1

backend/app/api/recommendations.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import logging
+import traceback
 from datetime import datetime
 from fastapi import APIRouter
 
@@ -114,6 +115,8 @@ async def _run_scan_background(scan_session: str):
         })
     except Exception as e:
         logger.error(f"后台扫描失败: {e}")
+        from app.db.error_logger import log_error
+        await log_error("recommender_api", f"后台扫描失败: {e}", detail=traceback.format_exc())
         await broadcast_update({
             "type": "scan_error",
             "session": scan_session,

backend/app/db/database.py

@@ -2,11 +2,21 @@
 
 from contextlib import asynccontextmanager
 from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
+from sqlalchemy import event
 from app.config import settings
 
 # SQLite 异步需要 aiosqlite
 db_url = settings.database_url.replace("sqlite:///", "sqlite+aiosqlite:///")
 engine = create_async_engine(db_url, echo=False)
+
+# 启用 WAL 模式：允许读写并发，写操作不阻塞读操作
+@event.listens_for(engine.sync_engine, "connect")
+def _set_sqlite_pragma(dbapi_connection, connection_record):
+    cursor = dbapi_connection.cursor()
+    cursor.execute("PRAGMA journal_mode=WAL")
+    cursor.execute("PRAGMA busy_timeout=5000")  # 写锁最多等待 5 秒
+    cursor.close()
+
 async_session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
 
 
@@ -26,8 +36,6 @@ async def init_db():
         await conn.run_sync(metadata.create_all)
         # 补充新增列（SQLite ALTER TABLE ADD COLUMN，已存在会忽略）
         for col_sql in [
-            "ALTER TABLE recommendations ADD COLUMN supply_demand_score REAL DEFAULT 0",
-            "ALTER TABLE recommendations ADD COLUMN price_action_score REAL DEFAULT 0",
             "ALTER TABLE recommendations ADD COLUMN supply_demand_score REAL DEFAULT 0",
             "ALTER TABLE recommendations ADD COLUMN price_action_score REAL DEFAULT 0",
             "ALTER TABLE recommendations ADD COLUMN position_score REAL",

backend/app/engine/recommender.py

@@ -39,7 +39,7 @@ async def refresh_recommendations(trade_date: str = None, scan_session: str = "m
                 rec.scan_session = scan_session
                 rec.created_at = datetime.now()
 
-            # 持久化到数据库
+            # 持久化到数据库（这是 async 操作，需要在主线程中执行）
             await _save_to_db(result)
 
             # 更新历史推荐跟踪（检查之前推荐的后续表现）
@@ -392,73 +392,79 @@ async def _save_to_db(result: dict):
                     "temp": mt.temperature,
                 })
 
-            # 保存板块热度（先清除同一 trade_date 的旧数据，避免重复）
+            # 保存板块热度（先清除同一 trade_date 的旧数据，再批量插入）
             trade_date_val = mt.trade_date if mt else ""
             if trade_date_val:
                 await db.execute(
                     text("DELETE FROM sector_heat WHERE trade_date = :td"),
                     {"td": trade_date_val},
                 )
-            for sector in result.get("hot_sectors", []):
+            sector_values = [
-                stmt = tables.sector_heat_table.insert().values(
+                {
-                    sector_code=sector.sector_code,
+                    "sector_code": sector.sector_code,
-                    sector_name=sector.sector_name,
+                    "sector_name": sector.sector_name,
-                    pct_change=sector.pct_change,
+                    "pct_change": sector.pct_change,
-                    capital_inflow=sector.capital_inflow,
+                    "capital_inflow": sector.capital_inflow,
-                    limit_up_count=sector.limit_up_count,
+                    "limit_up_count": sector.limit_up_count,
-                    heat_score=sector.heat_score,
+                    "heat_score": sector.heat_score,
-                    stage=sector.stage,
+                    "stage": sector.stage,
-                    days_continuous=sector.days_continuous,
+                    "days_continuous": sector.days_continuous,
-                    member_count=sector.member_count,
+                    "member_count": sector.member_count,
-                    leading_stocks=json.dumps(sector.leading_stocks, ensure_ascii=False),
+                    "leading_stocks": json.dumps(sector.leading_stocks, ensure_ascii=False),
-                    pct_trend=json.dumps(sector.pct_trend, ensure_ascii=False),
+                    "pct_trend": json.dumps(sector.pct_trend, ensure_ascii=False),
-                    turnover_avg=sector.turnover_avg,
+                    "turnover_avg": sector.turnover_avg,
-                    main_force_ratio=sector.main_force_ratio,
+                    "main_force_ratio": sector.main_force_ratio,
-                    trade_date=trade_date_val,
+                    "trade_date": trade_date_val,
-                )
+                }
-                await db.execute(stmt)
+                for sector in result.get("hot_sectors", [])
+            ]
+            if sector_values:
+                await db.execute(tables.sector_heat_table.insert(), sector_values)
 
-            # 保存推荐（按 ts_code 清除当日旧记录，避免同一天多次扫描产生重复）
+            # 保存推荐：先批量清除当日旧记录，再批量插入
             today_str = datetime.now().strftime("%Y-%m-%d")
             now_dt = datetime.now()
-            saved_count = 0
+            qualified_recs = [rec for rec in result.get("recommendations", []) if rec.score >= 60]
-            for rec in result.get("recommendations", []):
+            if qualified_recs:
-                if rec.score < 60:
+                # 批量删除当日同一 ts_code 的旧记录
-                    continue
+                codes = [rec.ts_code for rec in qualified_recs]
                 await db.execute(
-                    text("DELETE FROM recommendations WHERE date(created_at) = :today AND ts_code = :code"),
+                    text("DELETE FROM recommendations WHERE date(created_at) = :today AND ts_code IN :codes"),
-                    {"today": today_str, "code": rec.ts_code},
+                    {"today": today_str, "codes": tuple(codes)},
                 )
-                stmt = tables.recommendations_table.insert().values(
+                # 批量插入新记录
-                    ts_code=rec.ts_code,
+                rec_values = [
-                    name=rec.name,
+                    {
-                    sector=rec.sector,
+                        "ts_code": rec.ts_code,
-                    score=rec.score,
+                        "name": rec.name,
-                    market_temp_score=rec.market_temp_score,
+                        "sector": rec.sector,
-                    sector_score=rec.sector_score,
+                        "score": rec.score,
-                    capital_score=rec.capital_score,
+                        "market_temp_score": rec.market_temp_score,
-                    technical_score=rec.technical_score,
+                        "sector_score": rec.sector_score,
-                    supply_demand_score=rec.supply_demand_score,
+                        "capital_score": rec.capital_score,
-                    price_action_score=rec.price_action_score,
+                        "technical_score": rec.technical_score,
-                    position_score=rec.position_score,
+                        "supply_demand_score": rec.supply_demand_score,
-                    valuation_score=rec.valuation_score,
+                        "price_action_score": rec.price_action_score,
-                    signal=rec.signal,
+                        "position_score": rec.position_score,
-                    entry_price=rec.entry_price,
+                        "valuation_score": rec.valuation_score,
-                    target_price=rec.target_price,
+                        "signal": rec.signal,
-                    stop_loss=rec.stop_loss,
+                        "entry_price": rec.entry_price,
-                    reasons=json.dumps(rec.reasons, ensure_ascii=False),
+                        "target_price": rec.target_price,
-                    llm_analysis=rec.llm_analysis,
+                        "stop_loss": rec.stop_loss,
-                    strategy=rec.strategy,
+                        "reasons": json.dumps(rec.reasons, ensure_ascii=False),
-                    entry_signal_type=rec.entry_signal_type,
+                        "llm_analysis": rec.llm_analysis,
-                    llm_score=rec.llm_score,
+                        "strategy": rec.strategy,
-                    scan_session=rec.scan_session,
+                        "entry_signal_type": rec.entry_signal_type,
-                    created_at=now_dt,
+                        "llm_score": rec.llm_score,
-                )
+                        "scan_session": rec.scan_session,
-                await db.execute(stmt)
+                        "created_at": now_dt,
-                saved_count += 1
+                    }
+                    for rec in qualified_recs
+                ]
+                await db.execute(tables.recommendations_table.insert(), rec_values)
 
             await db.commit()
-            logger.info(f"已保存 {saved_count} 条推荐到数据库（共 {len(result.get('recommendations', []))} 条，过滤掉 <60 分）")
+            logger.info(f"已保存 {len(qualified_recs)} 条推荐到数据库（共 {len(result.get('recommendations', []))} 条，过滤掉 <60 分）")
     except Exception as e:
         logger.error(f"保存推荐到数据库失败: {e}")
         from app.db.error_logger import log_error

backend/app/engine/screener.py

@@ -17,6 +17,7 @@
 止损止盈：基于市场结构（阻力位/支撑MA/近期低点），而非固定百分比。
 """
 
+import asyncio
 import logging
 import traceback
 

frontend/.next/app-build-manifest.json

@@ -1,21 +1,5 @@
 {
   "pages": {
-    "/(public)/page": [
-      "static/chunks/webpack.js",
-      "static/chunks/main-app.js",
-      "static/chunks/app/(public)/page.js"
-    ],
-    "/(public)/layout": [
-      "static/chunks/webpack.js",
-      "static/chunks/main-app.js",
-      "static/chunks/app/(public)/layout.js"
-    ],
-    "/layout": [
-      "static/chunks/webpack.js",
-      "static/chunks/main-app.js",
-      "static/css/app/layout.css",
-      "static/chunks/app/layout.js"
-    ],
     "/(auth)/dashboard/page": [
       "static/chunks/webpack.js",
       "static/chunks/main-app.js",
@@ -26,6 +10,12 @@
       "static/chunks/main-app.js",
       "static/chunks/app/(auth)/layout.js"
     ],
+    "/layout": [
+      "static/chunks/webpack.js",
+      "static/chunks/main-app.js",
+      "static/css/app/layout.css",
+      "static/chunks/app/layout.js"
+    ],
     "/(auth)/recommendations/page": [
       "static/chunks/webpack.js",
       "static/chunks/main-app.js",
@@ -35,6 +25,11 @@
       "static/chunks/webpack.js",
       "static/chunks/main-app.js",
       "static/chunks/app/(auth)/settings/page.js"
+    ],
+    "/_not-found/page": [
+      "static/chunks/webpack.js",
+      "static/chunks/main-app.js",
+      "static/chunks/app/_not-found/page.js"
     ]
   }
 }

frontend/.next/server/app-paths-manifest.json

@@ -1,5 +1,5 @@
 {
-  "/(public)/page": "app/(public)/page.js",
+  "/_not-found/page": "app/_not-found/page.js",
   "/(auth)/dashboard/page": "app/(auth)/dashboard/page.js",
   "/(auth)/recommendations/page": "app/(auth)/recommendations/page.js",
   "/(auth)/settings/page": "app/(auth)/settings/page.js"

frontend/.next/server/interception-route-rewrite-manifest.js

@@ -1 +1 @@
-self.__INTERCEPTION_ROUTE_REWRITE_MANIFEST="[]"
+self.__INTERCEPTION_ROUTE_REWRITE_MANIFEST="[]";

frontend/.next/server/server-reference-manifest.json

@@ -1,5 +1,5 @@
 {
   "node": {},
   "edge": {},
-  "encryptionKey": "q9MinLsRBzgOwAUsxhHLGTEgY9kBDWvoZzf7iynqzyI="
+  "encryptionKey": "f4eykmt9lLjeIDNHjaA0ZKJupk05dXT0k2cBaExPwP8="
 }

frontend/src/app/(auth)/dashboard/page.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import { useEffect, useState, useCallback } from "react";
+import { useEffect, useState, useCallback, useRef } from "react";
 import { fetchAPI, postAPI } from "@/lib/api";
 import type { LatestResult, SectorData, IndexOverview, DailyReviewResponse } from "@/lib/api";
 import MarketTemp from "@/components/market-temp";
@@ -17,6 +17,8 @@ interface ScanStatus {
   description: string;
 }
 
+const SCAN_TIMEOUT_MS = 120_000; // 扫描超时：120秒后自动刷新数据
+
 export default function DashboardPage() {
   const { user } = useAuth();
   const [data, setData] = useState<LatestResult | null>(null);
@@ -29,6 +31,7 @@ export default function DashboardPage() {
   const [indices, setIndices] = useState<IndexOverview[]>([]);
   const [dailyReview, setDailyReview] = useState<string | null>(null);
   const [generatingReview, setGeneratingReview] = useState(false);
+  const scanTimeoutRef = useRef<ReturnType<typeof setTimeout> | null>(null);
 
   const loadData = useCallback(async () => {
     try {
@@ -55,12 +58,21 @@ export default function DashboardPage() {
     }
   }, []);
 
+  // 清除扫描超时计时器
+  const clearScanTimeout = useCallback(() => {
+    if (scanTimeoutRef.current) {
+      clearTimeout(scanTimeoutRef.current);
+      scanTimeoutRef.current = null;
+    }
+  }, []);
+
   useEffect(() => {
     loadData();
   }, [loadData]);
 
   useWebSocket(
     useCallback((msg: { type: string; count?: number; scan_mode?: string; message?: string }) => {
+      clearScanTimeout();
       if (msg.type === "scan_update") {
         const modeLabel = msg.scan_mode === "intraday" ? "盘中实时" : "盘后";
         setRefreshResult(`${modeLabel}扫描完成，发现 ${msg.count ?? 0} 只股票`);
@@ -75,7 +87,7 @@ export default function DashboardPage() {
         // 其他消息类型（如 llm_analysis_ready），刷新数据
         loadData();
       }
-    }, [loadData]),
+    }, [loadData, clearScanTimeout]),
     ["scan_update", "scan_error", "llm_analysis_ready", "sector_scan_ready", "scan_complete"]
   );
 
@@ -96,6 +108,14 @@ export default function DashboardPage() {
         setRefreshResult("扫描已启动，完成后自动刷新...");
         // 保持 refreshing，等待 WS 推送
       }
+
+      // 设置超时：如果 120 秒内没收到 WebSocket 消息，自动停止加载状态并刷新数据
+      scanTimeoutRef.current = setTimeout(() => {
+        setRefreshResult("扫描超时，已自动刷新数据");
+        setRefreshing(false);
+        loadData();
+        setTimeout(() => setRefreshResult(null), 5000);
+      }, SCAN_TIMEOUT_MS);
     } catch (e) {
       console.error("触发扫描失败:", e);
       setRefreshResult("触发扫描失败，请重试");
@@ -104,6 +124,11 @@ export default function DashboardPage() {
     }
   };
 
+  // 清理超时计时器
+  useEffect(() => {
+    return () => clearScanTimeout();
+  }, [clearScanTimeout]);
+
   if (loading) {
     return (
       <div className="max-w-7xl mx-auto px-4 md:px-8 pt-6 space-y-5">

frontend/src/app/(auth)/recommendations/page.tsx

@@ -1,10 +1,9 @@
 "use client";
 
 import { useEffect, useState, useCallback } from "react";
-import { fetchAPI, postAPI } from "@/lib/api";
+import { fetchAPI } from "@/lib/api";
 import type { DayGroup, PerformanceStats } from "@/lib/api";
 import StockCard from "@/components/stock-card";
-import { useWebSocket } from "@/hooks/use-websocket";
 
 function formatDate(dateStr: string): string {
   const d = new Date(dateStr);
@@ -23,20 +22,15 @@ export default function RecommendationsPage() {
   const [dayGroups, setDayGroups] = useState<DayGroup[]>([]);
   const [expandedDays, setExpandedDays] = useState<Set<string>>(new Set());
   const [filter, setFilter] = useState<string>("all");
-  const [llmEnabled, setLlmEnabled] = useState(false);
-  const [refreshing, setRefreshing] = useState(false);
-  const [refreshResult, setRefreshResult] = useState<string | null>(null);
   const [performance, setPerformance] = useState<PerformanceStats | null>(null);
 
   const loadData = useCallback(async () => {
     try {
-      const [history, health, perf] = await Promise.all([
+      const [history, perf] = await Promise.all([
         fetchAPI<DayGroup[]>("/api/recommendations/history?days=14"),
-        fetchAPI<{ llm_enabled: boolean }>("/api/health"),
         fetchAPI<PerformanceStats>("/api/recommendations/performance").catch(() => null),
       ]);
       setDayGroups(history);
-      setLlmEnabled(health.llm_enabled);
       setPerformance(perf);
 
       // 默认展开最近一天
@@ -56,50 +50,6 @@ export default function RecommendationsPage() {
     loadData();
   }, [loadData]);
 
-  // WebSocket 监听扫描结果
-  useWebSocket(
-    useCallback((data: { type: string; session?: string; count?: number; temperature?: number; scan_mode?: string; message?: string }) => {
-      if (data.type === "scan_update") {
-        const modeLabel = data.scan_mode === "intraday" ? "盘中实时" : "盘后";
-        setRefreshResult(`${modeLabel}扫描完成，发现 ${data.count ?? 0} 只股票`);
-        setRefreshing(false);
-        loadData();
-        setTimeout(() => setRefreshResult(null), 5000);
-      } else if (data.type === "scan_error") {
-        setRefreshResult("扫描失败，请重试");
-        setRefreshing(false);
-        setTimeout(() => setRefreshResult(null), 5000);
-      }
-    }, [loadData]),
-    ["scan_update", "scan_error"]
-  );
-
-  const handleRefresh = async () => {
-    setRefreshing(true);
-    setRefreshResult(null);
-    try {
-      const res = await postAPI<{
-        status: string;
-        message?: string;
-        is_trading: boolean;
-      }>("/api/recommendations/refresh?scan_session=manual");
-
-      if (res.status === "already_running") {
-        setRefreshResult(res.message || "扫描正在执行中，请稍候");
-        setTimeout(() => setRefreshResult(null), 5000);
-        // 不关闭 refreshing，等待 WS 推送完成通知
-      } else if (res.status === "scanning") {
-        setRefreshResult("扫描已启动，完成后自动刷新...");
-        // 保持 refreshing 状态，等待 WS 推送
-      }
-    } catch (e) {
-      console.error("触发扫描失败:", e);
-      setRefreshResult("触发扫描失败，请重试");
-      setRefreshing(false);
-      setTimeout(() => setRefreshResult(null), 5000);
-    }
-  };
-
   const toggleDay = (date: string) => {
     setExpandedDays((prev) => {
       const next = new Set(prev);
@@ -135,30 +85,8 @@ export default function RecommendationsPage() {
             <span className="font-mono tabular-nums ml-1">{dayGroups.length}</span> 天记录
           </p>
         </div>
-        <button
-          onClick={handleRefresh}
-          disabled={refreshing}
-          className="text-xs px-3 py-1.5 sm:px-4 sm:py-2 bg-gradient-to-r from-amber-500/20 to-amber-600/15 text-amber-400 rounded-lg sm:rounded-xl hover:from-amber-500/30 hover:to-amber-600/25 disabled:opacity-40 transition-all duration-200 border border-amber-500/10 font-medium self-start sm:self-auto"
-        >
-          {refreshing ? (
-            <span className="inline-flex items-center gap-1.5">
-              <span className="w-3 h-3 border border-amber-400/40 border-t-amber-400 rounded-full animate-spin" />
-              扫描中...
-            </span>
-          ) : (
-            "立即扫描"
-          )}
-        </button>
       </div>
 
-      {/* Scan result toast */}
-      {refreshResult && (
-        <div className="glass-card-static border-amber-500/15 px-4 py-2.5 text-xs text-amber-400 animate-fade-in-up flex items-center gap-2 mb-5">
-          <span className="w-1 h-1 rounded-full bg-amber-400" />
-          {refreshResult}
-        </div>
-      )}
-
       {/* Performance Stats */}
       {performance && performance.total_recommendations > 0 && (
         <div className="glass-card-static p-4 mb-5 animate-fade-in-up">
@@ -316,7 +244,7 @@ export default function RecommendationsPage() {
                         className="animate-fade-in-up"
                         style={{ animationDelay: `${i * 40}ms` }}
                       >
-                        <StockCard rec={rec} showLLMLoading={isToday && llmEnabled} />
+                        <StockCard rec={rec} />
                       </div>
                     ))}
                   </div>