon,
+  RefreshOutline as RefreshIcon,
+  AddOutline as AddIcon,
+  RemoveOutline as RemoveIcon,
+  CheckmarkCircle as SuccessIcon,
+  CloseCircle as ErrorIcon,
+  CodeSlashOutline as CodeIcon
+} from '@vicons/ionicons5';
+import { apiService } from '@/services/api';
+import { saveApiConfigToLocalStorage, loadApiConfig } from '@/utils';
+import type { ApiConfig } from '@/types';
+
+const props = defineProps<{
+  defaultApiUrl?: string;
+  defaultApiModel?: string;
+  defaultApiTimeout?: string;
+}>();
+
+const emit = defineEmits<{
+  (e: 'update:apiConfig', value: ApiConfig): void;
+}>();
+
+const message = useMessage();
+const expanded = ref(false);
+const testingConnection = ref(false);
+const isApiInfoVisible = ref(true);
+
+// 连接状态
+const connectionStatus = ref<{
+  type: 'success' | 'error';
+  message: string;
+  icon: any;
+} | null>(null);
+
+// 模型选项
+const modelOptions = [
+  { label: 'GPT-3.5', key: 'gpt-3.5-turbo' },
+  { label: 'GPT-4o', key: 'gpt-4o' },
+  { label: 'DeepSeek V3', key: 'deepseek-chat' },
+  { label: 'DeepSeek R1', key: 'deepseek-reasoner' },
+  { label: 'Claude 3.5 Sonnet', key: 'claude-3-5-sonnet' },
+  { label: 'Claude 3.5 Sonnet 20241022', key: 'claude-3-5-sonnet-20241022' },
+  { label: 'Gemini 1.5 Pro', key: 'gemini-1.5-pro' },
+  { label: 'Gemini 1.5 Flash', key: 'gemini-1.5-flash' },
+  { label: 'Gemini 2.0 Pro', key: 'gemini-2.0-pro' },
+  { label: 'Gemini 2.0 Flash', key: 'gemini-2.0-flash' }
+];
+
+// 常用模型（用于快速选择）
+const commonModels = [
+  { label: 'GPT-3.5', key: 'gpt-3.5-turbo' },
+  { label: 'GPT-4o', key: 'gpt-4o' },
+  { label: 'Claude 3.5', key: 'claude-3-5-sonnet' },
+  { label: 'Gemini 2.0', key: 'gemini-2.0-flash' },
+  { label: 'DeepSeek V3', key: 'deepseek-chat' },
+];
+
+// API配置
+const apiConfig = ref<ApiConfig>({
+  apiUrl: props.defaultApiUrl || '',
+  apiKey: '',
+  apiModel: props.defaultApiModel || '',
+  apiTimeout: props.defaultApiTimeout || '60',
+  saveApiConfig: false
+});
+
+const apiTimeout = computed({
+  get: () => parseInt(apiConfig.value.apiTimeout) || 60,
+  set: (val: number) => {
+    apiConfig.value.apiTimeout = val.toString();
+  }
+});
+
+const isConfigValid = computed(() => {
+  return apiConfig.value.apiUrl && apiConfig.value.apiKey;
+});
+
+const formattedUrl = computed(() => {
+  return formatApiUrl(apiConfig.value.apiUrl);
+});
+
+function toggleConfig() {
+  expanded.value = !expanded.value;
+}
+
+function handleConfigChange() {
+  console.log('API配置变更:', apiConfig.value);
+  
+  // 如果选择了保存配置，则自动保存
+  if (apiConfig.value.saveApiConfig) {
+    saveApiConfigToLocalStorage({
+      apiUrl: apiConfig.value.apiUrl,
+      apiKey: apiConfig.value.apiKey,
+      apiModel: apiConfig.value.apiModel,
+      apiTimeout: apiConfig.value.apiTimeout,
+      saveApiConfig: true
+    });
+  }
+  
+  // 向父组件发送更新事件
+  emit('update:apiConfig', { ...apiConfig.value });
+}
+
+function handleTimeoutChange(value: number | null) {
+  if (value !== null) {
+    apiConfig.value.apiTimeout = value.toString();
+    handleConfigChange();
+  }
+}
+
+function increaseTimeout() {
+  if (apiTimeout.value < 300) {
+    apiTimeout.value += 10;
+    handleTimeoutChange(apiTimeout.value);
+  }
+}
+
+function decreaseTimeout() {
+  if (apiTimeout.value > 10) {
+    apiTimeout.value -= 10;
+    handleTimeoutChange(apiTimeout.value);
+  }
+}
+
+function formatApiUrl(url: string): string {
+  if (!url) return '';
+  
+  try {
+    // 使用与后端一致的URL格式化逻辑
+    if (url.endsWith('/')) {
+      return `${url}chat/completions`;
+    } else if (url.endsWith('#')) {
+      return url.replace('#', '');
+    } else {
+      return `${url}/v1/chat/completions`;
+    }
+  } catch (e) {
+    // 如果URL格式错误，则返回原始字符串
+    return url;
+  }
+}
+
+async function testConnection() {
+  if (!isConfigValid.value) {
+    message.error('请填写完整的API配置信息');
+    return;
+  }
+  
+  testingConnection.value = true;
+  connectionStatus.value = null;
+  
+  try {
+    const response = await apiService.testApiConnection({
+      api_url: apiConfig.value.apiUrl,
+      api_key: apiConfig.value.apiKey,
+      api_model: apiConfig.value.apiModel,
+      api_timeout: apiConfig.value.apiTimeout
+    });
+    
+    if (response.success) {
+      message.success('API连接测试成功');
+      connectionStatus.value = {
+        type: 'success',
+        message: '连接成功！API配置有效。',
+        icon: SuccessIcon
+      };
+      
+      // 如果选择了保存配置，则保存
+      if (apiConfig.value.saveApiConfig) {
+        saveApiConfigToLocalStorage({
+          apiUrl: apiConfig.value.apiUrl,
+          apiKey: apiConfig.value.apiKey,
+          apiModel: apiConfig.value.apiModel,
+          apiTimeout: apiConfig.value.apiTimeout,
+          saveApiConfig: true
+        });
+      }
+    } else {
+      message.error(`API连接测试失败: ${response.message}`);
+      connectionStatus.value = {
+        type: 'error',
+        message: `连接失败: ${response.message}`,
+        icon: ErrorIcon
+      };
+    }
+  } catch (error: any) {
+    message.error(`测试连接出错: ${error.message || '未知错误'}`);
+    connectionStatus.value = {
+      type: 'error',
+      message: `连接错误: ${error.message || '未知错误'}`,
+      icon: ErrorIcon
+    };
+  } finally {
+    testingConnection.value = false;
+  }
+}
+
+function resetConfig() {
+  apiConfig.value = {
+    apiUrl: props.defaultApiUrl || '',
+    apiKey: '',
+    apiModel: props.defaultApiModel || '',
+    apiTimeout: props.defaultApiTimeout || '60',
+    saveApiConfig: false
+  };
+  
+  // 清除本地存储
+  if (window.localStorage) {
+    localStorage.removeItem('apiConfig');
+  }
+  
+  connectionStatus.value = null;
+  message.success('已重置API配置');
+  emit('update:apiConfig', { ...apiConfig.value });
+}
+
+// 选择模型
+function selectModel(key: string) {
+  console.log('选择模型:', key);
+  apiConfig.value.apiModel = key;
+  handleConfigChange();
+}
+
+onMounted(() => {
+  // 加载保存的配置
+  const savedConfig = loadApiConfig();
+  
+  if (savedConfig.saveApiConfig) {
+    apiConfig.value = {
+      apiUrl: savedConfig.apiUrl || props.defaultApiUrl || '',
+      apiKey: savedConfig.apiKey || '',
+      apiModel: savedConfig.apiModel || props.defaultApiModel || '',
+      apiTimeout: savedConfig.apiTimeout || props.defaultApiTimeout || '60',
+      saveApiConfig: true
+    };
+    
+    // 通知父组件配置已加载
+    emit('update:apiConfig', { ...apiConfig.value });
+  }
+});
+</script>
+
+<style scoped>
+.api-config-section {
+  margin-bottom: 1.5rem;
+  position: relative;
+}
+
+.toggle-button {
+  margin-bottom: 0.75rem;
+  font-weight: 500;
+  transition: all 0.3s ease;
+  border-radius: 16px;
+  padding: 4px 12px;
+}
+
+.toggle-button:hover {
+  transform: translateY(-1px);
+  box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+}
+
+.toggle-text {
+  margin-left: 4px;
+}
+
+.api-config-card {
+  margin-bottom: 1rem;
+  border-radius: 8px;
+  box-shadow: 0 2px 12px rgba(0, 0, 0, 0.08);
+  background: linear-gradient(to bottom, rgba(240, 240, 245, 0.5), rgba(250, 250, 252, 0.8));
+  padding: 16px;
+  transition: all 0.3s ease;
+}
+
+.api-info-alert {
+  margin-bottom: 16px;
+  border-radius: 8px;
+}
+
+.url-feedback {
+  padding: 6px 0;
+}
+
+.formatted-url {
+  color: var(--n-text-color-info);
+  font-size: 0.85rem;
+  display: block;
+  margin-bottom: 0.25rem;
+  font-weight: 500;
+}
+
+.url-tips {
+  color: var(--n-text-color-info);
+  font-size: 0.75rem;
+  opacity: 0.8;
+  line-height: 1.4;
+}
+
+.alert-actions {
+  margin-top: 0.5rem;
+  text-align: right;
+}
+
+.api-actions {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-top: 1.5rem;
+  flex-wrap: wrap;
+  gap: 12px;
+}
+
+.api-save-option {
+  display: flex;
+  align-items: center;
+  background-color: rgba(0, 0, 0, 0.02);
+  padding: 6px 12px;
+  border-radius: 16px;
+}
+
+.save-label {
+  margin-left: 0.5rem;
+  font-size: 0.875rem;
+  font-weight: 500;
+}
+
+.api-buttons {
+  display: flex;
+  gap: 0.75rem;
+}
+
+.timeout-input {
+  width: 100%;
+}
+
+.timeout-controls {
+  display: flex;
+  align-items: center;
+  margin-left: 8px;
+}
+
+.connection-status {
+  display: flex;
+  align-items: center;
+  padding: 10px 16px;
+  border-radius: 8px;
+  margin-top: 8px;
+  font-weight: 500;
+  animation: fadeIn 0.3s ease;
+}
+
+.connection-status.success {
+  background-color: rgba(24, 160, 88, 0.1);
+  color: var(--n-success-color);
+}
+
+.connection-status.error {
+  background-color: rgba(208, 48, 80, 0.1);
+  color: var(--n-error-color);
+}
+
+.status-icon {
+  margin-right: 8px;
+  font-size: 1.25rem;
+}
+
+.status-message {
+  font-size: 0.9rem;
+}
+
+@keyframes fadeIn {
+  from { opacity: 0; transform: translateY(5px); }
+  to { opacity: 1; transform: translateY(0); }
+}
+
+@media (max-width: 768px) {
+  .api-actions {
+    flex-direction: column;
+    align-items: flex-start;
+  }
+  
+  .api-buttons {
+    width: 100%;
+    justify-content: space-between;
+  }
+}
+
+.model-suggestions {
+  margin-top: 6px;
+  font-size: 0.75rem;
+  color: var(--n-text-color-3);
+}
+
+.model-chips {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 6px;
+  margin-top: 4px;
+}
+
+.model-chips :deep(.n-tag) {
+  cursor: pointer;
+  transition: all 0.2s ease;
+}
+
+.model-chips :deep(.n-tag:hover) {
+  background-color: rgba(32, 128, 240, 0.1);
+  transform: translateY(-1px);
+}
+
+.model-tip {
+  margin-bottom: 6px;
+  font-size: 0.75rem;
+  color: var(--n-text-color-3);
+  font-style: italic;
+}
+
+.model-dropdown-btn {
+  background-color: rgba(32, 128, 240, 0.1);
+  transition: all 0.2s ease;
+}
+
+.model-dropdown-btn:hover {
+  background-color: rgba(32, 128, 240, 0.2);
+  transform: translateY(-1px);
+}
+</style>

frontend/src/components/LoginPage.vue

@@ -0,0 +1,536 @@
+<template>
+  <div class="login-container">
+    <div class="login-background">
+      <div class="login-shape shape1"></div>
+      <div class="login-shape shape2"></div>
+      <div class="login-shape shape3"></div>
+      <div class="login-shape shape4"></div>
+      <div class="login-shape shape5"></div>
+      <div class="login-particle particle1"></div>
+      <div class="login-particle particle2"></div>
+      <div class="login-particle particle3"></div>
+      <div class="login-particle particle4"></div>
+      <div class="login-particle particle5"></div>
+      <div class="login-particle particle6"></div>
+    </div>
+    
+    <n-card class="login-card" :bordered="false">
+      <div class="login-header">
+        <div class="login-logo">
+          <n-icon :component="BarChartIcon" color="#2080f0" size="36" class="logo-icon" />
+        </div>
+        <h1 class="login-title">股票AI分析系统</h1>
+        <p class="login-subtitle">使用AI技术分析股票市场趋势</p>
+      </div>
+      
+      <n-form
+        ref="formRef"
+        :model="formValue"
+        :rules="rules"
+        label-placement="left"
+        label-width="0"
+        require-mark-placement="right-hanging"
+        class="login-form"
+      >
+        <n-form-item path="password">
+          <n-input
+            v-model:value="formValue.password"
+            type="password"
+            placeholder="请输入密码"
+            @keyup.enter="handleLogin"
+            size="large"
+            class="login-input"
+          >
+            <template #prefix>
+              <n-icon :component="LockClosedIcon" />
+            </template>
+          </n-input>
+        </n-form-item>
+        
+        <div class="login-button-container">
+          <n-button
+            type="primary"
+            size="large"
+            block
+            :loading="loading"
+            @click="handleLogin"
+            class="login-button"
+          >
+            {{ loading ? '登录中...' : '登 录' }}
+          </n-button>
+        </div>
+      </n-form>
+      
+      <div class="login-footer">
+        <n-text depth="3">© {{ new Date().getFullYear() }} 股票AI分析系统</n-text>
+      </div>
+    </n-card>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref, reactive, onMounted, h } from 'vue';
+import { useRouter } from 'vue-router';
+import { 
+  NCard, 
+  NForm, 
+  NFormItem, 
+  NInput, 
+  NButton, 
+  NIcon,
+  NText,
+  useMessage,
+  useNotification
+} from 'naive-ui';
+import type { FormInst, FormRules } from 'naive-ui';
+import { 
+  BarChartOutline as BarChartIcon, 
+  LockClosedOutline as LockClosedIcon,
+  NotificationsOutline as NotificationsIcon
+} from '@vicons/ionicons5';
+import { apiService } from '@/services/api';
+import type { LoginRequest } from '@/types';
+
+const message = useMessage();
+const notification = useNotification();
+const router = useRouter();
+const formRef = ref<FormInst | null>(null);
+const loading = ref(false);
+
+const formValue = reactive({
+  password: ''
+});
+
+const rules: FormRules = {
+  password: [
+    {
+      required: true,
+      message: '请输入密码'
+    }
+  ]
+};
+
+// 显示系统公告
+const showAnnouncement = (content: string) => {
+  if (!content) return;
+  
+  notification.info({
+    title: '系统公告',
+    content: () => h('div', { style: 'display: flex; align-items: center;' }, [
+      h(NIcon, { component: NotificationsIcon, style: 'margin-right: 8px; font-size: 18px;' }),
+      h('span', null, content)
+    ]),
+    duration: 10000,
+    keepAliveOnHover: true,
+    closable: true
+  });
+};
+
+// 页面加载时检查是否已登录并获取系统公告
+onMounted(async () => {
+  try {
+    // 获取系统配置
+    const config = await apiService.getConfig();
+    if (config.announcement) {
+      showAnnouncement(config.announcement);
+    }
+    
+    // 不重复检查是否需要登录，因为路由守卫已经做了这个检查
+    // 直接检查是否已登录
+    const token = localStorage.getItem('token');
+    if (!token) {
+      return; // 没有token，停留在登录页
+    }
+    
+    const isAuthenticated = await apiService.checkAuth();
+    console.log('登录页面认证检查结果:', isAuthenticated);
+    
+    if (isAuthenticated) {
+      // 已登录，跳转到主页
+      console.log('已登录，跳转到主页');
+      router.push('/');
+    }
+  } catch (error) {
+    console.error('认证检查或获取配置失败:', error);
+  }
+});
+
+const handleLogin = () => {
+  formRef.value?.validate(async (errors) => {
+    if (errors) {
+      return;
+    }
+    
+    loading.value = true;
+    
+    try {
+      const loginRequest: LoginRequest = {
+        password: formValue.password
+      };
+      
+      const response = await apiService.login(loginRequest);
+      
+      if (response.access_token) {
+        message.success('登录成功');
+        // 登录成功后跳转到主页
+        router.push('/');
+      } else {
+        message.error(response.message || '登录失败');
+      }
+    } catch (error: any) {
+      console.error('登录失败:', error);
+      message.error(error.message || '登录失败');
+    } finally {
+      loading.value = false;
+    }
+  });
+};
+</script>
+
+<style scoped>
+@keyframes float {
+  0% {
+    transform: translateY(0px) rotate(0deg);
+  }
+  50% {
+    transform: translateY(-20px) rotate(5deg);
+  }
+  100% {
+    transform: translateY(0px) rotate(0deg);
+  }
+}
+
+@keyframes pulse {
+  0% {
+    transform: scale(1);
+    opacity: 0.8;
+  }
+  50% {
+    transform: scale(1.05);
+    opacity: 0.6;
+  }
+  100% {
+    transform: scale(1);
+    opacity: 0.8;
+  }
+}
+
+@keyframes fadeIn {
+  from {
+    opacity: 0;
+    transform: translateY(20px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+@keyframes floatParticle {
+  0% {
+    transform: translateY(0) translateX(0);
+  }
+  25% {
+    transform: translateY(-15px) translateX(15px);
+  }
+  50% {
+    transform: translateY(0) translateX(30px);
+  }
+  75% {
+    transform: translateY(15px) translateX(15px);
+  }
+  100% {
+    transform: translateY(0) translateX(0);
+  }
+}
+
+html, body {
+  margin: 0;
+  padding: 0;
+  height: 100%;
+  width: 100%;
+  overflow: hidden;
+}
+
+.login-container {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  height: 100vh;
+  width: 100vw;
+  background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+  position: fixed;
+  top: 0;
+  left: 0;
+  overflow: hidden;
+}
+
+.login-background {
+  position: absolute;
+  width: 100%;
+  height: 100%;
+  z-index: 0;
+  top: 0;
+  left: 0;
+  overflow: hidden;
+}
+
+.login-shape {
+  position: absolute;
+  border-radius: 50%;
+  animation: pulse 8s infinite ease-in-out;
+}
+
+.shape1 {
+  width: 50vw;
+  height: 50vw;
+  max-width: 600px;
+  max-height: 600px;
+  background: linear-gradient(135deg, rgba(32, 128, 240, 0.2) 0%, rgba(32, 128, 240, 0.1) 100%);
+  top: -15%;
+  right: -10%;
+  animation-delay: 0s;
+}
+
+.shape2 {
+  width: 60vw;
+  height: 60vw;
+  max-width: 800px;
+  max-height: 800px;
+  background: linear-gradient(135deg, rgba(32, 128, 240, 0.1) 0%, rgba(32, 128, 240, 0.05) 100%);
+  bottom: -30%;
+  left: -15%;
+  animation-delay: 2s;
+}
+
+.shape3 {
+  width: 30vw;
+  height: 30vw;
+  max-width: 400px;
+  max-height: 400px;
+  background: linear-gradient(135deg, rgba(32, 128, 240, 0.15) 0%, rgba(32, 128, 240, 0.05) 100%);
+  top: 20%;
+  right: 15%;
+  animation-delay: 4s;
+}
+
+.shape4 {
+  width: 25vw;
+  height: 25vw;
+  max-width: 300px;
+  max-height: 300px;
+  background: linear-gradient(135deg, rgba(32, 128, 240, 0.1) 0%, rgba(32, 128, 240, 0.05) 100%);
+  top: 60%;
+  left: 10%;
+  animation-delay: 1s;
+}
+
+.shape5 {
+  width: 15vw;
+  height: 15vw;
+  max-width: 200px;
+  max-height: 200px;
+  background: linear-gradient(135deg, rgba(32, 128, 240, 0.15) 0%, rgba(32, 128, 240, 0.1) 100%);
+  top: 30%;
+  left: 20%;
+  animation-delay: 3s;
+}
+
+.login-particle {
+  position: absolute;
+  border-radius: 50%;
+  background-color: rgba(255, 255, 255, 0.6);
+  animation: floatParticle 15s infinite ease-in-out;
+}
+
+.particle1 {
+  width: 10px;
+  height: 10px;
+  top: 20%;
+  left: 30%;
+  animation-duration: 20s;
+}
+
+.particle2 {
+  width: 15px;
+  height: 15px;
+  top: 40%;
+  left: 70%;
+  animation-duration: 25s;
+}
+
+.particle3 {
+  width: 8px;
+  height: 8px;
+  top: 70%;
+  left: 40%;
+  animation-duration: 18s;
+}
+
+.particle4 {
+  width: 12px;
+  height: 12px;
+  top: 30%;
+  left: 60%;
+  animation-duration: 22s;
+}
+
+.particle5 {
+  width: 6px;
+  height: 6px;
+  top: 60%;
+  left: 20%;
+  animation-duration: 15s;
+}
+
+.particle6 {
+  width: 10px;
+  height: 10px;
+  top: 80%;
+  left: 80%;
+  animation-duration: 30s;
+}
+
+.login-card {
+  width: 420px;
+  max-width: 90%;
+  background: rgba(255, 255, 255, 0.9);
+  backdrop-filter: blur(10px);
+  border-radius: 16px;
+  box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1);
+  z-index: 1;
+  padding: 30px;
+  animation: fadeIn 0.8s ease-out;
+  transition: all 0.3s ease;
+  position: relative;
+}
+
+.login-card:hover {
+  box-shadow: 0 12px 40px rgba(0, 0, 0, 0.15);
+  transform: translateY(-5px);
+}
+
+.login-header {
+  text-align: center;
+  margin-bottom: 30px;
+}
+
+.login-logo {
+  display: flex;
+  justify-content: center;
+  margin-bottom: 16px;
+}
+
+.logo-icon {
+  animation: float 6s infinite ease-in-out;
+}
+
+.login-title {
+  font-size: 28px;
+  font-weight: 600;
+  color: #333;
+  margin: 0 0 8px;
+  background: linear-gradient(90deg, #2080f0, #44a4ff);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+}
+
+.login-subtitle {
+  font-size: 14px;
+  color: #666;
+  margin: 0;
+}
+
+.login-form {
+  animation: fadeIn 0.8s ease-out 0.2s both;
+}
+
+.login-input {
+  transition: all 0.3s ease;
+}
+
+.login-input:hover {
+  transform: translateY(-2px);
+}
+
+.login-button-container {
+  margin-top: 30px;
+  margin-bottom: 20px;
+  animation: fadeIn 0.8s ease-out 0.4s both;
+}
+
+.login-button {
+  height: 48px;
+  font-size: 16px;
+  font-weight: 500;
+  letter-spacing: 2px;
+  transition: all 0.3s ease;
+  background: linear-gradient(90deg, #2080f0, #44a4ff);
+  border: none;
+}
+
+.login-button:hover {
+  transform: translateY(-2px);
+  box-shadow: 0 5px 15px rgba(32, 128, 240, 0.3);
+  background: linear-gradient(90deg, #1c72d9, #3b9aff);
+}
+
+.login-footer {
+  text-align: center;
+  padding: 16px 0 0;
+  border-top: 1px solid rgba(0, 0, 0, 0.05);
+  margin-top: 20px;
+  animation: fadeIn 0.8s ease-out 0.6s both;
+}
+
+/* 响应式设计 */
+@media (max-width: 480px) {
+  .login-card {
+    width: 90%;
+    padding: 20px;
+  }
+  
+  .login-title {
+    font-size: 24px;
+  }
+  
+  .login-subtitle {
+    font-size: 12px;
+  }
+  
+  .login-button {
+    height: 44px;
+    font-size: 14px;
+  }
+  
+  /* 移动设备上的背景形状调整 */
+  .shape1 {
+    width: 70vw;
+    height: 70vw;
+    top: -30%;
+    right: -20%;
+  }
+  
+  .shape2 {
+    width: 80vw;
+    height: 80vw;
+    bottom: -40%;
+    left: -30%;
+  }
+  
+  .shape3 {
+    width: 50vw;
+    height: 50vw;
+    top: 50%;
+    right: -20%;
+  }
+  
+  .shape4, .shape5 {
+    display: none;
+  }
+  
+  .login-particle {
+    display: none;
+  }
+}
+</style> 

frontend/src/components/MarketTimeDisplay.vue

@@ -0,0 +1,188 @@
+<template>
+  <n-card class="market-time-card">
+    <n-grid :x-gap="16" :y-gap="16" :cols="gridCols">
+      <!-- 当前时间 -->
+      <n-grid-item>
+        <div class="time-block">
+          <p class="time-label">当前时间</p>
+          <p class="current-time">{{ marketInfo.currentTime }}</p>
+        </div>
+      </n-grid-item>
+      
+      <!-- A股状态 -->
+      <n-grid-item>
+        <div class="time-block">
+          <p class="time-label">A股市场</p>
+          <div class="market-status" :class="marketInfo.cnMarket.isOpen ? 'status-open' : 'status-closed'">
+            <n-tag v-if="marketInfo.cnMarket.isOpen" type="success" size="medium" round>
+              <template #icon><n-icon size="18"><pulse-icon /></n-icon></template>
+              交易中
+            </n-tag>
+            <n-tag v-else type="default" size="medium" round>
+              <template #icon><n-icon size="18"><time-icon /></n-icon></template>
+              已休市
+            </n-tag>
+          </div>
+          <p class="time-counter">{{ marketInfo.cnMarket.nextTime }}</p>
+        </div>
+      </n-grid-item>
+
+      <!-- 港股状态 -->
+      <n-grid-item>
+        <div class="time-block">
+          <p class="time-label">港股市场</p>
+          <div class="market-status" :class="marketInfo.hkMarket.isOpen ? 'status-open' : 'status-closed'">
+            <n-tag v-if="marketInfo.hkMarket.isOpen" type="success" size="medium" round>
+              <template #icon><n-icon size="18"><pulse-icon /></n-icon></template>
+              交易中
+            </n-tag>
+            <n-tag v-else type="default" size="medium" round>
+              <template #icon><n-icon size="18"><time-icon /></n-icon></template>
+              已休市
+            </n-tag>
+          </div>
+          <p class="time-counter">{{ marketInfo.hkMarket.nextTime }}</p>
+        </div>
+      </n-grid-item>
+      
+      <!-- 美股状态 -->
+      <n-grid-item>
+        <div class="time-block">
+          <p class="time-label">美股市场</p>
+          <div class="market-status" :class="marketInfo.usMarket.isOpen ? 'status-open' : 'status-closed'">
+            <n-tag v-if="marketInfo.usMarket.isOpen" type="success" size="medium" round>
+              <template #icon><n-icon size="18"><pulse-icon /></n-icon></template>
+              交易中
+            </n-tag>
+            <n-tag v-else type="default" size="medium" round>
+              <template #icon><n-icon size="18"><time-icon /></n-icon></template>
+              已休市
+            </n-tag>
+          </div>
+          <p class="time-counter">{{ marketInfo.usMarket.nextTime }}</p>
+        </div>
+      </n-grid-item>
+    </n-grid>
+  </n-card>
+</template>
+
+<script setup lang="ts">
+import { ref, computed, onMounted, onBeforeUnmount } from 'vue';
+import { NCard, NGrid, NGridItem, NTag, NIcon } from 'naive-ui';
+import { 
+  PulseOutline as PulseIcon,
+  TimeOutline as TimeIcon
+} from '@vicons/ionicons5';
+import { updateMarketTimeInfo } from '@/utils';
+import type { MarketTimeInfo } from '@/types';
+
+const props = defineProps({
+  isMobile: {
+    type: Boolean,
+    default: false
+  }
+});
+
+const marketInfo = ref<MarketTimeInfo>({
+  currentTime: '',
+  cnMarket: { isOpen: false, nextTime: '' },
+  hkMarket: { isOpen: false, nextTime: '' },
+  usMarket: { isOpen: false, nextTime: '' }
+});
+
+const gridCols = computed(() => {
+  return props.isMobile ? 1 : 4;
+});
+
+let intervalId: number | null = null;
+
+function updateMarketTime() {
+  marketInfo.value = updateMarketTimeInfo();
+}
+
+onMounted(() => {
+  updateMarketTime(); // 立即更新一次
+  intervalId = window.setInterval(updateMarketTime, 1000);
+});
+
+onBeforeUnmount(() => {
+  if (intervalId !== null) {
+    window.clearInterval(intervalId);
+    intervalId = null;
+  }
+});
+</script>
+
+<style scoped>
+.market-time-card {
+  margin-bottom: 1.5rem;
+  padding: 0.5rem;
+}
+
+.time-block {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  text-align: center;
+  padding: 0.5rem;
+}
+
+.time-label {
+  font-size: 1rem;
+  color: var(--n-text-color-3);
+  margin-bottom: 0.75rem;
+  font-weight: 500;
+}
+
+.current-time {
+  font-size: 1.75rem;
+  font-weight: bold;
+  color: var(--n-text-color);
+}
+
+.market-status {
+  margin-bottom: 0.5rem;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  min-height: 32px;
+}
+
+.market-status :deep(.n-tag) {
+  padding: 0 12px;
+  height: 32px;
+  font-size: 1rem;
+}
+
+.market-status :deep(.n-tag__icon) {
+  margin-right: 6px;
+}
+
+.status-open :deep(.n-tag) {
+  background-color: rgba(var(--success-color), 0.15);
+  border: 1px solid var(--n-success-color);
+  animation: pulse 2s infinite;
+}
+
+.status-closed :deep(.n-tag) {
+  background-color: rgba(var(--n-text-color-3), 0.1);
+}
+
+.time-counter {
+  font-size: 0.875rem;
+  color: var(--n-text-color-3);
+  margin-top: 0.5rem;
+}
+
+@keyframes pulse {
+  0% {
+    box-shadow: 0 0 0 0 rgba(var(--success-color), 0.4);
+  }
+  70% {
+    box-shadow: 0 0 0 6px rgba(var(--success-color), 0);
+  }
+  100% {
+    box-shadow: 0 0 0 0 rgba(var(--success-color), 0);
+  }
+}
+</style>

frontend/src/components/StockAnalysisApp.vue

@@ -0,0 +1,994 @@
+<template>
+  <div class="app-container">
+    <n-layout class="main-layout">
+      <n-layout-content class="main-content">
+        
+        <!-- 市场时间显示 -->
+        <MarketTimeDisplay />
+        
+        <!-- API配置面板 -->
+        <ApiConfigPanel
+          :default-api-url="defaultApiUrl"
+          :default-api-model="defaultApiModel"
+          :default-api-timeout="defaultApiTimeout"
+          @update:api-config="updateApiConfig"
+        />
+        
+        <!-- 主要内容 -->
+        <n-card class="analysis-container">
+          
+          <n-grid :cols="24" :x-gap="16" :y-gap="16">
+            <!-- 左侧配置区域 -->
+            <n-grid-item :span="24" :lg-span="8">
+              <div class="config-section">
+                <n-form-item label="选择市场类型">
+                  <n-select
+                    v-model:value="marketType"
+                    :options="marketOptions"
+                    @update:value="handleMarketTypeChange"
+                  />
+                </n-form-item>
+                
+                <n-form-item label="股票搜索" v-if="marketType === 'US'">
+                  <StockSearch :market-type="marketType" @select="addSelectedStock" />
+                </n-form-item>
+                
+                <n-form-item label="输入代码">
+                  <n-input
+                    v-model:value="stockCodes"
+                    type="textarea"
+                    placeholder="输入股票代码，多个代码用逗号、空格或换行分隔"
+                    :autosize="{ minRows: 3, maxRows: 6 }"
+                  />
+                </n-form-item>
+                
+                <div class="action-buttons">
+                  <n-button
+                    type="primary"
+                    :loading="isAnalyzing"
+                    :disabled="!stockCodes.trim()"
+                    @click="analyzeStocks"
+                  >
+                    {{ isAnalyzing ? '分析中...' : '开始分析' }}
+                  </n-button>
+                  
+                  <n-button
+                    :disabled="analyzedStocks.length === 0"
+                    @click="copyAnalysisResults"
+                  >
+                    复制结果
+                  </n-button>
+                </div>
+              </div>
+            </n-grid-item>
+            
+            <!-- 右侧结果区域 -->
+            <n-grid-item :span="24" :lg-span="16">
+              <div class="results-section">
+                <div class="results-header">
+                  <n-space align="center" justify="space-between">
+                    <n-text>分析结果 ({{ analyzedStocks.length }})</n-text>
+                    <n-space>
+                      <n-select 
+                        v-model:value="displayMode" 
+                        size="small" 
+                        style="width: 120px"
+                        :options="[
+                          { label: '卡片视图', value: 'card' },
+                          { label: '表格视图', value: 'table' }
+                        ]"
+                      />
+                      <n-button 
+                        size="small" 
+                        :disabled="analyzedStocks.length === 0"
+                        @click="copyAnalysisResults"
+                      >
+                        复制结果
+                      </n-button>
+                      <n-dropdown 
+                        trigger="click" 
+                        :disabled="analyzedStocks.length === 0"
+                        :options="exportOptions"
+                        @select="handleExportSelect"
+                      >
+                        <n-button size="small" :disabled="analyzedStocks.length === 0">
+                          导出
+                          <template #icon>
+                            <n-icon>
+                              <DownloadIcon />
+                            </n-icon>
+                          </template>
+                        </n-button>
+                      </n-dropdown>
+                    </n-space>
+                  </n-space>
+                </div>
+                
+                <template v-if="analyzedStocks.length === 0 && !isAnalyzing">
+                  <n-empty description="尚未分析股票" size="large">
+                    <template #icon>
+                      <n-icon :component="DocumentTextIcon" />
+                    </template>
+                  </n-empty>
+                </template>
+                
+                <template v-else-if="displayMode === 'card'">
+                  <n-grid :cols="1" :x-gap="16" :y-gap="16" :lg-cols="2">
+                    <n-grid-item v-for="stock in analyzedStocks" :key="stock.code">
+                      <StockCard :stock="stock" />
+                    </n-grid-item>
+                  </n-grid>
+                </template>
+                
+                <template v-else>
+                  <n-data-table
+                    :columns="stockTableColumns"
+                    :data="analyzedStocks"
+                    :pagination="{ pageSize: 10 }"
+                    :row-key="(row: StockInfo) => row.code"
+                    :bordered="false"
+                    :single-line="false"
+                    striped
+                  />
+                </template>
+              </div>
+            </n-grid-item>
+          </n-grid>
+        </n-card>
+      </n-layout-content>
+    </n-layout>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref, onMounted, h } from 'vue';
+import { 
+  NLayout, 
+  NLayoutContent, 
+  NCard, 
+  NIcon, 
+  NGrid, 
+  NGridItem, 
+  NFormItem, 
+  NSelect, 
+  NInput, 
+  NButton,
+  NEmpty,
+  useMessage,
+  useNotification,
+  NSpace,
+  NText,
+  NDataTable,
+  NDropdown,
+  type DataTableColumns
+} from 'naive-ui';
+import { useClipboard } from '@vueuse/core'
+import { 
+  DocumentTextOutline as DocumentTextIcon,
+  DownloadOutline as DownloadIcon,
+  NotificationsOutline as NotificationsIcon
+} from '@vicons/ionicons5';
+
+import MarketTimeDisplay from './MarketTimeDisplay.vue';
+import ApiConfigPanel from './ApiConfigPanel.vue';
+import StockSearch from './StockSearch.vue';
+import StockCard from './StockCard.vue';
+
+import { apiService } from '@/services/api';
+import type { StockInfo, ApiConfig, StreamInitMessage, StreamAnalysisUpdate } from '@/types';
+import { loadApiConfig } from '@/utils';
+import { validateMultipleStockCodes, MarketType } from '@/utils/stockValidator';
+
+// 使用Naive UI的组件API
+const message = useMessage();
+const notification = useNotification();
+const { copy } = useClipboard();
+
+// 从环境变量获取的默认配置
+const defaultApiUrl = ref('');
+const defaultApiModel = ref('');
+const defaultApiTimeout = ref('60');
+const announcement = ref('');
+
+// 股票分析配置
+const marketType = ref('A');
+const stockCodes = ref('');
+const isAnalyzing = ref(false);
+const analyzedStocks = ref<StockInfo[]>([]);
+const displayMode = ref<'card' | 'table'>('card');
+
+// API配置
+const apiConfig = ref<ApiConfig>({
+  apiUrl: '',
+  apiKey: '',
+  apiModel: '',
+  apiTimeout: '60',
+  saveApiConfig: false
+});
+
+// 显示系统公告
+const showAnnouncement = (content: string) => {
+  if (!content) return;
+  
+  notification.info({
+    title: '系统公告',
+    content: () => h('div', { style: 'display: flex; align-items: center;' }, [
+      h(NIcon, { component: NotificationsIcon, style: 'margin-right: 8px; font-size: 18px;' }),
+      h('span', null, content)
+    ]),
+    duration: 0, // 设置为0表示不会自动关闭
+    keepAliveOnHover: true,
+    closable: true
+  });
+};
+
+// 市场选项
+const marketOptions = [
+  { label: 'A股', value: 'A' },
+  { label: '港股', value: 'HK' },
+  { label: '美股', value: 'US' },
+  { label: 'ETF', value: 'ETF' },
+  { label: 'LOF', value: 'LOF' }
+];
+
+// 表格列定义
+const stockTableColumns = ref<DataTableColumns<StockInfo>>([
+  {
+    title: '代码',
+    key: 'code',
+    width: 100,
+    fixed: 'left'
+  },
+  {
+    title: '状态',
+    key: 'analysisStatus',
+    width: 100,
+    render(row: StockInfo) {
+      const statusMap = {
+        'waiting': '等待分析',
+        'analyzing': '分析中',
+        'completed': '已完成',
+        'error': '出错'
+      };
+      return statusMap[row.analysisStatus] || row.analysisStatus;
+    }
+  },
+  {
+    title: '价格',
+    key: 'price',
+    width: 100,
+    render(row: StockInfo) {
+      return row.price !== undefined ? row.price.toFixed(2) : '--';
+    }
+  },
+  {
+    title: '涨跌额',
+    key: 'price_change',
+    width: 100,
+    render(row: StockInfo) {
+      if (row.price_change === undefined) return '--';
+      const sign = row.price_change > 0 ? '+' : '';
+      return `${sign}${row.price_change.toFixed(2)}`;
+    }
+  },
+  {
+    title: '涨跌幅',
+    key: 'changePercent',
+    width: 100,
+    render(row: StockInfo) {
+      if (row.changePercent === undefined) {
+        // 如果没有changePercent但有price_change和price，尝试计算
+        if (row.price_change !== undefined && row.price !== undefined) {
+          const basePrice = row.price - row.price_change;
+          if (basePrice !== 0) {
+            const calculatedPercent = (row.price_change / basePrice) * 100;
+            const sign = calculatedPercent > 0 ? '+' : '';
+            return `${sign}${calculatedPercent.toFixed(2)}%`;
+          }
+        }
+        return '--';
+      }
+      const sign = row.changePercent > 0 ? '+' : '';
+      return `${sign}${row.changePercent.toFixed(2)}%`;
+    }
+  },
+  {
+    title: 'RSI',
+    key: 'rsi',
+    width: 80,
+    render(row: StockInfo) {
+      return row.rsi !== undefined ? row.rsi.toFixed(2) : '--';
+    }
+  },
+  {
+    title: '均线趋势',
+    key: 'ma_trend',
+    width: 100,
+    render(row: StockInfo) {
+      const trendMap: Record<string, string> = {
+        'UP': '上升',
+        'DOWN': '下降',
+        'NEUTRAL': '平稳'
+      };
+      return row.ma_trend ? trendMap[row.ma_trend] || row.ma_trend : '--';
+    }
+  },
+  {
+    title: 'MACD信号',
+    key: 'macd_signal',
+    width: 100,
+    render(row: StockInfo) {
+      const signalMap: Record<string, string> = {
+        'BUY': '买入',
+        'SELL': '卖出',
+        'HOLD': '持有',
+        'NEUTRAL': '中性'
+      };
+      return row.macd_signal ? signalMap[row.macd_signal] || row.macd_signal : '--';
+    }
+  },
+  {
+    title: '评分',
+    key: 'score',
+    width: 80,
+    render(row: StockInfo) {
+      return row.score !== undefined ? row.score : '--';
+    }
+  },
+  {
+    title: '推荐',
+    key: 'recommendation',
+    width: 100
+  },
+  {
+    title: '分析日期',
+    key: 'analysis_date',
+    width: 120,
+    render(row: StockInfo) {
+      if (!row.analysis_date) return '--';
+      try {
+        const date = new Date(row.analysis_date);
+        if (isNaN(date.getTime())) {
+          return row.analysis_date;
+        }
+        return date.toISOString().split('T')[0];
+      } catch (e) {
+        return row.analysis_date;
+      }
+    }
+  },
+  {
+    title: '分析结果',
+    key: 'analysis',
+    ellipsis: {
+      tooltip: true
+    },
+    width: 300,
+    className: 'analysis-cell'
+  }
+]);
+
+// 导出选项
+const exportOptions = [
+  {
+    label: '导出为CSV',
+    key: 'csv'
+  },
+  {
+    label: '导出为Excel',
+    key: 'excel'
+  },
+  {
+    label: '导出为PDF',
+    key: 'pdf'
+  }
+];
+
+// 更新API配置
+function updateApiConfig(config: ApiConfig) {
+  apiConfig.value = { ...config };
+}
+
+// 处理市场类型变更
+function handleMarketTypeChange() {
+  stockCodes.value = '';
+  analyzedStocks.value = [];
+}
+
+// 添加选择的股票
+function addSelectedStock(symbol: string) {
+  if (stockCodes.value) {
+    stockCodes.value += ', ' + symbol;
+  } else {
+    stockCodes.value = symbol;
+  }
+}
+
+// 处理流式响应的数据
+function processStreamData(text: string) {
+  try {
+    // 尝试解析为JSON
+    const data = JSON.parse(text);
+    
+    // 判断是初始消息还是更新消息
+    if (data.stream_type === 'single' || data.stream_type === 'batch') {
+      // 初始消息
+      handleStreamInit(data as StreamInitMessage);
+    } else if (data.stock_code) {
+      // 更新消息
+      handleStreamUpdate(data as StreamAnalysisUpdate);
+    } else if (data.scan_completed) {
+      // 扫描完成消息
+      message.success(`分析完成，共扫描 ${data.total_scanned} 只股票，符合条件 ${data.total_matched} 只`);
+      
+      // 将所有分析中的股票状态更新为已完成
+      analyzedStocks.value = analyzedStocks.value.map(stock => {
+        if (stock.analysisStatus === 'analyzing') {
+          return { 
+            ...stock, 
+            analysisStatus: 'completed' as const 
+          };
+        }
+        return stock;
+      });
+      
+      isAnalyzing.value = false;
+    }
+  } catch (e) {
+    console.error('解析流数据出错:', e);
+  }
+}
+
+// 处理流式初始化消息
+function handleStreamInit(data: StreamInitMessage) {
+  if (data.stream_type === 'single' && data.stock_code) {
+    // 单个股票分析
+    analyzedStocks.value = [{
+      code: data.stock_code,
+      name: '',
+      marketType: marketType.value,
+      analysisStatus: 'waiting'
+    }];
+  } else if (data.stream_type === 'batch' && data.stock_codes) {
+    // 批量分析
+    analyzedStocks.value = data.stock_codes.map(code => ({
+      code,
+      name: '',
+      marketType: marketType.value,
+      analysisStatus: 'waiting'
+    }));
+  }
+}
+
+// 处理流式更新消息
+function handleStreamUpdate(data: StreamAnalysisUpdate) {
+  const stockIndex = analyzedStocks.value.findIndex((s: StockInfo) => s.code === data.stock_code);
+  
+  if (stockIndex >= 0) {
+    const stock = { ...analyzedStocks.value[stockIndex] };
+    
+    // 确保所有数值类型的字段都有默认值
+    stock.price = data.price ?? stock.price ?? undefined;
+    stock.price_change = data.price_change ?? stock.price_change ?? undefined;
+    // 使用change_percent作为涨跌幅
+    stock.changePercent = data.change_percent ?? stock.changePercent ?? undefined;
+    stock.marketValue = data.market_value ?? stock.marketValue ?? undefined;
+    stock.score = data.score ?? stock.score ?? undefined;
+    stock.rsi = data.rsi ?? stock.rsi ?? undefined;
+
+    // 如果没有change_percent但有price_change和price，尝试计算changePercent
+    if (stock.changePercent === undefined && stock.price_change !== undefined && stock.price !== undefined) {
+      const basePrice = stock.price - stock.price_change;
+      if (basePrice !== 0) {
+        stock.changePercent = (stock.price_change / basePrice) * 100;
+      }
+    }
+    
+    // 更新分析状态
+    if (data.status) {
+      stock.analysisStatus = data.status;
+    }
+    
+    // 如果有分析结果，则更新
+    if (data.analysis !== undefined) {
+      stock.analysis = data.analysis;
+    }
+    
+    // 处理AI分析片段
+    if (data.ai_analysis_chunk !== undefined) {
+      stock.analysis = (stock.analysis || '') + data.ai_analysis_chunk;
+      stock.analysisStatus = 'analyzing';
+    }
+    
+    // 如果有错误，则更新
+    if (data.error !== undefined) {
+      stock.error = data.error;
+      stock.analysisStatus = 'error';
+    }
+    
+    // 更新其他字段
+    if (data.name !== undefined) {
+      stock.name = data.name;
+    }
+    
+    if (data.recommendation !== undefined) {
+      stock.recommendation = data.recommendation;
+    }
+    
+    if (data.ma_trend !== undefined) {
+      stock.ma_trend = data.ma_trend;
+    }
+    
+    if (data.macd_signal !== undefined) {
+      stock.macd_signal = data.macd_signal;
+    }
+    
+    if (data.volume_status !== undefined) {
+      stock.volume_status = data.volume_status;
+    }
+    
+    if (data.analysis_date !== undefined) {
+      stock.analysis_date = data.analysis_date;
+    }
+    
+    // 使用Vue的响应式API更新数组
+    analyzedStocks.value[stockIndex] = stock;
+  }
+}
+
+// 分析股票
+async function analyzeStocks() {
+  if (!stockCodes.value.trim()) {
+    message.warning('请输入股票代码');
+    return;
+  }
+  
+  // 解析股票代码
+  const codes = stockCodes.value
+    .split(/[,\s\n]+/)
+    .map((code: string) => code.trim())
+    .filter((code: string) => code);
+  
+  if (codes.length === 0) {
+    message.warning('未找到有效的股票代码');
+    return;
+  }
+  
+  // 去除重复的股票代码
+  const uniqueCodes = Array.from(new Set(codes));
+  
+  // 检查是否有重复代码被移除
+  if (uniqueCodes.length < codes.length) {
+    message.info(`已自动去除${codes.length - uniqueCodes.length}个重复的股票代码`);
+  }
+  
+  // 在前端验证股票代码
+  const marketTypeEnum = marketType.value as keyof typeof MarketType;
+  const invalidCodes = validateMultipleStockCodes(
+    uniqueCodes, 
+    MarketType[marketTypeEnum]
+  );
+  
+  // 如果有无效代码，显示错误信息并返回
+  if (invalidCodes.length > 0) {
+    const errorMessages = invalidCodes.map(item => item.errorMessage).join('\n');
+    message.error(`股票代码验证失败:${errorMessages}`);
+    return;
+  }
+  
+  isAnalyzing.value = true;
+  analyzedStocks.value = [];
+  
+  try {
+    // 构建请求参数
+    const requestData = {
+      stock_codes: uniqueCodes,
+      market_type: marketType.value
+    } as any;
+    
+    // 添加自定义API配置
+    if (apiConfig.value.apiUrl) {
+      requestData.api_url = apiConfig.value.apiUrl;
+    }
+    
+    if (apiConfig.value.apiKey) {
+      requestData.api_key = apiConfig.value.apiKey;
+    }
+    
+    if (apiConfig.value.apiModel) {
+      requestData.api_model = apiConfig.value.apiModel;
+    }
+    
+    if (apiConfig.value.apiTimeout) {
+      requestData.api_timeout = apiConfig.value.apiTimeout;
+    }
+    
+    // 发送分析请求
+    const response = await fetch('/analyze', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify(requestData)
+    });
+    
+    if (!response.ok) {
+      if (response.status === 404) {
+        throw new Error('服务器接口未找到，请检查服务是否正常运行');
+      }
+      throw new Error(`服务器响应错误: ${response.status}`);
+    }
+    
+    // 处理流式响应
+    const reader = response.body?.getReader();
+    if (!reader) {
+      throw new Error('无法读取响应流');
+    }
+    
+    const decoder = new TextDecoder();
+    let buffer = '';
+    
+    while (true) {
+      const { done, value } = await reader.read();
+      
+      if (done) {
+        break;
+      }
+      
+      // 解码并处理数据
+      const text = decoder.decode(value, { stream: true });
+      buffer += text;
+      
+      // 按行处理数据
+      const lines = buffer.split('\n');
+      buffer = lines.pop() || ''; // 最后一行可能不完整，保留到下一次
+      
+      for (const line of lines) {
+        if (line.trim()) {
+          try {
+            processStreamData(line);
+          } catch (e: Error | unknown) {
+            console.error('处理数据流时出错:', e);
+            message.error(`处理数据时出错: ${e instanceof Error ? e.message : '未知错误'}`);
+          }
+        }
+      }
+    }
+    
+    // 处理最后可能剩余的数据
+    if (buffer.trim()) {
+      try {
+        processStreamData(buffer);
+      } catch (e: Error | unknown) {
+        console.error('处理最后的数据块时出错:', e);
+        message.error(`处理数据时出错: ${e instanceof Error ? e.message : '未知错误'}`);
+      }
+    }
+    
+    message.success('分析完成');
+  } catch (error: any) {
+    let errorMessage = '分析出错: ';
+    if (error.message.includes('404')) {
+      errorMessage += '服务器接口未找到，请确保后端服务正常运行';
+    } else {
+      errorMessage += error.message || '未知错误';
+    }
+    message.error(errorMessage);
+    console.error('分析股票时出错:', error);
+    
+    // 清空分析状态
+    analyzedStocks.value = [];
+  } finally {
+    isAnalyzing.value = false;
+  }
+}
+
+// 复制分析结果
+async function copyAnalysisResults() {
+  if (analyzedStocks.value.length === 0) {
+    message.warning('没有可复制的分析结果');
+    return;
+  }
+  
+  try {
+    // 格式化分析结果
+    const formattedResults = analyzedStocks.value
+      .filter((stock: StockInfo) => stock.analysisStatus === 'completed')
+      .map((stock: StockInfo) => {
+        let result = `【${stock.code} ${stock.name || ''}】\n`;
+        
+        // 添加分析日期
+        if (stock.analysis_date) {
+          try {
+            const date = new Date(stock.analysis_date);
+            if (!isNaN(date.getTime())) {
+              result += `分析日期: ${date.toISOString().split('T')[0]}\n`;
+            } else {
+              result += `分析日期: ${stock.analysis_date}\n`;
+            }
+          } catch (e) {
+            result += `分析日期: ${stock.analysis_date}\n`;
+          }
+        }
+        
+        // 添加评分和推荐信息
+        if (stock.score !== undefined) {
+          result += `评分: ${stock.score}\n`;
+        }
+        
+        if (stock.recommendation) {
+          result += `推荐: ${stock.recommendation}\n`;
+        }
+        
+        // 添加技术指标信息
+        if (stock.rsi !== undefined) {
+          result += `RSI: ${stock.rsi.toFixed(2)}\n`;
+        }
+        
+        if (stock.price_change !== undefined) {
+          const sign = stock.price_change > 0 ? '+' : '';
+          result += `涨跌额: ${sign}${stock.price_change.toFixed(2)}\n`;
+        }
+        
+        if (stock.ma_trend) {
+          const trendMap: Record<string, string> = {
+            'UP': '上升',
+            'DOWN': '下降',
+            'NEUTRAL': '平稳'
+          };
+          const trend = trendMap[stock.ma_trend] || stock.ma_trend;
+          result += `均线趋势: ${trend}\n`;
+        }
+        
+        if (stock.macd_signal) {
+          const signalMap: Record<string, string> = {
+            'BUY': '买入',
+            'SELL': '卖出',
+            'HOLD': '持有',
+            'NEUTRAL': '中性'
+          };
+          const signal = signalMap[stock.macd_signal] || stock.macd_signal;
+          result += `MACD信号: ${signal}\n`;
+        }
+        
+        if (stock.volume_status) {
+          const statusMap: Record<string, string> = {
+            'HIGH': '放量',
+            'LOW': '缩量',
+            'NORMAL': '正常'
+          };
+          const status = statusMap[stock.volume_status] || stock.volume_status;
+          result += `成交量: ${status}\n`;
+        }
+        
+        // 添加分析结果
+        result += `\n${stock.analysis || '无分析结果'}\n`;
+        
+        return result;
+      })
+      .join('\n');
+    
+    if (!formattedResults) {
+      message.warning('没有已完成的分析结果可复制');
+      return;
+    }
+    
+    // 复制到剪贴板
+    await copy(formattedResults);
+    message.success('已复制分析结果到剪贴板');
+  } catch (error) {
+    message.error('复制失败，请手动复制');
+    console.error('复制分析结果时出错:', error);
+  }
+}
+
+// 从本地存储恢复API配置
+function restoreLocalApiConfig() {
+  const savedConfig = loadApiConfig();
+  if (savedConfig && savedConfig.saveApiConfig) {
+    apiConfig.value = {
+      apiUrl: savedConfig.apiUrl || '',
+      apiKey: savedConfig.apiKey || '',
+      apiModel: savedConfig.apiModel || defaultApiModel.value,
+      apiTimeout: savedConfig.apiTimeout || defaultApiTimeout.value,
+      saveApiConfig: savedConfig.saveApiConfig
+    };
+    
+    // 通知父组件配置已更新
+    updateApiConfig(apiConfig.value);
+  }
+}
+
+// 处理导出选择
+function handleExportSelect(key: string) {
+  switch (key) {
+    case 'csv':
+      exportToCSV();
+      break;
+    case 'excel':
+      message.info('Excel导出功能即将推出');
+      break;
+    case 'pdf':
+      message.info('PDF导出功能即将推出');
+      break;
+  }
+}
+
+// 导出为CSV
+function exportToCSV() {
+  if (analyzedStocks.value.length === 0) {
+    message.warning('没有可导出的分析结果');
+    return;
+  }
+  
+  try {
+    // 创建CSV内容
+    const headers = ['代码', '名称', '价格', '涨跌幅', 'RSI', '均线趋势', 'MACD信号', '成交量状态', '评分', '推荐', '分析日期'];
+    let csvContent = headers.join(',') + '\n';
+    
+    // 添加数据行
+    analyzedStocks.value.forEach(stock => {
+      const row = [
+        `"${stock.code}"`,
+        `"${stock.name || ''}"`,
+        stock.price !== undefined ? stock.price.toFixed(2) : '',
+        stock.changePercent !== undefined ? `${stock.changePercent > 0 ? '+' : ''}${stock.changePercent.toFixed(2)}%` : '',
+        stock.rsi !== undefined ? stock.rsi.toFixed(2) : '',
+        stock.ma_trend ? getChineseTrend(stock.ma_trend) : '',
+        stock.macd_signal ? getChineseSignal(stock.macd_signal) : '',
+        stock.volume_status ? getChineseVolumeStatus(stock.volume_status) : '',
+        stock.score !== undefined ? stock.score : '',
+        `"${stock.recommendation || ''}"`,
+        stock.analysis_date || ''
+      ];
+      
+      csvContent += row.join(',') + '\n';
+    });
+    
+    // 创建Blob对象
+    const blob = new Blob([csvContent], { type: 'text/csv;charset=utf-8;' });
+    const url = URL.createObjectURL(blob);
+    
+    // 创建下载链接
+    const link = document.createElement('a');
+    link.setAttribute('href', url);
+    link.setAttribute('download', `股票分析结果_${new Date().toISOString().split('T')[0]}.csv`);
+    link.style.visibility = 'hidden';
+    
+    // 添加到文档并触发点击
+    document.body.appendChild(link);
+    link.click();
+    
+    // 清理
+    document.body.removeChild(link);
+    URL.revokeObjectURL(url);
+    
+    message.success('已导出CSV文件');
+  } catch (error) {
+    message.error('导出失败');
+    console.error('导出CSV时出错:', error);
+  }
+}
+
+// 辅助函数：获取中文趋势描述
+function getChineseTrend(trend: string): string {
+  const trendMap: Record<string, string> = {
+    'UP': '上升',
+    'DOWN': '下降',
+    'NEUTRAL': '平稳'
+  };
+  return trendMap[trend] || trend;
+}
+
+// 辅助函数：获取中文信号描述
+function getChineseSignal(signal: string): string {
+  const signalMap: Record<string, string> = {
+    'BUY': '买入',
+    'SELL': '卖出',
+    'HOLD': '持有',
+    'NEUTRAL': '中性'
+  };
+  return signalMap[signal] || signal;
+}
+
+// 辅助函数：获取中文成交量状态描述
+function getChineseVolumeStatus(status: string): string {
+  const statusMap: Record<string, string> = {
+    'HIGH': '放量',
+    'LOW': '缩量',
+    'NORMAL': '正常'
+  };
+  return statusMap[status] || status;
+}
+
+// 页面加载时获取默认配置和公告
+onMounted(async () => {
+  try {
+    // 从API获取配置信息
+    const config = await apiService.getConfig();
+    
+    if (config.default_api_url) {
+      defaultApiUrl.value = config.default_api_url;
+    }
+    
+    if (config.default_api_model) {
+      defaultApiModel.value = config.default_api_model;
+    }
+    
+    if (config.default_api_timeout) {
+      defaultApiTimeout.value = config.default_api_timeout;
+    }
+    
+    if (config.announcement) {
+      announcement.value = config.announcement;
+      // 使用通知显示公告
+      showAnnouncement(config.announcement);
+    }
+    
+    // 初始化后恢复本地保存的配置
+    restoreLocalApiConfig();
+  } catch (error) {
+    console.error('获取默认配置时出错:', error);
+  }
+});
+</script>
+
+<style scoped>
+.app-container {
+  min-height: 100vh;
+  width: 100%;
+  max-width: 100vw;
+  overflow-x: hidden;
+}
+
+.main-layout {
+  background-color: #f6f6f6;
+  width: 100%;
+  max-width: 100vw;
+  overflow-x: hidden;
+}
+
+.main-content {
+  max-width: 1200px;
+  margin: 0 auto;
+  padding: 1rem;
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.card-title {
+  font-size: 1.25rem;
+  font-weight: 600;
+}
+
+.analysis-container {
+  margin-bottom: 2rem;
+}
+
+.config-section {
+  padding: 0.5rem;
+}
+
+.action-buttons {
+  display: flex;
+  gap: 0.75rem;
+  margin-top: 1rem;
+}
+
+.results-section {
+  padding: 0.5rem;
+  min-height: 200px;
+}
+
+.results-header {
+  margin-bottom: 1rem;
+}
+
+.n-data-table .analysis-cell {
+  max-width: 300px;
+  white-space: normal;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  word-break: break-word;
+}
+</style>

frontend/src/components/StockSearch.vue

@@ -0,0 +1,222 @@
+<template>
+  <div class="stock-search-container">
+    <n-input
+      v-model:value="searchKeyword"
+      placeholder="输入股票代码或名称搜索"
+      @input="handleSearchInput"
+      @blur="handleBlur"
+      @focus="handleFocus"
+      ref="searchInputRef"
+    >
+      <template #prefix>
+        <n-icon :component="SearchIcon" />
+      </template>
+    </n-input>
+    
+    <div class="search-results" v-show="showResults">
+      <div v-if="loading" class="loading-results">
+        <n-spin size="small" />
+        <span>搜索中...</span>
+      </div>
+      
+      <div v-else-if="results.length === 0 && searchKeyword" class="no-results">
+        未找到相关股票
+      </div>
+      
+      <template v-else>
+        <n-scrollbar style="max-height: 300px;">
+          <div
+            v-for="item in results"
+            :key="item.symbol"
+            class="search-result-item"
+            @click="selectStock(item)"
+          >
+            <div class="result-symbol-name">
+              <span class="result-symbol">{{ item.symbol }}</span>
+              <span class="result-name">{{ item.name }}</span>
+            </div>
+            <div class="result-meta">
+              <span class="result-market">{{ item.market }}</span>
+              <span v-if="item.marketValue" class="result-market-value">
+                市值: {{ formatMarketValue(item.marketValue) }}
+              </span>
+            </div>
+          </div>
+        </n-scrollbar>
+      </template>
+    </div>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref, onMounted, onBeforeUnmount } from 'vue';
+import { NInput, NIcon, NSpin, NScrollbar } from 'naive-ui';
+import { Search as SearchIcon } from '@vicons/ionicons5';
+import { apiService } from '@/services/api';
+import { debounce, formatMarketValue as formatMarketValueFn } from '@/utils';
+import type { SearchResult } from '@/types';
+
+const props = defineProps<{
+  marketType: string;
+}>();
+
+const emit = defineEmits<{
+  (e: 'select', symbol: string): void;
+}>();
+
+const searchKeyword = ref('');
+const results = ref<SearchResult[]>([]);
+const loading = ref(false);
+const showResults = ref(false);
+const searchInputRef = ref<any>(null);
+
+// 创建防抖搜索函数
+const debouncedSearch = debounce(async (keyword: string) => {
+  if (!keyword) {
+    results.value = [];
+    loading.value = false;
+    return;
+  }
+
+  loading.value = true;
+  
+  try {
+    if (props.marketType === 'US') {
+      // 美股搜索
+      const searchResults = await apiService.searchUsStocks(keyword);
+      // 限制只显示前10个结果
+      results.value = searchResults.slice(0, 10);
+    } else {
+      // 其他市场搜索 (后端需要实现对应的接口)
+      results.value = [];
+    }
+  } catch (error) {
+    console.error('搜索股票时出错:', error);
+    results.value = [];
+  } finally {
+    loading.value = false;
+  }
+}, 300);
+
+function handleSearchInput() {
+  showResults.value = true;
+  debouncedSearch(searchKeyword.value);
+}
+
+function selectStock(item: SearchResult) {
+  emit('select', item.symbol);
+  searchKeyword.value = '';
+  showResults.value = false;
+}
+
+function handleBlur() {
+  // 延迟隐藏，以便可以点击结果项
+  setTimeout(() => {
+    showResults.value = false;
+  }, 200);
+}
+
+function handleFocus() {
+  if (searchKeyword.value) {
+    showResults.value = true;
+  }
+}
+
+function formatMarketValue(value: number): string {
+  return formatMarketValueFn(value);
+}
+
+// 点击外部时隐藏搜索结果
+function handleClickOutside(event: MouseEvent) {
+  if (
+    searchInputRef.value &&
+    !searchInputRef.value.$el.contains(event.target as Node)
+  ) {
+    showResults.value = false;
+  }
+}
+
+onMounted(() => {
+  document.addEventListener('click', handleClickOutside);
+});
+
+onBeforeUnmount(() => {
+  document.removeEventListener('click', handleClickOutside);
+});
+</script>
+
+<style scoped>
+.stock-search-container {
+  position: relative;
+  width: 100%;
+}
+
+.search-results {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  right: 0;
+  z-index: 10;
+  margin-top: 0.25rem;
+  background-color: var(--n-color);
+  border-radius: 0.375rem;
+  box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+  border: 1px solid var(--n-border-color);
+}
+
+.loading-results,
+.no-results {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.5rem;
+  padding: 1rem;
+  color: var(--n-text-color-3);
+  font-size: 0.875rem;
+}
+
+.search-result-item {
+  padding: 0.75rem 1rem;
+  cursor: pointer;
+  transition: background-color 0.2s;
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+}
+
+.search-result-item:hover {
+  background-color: var(--n-color-hover);
+}
+
+.result-symbol-name {
+  display: flex;
+  flex-direction: column;
+}
+
+.result-symbol {
+  font-weight: 500;
+  color: var(--n-text-color);
+}
+
+.result-name {
+  font-size: 0.75rem;
+  color: var(--n-text-color-3);
+  margin-top: 0.25rem;
+}
+
+.result-meta {
+  display: flex;
+  flex-direction: column;
+  align-items: flex-end;
+}
+
+.result-market,
+.result-market-value {
+  font-size: 0.75rem;
+  color: var(--n-text-color-3);
+}
+
+.result-market-value {
+  margin-top: 0.25rem;
+}
+</style>

frontend/src/main.ts

@@ -0,0 +1,8 @@
+import { createApp } from 'vue'
+import './style.css'
+import App from './App.vue'
+import router from './router'
+
+const app = createApp(App)
+app.use(router)
+app.mount('#app')

frontend/src/router/index.ts

@@ -0,0 +1,89 @@
+import { createRouter, createWebHistory } from 'vue-router';
+import type { RouteRecordRaw } from 'vue-router';
+import { apiService } from '@/services/api';
+import StockAnalysisApp from '@/components/StockAnalysisApp.vue';
+import LoginPage from '@/components/LoginPage.vue';
+
+const routes: Array<RouteRecordRaw> = [
+  {
+    path: '/',
+    name: 'Home',
+    component: StockAnalysisApp,
+    meta: { requiresAuth: true }
+  },
+  {
+    path: '/login',
+    name: 'Login',
+    component: LoginPage,
+    meta: { requiresAuth: false }
+  },
+  {
+    path: '/:pathMatch(.*)*',
+    redirect: '/'
+  }
+];
+
+const router = createRouter({
+  history: createWebHistory(),
+  routes
+});
+
+// 全局前置守卫
+router.beforeEach(async (to, from, next) => {
+  console.log(`路由跳转: 从 ${from.path} 到 ${to.path}`);
+  
+  // 如果已经在登录页面，直接通过
+  if (to.path === '/login') {
+    next();
+    return;
+  }
+  
+  // 检查路由是否需要认证
+  if (to.matched.some(record => record.meta.requiresAuth)) {
+    console.log('当前路由需要认证');
+    
+    try {
+      // 先检查系统是否需要登录
+      const requireLogin = await apiService.checkNeedLogin();
+      console.log('系统是否需要登录:', requireLogin);
+      
+      if (!requireLogin) {
+        // 系统不需要登录，直接通过
+        console.log('系统不需要登录，允许访问');
+        next();
+        return;
+      }
+      
+      // 系统需要登录，检查本地是否有token
+      const token = localStorage.getItem('token');
+      if (!token) {
+        console.log('本地没有token，跳转到登录页');
+        next({ name: 'Login' });
+        return;
+      }
+      
+      const isAuthenticated = await apiService.checkAuth();
+      console.log('认证检查结果:', isAuthenticated);
+      
+      if (!isAuthenticated) {
+        // 未登录，重定向到登录页
+        console.log('认证失败，跳转到登录页');
+        next({ name: 'Login' });
+      } else {
+        // 已登录，允许访问
+        console.log('认证成功，允许访问');
+        next();
+      }
+    } catch (error) {
+      console.error('认证检查失败:', error);
+      // 认证检查失败，重定向到登录页
+      next({ name: 'Login' });
+    }
+  } else {
+    // 不需要认证的路由，直接访问
+    console.log('当前路由不需要认证，直接访问');
+    next();
+  }
+});
+
+export default router; 

frontend/src/services/api.ts

@@ -0,0 +1,146 @@
+import axios from 'axios';
+import type { AnalyzeRequest, TestApiRequest, TestApiResponse, SearchResult, LoginRequest, LoginResponse } from '@/types';
+
+// 在开发环境中使用完整URL
+const API_PREFIX = '';
+
+// 创建axios实例
+const axiosInstance = axios.create({
+  baseURL: API_PREFIX
+});
+
+// 请求拦截器，添加token
+axiosInstance.interceptors.request.use(
+  (config) => {
+    const token = localStorage.getItem('token');
+    if (token) {
+      config.headers.Authorization = `Bearer ${token}`;
+    }
+    return config;
+  },
+  (error) => {
+    return Promise.reject(error);
+  }
+);
+
+// 响应拦截器，处理401错误
+axiosInstance.interceptors.response.use(
+  (response) => {
+    return response;
+  },
+  (error) => {
+    if (error.response && error.response.status === 401) {
+      // 清除token
+      localStorage.removeItem('token');
+      // 不要在这里跳转，避免循环重定向
+    }
+    return Promise.reject(error);
+  }
+);
+
+export const apiService = {
+  // 用户登录
+  login: async (request: LoginRequest): Promise<LoginResponse> => {
+    try {
+      const response = await axios.post(`${API_PREFIX}/login`, request);
+      if (response.data.access_token) {
+        localStorage.setItem('token', response.data.access_token);
+      }
+      return response.data;
+    } catch (error: any) {
+      if (error.response) {
+        return {
+          success: false,
+          message: error.response.data.detail || '登录失败',
+        };
+      }
+      return {
+        success: false,
+        message: error.message || '登录失败'
+      };
+    }
+  },
+
+  // 检查认证状态
+  checkAuth: async (): Promise<boolean> => {
+    try {
+      const response = await axiosInstance.get(`${API_PREFIX}/check_auth`);
+      return response.data.authenticated === true;
+    } catch (error) {
+      // 认证失败，清除token
+      localStorage.removeItem('token');
+      return false;
+    }
+  },
+
+  // 登出
+  logout: () => {
+    localStorage.removeItem('token');
+    // 简化登出逻辑
+    window.location.href = '/login';
+  },
+
+  // 分析股票
+  analyzeStocks: async (request: AnalyzeRequest) => {
+    return axiosInstance.post(`${API_PREFIX}/analyze`, request, {
+      responseType: 'stream'
+    });
+  },
+
+  // 测试API连接
+  testApiConnection: async (request: TestApiRequest): Promise<TestApiResponse> => {
+    try {
+      const response = await axiosInstance.post(`${API_PREFIX}/test_api_connection`, request);
+      return response.data;
+    } catch (error: any) {
+      if (error.response) {
+        return error.response.data;
+      }
+      return {
+        success: false,
+        message: error.message || '连接失败'
+      };
+    }
+  },
+
+  // 搜索美股
+  searchUsStocks: async (keyword: string): Promise<SearchResult[]> => {
+    try {
+      const response = await axiosInstance.get(`${API_PREFIX}/search_us_stocks`, {
+        params: { keyword }
+      });
+      return response.data.results || [];
+    } catch (error) {
+      console.error('搜索美股时出错:', error);
+      return [];
+    }
+  },
+  
+  // 获取配置
+  getConfig: async () => {
+    try {
+      const response = await axios.get(`${API_PREFIX}/config`);
+      return response.data;
+    } catch (error) {
+      console.error('获取配置时出错:', error);
+      return {
+        announcement: '',
+        default_api_url: '',
+        default_api_model: 'gpt-3.5-turbo',
+        default_api_timeout: '60'
+      };
+    }
+  },
+
+  // 检查是否需要登录
+  checkNeedLogin: async (): Promise<boolean> => {
+    try {
+      const response = await axios.get(`${API_PREFIX}/need_login`);
+      return response.data.require_login;
+    } catch (error) {
+      console.error('检查是否需要登录时出错:', error);
+      // 默认为需要登录，确保安全
+      return true;
+    }
+  }
+};

frontend/src/style.css

@@ -0,0 +1,79 @@
+:root {
+  font-family: system-ui, Avenir, Helvetica, Arial, sans-serif;
+  line-height: 1.5;
+  font-weight: 400;
+
+  color-scheme: light dark;
+  color: rgba(255, 255, 255, 0.87);
+  background-color: #242424;
+
+  font-synthesis: none;
+  text-rendering: optimizeLegibility;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+a {
+  font-weight: 500;
+  color: #646cff;
+  text-decoration: inherit;
+}
+a:hover {
+  color: #535bf2;
+}
+
+body {
+  margin: 0;
+  display: flex;
+  place-items: center;
+  min-width: 320px;
+  min-height: 100vh;
+}
+
+h1 {
+  font-size: 3.2em;
+  line-height: 1.1;
+}
+
+button {
+  border-radius: 8px;
+  border: 1px solid transparent;
+  padding: 0.6em 1.2em;
+  font-size: 1em;
+  font-weight: 500;
+  font-family: inherit;
+  background-color: #1a1a1a;
+  cursor: pointer;
+  transition: border-color 0.25s;
+}
+button:hover {
+  border-color: #646cff;
+}
+button:focus,
+button:focus-visible {
+  outline: 4px auto -webkit-focus-ring-color;
+}
+
+.card {
+  padding: 2em;
+}
+
+#app {
+  max-width: 1280px;
+  margin: 0 auto;
+  padding: 2rem;
+  text-align: center;
+}
+
+@media (prefers-color-scheme: light) {
+  :root {
+    color: #213547;
+    background-color: #ffffff;
+  }
+  a:hover {
+    color: #747bff;
+  }
+  button {
+    background-color: #f9f9f9;
+  }
+}

frontend/src/types/index.ts

@@ -0,0 +1,109 @@
+// API接口相关类型
+export interface ApiConfig {
+  apiUrl: string;
+  apiKey: string;
+  apiModel: string;
+  apiTimeout: string;
+  saveApiConfig: boolean;
+}
+
+// 登录相关类型
+export interface LoginRequest {
+  password: string;
+}
+
+export interface LoginResponse {
+  access_token?: string;
+  token_type?: string;
+  success?: boolean;
+  message?: string;
+}
+
+export interface StockInfo {
+  code: string;
+  name: string;
+  marketType: string;
+  price?: number;
+  changePercent?: number;
+  marketValue?: number;
+  analysis?: string;
+  analysisStatus: 'waiting' | 'analyzing' | 'completed' | 'error';
+  error?: string;
+  score?: number;
+  recommendation?: string;
+  price_change?: number;
+  rsi?: number;
+  ma_trend?: string;
+  macd_signal?: string;
+  volume_status?: string;
+  analysis_date?: string;
+}
+
+export interface SearchResult {
+  symbol: string;
+  name: string;
+  market: string;
+  marketValue?: number;
+}
+
+export interface MarketStatus {
+  isOpen: boolean;
+  nextTime: string;
+}
+
+export interface MarketTimeInfo {
+  currentTime: string;
+  cnMarket: MarketStatus;
+  hkMarket: MarketStatus;
+  usMarket: MarketStatus;
+}
+
+// 分析请求和响应
+export interface AnalyzeRequest {
+  stock_codes: string[];
+  market_type: string;
+  api_url?: string;
+  api_key?: string;
+  api_model?: string;
+  api_timeout?: string;
+}
+
+export interface TestApiRequest {
+  api_url: string;
+  api_key: string;
+  api_model: string;
+  api_timeout: string;
+}
+
+export interface TestApiResponse {
+  success: boolean;
+  message: string;
+  status_code?: number;
+}
+
+// 流式响应类型
+export interface StreamInitMessage {
+  stream_type: 'single' | 'batch';
+  stock_code?: string;
+  stock_codes?: string[];
+}
+
+export interface StreamAnalysisUpdate {
+  stock_code: string;
+  analysis?: string;
+  status: 'analyzing' | 'completed' | 'error';
+  error?: string;
+  name?: string;
+  price?: number;
+  change_percent?: number;
+  market_value?: number;
+  score?: number;
+  recommendation?: string;
+  price_change?: number;
+  rsi?: number;
+  ma_trend?: string;
+  macd_signal?: string;
+  volume_status?: string;
+  analysis_date?: string;
+  ai_analysis_chunk?: string;
+}

frontend/src/utils/index.ts

@@ -0,0 +1,201 @@
+import type { MarketTimeInfo } from '@/types';
+import { marked } from 'marked';
+
+// 防抖函数
+export function debounce<T extends (...args: any[]) => any>(
+  func: T,
+  wait: number
+): (...args: Parameters<T>) => void {
+  let timeout: number | null = null;
+  
+  return function(...args: Parameters<T>): void {
+    const later = () => {
+      timeout = null;
+      func(...args);
+    };
+    
+    if (timeout !== null) {
+      clearTimeout(timeout);
+    }
+    timeout = window.setTimeout(later, wait);
+  };
+}
+
+// 格式化市值
+export function formatMarketValue(value: number): string {
+  if (!value) return '未知';
+  
+  if (value >= 1000000000000) {
+    return (value / 1000000000000).toFixed(2) + '万亿';
+  } else if (value >= 100000000) {
+    return (value / 100000000).toFixed(2) + '亿';
+  } else if (value >= 10000) {
+    return (value / 10000).toFixed(2) + '万';
+  } else {
+    return value.toFixed(2);
+  }
+}
+
+// 解析Markdown
+export function parseMarkdown(text: string): string {
+  try {
+    const result = marked(text);
+    if (typeof result === 'string') {
+      return result;
+    }
+    return '';
+  } catch (e) {
+    console.error('解析Markdown出错:', e);
+    return text;
+  }
+}
+
+// 更新市场时间信息
+export function updateMarketTimeInfo(): MarketTimeInfo {
+  const now = new Date();
+  
+  // 当前时间
+  const currentTime = now.toLocaleTimeString('zh-CN', { hour12: false });
+  
+  // 中国时间
+  const cnOptions = { timeZone: 'Asia/Shanghai', hour12: false } as Intl.DateTimeFormatOptions;
+  const cnTime = now.toLocaleString('zh-CN', cnOptions);
+  const cnHour = new Date(cnTime).getHours();
+  const cnMinute = new Date(cnTime).getMinutes();
+  
+  // A股市场状态
+  const cnMarketOpen = (cnHour === 9 && cnMinute >= 30) || (cnHour === 10) ||
+                     (cnHour === 11 && cnMinute <= 30) ||
+                     (cnHour >= 13 && cnHour < 15);
+                     
+  const cnNextTime = getNextTimeText(cnMarketOpen, cnHour, cnMinute, 9, 30, 15, 0);
+
+  // 港股市场状态（与A股相同时区）
+  const hkMarketOpen = (cnHour === 9 && cnMinute >= 30) || 
+                     (cnHour === 10) || (cnHour === 11) ||
+                     (cnHour >= 13 && cnHour < 16);
+                     
+  const hkNextTime = getNextTimeText(hkMarketOpen, cnHour, cnMinute, 9, 30, 16, 0);
+  
+  // 获取美国东部时间
+  const usOptions = { timeZone: 'America/New_York', hour12: false } as Intl.DateTimeFormatOptions;
+  const usTime = now.toLocaleString('zh-CN', usOptions);
+  const usHour = new Date(usTime).getHours();
+  const usMinute = new Date(usTime).getMinutes();
+  
+  // 美股市场状态
+  const usMarketOpen = (usHour >= 9 && usHour < 16) || 
+                     (usHour === 16 && usMinute === 0);
+                     
+  const usNextTime = getNextTimeText(usMarketOpen, usHour, usMinute, 9, 30, 16, 0);
+  
+  return {
+    currentTime,
+    cnMarket: { isOpen: cnMarketOpen, nextTime: cnNextTime },
+    hkMarket: { isOpen: hkMarketOpen, nextTime: hkNextTime },
+    usMarket: { isOpen: usMarketOpen, nextTime: usNextTime }
+  };
+}
+
+// 辅助函数：获取距离下一次开/闭市的时间文本
+function getNextTimeText(
+  isOpen: boolean,
+  currentHour: number,
+  currentMinute: number,
+  openHour: number,
+  openMinute: number,
+  closeHour: number,
+  closeMinute: number
+): string {
+  if (isOpen) {
+    // 计算距离收盘时间
+    let timeToCloseMinutes = (closeHour - currentHour) * 60 + (closeMinute - currentMinute);
+    
+    if (timeToCloseMinutes <= 0) {
+      return '即将收盘';
+    }
+    
+    const hours = Math.floor(timeToCloseMinutes / 60);
+    const minutes = timeToCloseMinutes % 60;
+    
+    return `距离收盘还有 ${hours}小时${minutes}分钟`;
+  } else {
+    // 计算距离开盘时间
+    let nextOpenHour = openHour;
+    let nextOpenMinute = openMinute;
+    let isNextDay = false;
+    
+    if (currentHour >= closeHour) {
+      // 已经过了今天的收盘时间，下一个开盘是明天
+      isNextDay = true;
+    } else if (currentHour < openHour || (currentHour === openHour && currentMinute < openMinute)) {
+      // 还没到今天的开盘时间
+      isNextDay = false;
+    } else {
+      // 当前处于盘中休息时间，下一个开盘时间是当天下午
+      nextOpenHour = 13;
+      nextOpenMinute = 0;
+    }
+    
+    let timeToOpenMinutes;
+    
+    if (isNextDay) {
+      timeToOpenMinutes = (24 - currentHour + nextOpenHour) * 60 + (nextOpenMinute - currentMinute);
+    } else {
+      timeToOpenMinutes = (nextOpenHour - currentHour) * 60 + (nextOpenMinute - currentMinute);
+    }
+    
+    if (timeToOpenMinutes <= 0) {
+      return '即将开盘';
+    }
+    
+    const hours = Math.floor(timeToOpenMinutes / 60);
+    const minutes = timeToOpenMinutes % 60;
+    
+    return `距离开盘还有 ${hours}小时${minutes}分钟`;
+  }
+}
+
+// 保存API配置到localStorage
+export function saveApiConfigToLocalStorage(config: Partial<Pick<
+  { apiUrl: string, apiKey: string, apiModel: string, apiTimeout: string, saveApiConfig: boolean },
+  'apiUrl' | 'apiKey' | 'apiModel' | 'apiTimeout' | 'saveApiConfig'
+>>): void {
+  if (window.localStorage) {
+    localStorage.setItem('apiConfig', JSON.stringify(config));
+  }
+}
+
+// 从localStorage加载API配置
+export function loadApiConfig(): Partial<{ 
+  apiUrl: string, 
+  apiKey: string, 
+  apiModel: string, 
+  apiTimeout: string, 
+  saveApiConfig: boolean 
+}> {
+  if (window.localStorage) {
+    const saved = localStorage.getItem('apiConfig');
+    if (saved) {
+      try {
+        return JSON.parse(saved);
+      } catch (e) {
+        console.error('解析保存的API配置出错:', e);
+      }
+    }
+  }
+  return {
+    apiUrl: '',
+    apiKey: '',
+    apiModel: '',
+    apiTimeout: '',
+    saveApiConfig: false
+  };
+}
+
+// 清除API配置
+export function clearApiConfig(): void {
+  if (window.localStorage) {
+    localStorage.removeItem('apiConfig');
+  }
+}

frontend/src/utils/stockValidator.ts

@@ -0,0 +1,172 @@
+ /**
+ * 股票代码验证工具
+ * 用于验证不同市场类型的股票代码格式
+ */
+
+/**
+ * 市场类型枚举
+ */
+export enum MarketType {
+  A = 'A',      // A股
+  HK = 'HK',    // 港股
+  US = 'US',    // 美股
+  ETF = 'ETF',  // ETF基金
+  LOF = 'LOF'   // LOF基金
+}
+
+/**
+ * 验证A股股票代码
+ * @param code 股票代码
+ * @returns 是否为有效的A股代码
+ */
+export const validateAStock = (code: string): boolean => {
+  // 上海证券交易所股票代码以6开头，6位数字
+  // 深圳证券交易所股票代码以0或3开头，6位数字
+  // 科创板股票代码以688开头，6位数字
+  // 北京证券交易所股票代码以8开头，一般为5位数字（如80XXX）
+  // 注意：中小板、创业板代码格式已合并处理
+  
+  // 验证上海证券交易所（以6开头的6位数字）
+  if (code.startsWith('6') && /^\d{6}$/.test(code)) {
+    return true;
+  }
+  
+  // 验证深圳证券交易所（以0或3开头的6位数字）
+  if ((code.startsWith('0') || code.startsWith('3')) && /^\d{6}$/.test(code)) {
+    return true;
+  }
+  
+  // 验证科创板（以688开头的6位数字）
+  if (code.startsWith('688') && /^\d{6}$/.test(code)) {
+    return true;
+  }
+  
+  // 验证北京证券交易所（以8开头的股票）
+  // 北交所股票一般是5位数字，格式为8xxxx
+  if (code.startsWith('8') && /^\d{5}$/.test(code)) {
+    return true;
+  }
+  
+  return false;
+};
+
+/**
+ * 验证港股股票代码
+ * @param code 股票代码
+ * @returns 是否为有效的港股代码
+ */
+export const validateHKStock = (code: string): boolean => {
+  // 港股通常是5位数字
+  return /^\d{5}$/.test(code);
+};
+
+/**
+ * 验证美股股票代码
+ * @param code 股票代码
+ * @returns 是否为有效的美股代码
+ */
+export const validateUSStock = (code: string): boolean => {
+  // 美股代码通常由字母组成，长度在1-5之间
+  return /^[A-Za-z]{1,5}$/.test(code);
+};
+
+/**
+ * 验证ETF/LOF基金代码
+ * @param code 基金代码
+ * @returns 是否为有效的基金代码
+ */
+export const validateFund = (code: string): boolean => {
+  // 基金代码通常为6位数字
+  return /^\d{6}$/.test(code);
+};
+
+/**
+ * 根据市场类型验证股票代码
+ * @param code 股票代码
+ * @param marketType 市场类型
+ * @returns 包含验证结果和错误信息的对象
+ */
+export const validateStockCode = (
+  code: string, 
+  marketType: MarketType
+): { valid: boolean; errorMessage?: string } => {
+  
+  if (!code || code.trim() === '') {
+    return { 
+      valid: false, 
+      errorMessage: '股票代码不能为空' 
+    };
+  }
+  
+  switch (marketType) {
+    case MarketType.A:
+      if (!validateAStock(code)) {
+        return { 
+          valid: false, 
+          errorMessage: `无效的A股股票代码格式: ${code}。A股代码应以0、3、6、688或8开头，且为6位数字或5位数字`
+        };
+      }
+      break;
+      
+    case MarketType.HK:
+      if (!validateHKStock(code)) {
+        return { 
+          valid: false, 
+          errorMessage: `无效的港股代码格式: ${code}。港股代码应为5位数字` 
+        };
+      }
+      break;
+      
+    case MarketType.US:
+      if (!validateUSStock(code)) {
+        return { 
+          valid: false, 
+          errorMessage: `无效的美股代码格式: ${code}。美股代码应为1-5位字母` 
+        };
+      }
+      break;
+      
+    case MarketType.ETF:
+    case MarketType.LOF:
+      if (!validateFund(code)) {
+        return { 
+          valid: false, 
+          errorMessage: `无效的${marketType}基金代码格式: ${code}。基金代码应为6位数字` 
+        };
+      }
+      break;
+      
+    default:
+      return { 
+        valid: false, 
+        errorMessage: `不支持的市场类型: ${marketType}` 
+      };
+  }
+  
+  return { valid: true };
+};
+
+/**
+ * 批量验证多个股票代码
+ * @param codes 股票代码数组
+ * @param marketType 市场类型
+ * @returns 包含所有无效代码及其错误信息的数组
+ */
+export const validateMultipleStockCodes = (
+  codes: string[], 
+  marketType: MarketType
+): { code: string; errorMessage: string }[] => {
+  const invalidCodes: { code: string; errorMessage: string }[] = [];
+  
+  for (const code of codes) {
+    const result = validateStockCode(code, marketType);
+    if (!result.valid && result.errorMessage) {
+      invalidCodes.push({
+        code,
+        errorMessage: result.errorMessage
+      });
+    }
+  }
+  
+  return invalidCodes;
+};

frontend/src/vite-env.d.ts

@@ -0,0 +1 @@
+/// <reference types="vite/client" />

frontend/tsconfig.app.json

@@ -0,0 +1,18 @@
+{
+  "extends": "@vue/tsconfig/tsconfig.dom.json",
+  "compilerOptions": {
+    "incremental": true,  // 启用增量编译
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true,
+    "baseUrl": ".",
+    "paths": {
+      "@/*": ["src/*"]
+    }
+  },
+  "include": ["src/**/*.ts", "src/**/*.tsx", "src/**/*.vue"]
+}

frontend/tsconfig.json

@@ -0,0 +1,7 @@
+{
+  "files": [],
+  "references": [
+    { "path": "./tsconfig.app.json" },
+    { "path": "./tsconfig.node.json" }
+  ]
+}

frontend/tsconfig.node.json

@@ -0,0 +1,25 @@
+{
+  "compilerOptions": {
+    "incremental": true,  // 启用增量编译
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+    "target": "ES2022",
+    "lib": ["ES2023"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "isolatedModules": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["vite.config.ts"]
+}

frontend/vite.config.ts

@@ -0,0 +1,68 @@
+import { defineConfig } from 'vite'
+import vue from '@vitejs/plugin-vue'
+import { fileURLToPath } from 'url'
+import { dirname, resolve } from 'path'
+
+// 获取当前文件的目录路径（在ESM中替代__dirname）
+const __filename = fileURLToPath(import.meta.url)
+const __dirname = dirname(__filename)
+
+// https://vitejs.dev/config/
+export default defineConfig({
+  plugins: [vue()],
+  resolve: {
+    alias: {
+      '@': resolve(__dirname, 'src'),
+    },
+  },
+  server: {
+    cors: true,
+    proxy: {
+      '/api': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+        rewrite: (path) => path.replace(/^\/api/, ''),
+      },
+      '/analyze': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/test_api_connection': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/search_us_stocks': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/config': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/login': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/check_auth': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/need_login': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/us_stock_detail': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/fund_detail': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+      '/search_funds': {
+        target: 'http://127.0.0.1:8888',
+        changeOrigin: true,
+      },
+    },
+  },
+})

fund_service.py

@@ -1,51 +0,0 @@
-import akshare as ak
-import pandas as pd
-
-class FundService:
-    def search_funds(self, keyword, market_type='ETF'):
-        """
-        搜索基金代码
-        :param keyword: 搜索关键词
-        :return: 匹配的基金列表
-        """
-        try:
-            # 获取ETF和LOF数据
-            if market_type == 'ETF':
-                df = ak.fund_etf_spot_em()
-            else:
-                df = ak.fund_lof_spot_em()
-            
-            # 转换列名
-            df = df.rename(columns={
-                "代码": "symbol",
-                "名称": "name",
-                "最新价": "price",
-                "涨跌额": "price_change",
-                "涨跌幅": "price_change_percent",
-                "成交量": "volume",
-                "流通市值": "market_value",
-                "总市值": "total_value",
-                "基金折价率": "discount_rate",
-            })
-            
-            # 模糊匹配搜索（同时匹配代码和名称）
-            mask = (df['name'].str.contains(keyword, case=False, na=False) | 
-                   df['symbol'].str.contains(keyword, case=False, na=False))
-            results = df[mask]
-            
-            # 格式化返回结果并处理 NaN 值
-            formatted_results = []
-            for _, row in results.iterrows():
-                formatted_results.append({
-                    'name': row['name'] if pd.notna(row['name']) else '',
-                    'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
-                    'price': float(row['price']) if pd.notna(row['price']) else 0.0,
-                    'volume': float(row['volume']) if pd.notna(row['volume']) else 0.0,
-                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0,
-                    'total_value': float(row['total_value']) if pd.notna(row['total_value']) else 0.0,
-                })
-                
-            return formatted_results
-            
-        except Exception as e:
-            raise Exception(f"搜索基金代码失败: {str(e)}")

requirements.txt

@@ -10,11 +10,14 @@ scipy==1.15.1
 akshare==1.16.22
 tqdm==4.67.1
 
+# Web框架与异步处理
+fastapi==0.115.11
+uvicorn[standard]==0.34.0
+pydantic==2.10.6
+httpx==0.28.1
 
-# 网络和API请求
+# 环境配置
-requests==2.32.3
 python-dotenv==1.0.1
-flask==3.1.0
 
 # 日志和系统工具
 loguru==0.7.2
@@ -32,3 +35,5 @@ html5lib==1.1
 lxml==4.9.4
 jsonpath==0.82.2
 openpyxl==3.1.5
+python-jose[cryptography]==3.4.0
+passlib==1.7.4

services/__init__.py

@@ -0,0 +1,2 @@
+# services包初始化文件
+# 用于组织股票分析服务的各个模块 

services/ai_analyzer.py

@@ -0,0 +1,456 @@
+import pandas as pd
+import numpy as np
+import os
+import json
+import asyncio
+import httpx
+import re
+from typing import Dict, List, Optional, Any, Generator, AsyncGenerator
+from dotenv import load_dotenv
+from logger import get_logger
+from utils.api_utils import APIUtils
+from datetime import datetime
+
+# 获取日志器
+logger = get_logger()
+
+class AIAnalyzer:
+    """
+    异步AI分析服务
+    负责调用AI API对股票数据进行分析
+    """
+    
+    def __init__(self, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
+        """
+        初始化AI分析服务
+        
+        Args:
+            custom_api_url: 自定义API URL
+            custom_api_key: 自定义API密钥
+            custom_api_model: 自定义API模型
+            custom_api_timeout: 自定义API超时时间
+        """
+        # 加载环境变量
+        load_dotenv()
+        
+        # 设置API配置
+        self.API_URL = custom_api_url or os.getenv('API_URL')
+        self.API_KEY = custom_api_key or os.getenv('API_KEY')
+        self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo')
+        self.API_TIMEOUT = int(custom_api_timeout or os.getenv('API_TIMEOUT', 60))
+        
+        logger.debug(f"初始化AIAnalyzer: API_URL={self.API_URL}, API_MODEL={self.API_MODEL}, API_KEY={'已提供' if self.API_KEY else '未提供'}, API_TIMEOUT={self.API_TIMEOUT}")
+    
+    async def get_ai_analysis(self, df: pd.DataFrame, stock_code: str, market_type: str = 'A', stream: bool = False) -> AsyncGenerator[str, None]:
+        """
+        对股票数据进行AI分析
+        
+        Args:
+            df: 包含技术指标的DataFrame
+            stock_code: 股票代码
+            market_type: 市场类型，默认为'A'股
+            stream: 是否使用流式响应
+            
+        Returns:
+            异步生成器，生成分析结果字符串
+        """
+        try:
+            logger.info(f"开始AI分析 {stock_code}, 流式模式: {stream}")
+            
+            # 提取关键技术指标
+            latest_data = df.iloc[-1]
+            
+            # 计算技术指标
+            rsi = latest_data.get('RSI')
+            price = latest_data.get('Close')
+            price_change = latest_data.get('Change')
+            
+            # 确定MA趋势
+            ma_trend = 'UP' if latest_data.get('MA5', 0) > latest_data.get('MA20', 0) else 'DOWN'
+            
+            # 确定MACD信号
+            macd = latest_data.get('MACD', 0)
+            macd_signal = latest_data.get('MACD_Signal', 0)
+            macd_signal_type = 'BUY' if macd > macd_signal else 'SELL'
+            
+            # 确定成交量状态
+            volume_ratio = latest_data.get('Volume_Ratio', 1)
+            volume_status = 'HIGH' if volume_ratio > 1.5 else ('LOW' if volume_ratio < 0.5 else 'NORMAL')
+            
+            # AI 分析内容
+            # 最近14天的股票数据记录
+            recent_data = df.tail(14).to_dict('records')
+            
+            # 包含trend, volatility, volume_trend, rsi_level的字典
+            technical_summary = {
+                'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
+                'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
+                'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
+                'rsi_level': df.iloc[-1]['RSI']
+            }
+            
+            # 根据市场类型调整分析提示
+            if market_type in ['ETF', 'LOF']:
+                prompt = f"""
+                分析基金 {stock_code}：
+
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请提供：
+                1. 净值走势分析（包含支撑位和压力位）
+                2. 成交量分析及其对净值的影响
+                3. 风险评估（包含波动率和折溢价分析）
+                4. 短期和中期净值预测
+                5. 关键价格位分析
+                6. 申购赎回建议（包含止损位）
+                
+                请基于技术指标和市场表现进行分析，给出具体数据支持。
+                """
+            elif market_type == 'US':
+                prompt = f"""
+                分析美股 {stock_code}：
+
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请提供：
+                1. 趋势分析（包含支撑位和压力位，美元计价）
+                2. 成交量分析及其含义
+                3. 风险评估（包含波动率和美股市场特有风险）
+                4. 短期和中期目标价位（美元）
+                5. 关键技术位分析
+                6. 具体交易建议（包含止损位）
+                
+                请基于技术指标和美股市场特点进行分析，给出具体数据支持。
+                """
+            elif market_type == 'HK':
+                prompt = f"""
+                分析港股 {stock_code}：
+
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请提供：
+                1. 趋势分析（包含支撑位和压力位，港币计价）
+                2. 成交量分析及其含义
+                3. 风险评估（包含波动率和港股市场特有风险）
+                4. 短期和中期目标价位（港币）
+                5. 关键技术位分析
+                6. 具体交易建议（包含止损位）
+                
+                请基于技术指标和港股市场特点进行分析，给出具体数据支持。
+                """
+            else:  # A股
+                prompt = f"""
+                分析A股 {stock_code}：
+
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请提供：
+                1. 趋势分析（包含支撑位和压力位）
+                2. 成交量分析及其含义
+                3. 风险评估（包含波动率分析）
+                4. 短期和中期目标价位
+                5. 关键技术位分析
+                6. 具体交易建议（包含止损位）
+                
+                请基于技术指标和A股市场特点进行分析，给出具体数据支持。
+                """
+            
+            # 格式化API URL
+            api_url = APIUtils.format_api_url(self.API_URL)
+            
+            # 准备请求数据
+            request_data = {
+                "model": self.API_MODEL,
+                "messages": [{"role": "user", "content": prompt}],
+                "temperature": 0.7,
+                "stream": stream
+            }
+            
+            # 准备请求头
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.API_KEY}"
+            }
+            
+            # 获取当前日期作为分析日期
+            analysis_date = datetime.now().strftime("%Y-%m-%d")
+            
+            # 异步请求API
+            async with httpx.AsyncClient(timeout=self.API_TIMEOUT) as client:
+                # 记录请求
+                logger.debug(f"发送AI请求: URL={api_url}, MODEL={self.API_MODEL}, STREAM={stream}")
+                
+                # 先发送技术指标数据
+                yield json.dumps({
+                    "stock_code": stock_code,
+                    "status": "analyzing",
+                    "rsi": rsi,
+                    "price": price,
+                    "price_change": price_change,
+                    "ma_trend": ma_trend,
+                    "macd_signal": macd_signal_type,
+                    "volume_status": volume_status,
+                    "analysis_date": analysis_date
+                })
+                
+                if stream:
+                    # 流式响应处理
+                    async with client.stream("POST", api_url, json=request_data, headers=headers) as response:
+                        if response.status_code != 200:
+                            error_text = await response.aread()
+                            error_data = json.loads(error_text)
+                            error_message = error_data.get('error', {}).get('message', '未知错误')
+                            logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
+                            yield json.dumps({
+                                "stock_code": stock_code,
+                                "error": f"API请求失败: {error_message}",
+                                "status": "error"
+                            })
+                            return
+                            
+                        # 处理流式响应
+                        buffer = ""
+                        collected_messages = []
+                        chunk_count = 0
+                        
+                        async for chunk in response.aiter_text():
+                            if chunk:
+                                # 分割多行响应（处理某些API可能在一个chunk中返回多行）
+                                lines = chunk.strip().split('\n')
+                                for line in lines:
+                                    line = line.strip()
+                                    if not line:
+                                        continue
+                                        
+                                    # 处理以data:开头的行
+                                    if line.startswith("data: "):
+                                        line = line[6:]  # 去除"data: "前缀
+                                     
+                                    if line == "[DONE]":
+                                        logger.debug("收到流结束标记 [DONE]")
+                                        continue
+                                        
+                                    try:
+                                        # 处理特殊错误情况
+                                        if "error" in line.lower():
+                                            error_msg = line
+                                            try:
+                                                error_data = json.loads(line)
+                                                error_msg = error_data.get("error", line)
+                                            except:
+                                                pass
+                                            
+                                            logger.error(f"流式响应中收到错误: {error_msg}")
+                                            yield json.dumps({
+                                                "stock_code": stock_code,
+                                                "error": f"流式响应错误: {error_msg}",
+                                                "status": "error"
+                                            })
+                                            continue
+                                        
+                                        # 尝试解析JSON
+                                        chunk_data = json.loads(line)
+                                        
+                                        # 检查是否有finish_reason
+                                        finish_reason = chunk_data.get("choices", [{}])[0].get("finish_reason")
+                                        if finish_reason == "stop":
+                                            logger.debug("收到finish_reason=stop，流结束")
+                                            continue
+                                        
+                                        # 获取delta内容
+                                        delta = chunk_data.get("choices", [{}])[0].get("delta", {})
+                                        
+                                        # 检查delta是否为空对象
+                                        if not delta or delta == {}:
+                                            logger.debug("收到空的delta对象，跳过")
+                                            continue
+                                        
+                                        content = delta.get("content", "")
+                                        
+                                        if content:
+                                            chunk_count += 1
+                                            buffer += content
+                                            collected_messages.append(content)
+                                            
+                                            # 直接发送每个内容片段，不累积
+                                            yield json.dumps({
+                                                "stock_code": stock_code,
+                                                "ai_analysis_chunk": content,
+                                                "status": "analyzing"
+                                            })
+                                    except json.JSONDecodeError:
+                                        # 记录解析错误并尝试恢复
+                                        logger.error(f"JSON解析错误，块内容: {line}")
+                                        
+                                        # 如果是特定错误模式，处理它
+                                        if "streaming failed after retries" in line.lower():
+                                            logger.error("检测到流式传输失败")
+                                            yield json.dumps({
+                                                "stock_code": stock_code,
+                                                "error": "流式传输失败，请稍后重试",
+                                                "status": "error"
+                                            })
+                                            return
+                                        continue
+                        
+                        logger.info(f"AI流式处理完成，共收到 {chunk_count} 个内容片段，总长度: {len(buffer)}")
+                        
+                        # 如果buffer不为空且不以换行符结束，发送一个换行符
+                        if buffer and not buffer.endswith('\n'):
+                            logger.debug("发送换行符")
+                            yield json.dumps({
+                                "stock_code": stock_code,
+                                "ai_analysis_chunk": "\n",
+                                "status": "analyzing"
+                            })
+                        
+                        # 完整的分析内容
+                        full_content = buffer
+                        
+                        # 尝试从分析内容中提取投资建议
+                        recommendation = self._extract_recommendation(full_content)
+                        
+                        # 计算分析评分
+                        score = self._calculate_analysis_score(full_content, technical_summary)
+                        
+                        # 发送完成状态和评分、建议
+                        yield json.dumps({
+                            "stock_code": stock_code,
+                            "status": "completed",
+                            "score": score,
+                            "recommendation": recommendation
+                        })
+                else:
+                    # 非流式响应处理
+                    response = await client.post(api_url, json=request_data, headers=headers)
+                    
+                    if response.status_code != 200:
+                        error_data = response.json()
+                        error_message = error_data.get('error', {}).get('message', '未知错误')
+                        logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
+                        yield json.dumps({
+                            "stock_code": stock_code,
+                            "error": f"API请求失败: {error_message}",
+                            "status": "error"
+                        })
+                        return
+                    
+                    response_data = response.json()
+                    analysis_text = response_data.get("choices", [{}])[0].get("message", {}).get("content", "")
+                    
+                    # 尝试从分析内容中提取投资建议
+                    recommendation = self._extract_recommendation(analysis_text)
+                    
+                    # 计算分析评分
+                    score = self._calculate_analysis_score(analysis_text, technical_summary)
+                    
+                    # 发送完整的分析结果
+                    yield json.dumps({
+                        "stock_code": stock_code,
+                        "status": "completed",
+                        "analysis": analysis_text,
+                        "score": score,
+                        "recommendation": recommendation,
+                        "rsi": rsi,
+                        "price": price,
+                        "price_change": price_change,
+                        "ma_trend": ma_trend,
+                        "macd_signal": macd_signal_type,
+                        "volume_status": volume_status,
+                        "analysis_date": analysis_date
+                    })
+                    
+        except Exception as e:
+            logger.error(f"AI分析出错: {str(e)}", exc_info=True)
+            yield json.dumps({
+                "stock_code": stock_code,
+                "error": f"分析出错: {str(e)}",
+                "status": "error"
+            })
+            
+    def _extract_recommendation(self, analysis_text: str) -> str:
+        """从分析文本中提取投资建议"""
+        # 查找投资建议部分
+        investment_advice_pattern = r"##\s*投资建议\s*\n(.*?)(?:\n##|\Z)"
+        match = re.search(investment_advice_pattern, analysis_text, re.DOTALL)
+        
+        if match:
+            advice_text = match.group(1).strip()
+            
+            # 提取关键建议
+            if "买入" in advice_text or "增持" in advice_text:
+                return "买入"
+            elif "卖出" in advice_text or "减持" in advice_text:
+                return "卖出"
+            elif "持有" in advice_text:
+                return "持有"
+            else:
+                return "观望"
+        
+        return "观望"  # 默认建议
+        
+    def _calculate_analysis_score(self, analysis_text: str, technical_summary: dict) -> int:
+        """计算分析评分"""
+        score = 50  # 基础分数
+        
+        # 根据技术指标调整分数
+        if technical_summary['trend'] == 'upward':
+            score += 10
+        else:
+            score -= 10
+            
+        if technical_summary['volume_trend'] == 'increasing':
+            score += 5
+        else:
+            score -= 5
+            
+        rsi = technical_summary['rsi_level']
+        if rsi < 30:  # 超卖
+            score += 15
+        elif rsi > 70:  # 超买
+            score -= 15
+            
+        # 根据分析文本中的关键词调整分数
+        if "强烈买入" in analysis_text or "显著上涨" in analysis_text:
+            score += 20
+        elif "买入" in analysis_text or "看涨" in analysis_text:
+            score += 10
+        elif "强烈卖出" in analysis_text or "显著下跌" in analysis_text:
+            score -= 20
+        elif "卖出" in analysis_text or "看跌" in analysis_text:
+            score -= 10
+            
+        # 确保分数在0-100范围内
+        return max(0, min(100, score))
+    
+    def _truncate_json_for_logging(self, json_obj, max_length=500):
+        """
+        截断JSON对象以便记录日志
+        
+        Args:
+            json_obj: JSON对象
+            max_length: 最大长度
+            
+        Returns:
+            截断后的字符串
+        """
+        json_str = json.dumps(json_obj, ensure_ascii=False)
+        if len(json_str) <= max_length:
+            return json_str
+        return json_str[:max_length] + "..." 

services/fund_service_async.py

@@ -0,0 +1,228 @@
+import asyncio
+import pandas as pd
+from typing import List, Dict, Any, Optional
+from logger import get_logger
+from datetime import datetime, timedelta
+
+# 获取日志器
+logger = get_logger()
+
+class FundServiceAsync:
+    """
+    异步基金服务
+    提供基金数据的异步搜索和获取功能
+    """
+    
+    def __init__(self):
+        """初始化异步基金服务"""
+        logger.debug("初始化FundServiceAsync")
+        
+        # 添加缓存
+        self._etf_cache = None
+        self._lof_cache = None
+        self._cache_timestamp = None
+        self._cache_duration = timedelta(minutes=30)  # 缓存30分钟
+    
+    async def search_funds(self, keyword: str, market_type: str = 'ETF') -> List[Dict[str, Any]]:
+        """
+        异步搜索基金代码
+        
+        Args:
+            keyword: 搜索关键词
+            market_type: 市场类型，'ETF'或'LOF'
+            
+        Returns:
+            匹配的基金列表
+        """
+        try:
+            logger.info(f"异步搜索基金: {keyword}, 类型: {market_type}")
+            
+            # 获取基金数据
+            df = await self._get_funds_data(market_type)
+            
+            # 模糊匹配搜索（同时匹配代码和名称）
+            mask = (df['name'].str.contains(keyword, case=False, na=False) | 
+                   df['symbol'].str.contains(keyword, case=False, na=False))
+            results = df[mask]
+            
+            # 格式化返回结果并处理 NaN 值
+            formatted_results = []
+            for _, row in results.iterrows():
+                formatted_results.append({
+                    'name': row['name'] if pd.notna(row['name']) else '',
+                    'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
+                    'price': float(row['price']) if pd.notna(row['price']) else 0.0,
+                    'volume': float(row['volume']) if pd.notna(row['volume']) else 0.0,
+                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0,
+                    'total_value': float(row['total_value']) if pd.notna(row['total_value']) else 0.0,
+                })
+                # 限制只返回前10个结果
+                if len(formatted_results) >= 10:
+                    break
+            
+            logger.info(f"基金搜索完成，找到 {len(formatted_results)} 个匹配项（限制显示前10个）")
+            return formatted_results
+            
+        except Exception as e:
+            error_msg = f"搜索基金代码失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg)
+    
+    async def _get_funds_data(self, market_type: str = 'ETF') -> pd.DataFrame:
+        """
+        异步获取基金数据，支持缓存
+        
+        Args:
+            market_type: 市场类型，'ETF'或'LOF'
+            
+        Returns:
+            包含基金数据的DataFrame
+        """
+        # 检查缓存是否有效
+        now = datetime.now()
+        cache_valid = (
+            self._cache_timestamp is not None and 
+            (now - self._cache_timestamp) < self._cache_duration
+        )
+        
+        if market_type == 'ETF' and cache_valid and self._etf_cache is not None:
+            logger.debug("使用ETF缓存数据")
+            return self._etf_cache
+        elif market_type == 'LOF' and cache_valid and self._lof_cache is not None:
+            logger.debug("使用LOF缓存数据")
+            return self._lof_cache
+        
+        # 缓存无效，重新获取数据
+        try:
+            logger.debug(f"从API获取{market_type}数据")
+            
+            # 使用线程池执行同步的akshare调用
+            if market_type == 'ETF':
+                df = await asyncio.to_thread(self._get_etf_data)
+                self._etf_cache = df
+            else:
+                df = await asyncio.to_thread(self._get_lof_data)
+                self._lof_cache = df
+                
+            self._cache_timestamp = now
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取{market_type}数据失败: {str(e)}")
+            logger.exception(e)
+            raise
+    
+    def _get_etf_data(self) -> pd.DataFrame:
+        """
+        获取ETF数据（同步方法，将被异步方法调用）
+        
+        Returns:
+            包含ETF数据的DataFrame
+        """
+        import akshare as ak
+        
+        try:
+            # 获取ETF基金数据
+            df = ak.fund_etf_spot_em()
+            
+            # 转换列名
+            df = df.rename(columns={
+                "代码": "symbol",
+                "名称": "name",
+                "最新价": "price",
+                "涨跌额": "price_change",
+                "涨跌幅": "price_change_percent",
+                "成交量": "volume",
+                "流通市值": "market_value",
+                "总市值": "total_value",
+                "基金折价率": "discount_rate",
+            })
+            
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取ETF数据失败: {str(e)}")
+            logger.exception(e)
+            raise Exception(f"获取ETF数据失败: {str(e)}")
+    
+    def _get_lof_data(self) -> pd.DataFrame:
+        """
+        获取LOF数据（同步方法，将被异步方法调用）
+        
+        Returns:
+            包含LOF数据的DataFrame
+        """
+        import akshare as ak
+        
+        try:
+            # 获取LOF基金数据
+            df = ak.fund_lof_spot_em()
+            
+            # 转换列名
+            df = df.rename(columns={
+                "代码": "symbol",
+                "名称": "name",
+                "最新价": "price",
+                "涨跌额": "price_change",
+                "涨跌幅": "price_change_percent",
+                "成交量": "volume",
+                "流通市值": "market_value",
+                "总市值": "total_value",
+                "基金折价率": "discount_rate",
+            })
+            
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取LOF数据失败: {str(e)}")
+            logger.exception(e)
+            raise Exception(f"获取LOF数据失败: {str(e)}")
+            
+    async def get_fund_detail(self, symbol: str, market_type: str = 'ETF') -> Dict[str, Any]:
+        """
+        异步获取单个基金详细信息
+        
+        Args:
+            symbol: 基金代码
+            market_type: 市场类型，'ETF'或'LOF'
+            
+        Returns:
+            基金详细信息
+        """
+        try:
+            logger.info(f"获取{market_type}基金详情: {symbol}")
+            
+            # 获取基金数据
+            df = await self._get_funds_data(market_type)
+            
+            # 精确匹配基金代码
+            result = df[df['symbol'] == symbol]
+            
+            if len(result) == 0:
+                raise Exception(f"未找到基金代码: {symbol}")
+            
+            # 获取第一行数据
+            row = result.iloc[0]
+            
+            # 格式化为字典
+            fund_detail = {
+                'name': row['name'] if pd.notna(row['name']) else '',
+                'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
+                'price': float(row['price']) if pd.notna(row['price']) else 0.0,
+                'price_change': float(row['price_change']) if pd.notna(row['price_change']) else 0.0,
+                'price_change_percent': float(row['price_change_percent'].strip('%'))/100 if pd.notna(row['price_change_percent']) else 0.0,
+                'volume': float(row['volume']) if pd.notna(row['volume']) else 0.0,
+                'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0,
+                'total_value': float(row['total_value']) if pd.notna(row['total_value']) else 0.0,
+                'discount_rate': float(row['discount_rate'].strip('%'))/100 if pd.notna(row['discount_rate']) else 0.0
+            }
+            
+            logger.info(f"获取基金详情成功: {symbol}")
+            return fund_detail
+            
+        except Exception as e:
+            error_msg = f"获取基金详情失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg) 

services/stock_analyzer_service.py

@@ -0,0 +1,269 @@
+import pandas as pd
+import numpy as np
+import asyncio
+import json
+from datetime import datetime
+from typing import Dict, List, Optional, Tuple, Any, AsyncGenerator
+from logger import get_logger
+from services.stock_data_provider import StockDataProvider
+from services.technical_indicator import TechnicalIndicator
+from services.stock_scorer import StockScorer
+from services.ai_analyzer import AIAnalyzer
+
+# 获取日志器
+logger = get_logger()
+
+class StockAnalyzerService:
+    """
+    股票分析服务
+    作为门面类协调数据提供、指标计算、评分和AI分析等组件
+    """
+    
+    def __init__(self, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
+        """
+        初始化股票分析服务
+        
+        Args:
+            custom_api_url: 自定义API URL
+            custom_api_key: 自定义API密钥
+            custom_api_model: 自定义API模型
+            custom_api_timeout: 自定义API超时时间
+        """
+        # 初始化各个组件
+        self.data_provider = StockDataProvider()
+        self.indicator = TechnicalIndicator()
+        self.scorer = StockScorer()
+        self.ai_analyzer = AIAnalyzer(
+            custom_api_url=custom_api_url,
+            custom_api_key=custom_api_key,
+            custom_api_model=custom_api_model,
+            custom_api_timeout=custom_api_timeout
+        )
+        
+        logger.info("初始化StockAnalyzerService完成")
+    
+    async def analyze_stock(self, stock_code: str, market_type: str = 'A', stream: bool = False) -> AsyncGenerator[str, None]:
+        """
+        分析单只股票
+        
+        Args:
+            stock_code: 股票代码
+            market_type: 市场类型，默认为'A'股
+            stream: 是否使用流式响应
+            
+        Returns:
+            异步生成器，生成分析结果的JSON字符串
+        """
+        try:
+            logger.info(f"开始分析股票: {stock_code}, 市场: {market_type}")
+            
+            # 获取股票数据
+            df = await self.data_provider.get_stock_data(stock_code, market_type)
+            
+            # 检查是否有错误
+            if hasattr(df, 'error'):
+                error_msg = df.error
+                logger.error(f"获取股票数据时出错: {error_msg}")
+                yield json.dumps({
+                    "stock_code": stock_code,
+                    "market_type": market_type,
+                    "error": error_msg,
+                    "status": "error"
+                })
+                return
+            
+            # 检查数据是否为空
+            if df.empty:
+                error_msg = f"获取到的股票 {stock_code} 数据为空"
+                logger.error(error_msg)
+                yield json.dumps({
+                    "stock_code": stock_code,
+                    "market_type": market_type,
+                    "error": error_msg,
+                    "status": "error"
+                })
+                return
+            
+            # 计算技术指标
+            df_with_indicators = self.indicator.calculate_indicators(df)
+            
+            # 计算评分
+            score = self.scorer.calculate_score(df_with_indicators)
+            recommendation = self.scorer.get_recommendation(score)
+            
+            # 获取最新数据
+            latest_data = df_with_indicators.iloc[-1]
+            previous_data = df_with_indicators.iloc[-2] if len(df_with_indicators) > 1 else latest_data
+            
+            # 计算价格变化百分比
+            price_change = ((latest_data['Close'] - previous_data['Close']) / previous_data['Close']) * 100
+            
+            # 确定MA趋势
+            ma_short = latest_data.get('MA5', 0)
+            ma_medium = latest_data.get('MA20', 0)
+            ma_long = latest_data.get('MA60', 0)
+            
+            if ma_short > ma_medium > ma_long:
+                ma_trend = "UP"
+            elif ma_short < ma_medium < ma_long:
+                ma_trend = "DOWN"
+            else:
+                ma_trend = "FLAT"
+                
+            # 确定MACD信号
+            macd = latest_data.get('MACD', 0)
+            signal = latest_data.get('Signal', 0)
+            
+            if macd > signal:
+                macd_signal = "BUY"
+            elif macd < signal:
+                macd_signal = "SELL"
+            else:
+                macd_signal = "HOLD"
+                
+            # 确定成交量状态
+            volume = latest_data.get('Volume', 0)
+            volume_ma = latest_data.get('Volume_MA', 0)
+            
+            if volume > volume_ma * 1.5:
+                volume_status = "HIGH"
+            elif volume < volume_ma * 0.5:
+                volume_status = "LOW"
+            else:
+                volume_status = "NORMAL"
+                
+            # 当前分析日期
+            analysis_date = datetime.now().strftime('%Y-%m-%d')
+            
+            # 生成基本分析结果
+            basic_result = {
+                "stock_code": stock_code,
+                "market_type": market_type,
+                "analysis_date": analysis_date,
+                "score": score,
+                "price": latest_data['Close'],
+                "price_change": price_change,
+                "ma_trend": ma_trend,
+                "rsi": latest_data.get('RSI', 0),
+                "macd_signal": macd_signal,
+                "volume_status": volume_status,
+                "recommendation": recommendation,
+                "ai_analysis": ""
+            }
+            
+            # 输出基本分析结果
+            logger.info(f"基本分析结果: {json.dumps(basic_result)}")
+            yield json.dumps(basic_result)
+            
+            # 使用AI进行深入分析
+            async for analysis_chunk in self.ai_analyzer.get_ai_analysis(df_with_indicators, stock_code, market_type, stream):
+                yield analysis_chunk
+                
+            logger.info(f"完成股票分析: {stock_code}")
+            
+        except Exception as e:
+            error_msg = f"分析股票 {stock_code} 时出错: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            yield json.dumps({"error": error_msg})
+    
+    async def scan_stocks(self, stock_codes: List[str], market_type: str = 'A', min_score: int = 0, stream: bool = False) -> AsyncGenerator[str, None]:
+        """
+        批量扫描股票
+        
+        Args:
+            stock_codes: 股票代码列表
+            market_type: 市场类型
+            min_score: 最低评分阈值
+            stream: 是否使用流式响应
+            
+        Returns:
+            异步生成器，生成扫描结果的JSON字符串
+        """
+        try:
+            logger.info(f"开始批量扫描 {len(stock_codes)} 只股票, 市场: {market_type}")
+            
+            # 输出初始状态 - 发送批量分析初始化消息
+            yield json.dumps({
+                "stream_type": "batch",
+                "stock_codes": stock_codes,
+                "market_type": market_type,
+                "min_score": min_score
+            })
+            
+            # 批量获取股票数据
+            stock_data_dict = await self.data_provider.get_multiple_stocks_data(stock_codes, market_type)
+            
+            # 计算技术指标
+            stock_with_indicators = {}
+            for code, df in stock_data_dict.items():
+                try:
+                    stock_with_indicators[code] = self.indicator.calculate_indicators(df)
+                except Exception as e:
+                    logger.error(f"计算 {code} 技术指标时出错: {str(e)}")
+                    # 发送错误状态
+                    yield json.dumps({
+                        "stock_code": code,
+                        "error": f"计算技术指标时出错: {str(e)}",
+                        "status": "error"
+                    })
+            
+            # 评分股票
+            results = self.scorer.batch_score_stocks(stock_with_indicators)
+            
+            # 过滤低于最低评分的股票
+            filtered_results = [r for r in results if r[1] >= min_score]
+            
+            # 为每只股票发送基本评分和推荐信息
+            for code, score, rec in results:
+                df = stock_with_indicators.get(code)
+                if df is not None and len(df) > 0:
+                    # 获取最新数据
+                    latest_data = df.iloc[-1]
+                    
+                    # 发送股票基本信息和评分
+                    yield json.dumps({
+                        "stock_code": code,
+                        "score": score,
+                        "recommendation": rec,
+                        "price": float(latest_data.get('Close', 0)),
+                        "price_change": float(latest_data.get('Change', 0)),
+                        "rsi": float(latest_data.get('RSI', 0)) if 'RSI' in latest_data else None,
+                        "ma_trend": "UP" if latest_data.get('MA5', 0) > latest_data.get('MA20', 0) else "DOWN",
+                        "macd_signal": "BUY" if latest_data.get('MACD', 0) > latest_data.get('MACD_Signal', 0) else "SELL",
+                        "volume_status": "HIGH" if latest_data.get('Volume_Ratio', 1) > 1.5 else ("LOW" if latest_data.get('Volume_Ratio', 1) < 0.5 else "NORMAL"),
+                        "status": "completed" if score < min_score else "waiting"
+                    })
+            
+            # 如果需要进一步分析，对评分较高的股票进行AI分析
+            if stream and filtered_results:
+                # 只分析前5只评分最高的股票，避免分析过多导致前端卡顿
+                top_stocks = filtered_results[:5]
+                
+                for stock_code, score, _ in top_stocks:
+                    df = stock_with_indicators.get(stock_code)
+                    if df is not None:
+                        # 输出正在分析的股票信息
+                        yield json.dumps({
+                            "stock_code": stock_code,
+                            "status": "analyzing"
+                        })
+                        
+                        # AI分析
+                        async for analysis_chunk in self.ai_analyzer.get_ai_analysis(df, stock_code, market_type, stream):
+                            yield analysis_chunk
+            
+            # 输出扫描完成信息
+            yield json.dumps({
+                "scan_completed": True,
+                "total_scanned": len(results),
+                "total_matched": len(filtered_results)
+            })
+            
+            logger.info(f"完成批量扫描 {len(stock_codes)} 只股票, 符合条件: {len(filtered_results)}")
+            
+        except Exception as e:
+            error_msg = f"批量扫描股票时出错: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            yield json.dumps({"error": error_msg})

services/stock_data_provider.py

@@ -0,0 +1,260 @@
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import asyncio
+import os
+from typing import Dict, List, Optional, Tuple, Any
+from logger import get_logger
+import re
+
+# 获取日志器
+logger = get_logger()
+
+class StockDataProvider:
+    """
+    异步股票数据提供服务
+    负责获取股票、基金等金融产品的历史数据
+    """
+    
+    def __init__(self):
+        """初始化数据提供者服务"""
+        logger.debug("初始化StockDataProvider")
+    
+    async def get_stock_data(self, stock_code: str, market_type: str = 'A', 
+                            start_date: Optional[str] = None, 
+                            end_date: Optional[str] = None) -> pd.DataFrame:
+        """
+        异步获取股票或基金数据
+        
+        Args:
+            stock_code: 股票代码
+            market_type: 市场类型，默认为'A'股
+            start_date: 开始日期，格式YYYYMMDD，默认为一年前
+            end_date: 结束日期，格式YYYYMMDD，默认为今天
+            
+        Returns:
+            包含历史数据的DataFrame
+        """
+        # 使用线程池执行同步的akshare调用
+        return await asyncio.to_thread(
+            self._get_stock_data_sync, 
+            stock_code, 
+            market_type, 
+            start_date, 
+            end_date
+        )
+    
+    def _get_stock_data_sync(self, stock_code: str, market_type: str = 'A', 
+                           start_date: Optional[str] = None, 
+                           end_date: Optional[str] = None) -> pd.DataFrame:
+        """
+        同步获取股票数据的实现
+        将被异步方法调用
+        """
+        import akshare as ak
+        
+        if start_date is None:
+            start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
+        if end_date is None:
+            end_date = datetime.now().strftime('%Y%m%d')
+            
+        # 确保日期格式统一（移除可能的'-'符号）
+        if isinstance(start_date, str) and '-' in start_date:
+            start_date = start_date.replace('-', '')
+        if isinstance(end_date, str) and '-' in end_date:
+            end_date = end_date.replace('-', '')
+            
+        try:
+            if market_type == 'A':
+                logger.debug(f"获取A股数据: {stock_code}")
+                
+                df = ak.stock_zh_a_hist(
+                    symbol=stock_code,
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+                
+            elif market_type in ['HK']:
+                logger.debug(f"获取港股数据: {stock_code}")
+                df = ak.stock_hk_daily(
+                    symbol=stock_code,
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+                
+            elif market_type in ['US']:
+                logger.debug(f"获取美股数据: {stock_code}")
+                try:
+                    df = ak.stock_us_daily(
+                        symbol=stock_code,
+                        adjust="qfq"
+                    )
+                    logger.debug(f"美股数据原始列: {df.columns.tolist()}")
+                    logger.debug(f"美股数据形状: {df.shape}")
+                    
+                    # 确保索引是日期时间类型
+                    if not isinstance(df.index, pd.DatetimeIndex):
+                        # 如果存在命名为'date'的列，将其设为索引
+                        if 'date' in df.columns:
+                            df['date'] = pd.to_datetime(df['date'])
+                            df.set_index('date', inplace=True)
+                            logger.debug("已将'date'列设置为索引")
+                        else:
+                            # 否则将当前索引转换为日期类型
+                            df.index = pd.to_datetime(df.index)
+                            logger.debug("已将索引转换为DatetimeIndex")
+                    
+                    # 计算美股的成交额（Amount）= 成交量（Volume）× 收盘价（Close）
+                    volume_col = next((col for col in df.columns if col.lower() == 'volume'), None)
+                    close_col = next((col for col in df.columns if col.lower() == 'close'), None)
+                    
+                    if volume_col and close_col:
+                        df['amount'] = df[volume_col] * df[close_col]
+                        logger.debug("已为美股数据计算成交额(amount)字段")
+                    else:
+                        logger.warning(f"美股数据缺少volume或close列，无法计算amount。当前列: {df.columns.tolist()}")
+                        # 添加空的amount列，避免后续处理错误
+                        df['amount'] = 0.0
+                        
+                    # 将所有列名转为小写以进行统一处理
+                    df.columns = [col.lower() for col in df.columns]
+                    
+                except Exception as e:
+                    logger.error(f"获取美股数据失败 {stock_code}: {str(e)}")
+                    raise ValueError(f"获取美股数据失败 {stock_code}: {str(e)}")
+                
+                # 将字符串日期转换为日期时间对象进行比较
+                try:
+                    # 尝试多种格式解析日期
+                    # 如果日期是数字格式（20220101），使用适当的格式
+                    if start_date.isdigit() and len(start_date) == 8:
+                        start_date_dt = pd.to_datetime(start_date, format='%Y%m%d')
+                    else:
+                        # 否则让pandas自动推断格式
+                        start_date_dt = pd.to_datetime(start_date)
+                        
+                    if end_date.isdigit() and len(end_date) == 8:
+                        end_date_dt = pd.to_datetime(end_date, format='%Y%m%d')
+                    else:
+                        end_date_dt = pd.to_datetime(end_date)
+                except Exception as e:
+                    logger.warning(f"日期转换出错: {str(e)}，使用默认值")
+                    # 如果转换失败，使用合理的默认值
+                    start_date_dt = pd.to_datetime('20000101', format='%Y%m%d')
+                    end_date_dt = pd.to_datetime(datetime.now().strftime('%Y%m%d'), format='%Y%m%d')
+                
+                # 过滤日期
+                try:
+                    df = df[(df.index >= start_date_dt) & (df.index <= end_date_dt)]
+                    logger.debug(f"日期过滤后数据点数: {len(df)}")
+                except Exception as e:
+                    logger.warning(f"日期过滤出错: {str(e)}，返回原始数据")
+                    
+            elif market_type in ['ETF', 'LOF']:
+                logger.debug(f"获取{market_type}基金数据: {stock_code}")
+                df = ak.fund_etf_hist_sina(
+                    symbol=stock_code,
+                    start_date=start_date.replace('-', ''),
+                    end_date=end_date.replace('-', '')
+                )
+                
+            else:
+                error_msg = f"不支持的市场类型: {market_type}"
+                logger.error(f"[市场类型错误] {error_msg}")
+                raise ValueError(error_msg)
+                
+            # 标准化列名
+            if market_type == 'A':
+                # 根据实际数据结构调整列名映射
+                # 实际数据列：['日期', '股票代码', '开盘', '收盘', '最高', '最低', '成交量', '成交额', '振幅', '涨跌幅', '涨跌额', '换手率']
+                df.columns = ['Date', 'Code', 'Open', 'Close', 'High', 'Low', 'Volume', 'Amount', 'Amplitude', 'Change_pct', 'Change', 'Turnover']
+            elif market_type in ['HK', 'US']:
+                # 美股数据列可能不同，需要通过映射处理
+                columns_mapping = {
+                    'open': 'Open',
+                    'high': 'High',
+                    'low': 'Low',
+                    'close': 'Close',
+                    'volume': 'Volume',
+                    'amount': 'Amount'
+                }
+                
+                # 创建新的DataFrame以确保列顺序和存在性
+                new_df = pd.DataFrame(index=df.index)
+                
+                # 遍历映射，填充新DataFrame
+                for orig_col, new_col in columns_mapping.items():
+                    if orig_col in df.columns:
+                        new_df[new_col] = df[orig_col]
+                    else:
+                        # 如果原始列不存在，创建一个填充0的列
+                        logger.warning(f"数据中缺少{orig_col}列，使用0值填充")
+                        new_df[new_col] = 0.0
+                
+                # 替换原始df
+                df = new_df
+                
+            elif market_type in ['ETF', 'LOF']:
+                # 基金数据可能有不同的列
+                df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Amount']
+                
+            # 确保日期列是日期类型
+            if 'Date' in df.columns:
+                df['Date'] = pd.to_datetime(df['Date'])
+                df.set_index('Date', inplace=True)
+                
+            # 确保按日期升序排序
+            df.sort_index(inplace=True)
+                
+            logger.info(f"成功获取{market_type}数据 {stock_code}, 数据点数: {len(df)}")
+            return df
+            
+        except Exception as e:
+            error_msg = f"获取{market_type}数据失败 {stock_code}: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            # 使用空的DataFrame并添加错误信息，而不是抛出异常
+            # 这样上层调用者可以检查是否有错误并适当处理
+            df = pd.DataFrame()
+            df.error = error_msg  # 添加错误属性
+            return df
+            
+    async def get_multiple_stocks_data(self, stock_codes: List[str], 
+                                     market_type: str = 'A',
+                                     start_date: Optional[str] = None, 
+                                     end_date: Optional[str] = None,
+                                     max_concurrency: int = 5) -> Dict[str, pd.DataFrame]:
+        """
+        异步批量获取多只股票数据
+        
+        Args:
+            stock_codes: 股票代码列表
+            market_type: 市场类型，默认为'A'股
+            start_date: 开始日期，格式YYYYMMDD
+            end_date: 结束日期，格式YYYYMMDD
+            max_concurrency: 最大并发数，默认为5
+            
+        Returns:
+            字典，键为股票代码，值为对应的DataFrame
+        """
+        # 使用信号量控制并发数
+        semaphore = asyncio.Semaphore(max_concurrency)
+        
+        async def get_with_semaphore(code):
+            async with semaphore:
+                try:
+                    return code, await self.get_stock_data(code, market_type, start_date, end_date)
+                except Exception as e:
+                    logger.error(f"获取股票 {code} 数据时出错: {str(e)}")
+                    return code, None
+        
+        # 创建异步任务
+        tasks = [get_with_semaphore(code) for code in stock_codes]
+        
+        # 等待所有任务完成
+        results = await asyncio.gather(*tasks)
+        
+        # 构建结果字典，过滤掉失败的请求
+        return {code: df for code, df in results if df is not None}

services/stock_scorer.py

@@ -0,0 +1,128 @@
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional, Any, List, Tuple
+from logger import get_logger
+
+# 获取日志器
+logger = get_logger()
+
+class StockScorer:
+    """
+    股票评分服务
+    负责根据技术指标计算股票的综合评分
+    """
+    
+    def __init__(self):
+        """初始化股票评分服务"""
+        logger.debug("初始化StockScorer")
+    
+    def calculate_score(self, df: pd.DataFrame) -> int:
+        """
+        计算股票评分（满分100分）
+        
+        Args:
+            df: 包含技术指标的DataFrame
+            
+        Returns:
+            股票评分（0-100的整数）
+        """
+        try:
+            # 使用最新的数据点进行评分
+            latest = df.iloc[-1]
+            
+            # 初始得分为0
+            score = 0
+            
+            # 移动平均线评分（25分）
+            if latest['MA5'] > latest['MA20'] > latest['MA60']:
+                # 短期、中期和长期均线呈多头排列
+                score += 25
+            elif latest['MA5'] > latest['MA20']:
+                # 短期均线在中期均线之上
+                score += 15
+            elif latest['Close'] > latest['MA20']:
+                # 股价在中期均线之上
+                score += 10
+                
+            # RSI评分（25分）
+            rsi = latest['RSI']
+            if 45 <= rsi <= 55:
+                # RSI在中间区域，可能即将爆发
+                score += 15
+            elif 55 < rsi < 70:
+                # RSI在强势区域但未超买
+                score += 25
+            elif 30 < rsi < 45:
+                # RSI在弱势区域但未超卖
+                score += 10
+            elif rsi >= 70:
+                # RSI超买
+                score += 5
+            elif rsi <= 30:
+                # RSI超卖
+                score += 15
+                
+            # MACD得分（20分）
+            if latest['MACD'] > latest['Signal']:
+                score += 20
+                
+            # 成交量得分（30分）
+            if latest['Volume_Ratio'] > 1.5:
+                score += 30
+            elif latest['Volume_Ratio'] > 1:
+                score += 15
+                
+            return score
+            
+        except Exception as e:
+            logger.error(f"计算评分时出错: {str(e)}")
+            logger.exception(e)
+            raise
+            
+    def get_recommendation(self, score: int) -> str:
+        """
+        根据评分获取投资建议
+        
+        Args:
+            score: 股票评分（0-100）
+            
+        Returns:
+            投资建议文本
+        """
+        if score >= 80:
+            return "强烈推荐"
+        elif score >= 70:
+            return "推荐"
+        elif score >= 60:
+            return "谨慎推荐"
+        elif score >= 40:
+            return "观望"
+        elif score >= 20:
+            return "不推荐"
+        else:
+            return "强烈不推荐"
+            
+    def batch_score_stocks(self, stock_dfs: Dict[str, pd.DataFrame]) -> List[Tuple[str, int, str]]:
+        """
+        批量评分多只股票
+        
+        Args:
+            stock_dfs: 字典，键为股票代码，值为DataFrame
+            
+        Returns:
+            评分结果列表，每项为(股票代码, 评分, 推荐)的三元组
+        """
+        results = []
+        
+        for stock_code, df in stock_dfs.items():
+            try:
+                score = self.calculate_score(df)
+                recommendation = self.get_recommendation(score)
+                results.append((stock_code, score, recommendation))
+            except Exception as e:
+                logger.error(f"评分股票 {stock_code} 时出错: {str(e)}")
+                
+        # 按评分降序排序
+        results.sort(key=lambda x: x[1], reverse=True)
+        
+        return results 

services/technical_indicator.py

@@ -0,0 +1,187 @@
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional, Any
+from logger import get_logger
+
+# 获取日志器
+logger = get_logger()
+
+class TechnicalIndicator:
+    """
+    技术指标计算服务
+    负责计算常见的股票技术指标
+    """
+    
+    def __init__(self, params: Optional[Dict[str, Any]] = None):
+        """
+        初始化技术指标计算服务
+        
+        Args:
+            params: 技术指标参数配置
+        """
+        # 默认参数设置
+        self.params = params or {
+            'ma_periods': {'short': 5, 'medium': 20, 'long': 60},
+            'rsi_period': 14,
+            'bollinger_period': 20,
+            'bollinger_std': 2,
+            'volume_ma_period': 20,
+            'atr_period': 14
+        }
+        
+        logger.debug(f"初始化TechnicalIndicator，参数: {self.params}")
+    
+    def calculate_ema(self, series: pd.Series, period: int) -> pd.Series:
+        """
+        计算指数移动平均线
+        
+        Args:
+            series: 价格序列
+            period: 周期
+            
+        Returns:
+            EMA序列
+        """
+        return series.ewm(span=period, adjust=False).mean()
+    
+    def calculate_rsi(self, series: pd.Series, period: int) -> pd.Series:
+        """
+        计算相对强弱指标(RSI)
+        
+        Args:
+            series: 价格序列
+            period: 周期
+            
+        Returns:
+            RSI序列
+        """
+        delta = series.diff()
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0)
+        
+        avg_gain = gain.rolling(window=period).mean()
+        avg_loss = loss.rolling(window=period).mean()
+        
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+        
+        return rsi
+    
+    def calculate_macd(self, series: pd.Series) -> tuple:
+        """
+        计算MACD指标
+        
+        Args:
+            series: 价格序列
+            
+        Returns:
+            (MACD线, 信号线, 柱状图)的元组
+        """
+        ema12 = self.calculate_ema(series, 12)
+        ema26 = self.calculate_ema(series, 26)
+        
+        macd = ema12 - ema26
+        signal = self.calculate_ema(macd, 9)
+        histogram = macd - signal
+        
+        return macd, signal, histogram
+    
+    def calculate_bollinger_bands(self, series: pd.Series, period: int, std_dev: float) -> tuple:
+        """
+        计算布林带
+        
+        Args:
+            series: 价格序列
+            period: 周期
+            std_dev: 标准差倍数
+            
+        Returns:
+            (中轨, 上轨, 下轨)的元组
+        """
+        middle = series.rolling(window=period).mean()
+        std = series.rolling(window=period).std()
+        
+        upper = middle + std_dev * std
+        lower = middle - std_dev * std
+        
+        return middle, upper, lower
+    
+    def calculate_atr(self, df: pd.DataFrame, period: int) -> pd.Series:
+        """
+        计算平均真实波幅(ATR)
+        
+        Args:
+            df: 包含High, Low, Close列的DataFrame
+            period: 周期
+            
+        Returns:
+            ATR序列
+        """
+        high = df['High']
+        low = df['Low']
+        close = df['Close']
+        
+        tr1 = high - low
+        tr2 = abs(high - close.shift())
+        tr3 = abs(low - close.shift())
+        
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        atr = tr.rolling(window=period).mean()
+        
+        return atr
+    
+    def calculate_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        计算所有技术指标
+        
+        Args:
+            df: 原始价格数据，包含Open, High, Low, Close, Volume列
+            
+        Returns:
+            添加了技术指标的DataFrame
+        """
+        try:
+            # 复制数据框
+            result_df = df.copy()
+            
+            # 移动平均线
+            for name, period in self.params['ma_periods'].items():
+                result_df[f'MA{period}'] = result_df['Close'].rolling(window=period).mean()
+            
+            # RSI
+            result_df['RSI'] = self.calculate_rsi(result_df['Close'], self.params['rsi_period'])
+            
+            # MACD
+            macd, signal, histogram = self.calculate_macd(result_df['Close'])
+            result_df['MACD'] = macd
+            result_df['Signal'] = signal
+            result_df['Histogram'] = histogram
+            
+            # 布林带
+            middle, upper, lower = self.calculate_bollinger_bands(
+                result_df['Close'], 
+                self.params['bollinger_period'], 
+                self.params['bollinger_std']
+            )
+            result_df['BB_Middle'] = middle
+            result_df['BB_Upper'] = upper
+            result_df['BB_Lower'] = lower
+            
+            # 成交量移动平均
+            result_df['Volume_MA'] = result_df['Volume'].rolling(window=self.params['volume_ma_period']).mean()
+            
+            # 成交量比率
+            result_df['Volume_Ratio'] = result_df['Volume'] / result_df['Volume_MA']
+            
+            # ATR
+            result_df['ATR'] = self.calculate_atr(result_df, self.params['atr_period'])
+            
+            # 波动率 (过去20天收盘价的标准差/均值)
+            result_df['Volatility'] = result_df['Close'].rolling(window=20).std() / result_df['Close'].rolling(window=20).mean() * 100
+            
+            return result_df
+            
+        except Exception as e:
+            logger.error(f"计算技术指标时出错: {str(e)}")
+            logger.exception(e)
+            raise 

services/us_stock_service_async.py

@@ -0,0 +1,154 @@
+import asyncio
+import pandas as pd
+from typing import List, Dict, Any, Optional
+from logger import get_logger
+
+# 获取日志器
+logger = get_logger()
+
+class USStockServiceAsync:
+    """
+    异步美股服务
+    提供美股数据的异步搜索和获取功能
+    """
+    
+    def __init__(self):
+        """初始化异步美股服务"""
+        logger.debug("初始化USStockServiceAsync")
+        
+        # 可选：添加缓存以减少频繁请求
+        self._cache = None
+        self._cache_timestamp = None
+    
+    async def search_us_stocks(self, keyword: str) -> List[Dict[str, Any]]:
+        """
+        异步搜索美股代码
+        
+        Args:
+            keyword: 搜索关键词
+            
+        Returns:
+            匹配的股票列表
+        """
+        try:
+            logger.info(f"异步搜索美股: {keyword}")
+            
+            # 使用线程池执行同步的akshare调用
+            df = await asyncio.to_thread(self._get_us_stocks_data)
+            
+            # 模糊匹配搜索
+            mask = df['name'].str.contains(keyword, case=False, na=False)
+            results = df[mask]
+            
+            # 格式化返回结果并处理 NaN 值
+            formatted_results = []
+            for _, row in results.iterrows():
+                formatted_results.append({
+                    'name': row['name'] if pd.notna(row['name']) else '',
+                    'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
+                    'price': float(row['price']) if pd.notna(row['price']) else 0.0,
+                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0
+                })
+                # 限制只返回前10个结果
+                if len(formatted_results) >= 10:
+                    break
+            
+            logger.info(f"美股搜索完成，找到 {len(formatted_results)} 个匹配项（限制显示前10个）")
+            return formatted_results
+            
+        except Exception as e:
+            error_msg = f"搜索美股代码失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg)
+    
+    def _get_us_stocks_data(self) -> pd.DataFrame:
+        """
+        获取美股数据（同步方法，将被异步方法调用）
+        
+        Returns:
+            包含美股数据的DataFrame
+        """
+        import akshare as ak
+        
+        try:
+            # 获取美股数据
+            df = ak.stock_us_spot_em()
+            
+            # 转换列名
+            df = df.rename(columns={
+                "序号": "index",
+                "名称": "name",
+                "最新价": "price",
+                "涨跌额": "price_change",
+                "涨跌幅": "price_change_percent",
+                "开盘价": "open",
+                "最高价": "high",
+                "最低价": "low",
+                "昨收价": "pre_close",
+                "总市值": "market_value",
+                "市盈率": "pe_ratio",
+                "成交量": "volume",
+                "成交额": "turnover",
+                "振幅": "amplitude",
+                "换手率": "turnover_rate",
+                "代码": "symbol"
+            })
+            
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取美股数据失败: {str(e)}")
+            logger.exception(e)
+            raise Exception(f"获取美股数据失败: {str(e)}")
+            
+    async def get_us_stock_detail(self, symbol: str) -> Dict[str, Any]:
+        """
+        异步获取单个美股详细信息
+        
+        Args:
+            symbol: 股票代码
+            
+        Returns:
+            股票详细信息
+        """
+        try:
+            logger.info(f"获取美股详情: {symbol}")
+            
+            # 使用线程池执行同步的akshare调用
+            df = await asyncio.to_thread(self._get_us_stocks_data)
+            
+            # 精确匹配股票代码
+            result = df[df['symbol'] == symbol]
+            
+            if len(result) == 0:
+                raise Exception(f"未找到股票代码: {symbol}")
+            
+            # 获取第一行数据
+            row = result.iloc[0]
+            
+            # 格式化为字典
+            stock_detail = {
+                'name': row['name'] if pd.notna(row['name']) else '',
+                'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
+                'price': float(row['price']) if pd.notna(row['price']) else 0.0,
+                'price_change': float(row['price_change']) if pd.notna(row['price_change']) else 0.0,
+                'price_change_percent': float(row['price_change_percent'].strip('%'))/100 if pd.notna(row['price_change_percent']) else 0.0,
+                'open': float(row['open']) if pd.notna(row['open']) else 0.0,
+                'high': float(row['high']) if pd.notna(row['high']) else 0.0,
+                'low': float(row['low']) if pd.notna(row['low']) else 0.0,
+                'pre_close': float(row['pre_close']) if pd.notna(row['pre_close']) else 0.0,
+                'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0,
+                'pe_ratio': float(row['pe_ratio']) if pd.notna(row['pe_ratio']) else 0.0,
+                'volume': float(row['volume']) if pd.notna(row['volume']) else 0.0,
+                'turnover': float(row['turnover']) if pd.notna(row['turnover']) else 0.0
+            }
+            
+            logger.info(f"获取美股详情成功: {symbol}")
+            return stock_detail
+            
+        except Exception as e:
+            error_msg = f"获取美股详情失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg) 

stock_analyzer.py

@@ -1,760 +0,0 @@
-import pandas as pd
-import numpy as np
-from datetime import datetime, timedelta
-import os
-import requests
-from typing import Dict, List, Optional, Tuple, Generator
-from dotenv import load_dotenv
-import json
-from logger import get_logger
-from utils.api_utils import APIUtils
-
-# 获取日志器
-logger = get_logger()
-
-class StockAnalyzer:
-    def __init__(self, initial_cash=1000000, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
-        
-        # 加载环境变量
-        load_dotenv()
-        
-        # 设置 API 配置，优先使用自定义配置，否则使用环境变量
-        self.API_URL = custom_api_url or os.getenv('API_URL')
-        self.API_KEY = custom_api_key or os.getenv('API_KEY')
-        self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo')
-        self.API_TIMEOUT = int(custom_api_timeout or os.getenv('API_TIMEOUT', 60))
-        
-        logger.debug(f"初始化StockAnalyzer: API_URL={self.API_URL}, API_MODEL={self.API_MODEL}, API_KEY={'已提供' if self.API_KEY else '未提供'}, API_TIMEOUT={self.API_TIMEOUT}")
-        
-        # 配置参数
-        self.params = {
-            'ma_periods': {'short': 5, 'medium': 20, 'long': 60},
-            'rsi_period': 14,
-            'bollinger_period': 20,
-            'bollinger_std': 2,
-            'volume_ma_period': 20,
-            'atr_period': 14
-        }
-
-        
-    def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None):
-        """获取股票或基金数据"""
-        import akshare as ak
-        
-        if start_date is None:
-            start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
-        if end_date is None:
-            end_date = datetime.now().strftime('%Y%m%d')
-            
-        try:
-            # 验证股票代码格式
-            if market_type == 'A':
-                # 上海证券交易所股票代码以6开头
-                # 深圳证券交易所股票代码以0或3开头
-                # 科创板股票代码以688开头
-                # 北京证券交易所股票代码以8开头
-                valid_prefixes = ['0', '3', '6', '688', '8']
-                valid_format = False
-                
-                for prefix in valid_prefixes:
-                    if stock_code.startswith(prefix):
-                        valid_format = True
-                        break
-                
-                if not valid_format:
-                    error_msg = f"无效的A股股票代码格式: {stock_code}。A股代码应以0、3、6、688或8开头"
-                    logger.error(f"[股票代码格式错误] {error_msg}")
-                    raise ValueError(error_msg)
-
-                df = ak.stock_zh_a_hist(
-                    symbol=stock_code,
-                    start_date=start_date,
-                    end_date=end_date,
-                    adjust="qfq"
-                )
-            elif market_type == 'HK':
-                df = ak.stock_hk_daily(
-                    symbol=stock_code,
-                    adjust="qfq"
-                )
-            elif market_type == 'US':
-                df = ak.stock_us_hist(
-                    symbol=stock_code,
-                    start_date=start_date,
-                    end_date=end_date,
-                    adjust="qfq"
-                )
-            elif market_type == 'ETF':
-                df = ak.fund_etf_hist_em(
-                    symbol=stock_code,
-                    period="daily",
-                    start_date=start_date,
-                    end_date=end_date,
-                    adjust="qfq"
-                )
-            elif market_type == 'LOF':
-                df = ak.fund_lof_hist_em(
-                    symbol=stock_code,
-                    period="daily",
-                    start_date=start_date,
-                    end_date=end_date,
-                    adjust="qfq"
-                )
-            else:
-                raise ValueError(f"不支持的市场类型: {market_type}")
-            
-            # 重命名列名以匹配分析需求
-            df = df.rename(columns={
-                "日期": "date",
-                "开盘": "open",
-                "收盘": "close",
-                "最高": "high",
-                "最低": "low",
-                "成交量": "volume"
-            })
-            
-            # 确保日期格式正确
-            df['date'] = pd.to_datetime(df['date'])
-            
-            # 数据类型转换
-            numeric_columns = ['open', 'close', 'high', 'low', 'volume']
-            df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce')
-            
-            # 删除空值
-            df = df.dropna()
-            
-            return df.sort_values('date')
-            
-        # except ValueError as ve:
-        #     # 捕获格式验证错误
-        #     logger.error(f"[股票代码格式错误] {str(ve)}")
-        #     raise Exception(f"股票代码格式错误: {str(ve)}")
-        except Exception as e:
-            logger.error(f"[获取数据失败] {str(e)}")
-            raise Exception(f"获取数据失败: {str(e)}")
-            
-    def calculate_ema(self, series, period):
-        """计算指数移动平均线"""
-        return series.ewm(span=period, adjust=False).mean()
-        
-    def calculate_rsi(self, series, period):
-        """计算RSI指标"""
-        delta = series.diff()
-        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
-        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
-        rs = gain / loss
-        return 100 - (100 / (1 + rs))
-        
-    def calculate_macd(self, series):
-        """计算MACD指标"""
-        exp1 = series.ewm(span=12, adjust=False).mean()
-        exp2 = series.ewm(span=26, adjust=False).mean()
-        macd = exp1 - exp2
-        signal = macd.ewm(span=9, adjust=False).mean()
-        hist = macd - signal
-        return macd, signal, hist
-        
-    def calculate_bollinger_bands(self, series, period, std_dev):
-        """计算布林带"""
-        middle = series.rolling(window=period).mean()
-        std = series.rolling(window=period).std()
-        upper = middle + (std * std_dev)
-        lower = middle - (std * std_dev)
-        return upper, middle, lower
-        
-    def calculate_atr(self, df, period):
-        """计算ATR指标"""
-        high = df['high']
-        low = df['low']
-        close = df['close'].shift(1)
-        
-        tr1 = high - low
-        tr2 = abs(high - close)
-        tr3 = abs(low - close)
-        
-        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
-        return tr.rolling(window=period).mean()
-        
-    def calculate_indicators(self, df):
-        """计算技术指标"""
-        try:
-            # 计算移动平均线
-            df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short'])
-            df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium'])
-            df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long'])
-            
-            # 计算RSI
-            df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period'])
-            
-            # 计算MACD
-            df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close'])
-            
-            # 计算布林带
-            df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands(
-                df['close'],
-                self.params['bollinger_period'],
-                self.params['bollinger_std']
-            )
-            
-            # 成交量分析
-            df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean()
-            df['Volume_Ratio'] = df['volume'] / df['Volume_MA']
-            
-            # 计算ATR和波动率
-            df['ATR'] = self.calculate_atr(df, self.params['atr_period'])
-            df['Volatility'] = df['ATR'] / df['close'] * 100
-            
-            # 动量指标
-            df['ROC'] = df['close'].pct_change(periods=10) * 100
-            
-            return df
-            
-        except Exception as e:
-            print(f"计算技术指标时出错: {str(e)}")
-            raise
-            
-    def calculate_score(self, df):
-        """计算评分"""
-        try:
-            score = 0
-            latest = df.iloc[-1]
-            
-            # 趋势得分 (30分)
-            if latest['MA5'] > latest['MA20']:
-                score += 15
-            if latest['MA20'] > latest['MA60']:
-                score += 15
-                
-            # RSI得分 (20分)
-            if 30 <= latest['RSI'] <= 70:
-                score += 20
-            elif latest['RSI'] < 30:  # 超卖
-                score += 15
-                
-            # MACD得分 (20分)
-            if latest['MACD'] > latest['Signal']:
-                score += 20
-                
-            # 成交量得分 (30分)
-            if latest['Volume_Ratio'] > 1.5:
-                score += 30
-            elif latest['Volume_Ratio'] > 1:
-                score += 15
-                
-            return score
-            
-        except Exception as e:
-            print(f"计算评分时出错: {str(e)}")
-            raise
-            
-    def get_ai_analysis(self, df, stock_code, market_type='A', stream=False):
-        """使用 OpenAI 进行 AI 分析"""
-        try:
-            logger.info(f"开始AI分析 {stock_code}, 流式模式: {stream}")
-            recent_data = df.tail(14).to_dict('records')
-            
-            technical_summary = {
-                'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
-                'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
-                'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
-                'rsi_level': df.iloc[-1]['RSI']
-            }
-            
-            # 根据市场类型调整分析提示
-            if market_type in ['ETF', 'LOF']:
-                prompt = f"""
-                分析基金 {stock_code}：
-
-                技术指标概要：
-                {technical_summary}
-                
-                近14日交易数据：
-                {recent_data}
-                
-                请提供：
-                1. 净值走势分析（包含支撑位和压力位）
-                2. 成交量分析及其对净值的影响
-                3. 风险评估（包含波动率和折溢价分析）
-                4. 短期和中期净值预测
-                5. 关键价格位分析
-                6. 申购赎回建议（包含止损位）
-                
-                请基于技术指标和市场表现进行分析，给出具体数据支持。
-                """
-            elif market_type == 'US':
-                prompt = f"""
-                分析美股 {stock_code}：
-
-                技术指标概要：
-                {technical_summary}
-                
-                近14日交易数据：
-                {recent_data}
-                
-                请提供：
-                1. 趋势分析（包含支撑位和压力位，美元计价）
-                2. 成交量分析及其含义
-                3. 风险评估（包含波动率和美股市场特有风险）
-                4. 短期和中期目标价位（美元）
-                5. 关键技术位分析
-                6. 具体交易建议（包含止损位）
-                
-                请基于技术指标和美股市场特点进行分析，给出具体数据支持。
-                """
-            elif market_type == 'HK':
-                prompt = f"""
-                分析港股 {stock_code}：
-
-                技术指标概要：
-                {technical_summary}
-                
-                近14日交易数据：
-                {recent_data}
-                
-                请提供：
-                1. 趋势分析（包含支撑位和压力位，港币计价）
-                2. 成交量分析及其含义
-                3. 风险评估（包含波动率和港股市场特有风险）
-                4. 短期和中期目标价位（港币）
-                5. 关键技术位分析
-                6. 具体交易建议（包含止损位）
-                
-                请基于技术指标和港股市场特点进行分析，给出具体数据支持。
-                """
-            else:  # A股
-                prompt = f"""
-                分析A股 {stock_code}：
-
-                技术指标概要：
-                {technical_summary}
-                
-                近14日交易数据：
-                {recent_data}
-                
-                请提供：
-                1. 趋势分析（包含支撑位和压力位）
-                2. 成交量分析及其含义
-                3. 风险评估（包含波动率分析）
-                4. 短期和中期目标价位
-                5. 关键技术位分析
-                6. 具体交易建议（包含止损位）
-                
-                请基于技术指标和A股市场特点进行分析，给出具体数据支持。
-                """
-            
-            logger.debug(f"生成的AI分析提示词: {self._truncate_json_for_logging(prompt, 100)}...")
-            
-            # 检查API配置
-            if not self.API_URL:
-                error_msg = "API URL未配置，无法进行AI分析"
-                logger.error(f"[API配置错误] {error_msg}")
-                return error_msg if not stream else (yield json.dumps({"error": error_msg}))
-                
-            if not self.API_KEY:
-                error_msg = "API Key未配置，无法进行AI分析"
-                logger.error(f"[API配置错误] {error_msg}")
-                return error_msg if not stream else (yield json.dumps({"error": error_msg}))
-            
-            # 标准化API URL
-            api_url = APIUtils.format_api_url(self.API_URL)
-            
-            logger.debug(f"标准化后的API URL: {api_url}")
-            
-            # 构建请求头和请求体
-            headers = {
-                "Authorization": f"Bearer {self.API_KEY}",
-                "Content-Type": "application/json"
-            }
-            
-            payload = {
-                "model": self.API_MODEL,
-                "messages": [{"role": "user", "content": prompt}]
-            }
-            
-            # 流式处理设置
-            if stream:
-                logger.debug(f"配置流式参数，使用API URL: {api_url}")
-                payload["stream"] = True  # 明确设置stream参数为True
-                
-                try:
-                    logger.debug(f"发起流式API请求: {api_url}")
-                    logger.debug(f"请求载荷: {self._truncate_json_for_logging(payload)}")
-                    
-                    response = requests.post(
-                        api_url,
-                        headers=headers,
-                        json=payload,
-                        timeout=self.API_TIMEOUT,  # 增加超时时间
-                        stream=True
-                    )
-                    
-                    logger.debug(f"API流式响应状态码: {response.status_code}")
-                    
-                    if response.status_code == 200:
-                        logger.info(f"成功获取API流式响应，开始处理")
-                        yield from self._process_ai_stream(response, stock_code)
-                    else:
-                        try:
-                            error_response = response.json()
-                            error_text = self._truncate_json_for_logging(error_response)
-                        except:
-                            error_text = response.text[:500] if response.text else "无响应内容"
-                            
-                        error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}"
-                        logger.error(f"[API请求失败] {error_msg}")
-                        yield json.dumps({"stock_code": stock_code, "error": error_msg})
-                        
-                except Exception as e:
-                    error_msg = f"流式API请求异常: {str(e)}"
-                    logger.error(f"[流式API异常] {error_msg}")
-                    logger.exception(e)
-                    yield json.dumps({"stock_code": stock_code, "error": error_msg})
-            else:
-                # 非流式处理
-                logger.debug(f"发起非流式API请求: {api_url}")
-                
-                try:
-                    response = requests.post(
-                        api_url,
-                        headers=headers,
-                        json=payload,
-                        timeout=self.API_TIMEOUT
-                    )
-                    
-                    logger.debug(f"API非流式响应状态码: {response.status_code}")
-                    
-                    if response.status_code == 200:
-                        api_response = response.json()
-                        content = api_response['choices'][0]['message']['content']
-                        logger.info(f"成功获取AI分析结果，长度: {len(content)}")
-                        logger.debug(f"AI分析结果前100字符: {content[:100]}...")
-                        return content
-                    else:
-                        try:
-                            error_response = response.json()
-                            error_text = self._truncate_json_for_logging(error_response)
-                        except:
-                            error_text = response.text[:500] if response.text else "无响应内容"
-                            
-                        error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}"
-                        logger.error(f"[API请求失败] {error_msg}")
-                        return error_msg
-                        
-                except Exception as e:
-                    error_msg = f"非流式API请求异常: {str(e)}"
-                    logger.error(f"[非流式API异常] {error_msg}")
-                    logger.exception(e)
-                    return error_msg
-            
-        except Exception as e:
-            error_msg = f"AI 分析过程中发生错误: {str(e)}"
-            logger.error(f"[AI分析异常] {error_msg}")
-            logger.exception(e)
-            
-            if stream:
-                logger.debug("在流式模式下返回异常信息")
-                error_json = json.dumps({"stock_code": stock_code, "error": error_msg})
-                logger.info(f"流式异常输出: {error_json}")
-                yield error_json
-            else:
-                return error_msg
-    
-    def _truncate_json_for_logging(self, json_obj, max_length=500):
-        """截断JSON对象用于日志记录，避免日志过大
-        
-        Args:
-            json_obj: 要截断的JSON对象
-            max_length: 最大字符长度，默认500
-            
-        Returns:
-            str: 截断后的JSON字符串
-        """
-        json_str = json.dumps(json_obj, ensure_ascii=False)
-        if len(json_str) <= max_length:
-            return json_str
-        return json_str[:max_length] + f"... [截断，总长度: {len(json_str)}字符]"
-    
-    def _process_ai_stream(self, response, stock_code) -> Generator[str, None, None]:
-        """处理AI流式响应"""
-        logger.info(f"开始处理 {stock_code} 的AI流式响应")
-        buffer = ""
-        chunk_count = 0
-        
-        try:
-            for line in response.iter_lines():
-                if line:
-                    line = line.decode('utf-8')
-                    
-                    # 跳过保持连接的空行
-                    if line.strip() == '':
-                        logger.debug("跳过空行")
-                        continue
-                        
-                    # 数据行通常以"data: "开头
-                    if line.startswith('data: '):
-                        data_content = line[6:]  # 移除 "data: " 前缀
-                        
-                        # 检查是否为流的结束
-                        if data_content.strip() == '[DONE]':
-                            logger.debug("收到流结束标记 [DONE]")
-                            break
-                            
-                        try:
-                            json_data = json.loads(data_content)
-                            
-                            # 检查 choices 列表是否为空
-                            if 'choices' in json_data and json_data['choices']:
-                                delta = json_data['choices'][0].get('delta', {})
-                                content = delta.get('content', '')
-                                
-                                if content:
-                                    chunk_count += 1
-                                    buffer += content
-
-                                    # 创建包含AI分析片段的JSON
-                                    chunk_json = json.dumps({
-                                        "stock_code": stock_code,
-                                        "ai_analysis_chunk": content
-                                    })
-                                    yield chunk_json
-                            else:
-                                logger.warning(f"收到空的 choices 列表: {data_content}")
-                        except json.JSONDecodeError as e:
-                            logger.error(f"[JSON解析错误] {str(e)}, 行内容: {data_content}")
-                            # 忽略无法解析的JSON
-                            pass
-                    else:
-                        logger.warning(f"收到非'data:'开头的行: {line}")
-            
-            logger.info(f"AI流式处理完成，共收到 {chunk_count} 个内容片段，总长度: {len(buffer)}")
-            
-            # 如果buffer不为空，最后一次发送完整内容
-            if buffer and not buffer.endswith('\n'):
-                logger.debug("发送换行符")
-                yield json.dumps({"stock_code": stock_code, "ai_analysis_chunk": "\n"})
-        
-        except Exception as e:
-            error_msg = f"处理AI流式响应时出错: {str(e)}"
-            logger.error(f"[流式响应异常] {error_msg}")
-            logger.exception(e)
-            yield json.dumps({"stock_code": stock_code, "error": error_msg})
-
-    
-    def get_recommendation(self, score):
-        """根据得分给出建议"""
-        logger.debug(f"根据评分 {score} 生成投资建议")
-        if score >= 80:
-            return '强烈推荐买入'
-        elif score >= 60:
-            return '建议买入'
-        elif score >= 40:
-            return '观望'
-        elif score >= 20:
-            return '建议卖出'
-        else:
-            return '强烈建议卖出'
-    
-    def analyze_stock(self, stock_code, market_type='A', stream=False):
-        """分析单只"""
-        logger.info(f"开始分析 {stock_code}, 市场类型: {market_type}, 流式模式: {stream}")
-        
-        try:
-            # 获取股票数据
-            try:
-                df = self.get_stock_data(stock_code, market_type)
-            except Exception as e:
-                # 捕获股票数据获取异常
-                error_msg = str(e)
-                logger.error(f"[数据获取异常] {error_msg}")
-                
-                # 格式化错误响应
-                error_response = {
-                    'stock_code': stock_code, 
-                    'error': error_msg,
-                    'status': 'error',
-                    'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-                }
-                
-                if stream:
-                    return (yield json.dumps(error_response))
-                else:
-                    return error_response
-            
-            # 检查数据是否为空
-            if df.empty:
-                error_msg = f" {stock_code} 数据为空"
-                logger.error(f"[空数据] {error_msg}")
-                
-                # 格式化错误响应
-                error_response = {
-                    'stock_code': stock_code, 
-                    'error': error_msg,
-                    'status': 'error',
-                    'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-                }
-                
-                if stream:
-                    return (yield json.dumps(error_response))
-                else:
-                    return error_response
-            
-            # 计算技术指标
-            logger.debug(f"计算 {stock_code} 技术指标")
-            df = self.calculate_indicators(df)
-            
-            # 评分系统
-            logger.debug(f"计算 {stock_code} 评分")
-            score = self.calculate_score(df)
-            logger.info(f"{stock_code} 评分结果: {score}")
-            
-            # 获取最新数据
-            latest = df.iloc[-1]
-            prev = df.iloc[-2]
-            
-            # 生成报告
-            report = {
-                'stock_code': stock_code,
-                'market_type': market_type,  # 添加市场类型
-                'analysis_date': datetime.now().strftime('%Y-%m-%d'),
-                'score': score,
-                'price': latest['close'],
-                'price_change': (latest['close'] - prev['close']) / prev['close'] * 100,
-                'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
-                'rsi': latest['RSI'] if not pd.isna(latest['RSI']) else None,
-                'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
-                'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL',
-                'recommendation': self.get_recommendation(score)
-            }
-            logger.debug(f"生成 {stock_code} 基础报告: {self._truncate_json_for_logging(report, 100)}...")
-            
-            if stream:
-                logger.info(f"以流式模式返回 {stock_code} 分析结果")
-                # 先返回基本报告结构
-                base_report = dict(report)
-                base_report['ai_analysis'] = ''
-                base_report_json = json.dumps(base_report)
-                logger.debug(f"基础报告JSON: {self._truncate_json_for_logging(base_report_json, 100)}...")
-                logger.info(f"发送基础报告: {base_report_json}")
-                yield base_report_json
-                
-                # 然后流式返回AI分析部分
-                logger.debug(f"开始获取 {stock_code} 的流式AI分析")
-                ai_chunks_count = 0
-                for ai_chunk in self.get_ai_analysis(df, stock_code, market_type, stream=True):
-                    ai_chunks_count += 1
-                    yield ai_chunk
-                logger.info(f" {stock_code} 流式AI分析完成，共发送 {ai_chunks_count} 个块")
-            else:
-                logger.info(f"以非流式模式返回 {stock_code} 分析结果")
-                logger.debug(f"开始获取 {stock_code} 的AI分析")
-                report['ai_analysis'] = self.get_ai_analysis(df, stock_code, market_type)
-                logger.debug(f"AI分析结果长度: {len(report['ai_analysis'])}")
-                return report
-            
-        except Exception as e:
-            error_msg = f"分析 {stock_code} 时出错: {str(e)}\n"
-            logger.error(f"[分析异常] {error_msg}")
-            logger.exception(e)
-            
-            # 格式化错误响应
-            error_response = {
-                'stock_code': stock_code, 
-                'error': error_msg,
-                'status': 'error',
-                'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-            }
-            
-            if stream:
-                return (yield json.dumps(error_response))
-            else:
-                return error_response
-            
-    def scan_stocks(self, stock_codes, market_type='A', min_score=60, stream=False):
-        """扫描多只"""
-        logger.info(f"开始扫描 {len(stock_codes)} 只, 市场类型: {market_type}, 最低评分: {min_score}, 流式模式: {stream}")
-        
-        if not stream:
-            # 非流式模式
-            recommended_stocks = []
-            stock_count = 0
-            error_count = 0
-            
-            for stock_code in stock_codes:
-                stock_count += 1
-                logger.info(f"扫描进度: {stock_count}/{len(stock_codes)}, 当前: {stock_code}")
-                
-                try:
-                    logger.debug(f"分析: {stock_code}")
-                    report = self.analyze_stock(stock_code, market_type)
-                    
-                    # 检查是否有错误
-                    if isinstance(report, dict) and 'error' in report:
-                        error_count += 1
-                        logger.warning(f"[扫描错误]  {stock_code}: {report['error']}")
-                        continue
-                    
-                    # 检查评分是否达到最低要求
-                    if report['score'] >= min_score:
-                        logger.info(f" {stock_code} 评分 {report['score']} >= {min_score}，添加到推荐列表")
-                        recommended_stocks.append(report)
-                    else:
-                        logger.debug(f" {stock_code} 评分 {report['score']} < {min_score}，不添加到推荐列表")
-                except Exception as e:
-                    error_count += 1
-                    error_msg = f"分析 {stock_code} 时出错: {str(e)}"
-                    logger.error(f"[扫描异常] {error_msg}")
-                    logger.exception(e)
-                    
-                    # 添加错误信息到推荐列表，确保前端能看到错误
-                    error_response = {
-                        'stock_code': stock_code, 
-                        'error': error_msg,
-                        'status': 'error',
-                        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-                    }
-                    recommended_stocks.append(error_response)
-                    continue
-            
-            logger.info(f"扫描完成，共 {stock_count} 只，{error_count} 只出错，{len(recommended_stocks)} 只推荐")
-            return recommended_stocks
-        else:
-            # 流式模式
-            stock_count = 0
-            error_count = 0
-            
-            for stock_code in stock_codes:
-                stock_count += 1
-                logger.info(f"流式扫描进度: {stock_count}/{len(stock_codes)}, 当前: {stock_code}")
-                
-                try:
-                    chunk_count = 0
-                    for chunk in self.analyze_stock(stock_code, market_type, stream=True):
-                        chunk_count += 1
-                        # 检查是否有错误信息
-                        try:
-                            chunk_data = json.loads(chunk)
-                            if 'error' in chunk_data:
-                                error_count += 1
-                                logger.warning(f"[流式扫描错误]  {stock_code}: {chunk_data['error']}")
-                        except:
-                            pass
-                        yield chunk
-                    logger.debug(f" {stock_code} 流式分析完成，共 {chunk_count} 个块")
-                except Exception as e:
-                    error_count += 1
-                    error_msg = f"分析 {stock_code} 时出错: {str(e)}"
-                    logger.error(f"[流式扫描异常] {error_msg}")
-                    logger.exception(e)
-                    
-                    # 格式化错误响应
-                    error_response = {
-                        'stock_code': stock_code, 
-                        'error': error_msg,
-                        'status': 'error',
-                        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-                    }
-                    error_json = json.dumps(error_response)
-                    logger.info(f"流式错误输出: {error_json}")
-                    yield error_json
-            
-            logger.info(f"流式扫描完成，共处理 {stock_count} ，{error_count} 只出错")

us_stock_service.py

@@ -1,53 +0,0 @@
-import akshare as ak
-import pandas as pd
-
-class USStockService:
-        
-    def search_us_stocks(self, keyword):
-        """
-        搜索美股代码
-        :param keyword: 搜索关键词
-        :return: 匹配的股票列表
-        """
-        try:
-            # 获取美股数据
-            df = ak.stock_us_spot_em()
-            
-            # 转换列名
-            df = df.rename(columns={
-                "序号": "index",
-                "名称": "name",
-                "最新价": "price",
-                "涨跌额": "price_change",
-                "涨跌幅": "price_change_percent",
-                "开盘价": "open",
-                "最高价": "high",
-                "最低价": "low",
-                "昨收价": "pre_close",
-                "总市值": "market_value",
-                "市盈率": "pe_ratio",
-                "成交量": "volume",
-                "成交额": "turnover",
-                "振幅": "amplitude",
-                "换手率": "turnover_rate",
-                "代码": "symbol"
-            })
-            
-            # 模糊匹配搜索
-            mask = df['name'].str.contains(keyword, case=False, na=False)
-            results = df[mask]
-            
-            # 格式化返回结果并处理 NaN 值
-            formatted_results = []
-            for _, row in results.iterrows():
-                formatted_results.append({
-                    'name': row['name'] if pd.notna(row['name']) else '',
-                    'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
-                    'price': float(row['price']) if pd.notna(row['price']) else 0.0,
-                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0
-                })
-                
-            return formatted_results
-            
-        except Exception as e:
-            raise Exception(f"搜索美股代码失败: {str(e)}")

web_server.py

@@ -1,60 +1,208 @@
-from flask import Flask, render_template, request, jsonify, Response, stream_with_context
+from fastapi import FastAPI, Request, Response, Depends, HTTPException, BackgroundTasks
-from stock_analyzer import StockAnalyzer
+from fastapi.responses import JSONResponse, StreamingResponse, FileResponse, RedirectResponse
-from us_stock_service import USStockService
+from fastapi.staticfiles import StaticFiles
-from fund_service import FundService  # 新增导入
+from fastapi.middleware.cors import CORSMiddleware
-import threading
+from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any, Generator
+from services.stock_analyzer_service import StockAnalyzerService
+from services.us_stock_service_async import USStockServiceAsync
+from services.fund_service_async import FundServiceAsync
 import os
-import traceback
+import httpx
-import requests
 from logger import get_logger
 from utils.api_utils import APIUtils
-# 加载环境变量
+from dotenv import load_dotenv, dotenv_values
-from dotenv import load_dotenv
+import uvicorn
+import json
+import secrets
+from datetime import datetime, timedelta
+from jose import JWTError, jwt
 
 load_dotenv()
 
 # 获取日志器
 logger = get_logger()
 
-app = Flask(__name__)
+# JWT相关配置
-analyzer = StockAnalyzer()
+SECRET_KEY = os.getenv("JWT_SECRET_KEY", secrets.token_hex(32))
-us_stock_service = USStockService()
+ALGORITHM = "HS256"
-fund_service = FundService()  # 新增服务实例
+ACCESS_TOKEN_EXPIRE_MINUTES = 10080  # Token过期时间一周
 
-@app.route('/')
+LOGIN_PASSWORD = os.getenv("LOGIN_PASSWORD", "")
-def index():
+print(LOGIN_PASSWORD)
-    announcement = os.getenv('ANNOUNCEMENT_TEXT') or None
-    # 获取默认API配置信息
-    default_api_url = os.getenv('API_URL', '')
-    default_api_model = os.getenv('API_MODEL', 'gpt-3.5-turbo')
-    default_api_timeout = os.getenv('API_TIMEOUT', '60')
-    # 不传递API_KEY到前端，出于安全考虑
-    return render_template('index.html', 
-                          announcement=announcement,
-                          default_api_url=default_api_url,
-                          default_api_model=default_api_model,
-                          default_api_timeout=default_api_timeout)
 
-@app.route('/analyze', methods=['POST'])
+# 是否需要登录
-def analyze():
+REQUIRE_LOGIN = bool(LOGIN_PASSWORD.strip())
+
+
+app = FastAPI(
+    title="Stock Scanner API",
+    description="异步股票分析API",
+    version="1.0.0"
+)
+
+# 添加CORS中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # 开发环境允许所有来源，生产环境应该限制
+    allow_credentials=True,
+    allow_methods=["*"],    
+    allow_headers=["*"],
+)
+
+# 设置静态文件
+frontend_dist = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'frontend', 'dist')
+if os.path.exists(frontend_dist):
+    app.mount("/assets", StaticFiles(directory=os.path.join(frontend_dist, "assets")), name="assets")
+
+# 初始化异步服务
+# StockAnalyzerService 不需要全局初始化，在 /analyze 接口中按需创建
+us_stock_service = USStockServiceAsync()
+fund_service = FundServiceAsync()
+
+# 定义请求和响应模型
+class AnalyzeRequest(BaseModel):
+    stock_codes: List[str]
+    market_type: str = "A"
+    api_url: Optional[str] = None
+    api_key: Optional[str] = None
+    api_model: Optional[str] = None
+    api_timeout: Optional[str] = None
+
+class TestAPIRequest(BaseModel):
+    api_url: str
+    api_key: str
+    api_model: Optional[str] = None
+    api_timeout: Optional[int] = 10
+
+class LoginRequest(BaseModel):
+    password: str
+
+class Token(BaseModel):
+    access_token: str
+    token_type: str
+
+# 自定义依赖项，在REQUIRE_LOGIN=False时不要求token
+class OptionalOAuth2PasswordBearer(OAuth2PasswordBearer):
+    async def __call__(self, request: Request) -> Optional[str]:
+        if not REQUIRE_LOGIN:
+            return None
+        try:
+            return await super().__call__(request)
+        except HTTPException:
+            if not REQUIRE_LOGIN:
+                return None
+            raise
+
+# 使用自定义的依赖项
+optional_oauth2_scheme = OptionalOAuth2PasswordBearer(tokenUrl="login")
+
+# 创建访问令牌
+def create_access_token(data: dict, expires_delta: Optional[timedelta] = None):
+    to_encode = data.copy()
+    if expires_delta:
+        expire = datetime.utcnow() + expires_delta
+    else:
+        expire = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
+    to_encode.update({"exp": expire})
+    encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
+    return encoded_jwt
+
+# 验证令牌
+async def verify_token(token: Optional[str] = Depends(optional_oauth2_scheme)):
+    # 如果未设置密码，则不需要验证
+    if not REQUIRE_LOGIN:
+        return "guest"
+        
+    # 如果没有token且不需要登录，返回guest
+    if token is None and not REQUIRE_LOGIN:
+        return "guest"
+        
+    credentials_exception = HTTPException(
+        status_code=401,
+        detail="无效的认证凭据",
+        headers={"WWW-Authenticate": "Bearer"},
+    )
+    
+    # 如果需要登录但没有token，抛出异常
+    if token is None:
+        raise credentials_exception
+        
+    try:
+        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
+        username: str = payload.get("sub")
+        if username is None:
+            raise credentials_exception
+        return username
+    except JWTError:
+        raise credentials_exception
+
+# 用户登录接口
+@app.post("/login")
+async def login(request: LoginRequest):
+    """用户登录接口"""
+    # 如果未设置密码，表示不需要登录
+    if not REQUIRE_LOGIN:
+        access_token = create_access_token(data={"sub": "guest"})
+        return {"access_token": access_token, "token_type": "bearer"}
+        
+    if request.password != LOGIN_PASSWORD:
+        logger.warning("登录失败：密码错误")
+        raise HTTPException(status_code=401, detail="密码错误")
+    
+    # 创建访问令牌
+    access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
+    access_token = create_access_token(
+        data={"sub": "user"}, expires_delta=access_token_expires
+    )
+    logger.info("用户登录成功")
+    return {"access_token": access_token, "token_type": "bearer"}
+
+# 检查用户认证状态
+@app.get("/check_auth")
+async def check_auth(username: str = Depends(verify_token)):
+    """检查用户认证状态"""
+    return {"authenticated": True, "username": username}
+
+# 获取系统配置
+@app.get("/config")
+async def get_config():
+    """返回系统配置信息"""
+    config = {
+        'announcement': os.getenv('ANNOUNCEMENT_TEXT') or '',
+        'default_api_url': os.getenv('API_URL', ''),
+        'default_api_model': os.getenv('API_MODEL', ''),
+        'default_api_timeout': os.getenv('API_TIMEOUT', '60')
+    }
+    return config
+
+# AI分析股票
+@app.post("/analyze")
+async def analyze(request: AnalyzeRequest, username: str = Depends(verify_token)):
     try:
         logger.info("开始处理分析请求")
-        data = request.json
+        stock_codes = request.stock_codes
-        stock_codes = data.get('stock_codes', [])
+        market_type = request.market_type
-        market_type = data.get('market_type', 'A') 
+        
+        # 后端再次去重，确保安全
+        original_count = len(stock_codes)
+        stock_codes = list(dict.fromkeys(stock_codes))  # 保持原有顺序的去重方法
+        if len(stock_codes) < original_count:
+            logger.info(f"后端去重: 从{original_count}个代码中移除了{original_count - len(stock_codes)}个重复项")
         
         logger.debug(f"接收到分析请求: stock_codes={stock_codes}, market_type={market_type}")
         
         # 获取自定义API配置
-        custom_api_url = data.get('api_url')
+        custom_api_url = request.api_url
-        custom_api_key = data.get('api_key')
+        custom_api_key = request.api_key
-        custom_api_model = data.get('api_model')
+        custom_api_model = request.api_model
-        custom_api_timeout = data.get('api_timeout')
+        custom_api_timeout = request.api_timeout
         
         logger.debug(f"自定义API配置: URL={custom_api_url}, 模型={custom_api_model}, API Key={'已提供' if custom_api_key else '未提供'}, Timeout={custom_api_timeout}")
         
         # 创建新的分析器实例，使用自定义配置
-        custom_analyzer = StockAnalyzer(
+        custom_analyzer = StockAnalyzerService(
             custom_api_url=custom_api_url,
             custom_api_key=custom_api_key,
             custom_api_model=custom_api_model,
@@ -63,34 +211,41 @@ def analyze():
         
         if not stock_codes:
             logger.warning("未提供股票代码")
-            return jsonify({'error': '请输入代码'}), 400
+            raise HTTPException(status_code=400, detail="请输入代码")
         
-        # 使用流式响应
+        # 定义流式生成器
-        def generate():
+        async def generate_stream():
             if len(stock_codes) == 1:
                 # 单个股票分析流式处理
                 stock_code = stock_codes[0].strip()
                 logger.info(f"开始单股流式分析: {stock_code}")
                 
-                init_message = f'{{"stream_type": "single", "stock_code": "{stock_code}"}}\n'
+                stock_code_json = json.dumps(stock_code)
+                init_message = f'{{"stream_type": "single", "stock_code": {stock_code_json}}}\n'
                 yield init_message
                 
                 logger.debug(f"开始处理股票 {stock_code} 的流式响应")
                 chunk_count = 0
-                for chunk in custom_analyzer.analyze_stock(stock_code, market_type, stream=True):
+                
+                # 使用异步生成器
+                async for chunk in custom_analyzer.analyze_stock(stock_code, market_type, stream=True):
                     chunk_count += 1
                     yield chunk + '\n'
+                
                 logger.info(f"股票 {stock_code} 流式分析完成，共发送 {chunk_count} 个块")
             else:
                 # 批量分析流式处理
                 logger.info(f"开始批量流式分析: {stock_codes}")
                 
-                init_message = f'{{"stream_type": "batch", "stock_codes": {stock_codes}}}\n'
+                stock_codes_json = json.dumps(stock_codes)
+                init_message = f'{{"stream_type": "batch", "stock_codes": {stock_codes_json}}}\n'
                 yield init_message
                 
                 logger.debug(f"开始处理批量股票的流式响应")
                 chunk_count = 0
-                for chunk in custom_analyzer.scan_stocks(
+                
+                # 使用异步生成器
+                async for chunk in custom_analyzer.scan_stocks(
                     [code.strip() for code in stock_codes], 
                     min_score=0, 
                     market_type=market_type,
@@ -98,106 +253,175 @@ def analyze():
                 ):
                     chunk_count += 1
                     yield chunk + '\n'
+                
                 logger.info(f"批量流式分析完成，共发送 {chunk_count} 个块")
         
         logger.info("成功创建流式响应生成器")
-        return Response(stream_with_context(generate()), mimetype='application/json')
+        return StreamingResponse(generate_stream(), media_type='application/json')
             
     except Exception as e:
         error_msg = f"分析时出错: {str(e)}"
         logger.error(error_msg)
         logger.exception(e)
-        return jsonify({'error': error_msg}), 500
+        raise HTTPException(status_code=500, detail=error_msg)
 
-@app.route('/search_us_stocks', methods=['GET'])
+# 搜索美股代码
-def search_us_stocks():
+@app.get("/search_us_stocks")
+async def search_us_stocks(keyword: str = "", username: str = Depends(verify_token)):
     try:
-        keyword = request.args.get('keyword', '')
         if not keyword:
-            return jsonify({'error': '请输入搜索关键词'}), 400
+            raise HTTPException(status_code=400, detail="请输入搜索关键词")
-            
+        
-        results = us_stock_service.search_us_stocks(keyword)
+        # 直接使用异步服务的异步方法
-        return jsonify({'results': results})
+        results = await us_stock_service.search_us_stocks(keyword)
+        return {"results": results}
         
     except Exception as e:
-        print(f"搜索美股代码时出错: {str(e)}")
+        logger.error(f"搜索美股代码时出错: {str(e)}")
-        return jsonify({'error': str(e)}), 500
+        raise HTTPException(status_code=500, detail=str(e))
 
-# 添加基金搜索路由
+# 搜索基金代码
-@app.route('/search_funds', methods=['GET'])
+@app.get("/search_funds")
-def search_funds():
+async def search_funds(keyword: str = "", market_type: str = "", username: str = Depends(verify_token)):
     try:
-        keyword = request.args.get('keyword', '')
-        market_type = request.args.get('market_type', '')
         if not keyword:
-            return jsonify({'error': '请输入搜索关键词'}), 400
+            raise HTTPException(status_code=400, detail="请输入搜索关键词")
-            
+        
-        results = fund_service.search_funds(keyword, market_type)
+        # 直接使用异步服务的异步方法
-        return jsonify({'results': results})
+        results = await fund_service.search_funds(keyword, market_type)
+        return {"results": results}
         
     except Exception as e:
         logger.error(f"搜索基金代码时出错: {str(e)}")
-        return jsonify({'error': str(e)}), 500
+        raise HTTPException(status_code=500, detail=str(e))
 
-@app.route('/test_api_connection', methods=['POST'])
+# 获取美股详情
-def test_api_connection():
+@app.get("/us_stock_detail/{symbol}")
+async def get_us_stock_detail(symbol: str, username: str = Depends(verify_token)):
+    try:
+        if not symbol:
+            raise HTTPException(status_code=400, detail="请提供股票代码")
+        
+        # 使用异步服务获取详情
+        detail = await us_stock_service.get_us_stock_detail(symbol)
+        return detail
+        
+    except Exception as e:
+        logger.error(f"获取美股详情时出错: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 获取基金详情
+@app.get("/fund_detail/{symbol}")
+async def get_fund_detail(symbol: str, market_type: str = "ETF", username: str = Depends(verify_token)):
+    try:
+        if not symbol:
+            raise HTTPException(status_code=400, detail="请提供基金代码")
+        
+        # 使用异步服务获取详情
+        detail = await fund_service.get_fund_detail(symbol, market_type)
+        return detail
+        
+    except Exception as e:
+        logger.error(f"获取基金详情时出错: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 测试API连接
+@app.post("/test_api_connection")
+async def test_api_connection(request: TestAPIRequest, username: str = Depends(verify_token)):
     """测试API连接"""
     try:
         logger.info("开始测试API连接")
-        data = request.json
+        api_url = request.api_url
-        api_url = data.get('api_url')
+        api_key = request.api_key
-        api_key = data.get('api_key')
+        api_model = request.api_model
-        api_model = data.get('api_model')
+        api_timeout = request.api_timeout
-        api_timeout = data.get('api_timeout', 10)  # 默认测试连接超时为10秒
         
         logger.debug(f"测试API连接: URL={api_url}, 模型={api_model}, API Key={'已提供' if api_key else '未提供'}, Timeout={api_timeout}")
         
         if not api_url:
             logger.warning("未提供API URL")
-            return jsonify({'error': '请提供API URL'}), 400
+            raise HTTPException(status_code=400, detail="请提供API URL")
             
         if not api_key:
             logger.warning("未提供API Key")
-            return jsonify({'error': '请提供API Key'}), 400
+            raise HTTPException(status_code=400, detail="请提供API Key")
             
         # 构建API URL
         test_url = APIUtils.format_api_url(api_url)
         logger.debug(f"完整API测试URL: {test_url}")
         
-        # 发送测试请求
+        # 使用异步HTTP客户端发送测试请求
-        response = requests.post(
+        async with httpx.AsyncClient(timeout=float(api_timeout)) as client:
-            test_url,
+            response = await client.post(
-            headers={
+                test_url,
-                "Authorization": f"Bearer {api_key}",
+                headers={
-                "Content-Type": "application/json"
+                    "Authorization": f"Bearer {api_key}",
-            },
+                    "Content-Type": "application/json"
-            json={
+                },
-                "model": api_model or "gpt-3.5-turbo",
+                json={
-                "messages": [
+                    "model": api_model or "",
-                    {"role": "user", "content": "Hello, this is a test message. Please respond with 'API connection successful'."}
+                    "messages": [
-                ],
+                        {"role": "user", "content": "Hello, this is a test message. Please respond with 'API connection successful'."}
-                "max_tokens": 20
+                    ],
-            },
+                    "max_tokens": 20
-            timeout=int(api_timeout)
+                }
-        )
+            )
         
         # 检查响应
         if response.status_code == 200:
             logger.info(f"API 连接测试成功: {response.status_code}")
-            return jsonify({'success': True, 'message': 'API 连接测试成功'})
+            return {"success": True, "message": "API 连接测试成功"}
         else:
-            error_message = response.json().get('error', {}).get('message', '未知错误')
+            error_data = response.json()
+            error_message = error_data.get('error', {}).get('message', '未知错误')
             logger.warning(f"API连接测试失败: {response.status_code} - {error_message}")
-            return jsonify({'success': False, 'message': f'API 连接测试失败: {error_message}', 'status_code': response.status_code}), 400
+            return JSONResponse(
+                status_code=400,
+                content={"success": False, "message": f"API 连接测试失败: {error_message}", "status_code": response.status_code}
+            )
             
-    except requests.exceptions.RequestException as e:
+    except httpx.RequestError as e:
         logger.error(f"API 连接请求错误: {str(e)}")
-        return jsonify({'success': False, 'message': f'请求错误: {str(e)}'}), 400
+        return JSONResponse(
+            status_code=400,
+            content={"success": False, "message": f"请求错误: {str(e)}"}
+        )
     except Exception as e:
         logger.error(f"测试 API 连接时出错: {str(e)}")
         logger.exception(e)
-        return jsonify({'success': False, 'message': f'API 测试连接时出错: {str(e)}'}), 500
+        return JSONResponse(
+            status_code=500,
+            content={"success": False, "message": f"API 测试连接时出错: {str(e)}"}
+        )
+
+# 检查是否需要登录
+@app.get("/need_login")
+async def need_login():
+    """检查是否需要登录"""
+    return {"require_login": REQUIRE_LOGIN}
+
+# 前端路由处理，必须放在所有API路由之后
+@app.get("/{full_path:path}")
+async def serve_frontend(full_path: str, request: Request):
+    """处理所有前端路由请求，返回index.html"""
+    # 排除API路径和静态资源
+    if full_path.startswith(("api/", "assets/", "docs", "openapi.json")) or \
+       full_path in ["check_auth", "config", "analyze", 
+                    "search_us_stocks", "search_funds", 
+                    "test_api_connection", "us_stock_detail", 
+                    "fund_detail"]:
+        # 对于API路径，让FastAPI继续处理
+        raise HTTPException(status_code=404, detail="API路径不存在")
+    
+    # 检查是否使用前端构建版本
+    if os.path.exists(frontend_dist):
+        index_file = os.path.join(frontend_dist, 'index.html')
+        return FileResponse(index_file)
+    else:
+        # 不再使用模板渲染，而是重定向到API文档页面
+        logger.warning("前端构建目录不存在，重定向到API文档页面")
+        return RedirectResponse(url="/docs")
 
 if __name__ == '__main__':
-    logger.info("股票分析系统启动")
+    logger.info("股票AI分析系统启动")
-    app.run(host='0.0.0.0', port=8888, debug=True)
+    uvicorn.run("web_server:app", host="0.0.0.0", port=8888, reload=True)