From 4c115cf3258ddc4ad8e3fc96e7dafabe867391dc Mon Sep 17 00:00:00 2001 From: CaasianVale <1544257291@qq.com> Date: Fri, 7 Mar 2025 03:33:18 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E4=BC=98=E5=8C=96=E5=89=8D=E7=AB=AF?= =?UTF-8?q?=E6=98=BE=E7=A4=BA&=E4=BF=AE=E5=A4=8D=E8=8B=A5=E5=B9=B2bug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 2 + frontend/index.html | 4 +- frontend/package-lock.json | 24 +- frontend/package.json | 4 +- frontend/src/App.vue | 3 +- frontend/src/components/ApiConfigPanel.vue | 365 +++- frontend/src/components/LoginPage.vue | 536 ++++++ frontend/src/components/MarketTimeDisplay.vue | 97 +- frontend/src/components/StockAnalysisApp.vue | 209 ++- frontend/src/components/StockCard.vue | 385 ++++- frontend/src/components/StockSearch.vue | 8 +- frontend/src/main.ts | 2 + frontend/src/router/index.ts | 89 + frontend/src/services/api.ts | 97 +- frontend/src/types/index.ts | 12 + frontend/src/utils/stockValidator.ts | 172 ++ frontend/vite.config.ts | 25 + frontend/yarn.lock | 1485 ++++++++++++++++- fund_service.py | 51 - requirements.txt | 2 + services/ai_analyzer.py | 104 +- services/fund_service_async.py | 5 +- services/stock_analyzer_service.py | 24 + services/stock_data_provider.py | 132 +- services/us_stock_service_async.py | 5 +- stock_analyzer.py | 758 --------- us_stock_service.py | 53 - utils/api_control.py | 43 - web_server.py | 239 ++- 29 files changed, 3726 insertions(+), 1209 deletions(-) create mode 100644 frontend/src/components/LoginPage.vue create mode 100644 frontend/src/router/index.ts create mode 100644 frontend/src/utils/stockValidator.ts delete mode 100644 fund_service.py delete mode 100644 stock_analyzer.py delete mode 100644 us_stock_service.py delete mode 100644 utils/api_control.py diff --git a/README.md b/README.md index d72310e..d510ace 100644 --- a/README.md +++ b/README.md @@ -19,9 +19,11 @@ 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！！！ diff --git a/frontend/index.html b/frontend/index.html index cbb7c0b..ad351f4 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -3,9 +3,9 @@ - 股票分析系统 + 股票AI分析系统 - +
diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 96ad038..e9038b3 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -14,7 +14,8 @@ "axios": "^1.8.1", "marked": "^15.0.7", "naive-ui": "^2.41.0", - "vue": "^3.5.13" + "vue": "^3.5.13", + "vue-router": "^4.5.0" }, "devDependencies": { "@types/marked": "^5.0.2", @@ -298,6 +299,12 @@ "he": "^1.2.0" } }, + "node_modules/@vue/devtools-api": { + "version": "6.6.4", + "resolved": "https://registry.npmjs.org/@vue/devtools-api/-/devtools-api-6.6.4.tgz", + "integrity": "sha512-sGhTPMuXqZ1rVOk32RylztWkfXTRhuS7vgAKv0zjqk8gbsHkJ7xfFf+jbySxt7tWObEJwyKaHMikV/WGDiQm8g==", + "license": "MIT" + }, "node_modules/@vue/language-core": { "version": "2.2.8", "resolved": "https://registry.npmmirror.com/@vue/language-core/-/language-core-2.2.8.tgz", @@ -1217,6 +1224,21 @@ } } }, + "node_modules/vue-router": { + "version": "4.5.0", + "resolved": "https://registry.npmjs.org/vue-router/-/vue-router-4.5.0.tgz", + "integrity": "sha512-HDuk+PuH5monfNuY+ct49mNmkCRK4xJAV9Ts4z9UFc4rzdDnxQLyCMGGc8pKhZhHTVzfanpNwB/lwqevcBwI4w==", + "license": "MIT", + "dependencies": { + "@vue/devtools-api": "^6.6.4" + }, + "funding": { + "url": "https://github.com/sponsors/posva" + }, + "peerDependencies": { + "vue": "^3.2.0" + } + }, "node_modules/vue-tsc": { "version": "2.2.8", "resolved": "https://registry.npmmirror.com/vue-tsc/-/vue-tsc-2.2.8.tgz", diff --git a/frontend/package.json b/frontend/package.json index cbe1418..0a9d388 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -15,7 +15,9 @@ "axios": "^1.8.1", "marked": "^15.0.7", "naive-ui": "^2.41.0", - "vue": "^3.5.13" + "npm": "^11.2.0", + "vue": "^3.5.13", + "vue-router": "4" }, "devDependencies": { "@types/marked": "^5.0.2", diff --git a/frontend/src/App.vue b/frontend/src/App.vue index 84095a0..07a97be 100644 --- a/frontend/src/App.vue +++ b/frontend/src/App.vue @@ -4,7 +4,7 @@ - + @@ -21,7 +21,6 @@ import { NDialogProvider, NNotificationProvider, } from 'naive-ui' -import StockAnalysisApp from './components/StockAnalysisApp.vue' // 主题设置 (默认使用亮色主题) const theme = ref(null) // 可以切换为 darkTheme 以启用暗色模式 diff --git a/frontend/src/components/ApiConfigPanel.vue b/frontend/src/components/ApiConfigPanel.vue index f840b70..dde2b99 100644 --- a/frontend/src/components/ApiConfigPanel.vue +++ b/frontend/src/components/ApiConfigPanel.vue @@ -5,16 +5,17 @@ size="small" @click="toggleConfig" :quaternary="true" + :type="expanded ? 'primary' : 'default'" > - API配置 {{ expanded ? '收起' : '展开' }} + API配置 {{ expanded ? '收起' : '展开' }} - - + + @@ -28,21 +29,32 @@ - - + + + round + > + + - + + round + > + + - + - + round + > + + + + - + + :show-button="false" + class="timeout-input" + > + + @@ -92,22 +159,36 @@ :loading="testingConnection" :disabled="!isConfigValid" @click="testConnection" + round > + 测试连接 - + + 重置 + + + +
+ + {{ connectionStatus.message }} +
+ + \ No newline at end of file diff --git a/frontend/src/components/MarketTimeDisplay.vue b/frontend/src/components/MarketTimeDisplay.vue index 2b9e48a..3acda4a 100644 --- a/frontend/src/components/MarketTimeDisplay.vue +++ b/frontend/src/components/MarketTimeDisplay.vue @@ -13,9 +13,16 @@

A股市场

-

- {{ marketInfo.cnMarket.isOpen ? '交易中' : '已休市' }} -

+
+ + + 交易中 + + + + 已休市 + +

{{ marketInfo.cnMarket.nextTime }}

 @@ -24,9 +31,16 @@

港股市场

-

- {{ marketInfo.hkMarket.isOpen ? '交易中' : '已休市' }} -

+
+ + + 交易中 + + + + 已休市 + +

{{ marketInfo.hkMarket.nextTime }}

 @@ -35,9 +49,16 @@

美股市场

-

- {{ marketInfo.usMarket.isOpen ? '交易中' : '已休市' }} -

+
+ + + 交易中 + + + + 已休市 + +

{{ marketInfo.usMarket.nextTime }}

 @@ -47,7 +68,11 @@ diff --git a/frontend/src/components/StockSearch.vue b/frontend/src/components/StockSearch.vue index ed7eed7..44abb46 100644 --- a/frontend/src/components/StockSearch.vue +++ b/frontend/src/components/StockSearch.vue @@ -68,7 +68,7 @@ const searchKeyword = ref(''); const results = ref([]); const loading = ref(false); const showResults = ref(false); -const searchInputRef = ref(null); +const searchInputRef = ref(null); // 创建防抖搜索函数 const debouncedSearch = debounce(async (keyword: string) => { @@ -83,7 +83,9 @@ const debouncedSearch = debounce(async (keyword: string) => { try { if (props.marketType === 'US') { // 美股搜索 - results.value = await apiService.searchUsStocks(keyword); + 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 = []; @@ -128,7 +130,7 @@ function formatMarketValue(value: number): string { function handleClickOutside(event: MouseEvent) { if ( searchInputRef.value && - !searchInputRef.value.contains(event.target as Node) + !searchInputRef.value.$el.contains(event.target as Node) ) { showResults.value = false; } diff --git a/frontend/src/main.ts b/frontend/src/main.ts index e03fc18..e3a485c 100644 --- a/frontend/src/main.ts +++ b/frontend/src/main.ts @@ -1,6 +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') diff --git a/frontend/src/router/index.ts b/frontend/src/router/index.ts new file mode 100644 index 0000000..e2bd7f0 --- /dev/null +++ b/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 = [ + { + 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; \ No newline at end of file diff --git a/frontend/src/services/api.ts b/frontend/src/services/api.ts index 6c38f9a..082f02f 100644 --- a/frontend/src/services/api.ts +++ b/frontend/src/services/api.ts @@ -1,13 +1,88 @@ import axios from 'axios'; -import type { AnalyzeRequest, TestApiRequest, TestApiResponse, SearchResult } from '@/types'; +import type { AnalyzeRequest, TestApiRequest, TestApiResponse, SearchResult, LoginRequest, LoginResponse } from '@/types'; -// 在开发环境中前缀为空，因为已经在vite.config.ts中配置了代理 +// 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 ${token}`; + } + return config; + }, + (error) => { + return Promise.reject(error); + } +); + +// 响应拦截器，处理401错误 +axiosInstance.interceptors.response.use( + (response) => { + return response; + }, + (error) => { + if (error.response && error.response.status === 401) { + // 清除token + localStorage.removeItem('token'); + // 不要在这里跳转，避免循环重定向 + } + return Promise.reject(error); + } +); + export const apiService = { + // 用户登录 + login: async (request: LoginRequest): Promise => { + try { + const response = await axios.post(`${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 => { + try { + const response = await axiosInstance.get(`${API_PREFIX}/check_auth`); + return response.data.authenticated === true; + } catch (error) { + // 认证失败，清除token + localStorage.removeItem('token'); + return false; + } + }, + + // 登出 + logout: () => { + localStorage.removeItem('token'); + // 简化登出逻辑 + window.location.href = '/login'; + }, + // 分析股票 analyzeStocks: async (request: AnalyzeRequest) => { - return axios.post(`${API_PREFIX}/analyze`, request, { + return axiosInstance.post(`${API_PREFIX}/analyze`, request, { responseType: 'stream' }); }, @@ -15,7 +90,7 @@ export const apiService = { // 测试API连接 testApiConnection: async (request: TestApiRequest): Promise => { try { - const response = await axios.post(`${API_PREFIX}/test_api_connection`, request); + const response = await axiosInstance.post(`${API_PREFIX}/test_api_connection`, request); return response.data; } catch (error: any) { if (error.response) { @@ -31,7 +106,7 @@ export const apiService = { // 搜索美股 searchUsStocks: async (keyword: string): Promise => { try { - const response = await axios.get(`${API_PREFIX}/search_us_stocks`, { + const response = await axiosInstance.get(`${API_PREFIX}/search_us_stocks`, { params: { keyword } }); return response.data.results || []; @@ -55,5 +130,17 @@ export const apiService = { default_api_timeout: '60' }; } + }, + + // 检查是否需要登录 + checkNeedLogin: async (): Promise => { + try { + const response = await axios.get(`${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; + } } }; diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index d73a9f9..a6f75ff 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -7,6 +7,18 @@ export interface ApiConfig { 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; diff --git a/frontend/src/utils/stockValidator.ts b/frontend/src/utils/stockValidator.ts new file mode 100644 index 0000000..edbbc58 --- /dev/null +++ b/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; +}; \ No newline at end of file diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts index 181d624..8009f22 100644 --- a/frontend/vite.config.ts +++ b/frontend/vite.config.ts @@ -16,6 +16,7 @@ export default defineConfig({ }, }, server: { + cors: true, proxy: { '/api': { target: 'http://127.0.0.1:8888', @@ -38,6 +39,30 @@ export default defineConfig({ 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, + }, }, }, }) diff --git a/frontend/yarn.lock b/frontend/yarn.lock index 5584045..2adc86a 100644 --- a/frontend/yarn.lock +++ b/frontend/yarn.lock @@ -167,6 +167,30 @@ resolved "https://registry.npmmirror.com/@esbuild/win32-x64/-/win32-x64-0.25.0.tgz" integrity sha512-ZENoHJBxA20C2zFzh6AI4fT6RraMzjYw4xKWemRTRmRVtN9c5DcH9r/f2ihEkMjOW5eGgrwCslG/+Y/3bL+DHQ== +"@isaacs/cliui@^8.0.2": + version "8.0.2" + resolved "https://registry.yarnpkg.com/@isaacs/cliui/-/cliui-8.0.2.tgz#b37667b7bc181c168782259bab42474fbf52b550" + integrity sha512-O8jcjabXaleOG9DQ0+ARXWZBTfnP4WNAqzuiJK7ll44AmxGKv/J2M4TPjxjY3znBCfvBXFzucm1twdyFybFqEA== + dependencies: + string-width "^5.1.2" + string-width-cjs "npm:string-width@^4.2.0" + strip-ansi "^7.0.1" + strip-ansi-cjs "npm:strip-ansi@^6.0.1" + wrap-ansi "^8.1.0" + wrap-ansi-cjs "npm:wrap-ansi@^7.0.0" + +"@isaacs/fs-minipass@^4.0.0": + version "4.0.1" + resolved "https://registry.yarnpkg.com/@isaacs/fs-minipass/-/fs-minipass-4.0.1.tgz#2d59ae3ab4b38fb4270bfa23d30f8e2e86c7fe32" + integrity sha512-wgm9Ehl2jpeqP3zw/7mo3kRHFp5MEDhqAdwy1fTGkHAwnkGOVsgpvQhL8B5n1qlb01jV3n/bI0ZfZp5lWA1k4w== + dependencies: + minipass "^7.0.4" + +"@isaacs/string-locale-compare@^1.1.0": + version "1.1.0" + resolved 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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)}") \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index f758152..988cf92 100644 --- a/requirements.txt +++ b/requirements.txt @@ -35,3 +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 diff --git a/services/ai_analyzer.py b/services/ai_analyzer.py index f7fa01e..eed82ff 100644 --- a/services/ai_analyzer.py +++ b/services/ai_analyzer.py @@ -231,35 +231,83 @@ class AIAnalyzer: async for chunk in response.aiter_text(): if chunk: - chunk_str = chunk.strip() - if chunk_str.startswith("data: "): - chunk_str = chunk_str[6:] # 去除"data: "前缀 - - if chunk_str == "[DONE]": - logger.debug("收到流结束标记 [DONE]") - continue - - try: - # 解析数据块 - chunk_data = json.loads(chunk_str) - delta = chunk_data.get("choices", [{}])[0].get("delta", {}) - content = delta.get("content", "") - - if content: - chunk_count += 1 - buffer += content - collected_messages.append(content) + # 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 - # 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: - # 忽略无法解析的块 - logger.error(f"JSON解析错误，块内容: {chunk_str[:100]}...") - 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)}") diff --git a/services/fund_service_async.py b/services/fund_service_async.py index 8bafada..e232188 100644 --- a/services/fund_service_async.py +++ b/services/fund_service_async.py @@ -56,8 +56,11 @@ class FundServiceAsync: '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") + 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: diff --git a/services/stock_analyzer_service.py b/services/stock_analyzer_service.py index 191663f..d73738f 100644 --- a/services/stock_analyzer_service.py +++ b/services/stock_analyzer_service.py @@ -60,6 +60,30 @@ class StockAnalyzerService: # 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) diff --git a/services/stock_data_provider.py b/services/stock_data_provider.py index 48cebb4..af2afa0 100644 --- a/services/stock_data_provider.py +++ b/services/stock_data_provider.py @@ -5,6 +5,7 @@ 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() @@ -57,27 +58,16 @@ class StockDataProvider: 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: - # 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) - logger.debug(f"获取A股数据: {stock_code}") + df = ak.stock_zh_a_hist( symbol=stock_code, start_date=start_date, @@ -96,13 +86,72 @@ class StockDataProvider: elif market_type in ['US']: logger.debug(f"获取美股数据: {stock_code}") - df = ak.stock_us_daily( - symbol=stock_code, - adjust="qfq" - ) - # 过滤日期 - df = df[(df.index >= start_date) & (df.index <= end_date)] + 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( @@ -122,8 +171,31 @@ class StockDataProvider: # 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']: - # 根据实际情况调整 - df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Amount'] + # 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'] @@ -143,7 +215,11 @@ class StockDataProvider: error_msg = f"获取{market_type}数据失败 {stock_code}: {str(e)}" logger.error(error_msg) logger.exception(e) - raise Exception(error_msg) + # 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', @@ -181,4 +257,4 @@ class StockDataProvider: 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} \ No newline at end of file + return {code: df for code, df in results if df is not None} \ No newline at end of file diff --git a/services/us_stock_service_async.py b/services/us_stock_service_async.py index d0a5b0b..97c94b1 100644 --- a/services/us_stock_service_async.py +++ b/services/us_stock_service_async.py @@ -49,8 +49,11 @@ class USStockServiceAsync: '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") + 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: diff --git a/stock_analyzer.py b/stock_analyzer.py deleted file mode 100644 index c28d4ac..0000000 --- a/stock_analyzer.py +++ /dev/null @@ -1,758 +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"开始处理股票 {stock_code} 的AI流式响应\n") - 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 - 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") diff --git a/us_stock_service.py b/us_stock_service.py deleted file mode 100644 index 852d565..0000000 --- a/us_stock_service.py +++ /dev/null @@ -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)}") \ No newline at end of file diff --git a/utils/api_control.py b/utils/api_control.py deleted file mode 100644 index ba10342..0000000 --- a/utils/api_control.py +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -from typing import Optional, Any -from fastapi import Response -from pydantic import BaseModel - - -class ResponseModel(BaseModel): - """ - 统一返回模型 - """ - code: int = 200 - msg: str = "Success" - data: Optional[Any] = None - - -class ApiResponse: - @staticmethod - def __response(code: int, msg: str, data: Optional[Any] = None) -> ResponseModel: - return ResponseModel(code=code, msg=msg, data=data) - - @classmethod - def success(cls, *, code: int = 200, msg: str = 'Success', data: Optional[Any] = None) -> Response: - response_model = cls.__response(code=code, msg=msg, data=data) - return cls(content=response_model.model_dump()) - - @classmethod - def fail(cls, *, code: int = 400, msg: str = 'Bad Request', data: Optional[Any] = None) -> Response: - response_model = cls.__response(code=code, msg=msg, data=data) - return cls(content=response_model.model_dump()) - -response_api = ApiResponse() - -""" 示例 -@app.get("/example-success") -async def example_success(): - return response_api.success(data={"key": "value"}) - -@app.get("/example-fail") -async def example_fail(): - return response_api.fail(msg="Something went wrong", data={"error": "details"}) -""" diff --git a/web_server.py b/web_server.py index 5550a49..8e06659 100644 --- a/web_server.py +++ b/web_server.py @@ -2,28 +2,40 @@ from fastapi import FastAPI, Request, Response, Depends, HTTPException, Backgrou from fastapi.responses import JSONResponse, StreamingResponse, FileResponse, RedirectResponse from fastapi.staticfiles import StaticFiles from fastapi.middleware.cors import CORSMiddleware +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 -# 导入新的异步服务 from services.us_stock_service_async import USStockServiceAsync from services.fund_service_async import FundServiceAsync -import asyncio -import threading import os -import traceback import httpx from logger import get_logger from utils.api_utils import APIUtils -# 加载环境变量 -from dotenv import load_dotenv +from dotenv import load_dotenv, dotenv_values 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() +# JWT相关配置 +SECRET_KEY = os.getenv("JWT_SECRET_KEY", secrets.token_hex(32)) +ALGORITHM = "HS256" +ACCESS_TOKEN_EXPIRE_MINUTES = 10080 # Token过期时间一周 + +LOGIN_PASSWORD = os.getenv("LOGIN_PASSWORD", "") +print(LOGIN_PASSWORD) + +# 是否需要登录 +REQUIRE_LOGIN = bool(LOGIN_PASSWORD.strip()) + + app = FastAPI( title="Stock Scanner API", description="异步股票分析API", @@ -42,7 +54,7 @@ app.add_middleware( # 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("/", StaticFiles(directory=frontend_dist, html=True), name="frontend") + 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 @@ -64,35 +76,121 @@ class TestAPIRequest(BaseModel): api_model: Optional[str] = None api_timeout: Optional[int] = 10 -@app.get("/") -async def index(request: Request): - # 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") +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', 'gpt-3.5-turbo'), + '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): +async def analyze(request: AnalyzeRequest, username: str = Depends(verify_token)): try: logger.info("开始处理分析请求") stock_codes = request.stock_codes market_type = request.market_type + # 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 @@ -122,7 +220,8 @@ async def analyze(request: AnalyzeRequest): 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") @@ -138,7 +237,8 @@ async def analyze(request: AnalyzeRequest): # 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") @@ -165,8 +265,9 @@ async def analyze(request: AnalyzeRequest): logger.exception(e) raise HTTPException(status_code=500, detail=error_msg) +# 搜索美股代码 @app.get("/search_us_stocks") -async def search_us_stocks(keyword: str = ""): +async def search_us_stocks(keyword: str = "", username: str = Depends(verify_token)): try: if not keyword: raise HTTPException(status_code=400, detail="请输入搜索关键词") @@ -179,8 +280,9 @@ async def search_us_stocks(keyword: str = ""): logger.error(f"搜索美股代码时出错: {str(e)}") raise HTTPException(status_code=500, detail=str(e)) +# 搜索基金代码 @app.get("/search_funds") -async def search_funds(keyword: str = "", market_type: str = ""): +async def search_funds(keyword: str = "", market_type: str = "", username: str = Depends(verify_token)): try: if not keyword: raise HTTPException(status_code=400, detail="请输入搜索关键词") @@ -193,8 +295,39 @@ async def search_funds(keyword: str = "", market_type: str = ""): logger.error(f"搜索基金代码时出错: {str(e)}") raise HTTPException(status_code=500, detail=str(e)) +# 获取美股详情 +@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): +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") @@ -226,7 +359,7 @@ async def test_api_connection(request: TestAPIRequest): "Content-Type": "application/json" }, json={ - "model": api_model or "gpt-3.5-turbo", + "model": api_model or "", "messages": [ {"role": "user", "content": "Hello, this is a test message. Please respond with 'API connection successful'."} ], @@ -261,36 +394,34 @@ async def test_api_connection(request: TestAPIRequest): content={"success": False, "message": f"API 测试连接时出错: {str(e)}"} ) -# 新增 API 端点：获取美股详情 -@app.get("/us_stock_detail/{symbol}") -async def get_us_stock_detail(symbol: str): - 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)) +# 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} -# 新增 API 端点：获取基金详情 -@app.get("/fund_detail/{symbol}") -async def get_fund_detail(symbol: str, market_type: str = "ETF"): - 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)) +# 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") uvicorn.run("web_server:app", host="127.0.0.1", port=8888, reload=True) \ No newline at end of file