feat: Web 个股分析支持选择策略 (#1331)

* feat: support strategy selection in web analysis

* fix(review-feedback-1331): The worker receives the original skills list, while TaskInfo stores a

* fix(review-feedback-1331): 补充对应 Web/API 文档，或在 PR 中明确说明现有哪份文档已覆盖且本次无需更新的依据

api/v1/endpoints/analysis.py

@@ -324,6 +324,7 @@ def _handle_async_analysis_batch(
     original_query = request.original_query if (is_single or preserve_batch_metadata) else None
     selection_source = request.selection_source if (is_single or preserve_batch_metadata) else None
     notify = getattr(request, "notify", True)
+    skills = getattr(request, "skills", None)
 
     submit_kwargs = dict(
         stock_codes=stock_codes,
@@ -334,6 +335,8 @@ def _handle_async_analysis_batch(
         force_refresh=request.force_refresh,
         notify=notify,
     )
+    if skills is not None:
+        submit_kwargs["skills"] = skills
 
     accepted_tasks, duplicate_errors = task_queue.submit_tasks_batch(**submit_kwargs)
 
@@ -415,6 +418,7 @@ def _handle_sync_analysis(
             force_refresh=request.force_refresh,
             query_id=query_id,
             send_notification=getattr(request, "notify", True),
+            skills=getattr(request, "skills", None),
         )
 
         if result is None:
@@ -748,6 +752,7 @@ def get_analysis_status(task_id: str) -> TaskStatus:
             stock_name=task.stock_name,
             original_query=task.original_query,
             selection_source=task.selection_source,
+            skills=getattr(task, "skills", None),
         )
     
     # 2. 从数据库查询已完成的记录
@@ -789,8 +794,12 @@ def get_analysis_status(task_id: str) -> TaskStatus:
             # Extract current_price / change_pct from context_snapshot
             current_price = None
             change_pct = None
+            skills = None
             context_snapshot = parse_json_field(getattr(record, 'context_snapshot', None))
             if context_snapshot and isinstance(context_snapshot, dict):
+                raw_skills = context_snapshot.get("skills")
+                if isinstance(raw_skills, list):
+                    skills = [str(skill) for skill in raw_skills]
                 enhanced_context = context_snapshot.get('enhanced_context') or {}
                 realtime = enhanced_context.get('realtime') or {}
                 current_price = realtime.get('price')
@@ -841,7 +850,8 @@ def get_analysis_status(task_id: str) -> TaskStatus:
                     report=report_dict,
                     created_at=record.created_at.isoformat() if record.created_at else datetime.now().isoformat()
                 ),
-                error=None
+                error=None,
+                skills=skills,
             )
 
     except Exception as e:

api/v1/schemas/analysis.py

@@ -13,7 +13,7 @@
 from typing import Optional, List, Any
 from enum import Enum
 
-from pydantic import BaseModel, Field
+from pydantic import AliasChoices, BaseModel, Field
 from src.utils.analysis_metadata import SELECTION_SOURCE_PATTERN
 
 
@@ -71,6 +71,12 @@ class AnalyzeRequest(BaseModel):
         True,
         description="是否发送推送通知（Telegram/企业微信等）"
     )
+    skills: Optional[List[str]] = Field(
+        None,
+        validation_alias=AliasChoices("skills", "strategies"),
+        description="本次分析使用的策略 skill ID 列表；兼容 legacy strategies 字段",
+        example=["bull_trend", "growth_quality"]
+    )
 
     class Config:
         json_schema_extra = {
@@ -82,7 +88,8 @@ class AnalyzeRequest(BaseModel):
                 "stock_name": "贵州茅台",
                 "original_query": "茅台",
                 "selection_source": "autocomplete",
-                "notify": True
+                "notify": True,
+                "skills": ["bull_trend"]
             }
         }
 
@@ -259,6 +266,7 @@ class TaskStatus(BaseModel):
         description="选择来源",
         pattern=SELECTION_SOURCE_PATTERN,
     )
+    skills: Optional[List[str]] = Field(None, description="本次任务使用的策略 skill ID 列表")
     
     class Config:
         json_schema_extra = {
@@ -271,7 +279,8 @@ class TaskStatus(BaseModel):
                 "error": None,
                 "stock_name": "贵州茅台",
                 "original_query": "茅台",
-                "selection_source": "autocomplete"
+                "selection_source": "autocomplete",
+                "skills": ["bull_trend"]
             }
         }
 
@@ -300,6 +309,7 @@ class TaskInfo(BaseModel):
         description="选择来源",
         pattern=SELECTION_SOURCE_PATTERN,
     )
+    skills: Optional[List[str]] = Field(None, description="本次任务使用的策略 skill ID 列表")
     
     class Config:
         json_schema_extra = {
@@ -316,7 +326,8 @@ class TaskInfo(BaseModel):
                 "completed_at": None,
                 "error": None,
                 "original_query": "茅台",
-                "selection_source": "autocomplete"
+                "selection_source": "autocomplete",
+                "skills": ["bull_trend"]
             }
         }
 

apps/dsa-web/src/api/analysis.ts

@@ -30,6 +30,7 @@ export const analysisApi = {
       stock_name: data.stockName,
       original_query: data.originalQuery,
       selection_source: data.selectionSource,
+      skills: data.skills,
       ...(data.notify !== undefined && { notify: data.notify }),
     };
 
@@ -63,6 +64,7 @@ export const analysisApi = {
       stock_name: data.stockName,
       original_query: data.originalQuery,
       selection_source: data.selectionSource,
+      skills: data.skills,
       ...(data.notify !== undefined && { notify: data.notify }),
     };
 

apps/dsa-web/src/pages/HomePage.tsx

@@ -1,9 +1,10 @@
 import type React from 'react';
 import { useCallback, useEffect, useMemo, useRef, useState } from 'react';
-import { BarChart3 } from 'lucide-react';
+import { BarChart3, Check, SlidersHorizontal } from 'lucide-react';
 import { useNavigate } from 'react-router-dom';
 import { getParsedApiError, type ParsedApiError } from '../api/error';
 import { analysisApi } from '../api/analysis';
+import { agentApi, type SkillInfo } from '../api/agent';
 import { systemConfigApi } from '../api/systemConfig';
 import { ApiErrorAlert, ConfirmDialog, Button, EmptyState, InlineAlert } from '../components/common';
 import { DashboardStateBlock } from '../components/dashboard';
@@ -30,8 +31,15 @@ const HomePage: React.FC = () => {
   const [marketReviewError, setMarketReviewError] = useState<ParsedApiError | null>(null);
   const [marketReviewReport, setMarketReviewReport] = useState<string | null>(null);
   const [marketReviewReportCopied, setMarketReviewReportCopied] = useState(false);
+  const [analysisSkills, setAnalysisSkills] = useState<SkillInfo[]>([]);
+  const [selectedStrategyId, setSelectedStrategyId] = useState('');
+  const [strategyMenuOpen, setStrategyMenuOpen] = useState(false);
   const marketReviewPollTimer = useRef<number | null>(null);
   const dashboardScrollRef = useRef<HTMLElement | null>(null);
+  const strategyMenuRef = useRef<HTMLDivElement | null>(null);
+  const strategyButtonRef = useRef<HTMLButtonElement | null>(null);
+  const strategyItemRefs = useRef<Array<HTMLButtonElement | null>>([]);
+  const strategyInitialFocusIndexRef = useRef<number | null>(null);
 
   const stopMarketReviewPolling = useCallback(() => {
     if (marketReviewPollTimer.current !== null) {
@@ -117,9 +125,154 @@ const HomePage: React.FC = () => {
     };
   }, []);
 
+  useEffect(() => {
+    let active = true;
+    agentApi.getSkills()
+      .then((response) => {
+        if (active) {
+          setAnalysisSkills(response.skills);
+        }
+      })
+      .catch(() => {
+        if (active) {
+          setAnalysisSkills([]);
+        }
+      });
+
+    return () => {
+      active = false;
+    };
+  }, []);
+
+  useEffect(() => {
+    if (!strategyMenuOpen) {
+      return;
+    }
+
+    const handlePointerDown = (event: MouseEvent) => {
+      const target = event.target;
+      if (target instanceof Node && strategyMenuRef.current?.contains(target)) {
+        return;
+      }
+      setStrategyMenuOpen(false);
+    };
+
+    document.addEventListener('mousedown', handlePointerDown);
+    return () => document.removeEventListener('mousedown', handlePointerDown);
+  }, [strategyMenuOpen]);
+
+  useEffect(() => {
+    if (selectedStrategyId && !analysisSkills.some((skill) => skill.id === selectedStrategyId)) {
+      setSelectedStrategyId('');
+    }
+  }, [analysisSkills, selectedStrategyId]);
+
   const reportLanguage = normalizeReportLanguage(selectedReport?.meta.reportLanguage);
   const reportText = getReportText(reportLanguage);
   const isMarketReviewHistoryReport = selectedReport?.meta.reportType === 'market_review';
+  const selectedStrategy = useMemo(
+    () => analysisSkills.find((skill) => skill.id === selectedStrategyId),
+    [analysisSkills, selectedStrategyId],
+  );
+  const selectedAnalysisSkills = useMemo(
+    () => (selectedStrategyId ? [selectedStrategyId] : undefined),
+    [selectedStrategyId],
+  );
+  const strategyOptions = useMemo(
+    () => [
+      { id: '', name: '默认策略', description: '沿用系统默认分析框架' },
+      ...analysisSkills.map((skill) => ({
+        id: skill.id,
+        name: skill.name,
+        description: skill.description,
+      })),
+    ],
+    [analysisSkills],
+  );
+  const closeStrategyMenu = useCallback((restoreFocus = false) => {
+    setStrategyMenuOpen(false);
+    if (restoreFocus) {
+      strategyButtonRef.current?.focus();
+    }
+  }, []);
+  const selectStrategy = useCallback((strategyId: string) => {
+    setSelectedStrategyId(strategyId);
+    setStrategyMenuOpen(false);
+  }, []);
+  const focusStrategyItem = useCallback((index: number) => {
+    const itemCount = strategyOptions.length;
+    if (itemCount === 0) {
+      return;
+    }
+    const nextIndex = (index + itemCount) % itemCount;
+    strategyItemRefs.current[nextIndex]?.focus();
+  }, [strategyOptions.length]);
+  const getSelectedStrategyIndex = useCallback(() => {
+    const selectedIndex = strategyOptions.findIndex((option) => option.id === selectedStrategyId);
+    return selectedIndex >= 0 ? selectedIndex : 0;
+  }, [selectedStrategyId, strategyOptions]);
+  useEffect(() => {
+    strategyItemRefs.current = strategyItemRefs.current.slice(0, strategyOptions.length);
+  }, [strategyOptions.length]);
+  useEffect(() => {
+    if (!strategyMenuOpen) {
+      return undefined;
+    }
+
+    const targetIndex = strategyInitialFocusIndexRef.current ?? getSelectedStrategyIndex();
+    strategyInitialFocusIndexRef.current = null;
+    const timeout = window.setTimeout(() => focusStrategyItem(targetIndex), 0);
+    return () => window.clearTimeout(timeout);
+  }, [focusStrategyItem, getSelectedStrategyIndex, strategyMenuOpen]);
+  const handleStrategyButtonKeyDown = useCallback((event: React.KeyboardEvent<HTMLButtonElement>) => {
+    if (event.key !== 'ArrowDown' && event.key !== 'ArrowUp') {
+      return;
+    }
+
+    event.preventDefault();
+    const targetIndex = event.key === 'ArrowUp' ? strategyOptions.length - 1 : 0;
+    if (strategyMenuOpen) {
+      focusStrategyItem(targetIndex);
+      return;
+    }
+    strategyInitialFocusIndexRef.current = targetIndex;
+    setStrategyMenuOpen(true);
+  }, [focusStrategyItem, strategyMenuOpen, strategyOptions.length]);
+  const handleStrategyMenuKeyDown = useCallback((event: React.KeyboardEvent<HTMLDivElement>) => {
+    const itemCount = strategyOptions.length;
+    if (itemCount === 0) {
+      return;
+    }
+
+    const currentIndex = strategyItemRefs.current.findIndex((item) => item === document.activeElement);
+    switch (event.key) {
+      case 'Escape':
+        event.preventDefault();
+        closeStrategyMenu(true);
+        break;
+      case 'ArrowDown':
+        event.preventDefault();
+        focusStrategyItem(currentIndex >= 0 ? currentIndex + 1 : 0);
+        break;
+      case 'ArrowUp':
+        event.preventDefault();
+        focusStrategyItem(currentIndex >= 0 ? currentIndex - 1 : itemCount - 1);
+        break;
+      case 'Home':
+        event.preventDefault();
+        focusStrategyItem(0);
+        break;
+      case 'End':
+        event.preventDefault();
+        focusStrategyItem(itemCount - 1);
+        break;
+      case 'Tab':
+        setStrategyMenuOpen(false);
+        break;
+      default:
+        break;
+    }
+  }, [closeStrategyMenu, focusStrategyItem, strategyOptions.length]);
   const setupNeedsAction = setupStatus ? !setupStatus.isComplete : false;
   const setupMissingLabels = useMemo(() => {
     if (!setupStatus) {
@@ -156,9 +309,10 @@ const HomePage: React.FC = () => {
         stockName,
         originalQuery: query,
         selectionSource: selectionSource ?? 'manual',
+        skills: selectedAnalysisSkills,
       });
     },
-    [query, submitAnalysis],
+    [query, selectedAnalysisSkills, submitAnalysis],
   );
 
   const handleAskFollowUp = useCallback(() => {
@@ -183,8 +337,9 @@ const HomePage: React.FC = () => {
       originalQuery: selectedReport.meta.stockCode,
       selectionSource: 'manual',
       forceRefresh: true,
+      skills: selectedAnalysisSkills,
     });
-  }, [selectedReport, submitAnalysis]);
+  }, [selectedAnalysisSkills, selectedReport, submitAnalysis]);
 
   const pollMarketReviewStatus = useCallback(
     async (taskId: string) => {
@@ -388,7 +543,7 @@ const HomePage: React.FC = () => {
       className="flex h-[calc(100vh-5rem)] w-full flex-col overflow-hidden md:flex-row sm:h-[calc(100vh-5.5rem)] lg:h-[calc(100vh-2rem)]"
     >
       <div className="flex-1 flex flex-col min-h-0 min-w-0 max-w-full lg:max-w-6xl mx-auto w-full">
-        <header className="flex min-w-0 flex-shrink-0 items-center overflow-hidden px-3 py-3 md:px-4 md:py-4">
+        <header className="relative z-30 flex min-w-0 flex-shrink-0 items-center overflow-visible px-3 py-3 md:px-4 md:py-4">
           <div className="flex min-w-0 flex-1 flex-col gap-2.5 md:flex-row md:items-center">
             <div className="flex min-w-0 flex-1 items-center gap-2.5">
               <button
@@ -412,6 +567,58 @@ const HomePage: React.FC = () => {
                   className={inputError ? 'border-danger/50' : undefined}
                 />
               </div>
+              {analysisSkills.length > 0 ? (
+                <div ref={strategyMenuRef} className="relative flex-shrink-0">
+                  <button
+                    ref={strategyButtonRef}
+                    id="strategy-menu-button"
+                    type="button"
+                    aria-haspopup="menu"
+                    aria-expanded={strategyMenuOpen}
+                    aria-controls={strategyMenuOpen ? 'strategy-menu' : undefined}
+                    onClick={() => setStrategyMenuOpen((open) => !open)}
+                    onKeyDown={handleStrategyButtonKeyDown}
+                    disabled={isAnalyzing}
+                    className="home-surface-button flex h-10 max-w-[8.5rem] items-center gap-1.5 rounded-xl px-3 text-xs text-foreground disabled:cursor-not-allowed disabled:opacity-60 sm:max-w-[11rem]"
+                  >
+                    <SlidersHorizontal className="h-4 w-4 flex-shrink-0" aria-hidden="true" />
+                    <span className="truncate">{selectedStrategy?.name || '策略'}</span>
+                  </button>
+                  {strategyMenuOpen ? (
+                    <div
+                      id="strategy-menu"
+                      role="menu"
+                      aria-labelledby="strategy-menu-button"
+                      onKeyDown={handleStrategyMenuKeyDown}
+                      className="absolute right-0 top-11 z-[120] max-h-80 w-[min(18rem,calc(100vw-1.5rem))] overflow-y-auto rounded-xl border border-subtle bg-elevated p-1.5 text-sm text-foreground shadow-2xl"
+                    >
+                      {strategyOptions.map((option, index) => {
+                        const selected = selectedStrategyId === option.id;
+                        return (
+                          <button
+                            key={option.id || 'default'}
+                            ref={(node) => {
+                              strategyItemRefs.current[index] = node;
+                            }}
+                            type="button"
+                            role="menuitemradio"
+                            aria-checked={selected}
+                            tabIndex={-1}
+                            onClick={() => selectStrategy(option.id)}
+                            className="flex w-full items-start gap-2 rounded-lg px-2.5 py-2 text-left transition-colors hover:bg-hover"
+                          >
+                            <Check className={`mt-0.5 h-4 w-4 flex-shrink-0 ${selected ? 'opacity-100' : 'opacity-0'}`} aria-hidden="true" />
+                            <span className="min-w-0">
+                              <span className="block font-medium">{option.name}</span>
+                              <span className="mt-0.5 line-clamp-2 block text-xs leading-5 text-muted-text">{option.description}</span>
+                            </span>
+                          </button>
+                        );
+                      })}
+                    </div>
+                  ) : null}
+                </div>
+              ) : null}
             </div>
             <div className="flex min-w-0 flex-shrink-0 items-center gap-2.5">
               <label className="flex h-10 flex-shrink-0 cursor-pointer items-center gap-1.5 rounded-xl border border-subtle bg-surface/60 px-3 text-xs text-secondary-text select-none transition-colors hover:border-subtle-hover hover:text-foreground">

apps/dsa-web/src/pages/__tests__/HomePage.test.tsx

@@ -2,6 +2,7 @@ import { fireEvent, render, screen, waitFor } from '@testing-library/react';
 import { MemoryRouter } from 'react-router-dom';
 import { beforeEach, describe, expect, it, vi } from 'vitest';
 import { analysisApi, DuplicateTaskError } from '../../api/analysis';
+import { agentApi } from '../../api/agent';
 import { historyApi } from '../../api/history';
 import { systemConfigApi } from '../../api/systemConfig';
 import { useStockPoolStore } from '../../stores';
@@ -46,6 +47,12 @@ vi.mock('../../api/systemConfig', () => ({
   },
 }));
 
+vi.mock('../../api/agent', () => ({
+  agentApi: {
+    getSkills: vi.fn(),
+  },
+}));
+
 vi.mock('../../hooks/useTaskStream', () => ({
   useTaskStream: vi.fn(),
 }));
@@ -110,6 +117,7 @@ describe('HomePage', () => {
     vi.clearAllMocks();
     navigateMock.mockReset();
     useStockPoolStore.getState().resetDashboardState();
+    vi.mocked(agentApi.getSkills).mockResolvedValue({ skills: [], default_skill_id: '' });
     vi.mocked(systemConfigApi.getSetupStatus).mockResolvedValue({
       isComplete: true,
       readyForSmoke: true,
@@ -519,6 +527,89 @@ describe('HomePage', () => {
     }));
   });
 
+  it('passes the selected strategy when submitting stock analysis', async () => {
+    vi.mocked(agentApi.getSkills).mockResolvedValue({
+      default_skill_id: 'bull_trend',
+      skills: [
+        { id: 'bull_trend', name: '默认多头趋势', description: '趋势分析' },
+        { id: 'growth_quality', name: '成长质量', description: '成长股分析' },
+      ],
+    });
+    vi.mocked(historyApi.getList).mockResolvedValue({
+      total: 0,
+      page: 1,
+      limit: 20,
+      items: [],
+    });
+    vi.mocked(analysisApi.analyzeAsync).mockResolvedValue({
+      taskId: 'task-strategy-1',
+      status: 'pending',
+    });
+
+    render(
+      <MemoryRouter>
+        <HomePage />
+      </MemoryRouter>,
+    );
+
+    fireEvent.click(await screen.findByRole('button', { name: '策略' }));
+    fireEvent.click(screen.getByRole('menuitemradio', { name: /成长质量/ }));
+
+    const input = screen.getByPlaceholderText('输入股票代码或名称，如 600519、贵州茅台、AAPL');
+    fireEvent.change(input, { target: { value: '600519' } });
+    fireEvent.click(screen.getByRole('button', { name: '分析' }));
+
+    await waitFor(() => {
+      expect(analysisApi.analyzeAsync).toHaveBeenCalledWith(expect.objectContaining({
+        stockCode: '600519',
+        skills: ['growth_quality'],
+      }));
+    });
+  });
+
+  it('supports keyboard navigation in the strategy menu', async () => {
+    vi.mocked(agentApi.getSkills).mockResolvedValue({
+      default_skill_id: 'bull_trend',
+      skills: [
+        { id: 'bull_trend', name: '默认多头趋势', description: '趋势分析' },
+        { id: 'growth_quality', name: '成长质量', description: '成长股分析' },
+      ],
+    });
+    vi.mocked(historyApi.getList).mockResolvedValue({
+      total: 0,
+      page: 1,
+      limit: 20,
+      items: [],
+    });
+
+    render(
+      <MemoryRouter>
+        <HomePage />
+      </MemoryRouter>,
+    );
+
+    const trigger = await screen.findByRole('button', { name: '策略' });
+    fireEvent.keyDown(trigger, { key: 'ArrowDown' });
+
+    const defaultOption = await screen.findByRole('menuitemradio', { name: /默认策略/ });
+    await waitFor(() => {
+      expect(defaultOption).toHaveFocus();
+    });
+
+    const menu = screen.getByRole('menu');
+    fireEvent.keyDown(menu, { key: 'ArrowDown' });
+    expect(screen.getByRole('menuitemradio', { name: /默认多头趋势/ })).toHaveFocus();
+
+    fireEvent.keyDown(menu, { key: 'End' });
+    expect(screen.getByRole('menuitemradio', { name: /成长质量/ })).toHaveFocus();
+
+    fireEvent.keyDown(menu, { key: 'Escape' });
+    await waitFor(() => {
+      expect(screen.queryByRole('menu')).not.toBeInTheDocument();
+    });
+    expect(trigger).toHaveFocus();
+  });
+
   it('disables stock reanalysis and follow-up for market review history reports', async () => {
     vi.mocked(historyApi.getList).mockResolvedValue({
       total: 1,

apps/dsa-web/src/stores/stockPoolStore.ts

@@ -24,6 +24,7 @@ type SubmitAnalysisOptions = {
   selectionSource?: SelectionSource;
   notify?: boolean;
   forceRefresh?: boolean;
+  skills?: string[];
 };
 
 let reportRequestSeq = 0;
@@ -310,6 +311,7 @@ export const useStockPoolStore = create<StockPoolState>((set, get) => ({
     const originalQuery = (options?.originalQuery ?? state.query).trim();
     const notify = options?.notify ?? state.notify;
     const forceRefresh = options?.forceRefresh ?? false;
+    const skills = options?.skills;
 
     if (!stockCodeInput) {
       set({ inputError: '请输入股票代码', duplicateError: null });
@@ -348,6 +350,7 @@ export const useStockPoolStore = create<StockPoolState>((set, get) => ({
         selectionSource,
         notify,
         forceRefresh,
+        skills,
       });
 
       if (requestId !== analyzeRequestSeq) {

apps/dsa-web/src/types/analysis.ts

@@ -18,6 +18,7 @@ export interface AnalysisRequest {
   originalQuery?: string;
   selectionSource?: 'manual' | 'autocomplete' | 'import' | 'image';
   notify?: boolean;
+  skills?: string[];
 }
 
 export interface MarketReviewRequest {
@@ -166,6 +167,7 @@ export interface TaskStatus {
   stockName?: string;
   originalQuery?: string;
   selectionSource?: string;
+  skills?: string[];
 }
 
 /** Task details used by task list and SSE events */

docs/CHANGELOG.md

@@ -23,6 +23,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/).
 - [修复] AlphaVantage 适配器在 newest-first 原始数据下 pct_chg 计算错误：改为先按日期升序排序再计算涨跌幅。
 - [修复] 美股日线路由未包含 Finnhub / AlphaVantage：扩展 `get_daily_data()` 美股分支的 source_order 以覆盖新增数据源。
 - [文档] 新增小白客户端安装与配置指南，说明桌面客户端下载、基础模型配置、新闻源配置和常见问题。
+- [新功能] Web 首页个股分析支持选择策略。
 
 ## [3.17.1] - 2026-05-16
 

docs/full-guide.md

@@ -1152,6 +1152,7 @@ FastAPI 提供 RESTful API 服务，支持配置管理和触发分析。
 
 - 📝 **配置管理** - 查看/修改自选股列表
 - 🚀 **快速分析** - 通过 API 接口触发个股分析；首页也提供“大盘复盘”按钮，可在 Docker/server 模式下后台触发大盘复盘
+- 🎯 **策略选择** - 首页支持显式选择分析策略 skill；不传 `skills` 时按系统默认策略运行，便于保持与历史行为兼容
 - 🧭 **首次配置提示** - 首页会读取只读配置状态，缺少 LLM 主渠道、自选股等基础项时提示缺口并引导进入系统设置
 - 📊 **实时进度** - 分析任务状态实时更新，支持多任务并行；普通分析链路在进入 LLM 阶段后会优先尝试 LiteLLM 流式生成，并通过任务 SSE 回灌更细粒度的 `message/progress`
 - 🗂️ **大盘复盘任务可见性** - 首页触发大盘复盘后会返回 `task_id` 并轮询 `GET /api/v1/analysis/status/{task_id}`，在进行中/完成/失败场景给出可见反馈，失败时直接透出报错内容
@@ -1180,6 +1181,8 @@ FastAPI 提供 RESTful API 服务，支持配置管理和触发分析。
 | `/docs` | GET | API Swagger 文档 |
 
 > 说明：`POST /api/v1/analysis/analyze` 在 `async_mode=false` 时仅支持单只股票；批量 `stock_codes` 需使用 `async_mode=true`。异步 `202` 响应对单股返回 `task_id`，对批量返回 `accepted` / `duplicates` 汇总结构。
+> 说明：`POST /api/v1/analysis/analyze` 支持使用 `skills` 传入策略 skill ID 列表；若未传则按服务端默认策略执行。为兼容历史调用，`strategies` 字段仍作为兼容别名保留。
+> 说明：Web 侧首页策略下拉为显式可选策略入口。用户未手动选择时不会携带 `skills`，与历史客户端行为一致；选择策略后将透传到该接口并在任务状态与历史快照中保留。
 > 说明：`POST /api/v1/analysis/market-review` 采用后端与 CLI/Bot 共用的配置路径（`GeminiAnalyzer(config=...)` 与同样的搜索/提示词构造入口）。Provider 兼容路由会优先识别并使用 `litellm_model`、`llm_model_list`，若未配置则回退 legacy `GEMINI_*`、`OPENAI_*`、`ANTHROPIC_*`、`DEEPSEEK_*` 键；不会新增/调整 provider、Base URL 或 LiteLLM 路由语义。
 > 审计依据：优先级与回退语义以 `src/config.py` 的 `Config._load_from_env()` 为准（`LITELLM_CONFIG` > `LLM_CHANNELS` > legacy）。配套回归见 `tests/test_llm_channel_config.py`（配置源解析）与 `tests/test_market_review_runtime.py`（共享装配路径）。该接口当前仅提供单进程/单机级防重复能力，若为多实例部署需通过外部任务队列或分布式锁补齐全局幂等。
 > 说明：`POST /api/v1/analysis/market-review` 触发后，报告会以 `report_type=market_review` 写入历史库；你可直接查询 `/api/v1/history` 或 `/api/v1/history/{record_id}` 获取历史 Markdown，避免再次触发分析重算。
@@ -1209,6 +1212,11 @@ curl -X POST http://127.0.0.1:8000/api/v1/analysis/analyze \
   -H 'Content-Type: application/json' \
   -d '{"stock_code": "600519"}'
 
+# 透传策略（可选）
+curl -X POST http://127.0.0.1:8000/api/v1/analysis/analyze \
+  -H 'Content-Type: application/json' \
+  -d '{"stock_code": "600519", "skills": ["bull_trend", "growth_quality"]}'
+
 # 查询任务状态
 curl http://127.0.0.1:8000/api/v1/analysis/status/<task_id>
 

docs/full-guide_EN.md

@@ -1011,6 +1011,7 @@ FastAPI provides RESTful API service for configuration management and triggering
 
 - **Configuration Management** - View/modify watchlist
 - **Quick Analysis** - Trigger stock analysis via API; the Home page also provides a Market Review button that starts a background market recap in Docker/server mode
+- **Strategy selection** - The Home page supports explicitly selecting analysis strategy skills; when `skills` is omitted, analysis uses the server default strategy so legacy clients keep existing behavior
 - **First-run Setup Hint** - The Home page reads the read-only setup status and points users to Settings when required items such as the primary LLM channel or watchlist are missing
 - **Real-time Progress** - Analysis task status updates in real-time, supports parallel tasks; the regular stock-analysis path now prefers LiteLLM streaming during the LLM stage and pushes finer-grained `message/progress` updates through task SSE
 - **Market Review visibility** - After clicking Market Review, the API returns a `task_id` and the UI polls `GET /api/v1/analysis/status/{task_id}` to show progress; completed/failure states are rendered explicitly and failure messages are shown directly in the UI error area.
@@ -1037,6 +1038,8 @@ FastAPI provides RESTful API service for configuration management and triggering
 | `/docs` | GET | API Swagger documentation |
 
 > Note: `POST /api/v1/analysis/analyze` supports only one stock when `async_mode=false`; batch `stock_codes` requires `async_mode=true`. The async `202` response returns a single `task_id` for one stock, or an `accepted` / `duplicates` summary for batch requests.
+> Note: `POST /api/v1/analysis/analyze` accepts `skills` as an array of strategy IDs; if omitted, server defaults are used. The legacy field `strategies` is still accepted for backward compatibility.
+> Note: The Web Home page exposes an explicit strategy selector. When users do not pick one, `skills` is not sent and legacy behavior is preserved; when selected, it is passed through to this endpoint and persisted in task status/history snapshots.
 > Note: `POST /api/v1/analysis/market-review` follows the same runtime configuration path as CLI/Bot market review (`GeminiAnalyzer(config=...)`, search setup, and prompt/rendering pipeline). The provider compatibility path prioritizes `litellm_model` and `llm_model_list`, then falls back to existing legacy keys (`GEMINI_*`, `OPENAI_*`, `ANTHROPIC_*`, `DEEPSEEK_*`) when those are not set; provider names, Base URL, and LiteLLM routing semantics are otherwise unchanged.
 > Audit note: priority and fallback are defined by `Config._load_from_env()` in `src/config.py` (`LITELLM_CONFIG` > `LLM_CHANNELS` > legacy). Regression coverage is in `tests/test_llm_channel_config.py` (configuration source parsing) and `tests/test_market_review_runtime.py` (shared runtime assembly). The endpoint lock is process/host-level only; multi-instance deployments still need external distributed idempotency controls.
 > Note: Once `/api/v1/analysis/market-review` completes, the report is persisted with `report_type=market_review`; open `/api/v1/history` and `/api/v1/history/{record_id}` (or Markdown history endpoints) to view it directly without re-running analysis.
@@ -1066,6 +1069,11 @@ curl -X POST http://127.0.0.1:8000/api/v1/analysis/analyze \
   -H 'Content-Type: application/json' \
   -d '{"stock_code": "600519"}'
 
+# pass strategy list (optional)
+curl -X POST http://127.0.0.1:8000/api/v1/analysis/analyze \
+  -H 'Content-Type: application/json' \
+  -d '{"stock_code": "600519", "skills": ["bull_trend", "growth_quality"]}'
+
 # Query task status
 curl http://127.0.0.1:8000/api/v1/analysis/status/<task_id>
 

docs/openclaw-skill-integration.md

@@ -132,8 +132,9 @@ metadata:
 1. **提取股票代码**：从用户消息中识别股票代码（如 600519、AAPL、hk00700）。若用户仅提供中文名称（如「茅台」），需提示用户提供股票代码，或使用常见映射（茅台→600519）。
 2. **调用 API**：向 `{DSA_BASE_URL}/api/v1/analysis/analyze` 发送 POST 请求，请求体：
    ```json
-   {"stock_code": "<提取的代码>", "report_type": "detailed", "force_refresh": true, "async_mode": false}
+   {"stock_code": "<提取的代码>", "report_type": "detailed", "force_refresh": true, "async_mode": false, "skills": ["bull_trend"]}
    ```
+   > `skills` 为可选策略 ID 数组；历史字段 `strategies` 仍保留兼容，建议优先使用 `skills`。
 3. **等待响应**：同步模式下分析约需 2–5 分钟，请确保 HTTP 客户端超时足够（建议 ≥300 秒）。
 4. **解析结果**：从响应的 `report.summary` 中提取 `operation_advice`、`trend_prediction`、`analysis_summary`，从 `report.strategy` 中提取 `ideal_buy`、`stop_loss`、`take_profit`，以简洁格式呈现给用户。
 5. **错误处理**：

src/core/pipeline.py

@@ -84,6 +84,7 @@ class StockAnalysisPipeline:
         query_source: Optional[str] = None,
         save_context_snapshot: Optional[bool] = None,
         progress_callback: Optional[Callable[[int, str], None]] = None,
+        analysis_skills: Optional[List[str]] = None,
     ):
         """
         初始化调度器
@@ -101,13 +102,14 @@ class StockAnalysisPipeline:
             self.config.save_context_snapshot if save_context_snapshot is None else save_context_snapshot
         )
         self.progress_callback = progress_callback
+        self.analysis_skills = list(analysis_skills) if analysis_skills is not None else None
         
         # 初始化各模块
         self.db = get_db()
         self.fetcher_manager = DataFetcherManager()
         # 不再单独创建 akshare_fetcher，统一使用 fetcher_manager 获取增强数据
         self.trend_analyzer = StockTrendAnalyzer()  # 技术分析器
-        self.analyzer = GeminiAnalyzer(config=self.config)
+        self.analyzer = GeminiAnalyzer(config=self.config, skills=self.analysis_skills)
         self.notifier = NotificationService(source_message=source_message)
         self._single_stock_notify_lock = threading.Lock()
         
@@ -311,6 +313,10 @@ class StockAnalysisPipeline:
             # API Key for the traditional analysis path are not silently
             # switched to Agent mode (which is slower and more expensive).
             use_agent = getattr(self.config, 'agent_mode', False)
+            if not use_agent:
+                if self.analysis_skills:
+                    use_agent = True
+                    logger.info(f"{stock_name}({code}) Auto-enabled agent mode due to request skills: {self.analysis_skills}")
             if not use_agent:
                 # Auto-enable agent mode when specific skills are configured (e.g., scheduled task with strategy)
                 configured_skills = getattr(self.config, 'agent_skills', [])
@@ -790,8 +796,13 @@ class StockAnalysisPipeline:
             from src.agent.factory import build_agent_executor
             report_language = normalize_report_language(getattr(self.config, "report_language", "zh"))
 
+            requested_skills = (
+                self.analysis_skills
+                if self.analysis_skills is not None
+                else (getattr(self.config, 'agent_skills', None) or None)
+            )
             # Build executor from shared factory (ToolRegistry and SkillManager prototype are cached)
-            executor = build_agent_executor(self.config, getattr(self.config, 'agent_skills', None) or None)
+            executor = build_agent_executor(self.config, requested_skills)
 
             # Build initial context to avoid redundant tool calls
             initial_context = {
@@ -801,6 +812,8 @@ class StockAnalysisPipeline:
                 "report_language": report_language,
                 "fundamental_context": fundamental_context,
             }
+            if self.analysis_skills is not None:
+                initial_context["skills"] = self.analysis_skills
             
             if realtime_quote:
                 initial_context["realtime_quote"] = self._safe_to_dict(realtime_quote)
@@ -1518,12 +1531,15 @@ class StockAnalysisPipeline:
         """
         构建分析上下文快照
         """
-        return {
+        snapshot = {
             "enhanced_context": enhanced_context,
             "news_content": news_content,
             "realtime_quote_raw": self._safe_to_dict(realtime_quote),
             "chip_distribution_raw": self._safe_to_dict(chip_data),
         }
+        if self.analysis_skills is not None:
+            snapshot["skills"] = list(self.analysis_skills)
+        return snapshot
 
     @staticmethod
     def _resolve_resume_target_date(

src/services/analysis_service.py

@@ -12,7 +12,7 @@
 
 import logging
 import uuid
-from typing import Optional, Dict, Any, Callable
+from typing import Optional, Dict, Any, Callable, List
 
 from src.repositories.analysis_repo import AnalysisRepository
 from src.report_language import (
@@ -46,6 +46,7 @@ class AnalysisService:
         query_id: Optional[str] = None,
         send_notification: bool = True,
         progress_callback: Optional[Callable[[int, str], None]] = None,
+        skills: Optional[List[str]] = None,
     ) -> Optional[Dict[str, Any]]:
         """
         执行股票分析
@@ -83,6 +84,7 @@ class AnalysisService:
                 query_id=query_id,
                 query_source="api",
                 progress_callback=progress_callback,
+                analysis_skills=skills,
             )
             
             # 确定报告类型 (API: simple/detailed/full/brief -> ReportType)

src/services/task_queue.py

@@ -71,6 +71,7 @@ class TaskInfo:
     completed_at: Optional[datetime] = None
     original_query: Optional[str] = None
     selection_source: Optional[str] = None
+    skills: Optional[List[str]] = None
     
     def to_dict(self) -> Dict[str, Any]:
         """Convert task info into an API-friendly dictionary."""
@@ -88,6 +89,7 @@ class TaskInfo:
             "error": self.error,
             "original_query": self.original_query,
             "selection_source": self.selection_source,
+            "skills": self.skills,
         }
     
     def copy(self) -> 'TaskInfo':
@@ -107,6 +109,7 @@ class TaskInfo:
             completed_at=self.completed_at,
             original_query=self.original_query,
             selection_source=self.selection_source,
+            skills=list(self.skills) if self.skills is not None else None,
         )
 
 
@@ -300,6 +303,7 @@ class AnalysisTaskQueue:
         selection_source: Optional[str] = None,
         report_type: str = "detailed",
         force_refresh: bool = False,
+        skills: Optional[List[str]] = None,
     ) -> TaskInfo:
         """
         Submit a single analysis task.
@@ -329,6 +333,7 @@ class AnalysisTaskQueue:
             selection_source=selection_source,
             report_type=report_type,
             force_refresh=force_refresh,
+            skills=skills,
         )
         if duplicates:
             raise duplicates[0]
@@ -343,6 +348,7 @@ class AnalysisTaskQueue:
         report_type: str = "detailed",
         force_refresh: bool = False,
         notify: bool = True,
+        skills: Optional[List[str]] = None,
     ) -> Tuple[List[TaskInfo], List[DuplicateTaskError]]:
         """
         Submit analysis tasks in batch.
@@ -370,6 +376,7 @@ class AnalysisTaskQueue:
                     continue
 
                 task_id = uuid.uuid4().hex
+                task_skills = list(skills) if skills is not None else None
                 task_info = TaskInfo(
                     task_id=task_id,
                     stock_code=stock_code,
@@ -379,6 +386,7 @@ class AnalysisTaskQueue:
                     report_type=report_type,
                     original_query=original_query,
                     selection_source=selection_source,
+                    skills=task_skills,
                 )
                 self._tasks[task_id] = task_info
                 self._analyzing_stocks[dedupe_key] = task_id
@@ -391,6 +399,7 @@ class AnalysisTaskQueue:
                         report_type,
                         force_refresh,
                         notify,
+                        task_skills,
                     )
                 except Exception:
                     # Roll back the current batch to avoid partial submission.
@@ -573,6 +582,7 @@ class AnalysisTaskQueue:
         report_type: str,
         force_refresh: bool,
         notify: bool = True,
+        skills: Optional[List[str]] = None,
     ) -> Optional[Dict[str, Any]]:
         """
         执行分析任务（在线程池中运行）
@@ -615,6 +625,7 @@ class AnalysisTaskQueue:
                 query_id=task_id,
                 send_notification=notify,
                 progress_callback=_on_progress,
+                skills=skills,
             )
             
             if result:

tests/test_agent_pipeline.py

@@ -1248,6 +1248,46 @@ class TestPipelineRouting(unittest.TestCase):
             # Instead, verify analyzer.analyze was called (legacy path)
             pipeline.analyzer.analyze.assert_called_once()
 
+    def test_request_skills_auto_enable_agent_mode(self):
+        """Request-specific skills should route the stock analysis through Agent mode."""
+        with patch('src.core.pipeline.get_config') as mock_config, \
+             patch('src.core.pipeline.get_db'), \
+             patch('src.core.pipeline.DataFetcherManager'), \
+             patch('src.core.pipeline.GeminiAnalyzer'), \
+             patch('src.core.pipeline.NotificationService'), \
+             patch('src.core.pipeline.SearchService'):
+
+            mock_cfg = MagicMock()
+            mock_cfg.max_workers = 2
+            mock_cfg.agent_mode = False
+            mock_cfg.agent_max_steps = 5
+            mock_cfg.agent_skills = []
+            mock_cfg.bocha_api_keys = []
+            mock_cfg.tavily_api_keys = []
+            mock_cfg.brave_api_keys = []
+            mock_cfg.serpapi_keys = []
+            mock_cfg.searxng_base_urls = []
+            mock_cfg.searxng_public_instances_enabled = False
+            mock_cfg.news_max_age_days = 7
+            mock_cfg.enable_realtime_quote = True
+            mock_cfg.enable_chip_distribution = True
+            mock_cfg.realtime_source_priority = []
+            mock_cfg.save_context_snapshot = False
+            mock_config.return_value = mock_cfg
+
+            from src.core.pipeline import StockAnalysisPipeline
+            from src.enums import ReportType
+            pipeline = StockAnalysisPipeline(
+                config=mock_cfg,
+                analysis_skills=["growth_quality"],
+            )
+            pipeline._analyze_with_agent = MagicMock(return_value=None)
+
+            pipeline.analyze_stock("600519", ReportType.SIMPLE, "q1")
+
+            pipeline._analyze_with_agent.assert_called_once()
+            self.assertEqual(pipeline.analysis_skills, ["growth_quality"])
+
 
 class TestAnalyzeWithAgentStockName(unittest.TestCase):
     """Test stock-name handling in _analyze_with_agent."""

tests/test_analysis_api_contract.py

@@ -537,6 +537,26 @@ class AnalysisApiContractTestCase(unittest.TestCase):
 
         )
 
+    def test_analysis_service_passes_request_skills_to_pipeline(self) -> None:
+        service = object.__new__(AnalysisService)
+        pipeline_instance = MagicMock()
+        pipeline_instance.process_single_stock.return_value = object()
+        request_skills = ["growth_quality"]
+
+        with patch("src.config.get_config", return_value=SimpleNamespace()), \
+             patch("src.core.pipeline.StockAnalysisPipeline", return_value=pipeline_instance) as pipeline_cls, \
+             patch.object(AnalysisService, "_build_analysis_response", return_value={"stock_code": "600519"}):
+            result = AnalysisService.analyze_stock(
+                service,
+                "600519",
+                report_type="full",
+                query_id="q1",
+                skills=request_skills,
+            )
+
+        self.assertEqual(result, {"stock_code": "600519"})
+        self.assertEqual(pipeline_cls.call_args.kwargs["analysis_skills"], request_skills)
+
     def test_report_type_full_is_preserved_in_response_metadata(self) -> None:
         service = AnalysisService()
         pipeline_instance = MagicMock()
@@ -1524,6 +1544,42 @@ class BatchTaskQueueContractTestCase(unittest.TestCase):
         self.assertEqual(duplicates, [])
         self.assertEqual(sorted(task.stock_code for task in queue._tasks.values()), ["600519"])
 
+    def test_batch_submit_and_worker_use_copied_request_skills(self) -> None:
+        class CapturingExecutor:
+            def __init__(self) -> None:
+                self.calls = []
+
+            def submit(self, fn, *args, **kwargs):
+                self.calls.append((fn, args, kwargs))
+                return Future()
+
+        queue = AnalysisTaskQueue(max_workers=1)
+        executor = CapturingExecutor()
+        queue._executor = executor
+        request_skills = ["growth_quality"]
+
+        accepted, duplicates = queue.submit_tasks_batch(
+            ["600519"],
+            report_type="detailed",
+            skills=request_skills,
+        )
+        request_skills.append("mutated_after_submit")
+
+        self.assertEqual(duplicates, [])
+        self.assertEqual(accepted[0].skills, ["growth_quality"])
+        self.assertIs(executor.calls[0][1][-1], accepted[0].skills)
+
+        service_instance = MagicMock()
+        service_instance.analyze_stock.return_value = {"stock_name": "贵州茅台"}
+        with patch("src.services.analysis_service.AnalysisService", return_value=service_instance):
+            executor.calls[0][0](*executor.calls[0][1])
+
+        self.assertIs(
+            service_instance.analyze_stock.call_args.kwargs["skills"],
+            accepted[0].skills,
+        )
+        self.assertEqual(service_instance.analyze_stock.call_args.kwargs["skills"], ["growth_quality"])
+
     def test_batch_submit_deduplicates_equivalent_stock_code_shapes(self) -> None:
         queue = AnalysisTaskQueue(max_workers=1)
         queue._executor = type("ExecutorStub", (), {"submit": lambda self, *args, **kwargs: Future()})()

tests/test_autocomplete_pr0.py

@@ -44,6 +44,20 @@ class TestAnalyzeRequest:
         assert request.original_query is None
         assert request.selection_source is None
 
+    def test_analyze_request_accepts_strategy_skills_alias(self):
+        """Test analysis requests accept both skills and legacy strategies."""
+        request = AnalyzeRequest(
+            stock_code="600519",
+            skills=["growth_quality"],
+        )
+        assert request.skills == ["growth_quality"]
+
+        legacy_request = AnalyzeRequest(
+            stock_code="600519",
+            strategies=["event_driven"],
+        )
+        assert legacy_request.skills == ["event_driven"]
+
     def test_analyze_request_validation_selection_source(self):
         """Test selection_source field validation"""
         # Valid selection_source values