From ddc0180ae6fd02dff5677d7afddd415fa430f2ae Mon Sep 17 00:00:00 2001
From: "2569718930@qq.com" <2569718930@qq.com>
Date: Thu, 23 Apr 2026 23:49:52 +0800
Subject: [PATCH] Implement EMOS scan terminal with REST-only market data

---
 frontend/app/api/scan/terminal/route.ts       |   65 +
 .../components/dashboard/DashboardEntry.tsx   |    8 +-
 .../components/dashboard/OpportunityTable.tsx |  298 +-
 .../dashboard/PolyWeatherDashboard.tsx        | 3315 +----------------
 .../components/dashboard/ScanFilterPanel.tsx  |   98 +-
 frontend/components/dashboard/ScanKPIBar.tsx  |   43 +-
 .../dashboard/ScanTerminalDashboard.tsx       |  515 +++
 frontend/lib/dashboard-client.ts              |   38 +
 frontend/lib/dashboard-types.ts               |  128 +
 src/data_collection/polymarket_readonly.py    |  961 ++++-
 tests/test_polymarket_readonly.py             |  190 +
 tests/test_web_observability.py               |   64 +
 web/analysis_service.py                       |   13 +
 web/routes.py                                 |   34 +
 web/scan_terminal_service.py                  |  358 ++
 15 files changed, 2543 insertions(+), 3585 deletions(-)
 create mode 100644 frontend/app/api/scan/terminal/route.ts
 create mode 100644 frontend/components/dashboard/ScanTerminalDashboard.tsx
 create mode 100644 web/scan_terminal_service.py

diff --git a/frontend/app/api/scan/terminal/route.ts b/frontend/app/api/scan/terminal/route.ts
new file mode 100644
index 00000000..553f6bf4
--- /dev/null
+++ b/frontend/app/api/scan/terminal/route.ts
@@ -0,0 +1,65 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  const params = new URLSearchParams();
+  for (const key of [
+    "scan_mode",
+    "min_price",
+    "max_price",
+    "min_edge_pct",
+    "min_liquidity",
+    "high_liquidity_only",
+    "market_type",
+    "time_range",
+    "limit",
+    "force_refresh",
+  ]) {
+    const value = req.nextUrl.searchParams.get(key);
+    if (value != null && value !== "") {
+      params.set(key, value);
+    }
+  }
+
+  const url = `${API_BASE}/api/scan/terminal?${params.toString()}`;
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(url, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
+        { status: 502 },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data, {
+      headers: {
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch scan terminal data", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}
diff --git a/frontend/components/dashboard/DashboardEntry.tsx b/frontend/components/dashboard/DashboardEntry.tsx
index ee914549..74f53fda 100644
--- a/frontend/components/dashboard/DashboardEntry.tsx
+++ b/frontend/components/dashboard/DashboardEntry.tsx
@@ -3,10 +3,10 @@
 import dynamic from "next/dynamic";
 import { DashboardShellSkeleton } from "@/components/dashboard/DashboardShellSkeleton";
 
-const PolyWeatherDashboard = dynamic(
+const ScanTerminalDashboard = dynamic(
   () =>
-    import("@/components/dashboard/PolyWeatherDashboard").then(
-      (module) => module.PolyWeatherDashboard,
+    import("@/components/dashboard/ScanTerminalDashboard").then(
+      (module) => module.ScanTerminalDashboard,
     ),
   {
     ssr: false,
@@ -15,5 +15,5 @@ const PolyWeatherDashboard = dynamic(
 );
 
 export function DashboardEntry() {
-  return <PolyWeatherDashboard />;
+  return <ScanTerminalDashboard />;
 }
diff --git a/frontend/components/dashboard/OpportunityTable.tsx b/frontend/components/dashboard/OpportunityTable.tsx
index 5bdafc02..355fdbf3 100644
--- a/frontend/components/dashboard/OpportunityTable.tsx
+++ b/frontend/components/dashboard/OpportunityTable.tsx
@@ -1,160 +1,63 @@
 "use client";
 
-import React, { useMemo } from "react";
-import Image from "next/image";
+import React from "react";
 import { Star } from "lucide-react";
 import { useI18n } from "@/hooks/useI18n";
-import { useDashboardStore } from "@/hooks/useDashboardStore";
-import type {
-  CityDetail,
-  CityListItem,
-  CitySummary,
-} from "@/lib/dashboard-types";
-import { getLocalizedCityDisplay } from "@/lib/dashboard-home-copy";
+import type { ScanOpportunityRow } from "@/lib/dashboard-types";
+import { getLocalizedCityName } from "@/lib/dashboard-home-copy";
 
-interface OpportunityRow {
-  rank: number;
-  city: CityListItem;
-  summary?: CitySummary | null;
-  detail?: CityDetail | null;
-  score: number;
-  tradable: boolean;
-}
-
-function getTodayHighLabel(detail?: CityDetail | null, symbol = "°C"): string {
-  const current = detail?.current;
-  if (current?.temp != null) return `${current.temp.toFixed(1)}${symbol}`;
-  return "--";
-}
-
-function getTimeLabel(detail?: CityDetail | null): string {
-  if (!detail?.local_time) return "--";
-  return detail.local_time;
-}
-
-function getTimezone(detail?: CityDetail | null): string {
-  if (detail?.utc_offset_seconds != null) {
-    const hours = Math.round(detail.utc_offset_seconds / 3600);
-    return `UTC${hours >= 0 ? "+" : ""}${hours}`;
+function getStatusMeta(
+  row: ScanOpportunityRow,
+  locale: string,
+): { label: string; tone: "green" | "amber" | "purple" | "neutral" } {
+  const phase = String(row.window_phase || "").toLowerCase();
+  if (phase === "active_peak" || phase === "setup_today") {
+    return { label: locale === "en-US" ? "Tradable" : "可交易", tone: "green" };
   }
-  return "";
-}
-
-function getMarketVolume(detail?: CityDetail | null): string {
-  const vol =
-    detail?.market_scan?.volume ?? detail?.market_scan?.primary_market?.volume;
-  if (vol == null) return "--";
-  if (vol >= 1_000_000) return `$${(vol / 1_000_000).toFixed(1)}M`;
-  if (vol >= 1_000) return `$${Math.round(vol / 1_000)}K`;
-  return `$${vol.toFixed(0)}`;
-}
-
-function getScanStatus(
-  detail?: CityDetail | null,
-  locale = "zh-CN",
-): { label: string; tone: string } {
-  const scan = detail?.market_scan;
-  if (!scan?.available)
-    return {
-      label: locale === "en-US" ? "No Market" : "无盘口",
-      tone: "neutral",
-    };
-  const pa = scan?.price_analysis;
-  const bestSide = pa?.best_side;
-  const sideView = bestSide === "no" ? pa?.no : pa?.yes;
-  const edgeVal =
-    sideView?.edge != null
-      ? Math.abs(Number(sideView.edge)) > 1
-        ? Number(sideView.edge) / 100
-        : Number(sideView.edge)
-      : null;
-  if (edgeVal != null && edgeVal >= 0.05)
-    return {
-      label: locale === "en-US" ? "Touch Play" : "触达博弈",
-      tone: "amber",
-    };
-  if (edgeVal != null && edgeVal >= 0.02)
-    return {
-      label: locale === "en-US" ? "Tradable" : "即时确认",
-      tone: "green",
-    };
-  if (edgeVal != null && edgeVal > 0)
+  if (phase === "tomorrow" || phase === "week_ahead") {
     return { label: locale === "en-US" ? "Early" : "早期机会", tone: "purple" };
-  return { label: locale === "en-US" ? "Market" : "市场", tone: "neutral" };
-}
-
-function getBestAction(detail?: CityDetail | null, locale = "zh-CN"): string {
-  const scan = detail?.market_scan;
-  if (!scan?.available) return "--";
-  const pa = scan?.price_analysis;
-  const side = pa?.best_side;
-  const topBucket = scan?.top_buckets?.[0];
-  const label = topBucket?.label || topBucket?.slug || "";
-  if (side === "yes" && label)
-    return `${locale === "en-US" ? "Buy" : "买入"} Yes ${label}`;
-  if (side === "no" && label)
-    return `${locale === "en-US" ? "Buy" : "买入"} No ${label}`;
-  return "--";
-}
-
-function getEdge(detail?: CityDetail | null): number | null {
-  const pa = detail?.market_scan?.price_analysis;
-  const bestSide = pa?.best_side;
-  const sideView = bestSide === "no" ? pa?.no : pa?.yes;
-  const edge = sideView?.edge;
-  if (edge == null) return null;
-  const val = Number(edge);
-  return Math.abs(val) > 1 ? val / 100 : val;
-}
-
-function getProbabilityBuckets(
-  detail?: CityDetail | null,
-): Array<{ label: string; probability: number }> {
-  const view = detail?.probabilities?.distribution || [];
-  if (!Array.isArray(view)) return [];
-  return view.slice(0, 8).map((b) => ({
-    label: String(b.label || b.value || ""),
-    probability: Number(b.probability || 0),
-  }));
-}
-
-function MiniProbabilityChart({
-  buckets,
-}: {
-  buckets: Array<{ label: string; probability: number }>;
-}) {
-  if (!buckets.length) return <span className="scan-no-data">--</span>;
-  const maxProb = Math.max(...buckets.map((b) => b.probability), 0.01);
-  return (
-    <div className="scan-mini-chart">
-      {buckets.map((bucket, i) => (
-        <div key={i} className="scan-mini-bar-col">
-          <div
-            className="scan-mini-bar"
-            style={{
-              height: `${Math.max((bucket.probability / maxProb) * 100, 4)}%`,
-            }}
-            title={`${bucket.label}: ${(bucket.probability * 100).toFixed(1)}%`}
-          />
-          <span className="scan-mini-bar-label">
-            {bucket.label.replace(/[°CF]/g, "")}
-          </span>
-        </div>
-      ))}
-    </div>
-  );
-}
-
-function getDisplayScore(rawScore: number): number {
-  if (rawScore <= 0) return Math.max(0, 70 + rawScore / 1000);
-  if (rawScore > 1000) {
-    return Math.min(100, 80 + (rawScore - 1000) / 30);
   }
-  return Math.min(80, Math.max(0, (rawScore / 1000) * 80));
+  if (phase === "post_peak") {
+    return { label: locale === "en-US" ? "Post Peak" : "峰后确认", tone: "amber" };
+  }
+  return { label: locale === "en-US" ? "Watching" : "观察中", tone: "neutral" };
 }
 
-function ScoreRing({ score }: { score: number }) {
-  const displayScore = getDisplayScore(score);
+function formatPercent(value?: number | null, signed = false) {
+  if (value == null || Number.isNaN(Number(value))) return "--";
+  const numeric = Number(value);
+  if (!signed) return `${numeric.toFixed(1)}%`;
+  return `${numeric >= 0 ? "+" : ""}${numeric.toFixed(1)}%`;
+}
+
+function formatTimeBlock(row: ScanOpportunityRow, locale: string) {
+  const parts: string[] = [];
+  if (row.local_time) {
+    parts.push(row.local_time);
+  }
+  if (row.selected_date) {
+    parts.push(row.selected_date);
+  }
+  return parts.join(" · ") || (locale === "en-US" ? "No time" : "暂无时间");
+}
+
+function formatAction(row: ScanOpportunityRow, locale: string) {
+  if (row.side === "yes") {
+    return `${locale === "en-US" ? "Buy" : "买入"} Yes ${row.target_label || ""}`.trim();
+  }
+  if (row.side === "no") {
+    return `${locale === "en-US" ? "Buy" : "买入"} No ${row.target_label || ""}`.trim();
+  }
+  return row.action || "--";
+}
+
+function formatProbability(value?: number | null) {
+  if (value == null || Number.isNaN(Number(value))) return "--";
+  return `${(Number(value) * 100).toFixed(0)}%`;
+}
+
+function ScoreRing({ score }: { score?: number | null }) {
+  const displayScore = Math.max(0, Math.min(100, Number(score || 0)));
   const radius = 20;
   const circumference = 2 * Math.PI * radius;
   const progress = (displayScore / 100) * circumference;
@@ -191,27 +94,33 @@ function ScoreRing({ score }: { score: number }) {
   );
 }
 
-export function OpportunityTable({ rows }: { rows: OpportunityRow[] }) {
+export function OpportunityTable({
+  rows,
+  selectedRowId,
+  onSelectRow,
+}: {
+  rows: ScanOpportunityRow[];
+  selectedRowId?: string | null;
+  onSelectRow?: (row: ScanOpportunityRow) => void;
+}) {
   const { locale } = useI18n();
-  const store = useDashboardStore();
   const isEn = locale === "en-US";
 
   return (
     <div className="scan-table-container">
-      {/* Header */}
       <div className="scan-table-header">
         <span className="scan-th scan-th-rank">#</span>
         <span className="scan-th scan-th-city">
           {isEn ? "City / Market" : "城市 / 市场"}
         </span>
         <span className="scan-th scan-th-time">
-          {isEn ? "Local Time / Status" : "当前时间 / 阶段"}
+          {isEn ? "Time / Phase" : "时间 / 阶段"}
         </span>
         <span className="scan-th scan-th-prob">
-          {isEn ? "Prob. Dist. vs Market" : "模型分布 vs 市场分布"}
+          {isEn ? "EMOS vs Market" : "EMOS vs 市场"}
         </span>
         <span className="scan-th scan-th-action">
-          {isEn ? "Best Action" : "最佳机会"}
+          {isEn ? "Best Action" : "最佳动作"}
         </span>
         <span className="scan-th scan-th-edge">
           {isEn ? "Edge" : "边际优势"}
@@ -222,90 +131,99 @@ export function OpportunityTable({ rows }: { rows: OpportunityRow[] }) {
         <span className="scan-th scan-th-fav" />
       </div>
 
-      {/* Rows */}
-      {rows.map((row) => {
-        const cityName = getLocalizedCityDisplay(
+      {rows.map((row, index) => {
+        const status = getStatusMeta(row, locale);
+        const localizedCityName = getLocalizedCityName(
           row.city,
+          row.city_display_name || row.display_name || row.city,
           locale,
-          row.summary,
-          row.detail,
         );
-        const status = getScanStatus(row.detail, locale);
-        const edge = getEdge(row.detail);
-        const buckets = getProbabilityBuckets(row.detail);
-        const isSelected = store.selectedCity === row.city.name;
-        const symbol = row.detail?.temp_symbol || "°C";
+        const selected = selectedRowId === row.id;
+        const finalScore = Number(row.final_score || 0);
 
         return (
-          <div
-            key={row.city.name}
-            className={`scan-table-row ${isSelected ? "selected" : ""} ${row.tradable ? "tradable" : ""}`}
-            onClick={() => store.focusCity(row.city.name)}
+          <button
+            key={row.id}
+            type="button"
+            className={`scan-table-row ${selected ? "selected" : ""} ${row.tradable ? "tradable" : ""}`}
+            onClick={() => onSelectRow?.(row)}
           >
-            {/* Rank */}
             <span className={`scan-td scan-td-rank rank-${status.tone}`}>
-              <span className="scan-rank-circle">{row.rank}</span>
+              <span className="scan-rank-circle">{row.rank || index + 1}</span>
             </span>
 
-            {/* City */}
             <span className="scan-td scan-td-city">
               <span className="scan-city-thumb">
                 <span className="scan-city-img-placeholder" />
               </span>
               <span className="scan-city-info">
-                <span className="scan-city-name">{cityName}</span>
+                <span className="scan-city-name">{localizedCityName}</span>
                 <span className="scan-city-sub">
-                  {isEn ? "Today's high" : "今日最高温"} ·{" "}
-                  {getMarketVolume(row.detail)}
+                  {row.target_label || row.market_question || "--"}
                 </span>
               </span>
             </span>
 
-            {/* Time + Status */}
             <span className="scan-td scan-td-time">
-              <span className="scan-time-text">
-                {getTimeLabel(row.detail)} ({getTimezone(row.detail)})
-              </span>
+              <span className="scan-time-text">{formatTimeBlock(row, locale)}</span>
               <span className={`scan-status-badge tone-${status.tone}`}>
                 {status.label}
               </span>
             </span>
 
-            {/* Probability Chart */}
             <span className="scan-td scan-td-prob">
-              <MiniProbabilityChart buckets={buckets} />
+              <div className="scan-city-info">
+                <span className="scan-city-name">
+                  {formatProbability(row.model_event_probability)} /{" "}
+                  {formatProbability(row.market_event_probability)}
+                </span>
+                <span className="scan-city-sub">
+                  {isEn ? "Bias" : "偏移"}{" "}
+                  {row.distribution_bias_direction || "--"} ·{" "}
+                  {formatPercent(row.distribution_bias_score)}
+                </span>
+              </div>
             </span>
 
-            {/* Best Action */}
             <span className="scan-td scan-td-action">
               <span className="scan-action-text">
-                {getBestAction(row.detail, locale)}
+                {formatAction(row, locale)}
               </span>
             </span>
 
-            {/* Edge */}
             <span className="scan-td scan-td-edge">
               <span
-                className={`scan-edge-value ${edge != null && edge > 0 ? "positive" : "neutral"}`}
+                className={`scan-edge-value ${finalScore > 0 ? "positive" : "neutral"}`}
               >
-                {edge != null
-                  ? `${edge > 0 ? "+" : ""}${(edge * 100).toFixed(1)}%`
-                  : "--"}
+                {formatPercent(row.edge_percent, true)}
               </span>
             </span>
 
-            {/* Score */}
             <span className="scan-td scan-td-score">
-              <ScoreRing score={row.score} />
+              <ScoreRing score={row.final_score} />
             </span>
 
-            {/* Favorite */}
             <span className="scan-td scan-td-fav">
               <Star size={16} className="scan-fav-icon" />
             </span>
-          </div>
+          </button>
         );
       })}
+
+      {!rows.length ? (
+        <div className="scan-detail-empty">
+          <div>
+            <div className="scan-detail-section-title">
+              {isEn ? "No primary signal" : "当前无主信号"}
+            </div>
+            <p className="scan-city-sub">
+              {isEn
+                ? "No opportunity passed the price, spread, liquidity, and edge filters."
+                : "当前没有机会同时满足价格、点差、流动性和 edge 过滤。"}
+            </p>
+          </div>
+        </div>
+      ) : null}
     </div>
   );
 }
diff --git a/frontend/components/dashboard/PolyWeatherDashboard.tsx b/frontend/components/dashboard/PolyWeatherDashboard.tsx
index e950404b..54c7e0da 100644
--- a/frontend/components/dashboard/PolyWeatherDashboard.tsx
+++ b/frontend/components/dashboard/PolyWeatherDashboard.tsx
@@ -1,3318 +1,7 @@
 "use client";
-import clsx from "clsx";
-import dynamic from "next/dynamic";
-import Image from "next/image";
-import Link from "next/link";
-import {
-  useEffect,
-  useMemo,
-  useRef,
-  useState,
-  type FormEvent,
-  type PointerEvent as ReactPointerEvent,
-} from "react";
-import { Search } from "lucide-react";
-import styles from "./Dashboard.module.css";
-import detailChromeStyles from "./DetailPanelChrome.module.css";
-import modalChromeStyles from "./ModalChrome.module.css";
-import {
-  DashboardStoreProvider,
-  useDashboardStore,
-} from "@/hooks/useDashboardStore";
-import { I18nProvider, useI18n } from "@/hooks/useI18n";
-import { CitySidebar } from "@/components/dashboard/CitySidebar";
-import { HeaderBar } from "@/components/dashboard/HeaderBar";
-import { ScanFilterPanel } from "@/components/dashboard/ScanFilterPanel";
-import { ScanKPIBar } from "@/components/dashboard/ScanKPIBar";
-import { OpportunityTable } from "@/components/dashboard/OpportunityTable";
-import { ProFeaturePaywall } from "@/components/dashboard/ProFeaturePaywall";
-import type {
-  CityDetail,
-  CityListItem,
-  CitySummary,
-  MarketScan,
-  MarketTopBucket,
-  ProbabilityBucket,
-  RiskLevel,
-} from "@/lib/dashboard-types";
-import type {
-  AssistantContextPayload,
-  AssistantOpportunityContext,
-} from "@/lib/dashboard-client";
-import { dashboardClient, getCityRevision } from "@/lib/dashboard-client";
-import {
-  getLocalizedAirportDisplay,
-  getLocalizedCityDisplay,
-} from "@/lib/dashboard-home-copy";
-import {
-  getTemperatureChartData,
-  getWeatherSummary,
-} from "@/lib/dashboard-utils";
 
-const loadHistoryModal = () =>
-  import("@/components/dashboard/HistoryModal").then(
-    (module) => module.HistoryModal,
-  );
-
-const loadFutureForecastModal = () =>
-  import("@/components/dashboard/FutureForecastModal").then(
-    (module) => module.FutureForecastModal,
-  );
-
-const MapCanvas = dynamic(
-  () =>
-    import("@/components/dashboard/MapCanvas").then(
-      (module) => module.MapCanvas,
-    ),
-  {
-    ssr: false,
-    loading: () => <div className="map" aria-hidden="true" />,
-  },
-);
-
-const HistoryModal = dynamic(loadHistoryModal, {
-  ssr: false,
-  loading: () => null,
-});
-
-const FutureForecastModal = dynamic(loadFutureForecastModal, {
-  ssr: false,
-  loading: () => null,
-});
-
-type CitySnapshot = {
-  city: CityListItem;
-  detail?: CityDetail | null;
-  score: number;
-  summary?: CitySummary | null;
-  tradableOpportunity: boolean;
-};
-
-const RISK_SCORE: Record<string, number> = {
-  high: 3,
-  medium: 2,
-  low: 1,
-};
-
-function getRiskCopy(level: RiskLevel | undefined, locale: string) {
-  if (level === "high") return locale === "en-US" ? "High variance" : "高波动";
-  if (level === "medium") return locale === "en-US" ? "Watch list" : "重点观察";
-  if (level === "low") return locale === "en-US" ? "Stable" : "低波动";
-  return locale === "en-US" ? "Unrated" : "待评级";
-}
-
-function getTempSymbol(
-  city: CityListItem,
-  summary?: CitySummary | null,
-  detail?: CityDetail | null,
-) {
-  if (summary?.temp_symbol) return summary.temp_symbol;
-  if (detail?.temp_symbol) return detail.temp_symbol;
-  return city.temp_unit === "fahrenheit" ? "°F" : "°C";
-}
-
-function formatTemperature(value: number | null | undefined, symbol: string) {
-  if (!Number.isFinite(Number(value))) return "--";
-  return `${Math.round(Number(value))}${symbol}`;
-}
-
-function formatPercent(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return "--";
-  return `${Math.round(Number(value) * 100)}%`;
-}
-
-function formatDelta(
-  current: number | null | undefined,
-  forecast: number | null | undefined,
-  symbol: string,
-) {
-  if (!Number.isFinite(Number(current)) || !Number.isFinite(Number(forecast))) {
-    return "--";
-  }
-  const delta = Number(current) - Number(forecast);
-  const sign = delta > 0 ? "+" : "";
-  return `${sign}${delta.toFixed(1)}${symbol}`;
-}
-
-function buildSnapshot(
-  city: CityListItem,
-  summary?: CitySummary | null,
-  detail?: CityDetail | null,
-): CitySnapshot {
-  const tier =
-    city.deb_recent_tier ||
-    city.risk_level ||
-    summary?.risk?.level ||
-    detail?.risk?.level;
-  const hitRate = Number(city.deb_recent_hit_rate ?? 0);
-  const sampleCount = Number(city.deb_recent_sample_count ?? 0);
-  const normalizedEdge = normalizeEdgePercent(getMarketEdgeValue(detail));
-  const tradableOpportunity = isTradableMarketOpportunity(detail);
-  const score =
-    (detail?.market_scan && !tradableOpportunity ? -10_000 : 0) +
-    (tradableOpportunity && normalizedEdge != null
-      ? 1000 + normalizedEdge * 10
-      : 0) +
-    (RISK_SCORE[String(tier || "")] || 0) * 100 +
-    hitRate * 100 +
-    Math.min(sampleCount, 60) / 10;
-  return { city, detail, score, summary, tradableOpportunity };
-}
-
-function getProbabilityLabel(
-  bucket: {
-    label?: string | null;
-    value?: number | null;
-    bucket?: string | null;
-  },
-  symbol: string,
-) {
-  if (bucket.label)
-    return normalizeTemperatureBucketLabel(bucket.label, symbol);
-  if (bucket.bucket)
-    return normalizeTemperatureBucketLabel(bucket.bucket, symbol);
-  if (Number.isFinite(Number(bucket.value))) {
-    return `≥ ${Math.round(Number(bucket.value))}${symbol}`;
-  }
-  return "--";
-}
-
-function normalizeTemperatureBucketLabel(label: string, symbol: string) {
-  const normalizedSymbol = symbol.includes("F") ? "°F" : "°C";
-  return String(label || "")
-    .trim()
-    .replace(
-      /(-?\d+(?:\.\d+)?)\s*°?\s*[CF]\b/gi,
-      (_, value) => `${Math.round(Number(value))}${normalizedSymbol}`,
-    );
-}
-
-function parseBucketThreshold(bucket?: {
-  label?: string | null;
-  value?: number | null;
-  temp?: number | null;
-  bucket?: string | null;
-  range?: string | null;
-}) {
-  const directValue = bucket?.value ?? bucket?.temp;
-  if (Number.isFinite(Number(directValue))) return Number(directValue);
-  const text = String(bucket?.label || bucket?.bucket || bucket?.range || "");
-  const match = text.match(/-?\d+(?:\.\d+)?/);
-  if (!match) return null;
-  const parsed = Number(match[0]);
-  return Number.isFinite(parsed) ? parsed : null;
-}
-
-function getBucketMatchKey(bucket?: {
-  label?: string | null;
-  value?: number | null;
-  temp?: number | null;
-  bucket?: string | null;
-  range?: string | null;
-}) {
-  const threshold = parseBucketThreshold(bucket);
-  if (threshold != null) return `t:${threshold.toFixed(2)}`;
-  return `l:${String(bucket?.label || bucket?.bucket || bucket?.range || "")
-    .trim()
-    .toLowerCase()}`;
-}
-
-function normalizeProbabilityValue(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return null;
-  return Number(value);
-}
-
-function getDisplayScore(rawScore: number): number {
-  if (rawScore <= 0) return Math.max(0, 70 + rawScore / 1000); // Penalty cases
-  // Scale the positive scores to 80-100
-  // rawScore for tradable is roughly 1000 + edge*10 + risk*100 + hitRate*100
-  // typical high score is 1000 + 50 + 400 + 90 = 1540
-  if (rawScore > 1000) {
-    return Math.min(100, 80 + (rawScore - 1000) / 30);
-  }
-  return Math.min(80, Math.max(0, (rawScore / 1000) * 80));
-}
-
-function buildMarketAlignedProbabilities(
-  marketScan: MarketScan | null | undefined,
-  probabilityBuckets: ProbabilityBucket[],
-  symbol: string,
-) {
-  const modelByKey = new Map(
-    probabilityBuckets.map((bucket) => [getBucketMatchKey(bucket), bucket]),
-  );
-  const marketBuckets: Array<MarketTopBucket | ProbabilityBucket> = [
-    ...(Array.isArray(marketScan?.all_buckets) ? marketScan.all_buckets : []),
-    ...(Array.isArray(marketScan?.top_buckets) ? marketScan.top_buckets : []),
-    ...(marketScan?.temperature_bucket ? [marketScan.temperature_bucket] : []),
-  ];
-  const aligned: ProbabilityBucket[] = [];
-  const seen = new Set<string>();
-
-  for (const marketBucket of marketBuckets) {
-    const key = getBucketMatchKey(marketBucket);
-    if (!key || seen.has(key)) continue;
-    seen.add(key);
-
-    const matchedModelBucket = modelByKey.get(key);
-    aligned.push({
-      label: getProbabilityLabel(marketBucket, symbol),
-      value:
-        marketBucket.value ??
-        ("temp" in marketBucket ? marketBucket.temp : undefined) ??
-        matchedModelBucket?.value ??
-        null,
-      bucket:
-        ("bucket" in marketBucket ? marketBucket.bucket : undefined) ??
-        matchedModelBucket?.bucket ??
-        null,
-      range:
-        ("range" in marketBucket ? marketBucket.range : undefined) ??
-        matchedModelBucket?.range ??
-        null,
-      unit:
-        marketBucket.unit ??
-        matchedModelBucket?.unit ??
-        (symbol === "°F" ? "F" : "C"),
-      probability:
-        normalizeProbabilityValue(matchedModelBucket?.probability) ?? null,
-    });
-  }
-
-  return aligned.length ? aligned.slice(0, 4) : probabilityBuckets.slice(0, 4);
-}
-
-function formatProbability(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return "--";
-  const normalized =
-    Math.abs(Number(value)) <= 1 ? Number(value) * 100 : Number(value);
-  return `${Math.round(normalized)}%`;
-}
-
-function formatCents(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return "--";
-  const normalized =
-    Math.abs(Number(value)) <= 1 ? Number(value) * 100 : Number(value);
-  return `${Math.round(normalized)}¢`;
-}
-
-function formatEdge(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return "--";
-  const normalized =
-    Math.abs(Number(value)) <= 1 ? Number(value) * 100 : Number(value);
-  const sign = normalized > 0 ? "+" : "";
-  return `${sign}${normalized.toFixed(1)}%`;
-}
-
-function getScanStatusLabel(detail?: CityDetail | null): string {
-  const scan = detail?.market_scan;
-  if (!scan?.available) return "无盘口";
-  const pa = scan?.price_analysis;
-  const bestSide = pa?.best_side;
-  const sideView = bestSide === "no" ? pa?.no : pa?.yes;
-  const edgeVal =
-    sideView?.edge != null
-      ? Math.abs(Number(sideView.edge)) > 1
-        ? Number(sideView.edge) / 100
-        : Number(sideView.edge)
-      : null;
-  if (edgeVal != null && edgeVal >= 0.05) return "触达博弈";
-  if (edgeVal != null && edgeVal >= 0.02) return "即时确认";
-  if (edgeVal != null && edgeVal > 0) return "早期机会";
-  return "市场";
-}
-
-function formatVolume(value?: number | null): string {
-  if (value == null) return "--";
-  if (value >= 1_000_000) return `$${(value / 1_000_000).toFixed(1)}M`;
-  if (value >= 1_000) return `$${Math.round(value / 1_000)}K`;
-  return `$${value.toFixed(0)}`;
-}
-
-function normalizeEdgePercent(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return null;
-  const numeric = Number(value);
-  return Math.abs(numeric) <= 1 ? numeric * 100 : numeric;
-}
-
-function getBestSide(detail?: CityDetail | null) {
-  const bestSide = String(
-    detail?.market_scan?.price_analysis?.best_side || "",
-  ).toLowerCase();
-  if (bestSide.includes("yes")) return "yes";
-  if (bestSide.includes("no")) return "no";
-  return null;
-}
-
-function getSideEdgePercent(
-  detail: CityDetail | null | undefined,
-  side: "yes" | "no",
-) {
-  const sideAnalysis = detail?.market_scan?.price_analysis?.[side];
-  return normalizeEdgePercent(sideAnalysis?.edge_percent ?? sideAnalysis?.edge);
-}
-
-function getExecutableEdgePercent(detail?: CityDetail | null) {
-  const bestSide = getBestSide(detail);
-  if (bestSide) {
-    const bestEdge = getSideEdgePercent(detail, bestSide);
-    if (bestEdge != null) return bestEdge;
-  }
-
-  const sideEdges = [
-    getSideEdgePercent(detail, "yes"),
-    getSideEdgePercent(detail, "no"),
-  ].filter((value): value is number => value != null && value > 0);
-  if (sideEdges.length) return Math.max(...sideEdges);
-
-  return normalizeEdgePercent(detail?.market_scan?.edge_percent);
-}
-
-function getBestSideModelProbability(detail?: CityDetail | null) {
-  const marketScan = detail?.market_scan;
-  const pYes = normalizeProbabilityValue(
-    marketScan?.price_analysis?.model_probability ??
-      marketScan?.model_probability ??
-      marketScan?.temperature_bucket?.probability,
-  );
-  if (pYes == null) return null;
-  return getBestSide(detail) === "no" ? 1 - pYes : pYes;
-}
-
-function getBestSideAsk(detail?: CityDetail | null) {
-  const marketScan = detail?.market_scan;
-  const side = getBestSide(detail);
-  if (side === "yes") {
-    return (
-      marketScan?.yes_buy ??
-      marketScan?.yes_token?.buy_price ??
-      marketScan?.price_analysis?.yes?.ask
-    );
-  }
-  if (side === "no") {
-    return (
-      marketScan?.no_buy ??
-      marketScan?.no_token?.buy_price ??
-      marketScan?.price_analysis?.no?.ask
-    );
-  }
-  return marketScan?.market_price ?? marketScan?.midpoint ?? null;
-}
-
-function getTradeSideLabel(side: "yes" | "no" | null, locale: string) {
-  if (side === "yes") return locale === "en-US" ? "BUY YES" : "买 YES";
-  if (side === "no") return locale === "en-US" ? "BUY NO" : "买 NO";
-  return locale === "en-US" ? "WATCH" : "观察";
-}
-
-function getTradeFormulaLabel(side: "yes" | "no" | null, locale: string) {
-  if (side === "no") {
-    return locale === "en-US"
-      ? "Edge = EMOS P(NO) - NO ask"
-      : "Edge = EMOS P(NO) - NO 买价";
-  }
-  return locale === "en-US"
-    ? "Edge = EMOS P(YES) - YES ask"
-    : "Edge = EMOS P(YES) - YES 买价";
-}
-
-function isLiveMarketScan(detail?: CityDetail | null) {
-  const marketScan = detail?.market_scan;
-  if (!marketScan) return false;
-  if (marketScan.available === false) return false;
-  if (marketScan.primary_market?.closed === true) return false;
-  if (marketScan.primary_market?.active === false) return false;
-  if (marketScan.selected_date && detail?.local_date) {
-    if (marketScan.selected_date < detail.local_date) return false;
-  }
-  const endDateMs = marketScan.primary_market?.end_date
-    ? Date.parse(marketScan.primary_market.end_date)
-    : Number.NaN;
-  if (Number.isFinite(endDateMs) && endDateMs < Date.now()) return false;
-  return true;
-}
-
-function buildSparklinePoints(values: number[] | undefined) {
-  if (!values?.length) return "";
-  const width = 92;
-  const height = 28;
-  const min = Math.min(...values);
-  const max = Math.max(...values);
-  const span = max - min || 1;
-  return values
-    .map((value, index) => {
-      const x =
-        values.length === 1 ? width : (index / (values.length - 1)) * width;
-      const y = height - ((value - min) / span) * height;
-      return `${x.toFixed(1)},${y.toFixed(1)}`;
-    })
-    .join(" ");
-}
-
-type AssistantMessage = {
-  id: string;
-  role: "assistant" | "user";
-  content: string;
-  cached?: boolean;
-};
-
-type OpportunityScanState = "pending" | "complete" | "empty" | "error";
-const HOME_OPPORTUNITY_REFRESH_MS = 30_000;
-
-type AssistantDockPosition = {
-  right: number;
-  bottom: number;
-};
-
-type AssistantDockDragState = {
-  pointerId: number;
-  startX: number;
-  startY: number;
-  right: number;
-  bottom: number;
-  hasMoved: boolean;
-};
-
-const HOME_AI_DOCK_POSITION_STORAGE_KEY =
-  "polyweather_home_ai_dock_position_v2";
-
-function readAssistantDockPosition() {
-  if (typeof window === "undefined") return null;
-  try {
-    const raw = window.localStorage.getItem(HOME_AI_DOCK_POSITION_STORAGE_KEY);
-    if (!raw) return null;
-    const parsed = JSON.parse(raw) as AssistantDockPosition | null;
-    if (
-      parsed &&
-      Number.isFinite(Number(parsed.right)) &&
-      Number.isFinite(Number(parsed.bottom))
-    ) {
-      return {
-        right: Number(parsed.right),
-        bottom: Number(parsed.bottom),
-      };
-    }
-  } catch {
-    // Ignore malformed dock position cache.
-  }
-  return null;
-}
-
-function writeAssistantDockPosition(position: AssistantDockPosition | null) {
-  if (typeof window === "undefined") return;
-  if (!position) {
-    window.localStorage.removeItem(HOME_AI_DOCK_POSITION_STORAGE_KEY);
-    return;
-  }
-  window.localStorage.setItem(
-    HOME_AI_DOCK_POSITION_STORAGE_KEY,
-    JSON.stringify(position),
-  );
-}
-
-function getDefaultAssistantDockPosition() {
-  if (typeof window === "undefined" || window.innerWidth <= 960) {
-    return null;
-  }
-  return {
-    right: 24,
-    bottom: 24,
-  };
-}
-
-function clampAssistantDockPosition(
-  position: AssistantDockPosition | null,
-  dockElement?: HTMLElement | null,
-) {
-  if (typeof window === "undefined" || window.innerWidth <= 960) {
-    return null;
-  }
-  const rect = dockElement?.getBoundingClientRect();
-  const width = rect?.width || 340;
-  const height = rect?.height || 88;
-  const minOffset = 24;
-  const maxRight = Math.max(minOffset, window.innerWidth - width - minOffset);
-  const maxBottom = Math.max(
-    minOffset,
-    window.innerHeight - height - minOffset,
-  );
-  const base = position || getDefaultAssistantDockPosition();
-  if (!base) return null;
-  return {
-    right: Math.min(maxRight, Math.max(minOffset, Number(base.right))),
-    bottom: Math.min(maxBottom, Math.max(minOffset, Number(base.bottom))),
-  };
-}
-
-function hashSnapshotText(source: string) {
-  let hash = 5381;
-  for (let index = 0; index < source.length; index += 1) {
-    hash = (hash * 33) ^ source.charCodeAt(index);
-  }
-  return Math.abs(hash >>> 0).toString(16);
-}
-
-function normalizeAssistantPercent(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return null;
-  const numeric = Number(value);
-  return Math.abs(numeric) <= 1 ? numeric * 100 : numeric;
-}
-
-function normalizeAssistantCents(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return null;
-  const numeric = Number(value);
-  return Math.abs(numeric) <= 1 ? numeric * 100 : numeric;
-}
-
-function buildAssistantOpportunityContext(
-  snapshot: CitySnapshot,
-  locale: string,
-): AssistantOpportunityContext {
-  const { city, detail, summary, tradableOpportunity } = snapshot;
-  const symbol = getTempSymbol(city, summary, detail);
-  const marketScan = detail?.market_scan;
-  const marketBucket =
-    marketScan?.temperature_bucket || marketScan?.top_buckets?.[0] || null;
-  const marketQuestion =
-    marketScan?.primary_market?.question ||
-    (marketBucket
-      ? `${getProbabilityLabel(marketBucket, symbol)} ${
-          marketScan?.selected_date || detail?.local_date || ""
-        }`
-      : null);
-  const localizedCityName = getLocalizedCityDisplay(
-    city,
-    locale,
-    summary,
-    detail,
-  );
-  const localizedAirport = getLocalizedAirportDisplay(city, locale, detail);
-  const marketProbability =
-    marketScan?.market_price ??
-    readNumericField(marketBucket, "market_price") ??
-    readNumericField(marketBucket, "yes_buy") ??
-    marketScan?.yes_buy;
-  const modelProbability =
-    marketScan?.model_probability ?? marketBucket?.probability ?? null;
-  return {
-    city_name: city.name,
-    city_display_name: localizedCityName,
-    airport: localizedAirport,
-    risk_level: String(
-      city.deb_recent_tier ||
-        city.risk_level ||
-        summary?.risk?.level ||
-        detail?.risk?.level ||
-        "",
-    ),
-    tradable: tradableOpportunity,
-    local_time: summary?.local_time || detail?.local_time || null,
-    current_temperature:
-      summary?.current?.temp ?? detail?.current?.temp ?? null,
-    deb_prediction: summary?.deb?.prediction ?? detail?.deb?.prediction ?? null,
-    temp_symbol: symbol,
-    today_high:
-      detail?.forecast?.today_high ??
-      detail?.current?.max_so_far ??
-      detail?.airport_current?.max_so_far ??
-      null,
-    market_question: marketQuestion,
-    market_label: marketBucket
-      ? getProbabilityLabel(marketBucket, symbol)
-      : null,
-    selected_date: marketScan?.selected_date || detail?.local_date || null,
-    best_side: marketScan?.price_analysis?.best_side || null,
-    yes_price: normalizeAssistantCents(
-      marketScan?.yes_buy ??
-        readNumericField(marketBucket, "yes_buy") ??
-        marketScan?.yes_token?.buy_price ??
-        marketScan?.yes_token?.midpoint,
-    ),
-    no_price: normalizeAssistantCents(
-      marketScan?.no_buy ??
-        readNumericField(marketBucket, "no_buy") ??
-        marketScan?.no_token?.buy_price ??
-        marketScan?.no_token?.midpoint,
-    ),
-    edge_percent: normalizeAssistantPercent(getMarketEdgeValue(detail)),
-    market_probability: normalizeAssistantPercent(marketProbability),
-    model_probability: normalizeAssistantPercent(modelProbability),
-    status: tradableOpportunity
-      ? "tradable"
-      : marketScan
-        ? "inactive"
-        : "market_pending",
-  };
-}
-
-function buildAssistantContextPayload(
-  snapshots: CitySnapshot[],
-  selectedCity: string | null,
-  locale: string,
-): AssistantContextPayload {
-  const opportunities = snapshots.map((snapshot) =>
-    buildAssistantOpportunityContext(snapshot, locale),
-  );
-  const revisionSeed = snapshots
-    .map((snapshot) => {
-      const revision =
-        getCityRevision(snapshot.detail) || getCityRevision(snapshot.summary);
-      return `${snapshot.city.name}:${revision}:${snapshot.tradableOpportunity ? 1 : 0}`;
-    })
-    .join("|");
-  const snapshotId = `home-${hashSnapshotText(revisionSeed || String(Date.now()))}`;
-  const selected =
-    opportunities.find((item) => item.city_name === selectedCity) || null;
-
-  return {
-    snapshot_id: snapshotId,
-    locale,
-    generated_at: new Date().toISOString(),
-    totals: {
-      cities: snapshots.length,
-      tradable_markets: opportunities.filter((item) => item.tradable).length,
-      high_risk: opportunities.filter((item) => item.risk_level === "high")
-        .length,
-      medium_risk: opportunities.filter((item) => item.risk_level === "medium")
-        .length,
-      low_risk: opportunities.filter((item) => item.risk_level === "low")
-        .length,
-    },
-    selected_city: selected,
-    opportunities,
-    glossary:
-      locale === "en-US"
-        ? [
-            {
-              term: "edge",
-              meaning:
-                "Edge is the gap between model probability and market-implied probability. Positive edge means the model is more optimistic than the market.",
-            },
-            {
-              term: "market probability",
-              meaning:
-                "Market probability is the implied probability backed out from the live YES/NO price.",
-            },
-            {
-              term: "DEB",
-              meaning:
-                "DEB is the internal forecast anchor for the day-max settlement temperature.",
-            },
-            {
-              term: "EMOS",
-              meaning:
-                "EMOS is the calibrated probability ladder for the 24-hour max-temperature outcome buckets.",
-            },
-          ]
-        : [
-            {
-              term: "edge",
-              meaning:
-                "edge 是模型概率和市场隐含概率之间的差值。正值表示模型比市场更乐观。",
-            },
-            {
-              term: "市场概率",
-              meaning:
-                "市场概率来自 YES/NO 实时价格的隐含概率，而不是模型输出。",
-            },
-            {
-              term: "DEB",
-              meaning: "DEB 是系统对当日结算最高温的核心预测锚点之一。",
-            },
-            {
-              term: "EMOS",
-              meaning: "EMOS 是 24 小时最高温结果分桶使用的校准概率分布。",
-            },
-          ],
-  };
-}
-
-function buildAssistantGreeting(
-  locale: string,
-  selectedCityName?: string | null,
-) {
-  if (locale === "en-US") {
-    return selectedCityName
-      ? `Ask about ${selectedCityName}'s current temperature, today's forecast high, market edge, or live opportunities.`
-      : "Ask about current temperature, today's forecast high, market edge, or live opportunities.";
-  }
-  return selectedCityName
-    ? `可以直接问我 ${selectedCityName} 的当前温度、今日最高温预测、市场 edge 或实时机会。`
-    : "可以直接问我当前温度、今日最高温预测、市场 edge 或实时机会。";
-}
-
-type HomeWeatherIconKind =
-  | "clear"
-  | "partly"
-  | "cloudy"
-  | "rain"
-  | "storm"
-  | "mist"
-  | "wind";
-
-type HomeTrendChart = {
-  forecastPath: string;
-  legendText: string;
-  observationDots: Array<{ cx: number; cy: number; key: string }>;
-  hourlyReports: Array<{
-    key: string;
-    label: string;
-    temperatureText: string;
-  }>;
-  yAxisLabels: Array<{ key: string; label: string; y: number }>;
-  xAxisLabels: Array<{ key: string; label: string; x: number }>;
-};
-
-type HomeForecastDay = {
-  key: string;
-  label: string;
-  maxTemp: number;
-};
-
-type HomeSummaryCard = {
-  key: string;
-  title: string;
-  linkLabel?: string;
-  items: Array<{
-    label: string;
-    value: string;
-    accent?: "green" | "amber" | "red" | "cyan";
-  }>;
-};
-
-function buildDashboardSummaryCards(
-  snapshots: CitySnapshot[],
-  locale: string,
-): HomeSummaryCard[] {
-  const topTradable = snapshots.filter(
-    (snapshot) => snapshot.tradableOpportunity,
-  );
-  const highCount = snapshots.filter(
-    (snapshot) => snapshot.city.deb_recent_tier === "high",
-  ).length;
-  const mediumCount = snapshots.filter(
-    (snapshot) => snapshot.city.deb_recent_tier === "medium",
-  ).length;
-  const lowCount = snapshots.filter(
-    (snapshot) => snapshot.city.deb_recent_tier === "low",
-  ).length;
-  const strongAgreement = snapshots.filter(
-    (snapshot) => Number(snapshot.city.deb_recent_hit_rate ?? 0) >= 0.66,
-  ).length;
-  const mediumAgreement = snapshots.filter((snapshot) => {
-    const rate = Number(snapshot.city.deb_recent_hit_rate ?? 0);
-    return rate >= 0.4 && rate < 0.66;
-  }).length;
-  const weakAgreement = Math.max(
-    snapshots.length - strongAgreement - mediumAgreement,
-    0,
-  );
-  const avgEdge = topTradable.length
-    ? topTradable.reduce((sum, snapshot) => {
-        const edgeValue = Number(getMarketEdgeValue(snapshot.detail));
-        const normalized =
-          Number.isFinite(edgeValue) && Math.abs(edgeValue) <= 1
-            ? edgeValue * 100
-            : edgeValue;
-        return sum + (Number.isFinite(normalized) ? normalized : 0);
-      }, 0) / topTradable.length
-    : 0;
-
-  return [
-    {
-      key: "opportunities",
-      title: locale === "en-US" ? "Current Opportunities" : "当前机会分布",
-      items: [
-        {
-          label: locale === "en-US" ? "High risk" : "高风险",
-          value: String(highCount),
-          accent: "red",
-        },
-        {
-          label: locale === "en-US" ? "Medium risk" : "中风险",
-          value: String(mediumCount),
-          accent: "amber",
-        },
-        {
-          label: locale === "en-US" ? "Low risk" : "低风险",
-          value: String(lowCount),
-          accent: "green",
-        },
-      ],
-    },
-    {
-      key: "market-summary",
-      title: locale === "en-US" ? "Market Summary" : "市场概览",
-      items: [
-        {
-          label: locale === "en-US" ? "Total markets" : "总市场数",
-          value: String(snapshots.length),
-        },
-        {
-          label: locale === "en-US" ? "Active" : "活跃市场",
-          value: String(topTradable.length),
-          accent: "cyan",
-        },
-        {
-          label: locale === "en-US" ? "Avg. edge" : "平均优势",
-          value: formatEdge(avgEdge),
-          accent: "green",
-        },
-      ],
-    },
-    {
-      key: "model-agreement",
-      title: locale === "en-US" ? "Model Agreement" : "模型一致性",
-      items: [
-        {
-          label: locale === "en-US" ? "High" : "高",
-          value: `${strongAgreement}`,
-          accent: "green",
-        },
-        {
-          label: locale === "en-US" ? "Medium" : "中",
-          value: `${mediumAgreement}`,
-          accent: "amber",
-        },
-        {
-          label: locale === "en-US" ? "Low" : "低",
-          value: `${weakAgreement}`,
-          accent: "red",
-        },
-      ],
-    },
-    {
-      key: "impact-window",
-      title: locale === "en-US" ? "High Impact Window" : "高影响窗口",
-      items: [
-        {
-          label: locale === "en-US" ? "Next 6 hours" : "未来 6 小时",
-          value:
-            locale === "en-US"
-              ? `${Math.min(topTradable.length, 12)} markets`
-              : `${Math.min(topTradable.length, 12)} 个市场`,
-        },
-        {
-          label: locale === "en-US" ? "Focus city" : "焦点城市",
-          value: topTradable[0]?.city.display_name || "--",
-          accent: "cyan",
-        },
-        {
-          label: locale === "en-US" ? "Best edge" : "最佳优势",
-          value:
-            topTradable.length > 0
-              ? formatEdge(getMarketEdgeValue(topTradable[0]?.detail))
-              : "--",
-          accent: "green",
-        },
-      ],
-    },
-  ];
-}
-
-function projectHomeTrendPoint(
-  x: number,
-  y: number,
-  xMin: number,
-  xMax: number,
-  yMin: number,
-  yMax: number,
-) {
-  const width = 296;
-  const height = 78;
-  const left = 10;
-  const right = 10;
-  const top = 8;
-  const bottom = 12;
-  const plotWidth = width - left - right;
-  const plotHeight = height - top - bottom;
-  const normalizedX =
-    xMax === xMin ? 0.5 : Math.min(1, Math.max(0, (x - xMin) / (xMax - xMin)));
-  const normalizedY =
-    yMax === yMin ? 0.5 : Math.min(1, Math.max(0, (y - yMin) / (yMax - yMin)));
-  return {
-    cx: Number((left + normalizedX * plotWidth).toFixed(1)),
-    cy: Number((top + (1 - normalizedY) * plotHeight).toFixed(1)),
-  };
-}
-
-function getHomeWeatherIconKind(
-  detail?: CityDetail | null,
-  locale = "zh-CN",
-): HomeWeatherIconKind {
-  if (!detail) return "cloudy";
-  const summary = getWeatherSummary(
-    detail,
-    locale === "en-US" ? "en-US" : "zh-CN",
-  );
-  const weatherText =
-    `${summary.weatherIcon} ${summary.weatherText} ${detail.current?.wx_desc || ""} ${detail.current?.cloud_desc || ""}`.toLowerCase();
-  const cloudCover = Number(detail.hourly_next_48h?.cloud_cover?.[0]);
-  const windSpeed = Number(
-    detail.current?.wind_speed_kt ?? detail.airport_current?.wind_speed_kt,
-  );
-
-  if (/⛈|雷|storm|thunder/.test(weatherText)) return "storm";
-  if (/🌧|🌦|雨|drizzle|shower|rain/.test(weatherText)) return "rain";
-  if (/🌫|雾|mist|fog|haze/.test(weatherText)) return "mist";
-  if (/💨|飑|squall/.test(weatherText) || windSpeed >= 22) return "wind";
-  if (/☀|晴|clear|sunny/.test(weatherText)) return "clear";
-  if (/🌤|⛅|partly|few|scattered|少云|散云/.test(weatherText)) return "partly";
-  if (/☁|云|cloud|overcast|阴/.test(weatherText)) return "cloudy";
-  if (Number.isFinite(cloudCover) && cloudCover <= 15) return "clear";
-  if (Number.isFinite(cloudCover) && cloudCover <= 55) return "partly";
-  return "cloudy";
-}
-
-function buildHomeTrendChart(
-  detail?: CityDetail | null,
-  locale = "zh-CN",
-): HomeTrendChart | null {
-  if (!detail) return null;
-  const chartData = getTemperatureChartData(
-    detail,
-    locale === "en-US" ? "en-US" : "zh-CN",
-  );
-  if (!chartData) return null;
-  const forecastSeries = chartData.datasets.hasMgmHourly
-    ? chartData.datasets.mgmHourlySeries
-    : [
-        ...chartData.datasets.debPastSeries,
-        ...chartData.datasets.debFutureSeries,
-      ];
-  const observationSeries =
-    chartData.datasets.metarSeries.length > 0
-      ? chartData.datasets.metarSeries
-      : chartData.datasets.airportMetarSeries;
-  if (!forecastSeries.length && !observationSeries.length) return null;
-
-  const forecastPath = forecastSeries
-    .map((point) => {
-      const projected = projectHomeTrendPoint(
-        point.x,
-        point.y,
-        chartData.xMin,
-        chartData.xMax,
-        chartData.min,
-        chartData.max,
-      );
-      return `${projected.cx},${projected.cy}`;
-    })
-    .join(" ");
-  const observationDots = observationSeries.map((point, index) => {
-    const projected = projectHomeTrendPoint(
-      point.x,
-      point.y,
-      chartData.xMin,
-      chartData.xMax,
-      chartData.min,
-      chartData.max,
-    );
-    return {
-      cx: projected.cx,
-      cy: projected.cy,
-      key: `${point.labelTime}-${index}`,
-    };
-  });
-  const hoverSource =
-    observationSeries.length > 0 ? observationSeries : forecastSeries;
-  const hourlyReports = hoverSource
-    .filter((point) => {
-      const label = String(point.labelTime || "");
-      return /(^|\D)\d{1,2}:00($|\D)/.test(label) || hoverSource.length <= 8;
-    })
-    .slice(-6)
-    .map((point, index) => ({
-      key: `hourly-${point.labelTime}-${index}`,
-      label: point.labelTime,
-      temperatureText: formatTemperature(point.y, detail.temp_symbol || "°C"),
-    }));
-
-  const yAxisLabels = [
-    {
-      key: "max",
-      label: formatTemperature(chartData.max, detail.temp_symbol || "°C"),
-      y: 14,
-    },
-    {
-      key: "mid",
-      label: formatTemperature(
-        (chartData.max + chartData.min) / 2,
-        detail.temp_symbol || "°C",
-      ),
-      y: 36,
-    },
-    {
-      key: "min",
-      label: formatTemperature(chartData.min, detail.temp_symbol || "°C"),
-      y: 58,
-    },
-  ];
-
-  const xAxisSource =
-    hoverSource.length <= 4
-      ? hoverSource
-      : [
-          hoverSource[0],
-          hoverSource[Math.floor((hoverSource.length - 1) / 3)],
-          hoverSource[Math.floor(((hoverSource.length - 1) * 2) / 3)],
-          hoverSource[hoverSource.length - 1],
-        ];
-
-  const xAxisLabels = xAxisSource.map((point, index) => {
-    const projected = projectHomeTrendPoint(
-      point.x,
-      point.y,
-      chartData.xMin,
-      chartData.xMax,
-      chartData.min,
-      chartData.max,
-    );
-    return {
-      key: `axis-${point.labelTime}-${index}`,
-      label: point.labelTime,
-      x: Number(((projected.cx / 296) * 100).toFixed(2)),
-    };
-  });
-
-  return {
-    forecastPath,
-    legendText: chartData.legendText,
-    observationDots,
-    hourlyReports,
-    yAxisLabels,
-    xAxisLabels,
-  };
-}
-
-function buildHomeForecastDays(
-  detail?: CityDetail | null,
-  locale = "zh-CN",
-): HomeForecastDay[] {
-  if (!detail) return [];
-  const todayLabel = locale === "en-US" ? "Today" : "今天";
-  const tomorrowLabel = locale === "en-US" ? "Tomorrow" : "明天";
-  const formatter = new Intl.DateTimeFormat(
-    locale === "en-US" ? "en-US" : "zh-CN",
-    {
-      day: "2-digit",
-      month: "2-digit",
-    },
-  );
-  const rows = Array.isArray(detail.forecast?.daily)
-    ? detail.forecast.daily
-    : [];
-  const byDate = new Map<string, number>();
-
-  if (
-    detail.local_date &&
-    Number.isFinite(Number(detail.forecast?.today_high))
-  ) {
-    byDate.set(detail.local_date, Number(detail.forecast?.today_high));
-  }
-  rows.forEach((row) => {
-    if (!row?.date || !Number.isFinite(Number(row.max_temp))) return;
-    if (!byDate.has(row.date)) {
-      byDate.set(row.date, Number(row.max_temp));
-    }
-  });
-
-  return [...byDate.entries()].slice(0, 4).map(([date, maxTemp], index) => {
-    const parsed = new Date(`${date}T00:00:00`);
-    const label =
-      index === 0
-        ? todayLabel
-        : index === 1
-          ? tomorrowLabel
-          : Number.isNaN(parsed.getTime())
-            ? date
-            : formatter.format(parsed);
-    return {
-      key: date,
-      label,
-      maxTemp,
-    };
-  });
-}
-
-function readNumericField(source: unknown, key: string) {
-  if (!source || typeof source !== "object") return undefined;
-  const value = (source as Record<string, unknown>)[key];
-  return typeof value === "number" && Number.isFinite(value)
-    ? value
-    : undefined;
-}
-
-function normalizePercentValue(value: number | null | undefined) {
-  if (!Number.isFinite(Number(value))) return null;
-  const numeric = Number(value);
-  return Math.abs(numeric) <= 1 ? numeric * 100 : numeric;
-}
-
-function isBoundaryMarketPrice(value: number | null | undefined) {
-  const percent = normalizePercentValue(value);
-  return percent != null && (percent <= 2 || percent >= 98);
-}
-
-function getMarketEdgeValue(detail?: CityDetail | null) {
-  return getExecutableEdgePercent(detail);
-}
-
-function isTradableMarketOpportunity(detail?: CityDetail | null) {
-  const marketScan = detail?.market_scan;
-  if (!marketScan) return false;
-  if (marketScan.available === false) return false;
-  if (marketScan.primary_market?.closed === true) return false;
-  if (marketScan.primary_market?.active === false) return false;
-
-  const selectedDate = marketScan.selected_date;
-  const localDate = detail?.local_date;
-  if (selectedDate && localDate && selectedDate < localDate) return false;
-
-  const endDateMs = marketScan.primary_market?.end_date
-    ? Date.parse(marketScan.primary_market.end_date)
-    : Number.NaN;
-  if (Number.isFinite(endDateMs) && endDateMs < Date.now()) return false;
-
-  const marketBucket =
-    marketScan.temperature_bucket || marketScan.top_buckets?.[0] || null;
-  const yesPrice =
-    marketScan.yes_buy ??
-    readNumericField(marketBucket, "yes_buy") ??
-    marketScan.yes_token?.buy_price ??
-    marketScan.yes_token?.midpoint ??
-    marketScan.price_analysis?.yes?.ask;
-  const noPrice =
-    marketScan.no_buy ??
-    readNumericField(marketBucket, "no_buy") ??
-    marketScan.no_token?.buy_price ??
-    marketScan.no_token?.midpoint ??
-    marketScan.price_analysis?.no?.ask;
-  const marketPrice =
-    marketScan.market_price ??
-    readNumericField(marketBucket, "market_price") ??
-    yesPrice;
-
-  if (isBoundaryMarketPrice(yesPrice) || isBoundaryMarketPrice(noPrice)) {
-    return false;
-  }
-  if (isBoundaryMarketPrice(marketPrice)) return false;
-  const executableEdge = getExecutableEdgePercent(detail);
-  return executableEdge != null && executableEdge > 0;
-}
-
-function HomeIntelligencePanel({ snapshots }: { snapshots: CitySnapshot[] }) {
-  const store = useDashboardStore();
-  const { locale } = useI18n();
-  const spotlight = useMemo(
-    () =>
-      store.selectedCity
-        ? snapshots.find(
-            (snapshot) => snapshot.city.name === store.selectedCity,
-          ) || null
-        : null,
-    [snapshots, store.selectedCity],
-  );
-  const spotlightView = useMemo(() => {
-    if (!spotlight) return null;
-
-    const { city, detail, summary } = spotlight;
-    const symbol = getTempSymbol(city, summary, detail);
-    const currentTemp = summary?.current?.temp ?? detail?.current?.temp;
-    const debPrediction = summary?.deb?.prediction ?? detail?.deb?.prediction;
-    const maxSoFar =
-      detail?.current?.max_so_far ?? detail?.airport_current?.max_so_far;
-    const maxTime =
-      detail?.current?.max_temp_time ||
-      detail?.airport_current?.max_temp_time ||
-      "--";
-    const localTime = summary?.local_time || detail?.local_time || "--";
-    const riskLevel =
-      city.deb_recent_tier ||
-      city.risk_level ||
-      summary?.risk?.level ||
-      detail?.risk?.level;
-    const probabilityBuckets =
-      detail?.probabilities?.distribution ||
-      (detail?.local_date
-        ? detail?.multi_model_daily?.[detail.local_date]?.probabilities
-        : undefined) ||
-      [];
-    const marketScan = detail?.market_scan;
-    const displayedProbabilities = buildMarketAlignedProbabilities(
-      marketScan,
-      probabilityBuckets,
-      symbol,
-    );
-    const marketBucket =
-      marketScan?.temperature_bucket || marketScan?.top_buckets?.[0] || null;
-    const yesPrice =
-      marketScan?.yes_buy ??
-      readNumericField(marketBucket, "yes_buy") ??
-      marketScan?.yes_token?.buy_price ??
-      marketScan?.yes_token?.midpoint ??
-      marketScan?.price_analysis?.yes?.ask;
-    const noPrice =
-      marketScan?.no_buy ??
-      readNumericField(marketBucket, "no_buy") ??
-      marketScan?.no_token?.buy_price ??
-      marketScan?.no_token?.midpoint ??
-      marketScan?.price_analysis?.no?.ask;
-    const marketEdge =
-      marketScan?.edge_percent ??
-      marketScan?.price_analysis?.yes?.edge_percent ??
-      marketScan?.price_analysis?.yes?.edge ??
-      marketScan?.price_analysis?.no?.edge_percent ??
-      marketScan?.price_analysis?.no?.edge;
-
-    return {
-      city,
-      detail,
-      tradableOpportunity: spotlight.tradableOpportunity,
-      symbol,
-      currentTemp,
-      debPrediction,
-      maxSoFar,
-      maxTime,
-      localTime,
-      riskLevel,
-      displayedProbabilities,
-      marketScan,
-      showOpportunityLabel: !marketScan || spotlight.tradableOpportunity,
-      marketLabel: marketBucket
-        ? getProbabilityLabel(marketBucket, symbol)
-        : debPrediction != null
-          ? `> ${formatTemperature(debPrediction, symbol)}`
-          : "--",
-      yesPrice,
-      noPrice,
-      marketEdge,
-      marketProbability:
-        marketScan?.market_price ??
-        readNumericField(marketBucket, "market_price") ??
-        readNumericField(marketBucket, "yes_buy") ??
-        yesPrice ??
-        marketScan?.model_probability,
-      marketModelProbability:
-        marketScan?.model_probability ?? marketBucket?.probability,
-      sparklinePoints: buildSparklinePoints(marketScan?.sparkline),
-      isLoading:
-        store.loadingState.cityDetail && store.selectedCity === city.name,
-      isPro: store.proAccess.subscriptionActive,
-      cityCode: city.icao || detail?.risk?.icao || city.airport,
-      localizedCityName: getLocalizedCityDisplay(city, locale, summary, detail),
-      localizedAirportName: getLocalizedAirportDisplay(city, locale, detail),
-      highRiskLabel:
-        riskLevel === "high"
-          ? locale === "en-US"
-            ? "High risk"
-            : "高风险"
-          : getRiskCopy(riskLevel, locale),
-      weatherIconKind: getHomeWeatherIconKind(detail, locale),
-      trendChart: buildHomeTrendChart(detail, locale),
-      debLabel: locale === "en-US" ? "DEB forecast" : "DEB 预测",
-      dayMaxLabel: locale === "en-US" ? "24h max" : "24 小时最高",
-      probabilityTitle: locale === "en-US" ? "EMOS probability" : "EMOS 概率",
-      marketTitle: locale === "en-US" ? "Market edge" : "市场优势",
-      marketEdgeLabel: locale === "en-US" ? "Edge" : "优势",
-      marketImpliedLabel: locale === "en-US" ? "Implied" : "市场隐含",
-      marketModelLabel: locale === "en-US" ? "Model prob" : "模型概率",
-      proLabel: store.proAccess.subscriptionActive
-        ? locale === "en-US"
-          ? "Pro signal"
-          : "PRO 信号"
-        : locale === "en-US"
-          ? "Pro locked"
-          : "PRO 锁定",
-      forecastDays: buildHomeForecastDays(detail, locale),
-    };
-  }, [
-    locale,
-    spotlight,
-    store.loadingState.cityDetail,
-    store.proAccess.subscriptionActive,
-    store.selectedCity,
-  ]);
-
-  if (!spotlightView) return null;
-
-  const {
-    city,
-    detail,
-    symbol,
-    currentTemp,
-    debPrediction,
-    maxSoFar,
-    maxTime,
-    localTime,
-    riskLevel,
-    displayedProbabilities,
-    marketScan,
-    tradableOpportunity,
-    showOpportunityLabel,
-    marketLabel,
-    yesPrice,
-    noPrice,
-    marketEdge,
-    marketProbability,
-    marketModelProbability,
-    sparklinePoints,
-    isLoading,
-    isPro,
-    cityCode,
-    localizedCityName,
-    localizedAirportName,
-    highRiskLabel,
-    weatherIconKind,
-    trendChart,
-    debLabel,
-    dayMaxLabel,
-    probabilityTitle,
-    marketTitle,
-    marketEdgeLabel,
-    marketImpliedLabel,
-    marketModelLabel,
-    proLabel,
-    forecastDays,
-  } = spotlightView;
-  const subtitle = `${cityCode} · ${localizedAirportName}`;
-  const proCard = (
-    <div className={clsx("home-pro-card", isPro && "active")}>
-      <div>
-        <span>{proLabel}</span>
-        {!isPro ? (
-          <strong>
-            {locale === "en-US"
-              ? "History review and future dates stay paid."
-              : "历史复盘和未来日期保持付费。"}
-          </strong>
-        ) : null}
-      </div>
-      {isPro ? (
-        <button type="button" onClick={() => void store.openTodayModal()}>
-          {locale === "en-US" ? "Today intraday" : "今日日内分析"}
-        </button>
-      ) : (
-        <Link href="/account">{locale === "en-US" ? "Upgrade" : "升级"}</Link>
-      )}
-    </div>
-  );
-
-  return (
-    <aside
-      className="home-intelligence-panel full"
-      aria-label={localizedCityName}
-    >
-      <div className="home-panel-glow" aria-hidden="true" />
-
-      <div className="home-panel-header">
-        <div className="home-panel-header-left">
-          <span className="home-panel-live-indicator" aria-hidden="true" />
-          <span className="home-focus-title">
-            {showOpportunityLabel
-              ? locale === "en-US"
-                ? "TOP OPPORTUNITY"
-                : "最佳机会"
-              : locale === "en-US"
-                ? "FOCUS CITY"
-                : "焦点城市"}
-          </span>
-        </div>
-      </div>
-
-      <div className="home-card-titlebar">
-        <div>
-          <h2>{localizedCityName}</h2>
-          <p>{subtitle}</p>
-        </div>
-        <span className={clsx("home-risk-badge", String(riskLevel || "other"))}>
-          {highRiskLabel}
-        </span>
-      </div>
-
-      <div className="home-card-meta-row">
-        <span>
-          {locale === "en-US" ? "Local time" : "当地时间"} {localTime}
-        </span>
-        <span className="home-card-live-dot" />
-        <span>
-          {isLoading
-            ? locale === "en-US"
-              ? "Updating..."
-              : "更新中..."
-            : locale === "en-US"
-              ? "Updated now"
-              : "刚刚更新"}
-        </span>
-      </div>
-
-      {proCard}
-
-      <div className="home-weather-hero">
-        <div className="home-weather-main">
-          <span className="home-weather-label">
-            {locale === "en-US" ? "Observed now" : "当前实况"}
-          </span>
-          <strong>{formatTemperature(currentTemp, symbol)}</strong>
-        </div>
-        <div className="home-weather-side">
-          <div
-            className={clsx("home-weather-icon", `weather-${weatherIconKind}`)}
-            aria-hidden="true"
-          >
-            <span className="sun" />
-            <span className="cloud cloud-a" />
-            <span className="cloud cloud-b" />
-            <span className="mist mist-a" />
-            <span className="mist mist-b" />
-            <span className="wind wind-a" />
-            <span className="wind wind-b" />
-            <span className="rain rain-a" />
-            <span className="rain rain-b" />
-            <span className="rain rain-c" />
-            <span className="bolt" />
-          </div>
-          <div className="home-weather-stat">
-            <span>{locale === "en-US" ? "Day high" : "日内高点"}</span>
-            <strong>{formatTemperature(maxSoFar, symbol)}</strong>
-            <small>{maxTime}</small>
-          </div>
-        </div>
-      </div>
-
-      <div className="home-deb-card">
-        <div>
-          <span>
-            {debLabel} <small>({dayMaxLabel})</small>
-          </span>
-          <strong>{formatTemperature(debPrediction, symbol)}</strong>
-          <em>{formatDelta(debPrediction, currentTemp, symbol)}</em>
-        </div>
-      </div>
-
-      {trendChart ? (
-        <div className="home-card-section intraday">
-          <h3>
-            {locale === "en-US" ? "Hourly reports" : "小时准点报"}{" "}
-            <small>{locale === "en-US" ? "1h cadence" : "1h 级别"}</small>
-          </h3>
-          <div className="home-intraday-chart">
-            <div className="home-intraday-y-axis" aria-hidden="true">
-              {trendChart.yAxisLabels.map((axisLabel) => (
-                <span
-                  key={axisLabel.key}
-                  className="home-intraday-y-label"
-                  style={{ top: `${axisLabel.y}px` }}
-                >
-                  {axisLabel.label}
-                </span>
-              ))}
-            </div>
-            <svg viewBox="0 0 296 78" aria-hidden="true">
-              <line x1="10" y1="14" x2="286" y2="14" />
-              <line x1="10" y1="36" x2="286" y2="36" />
-              <line x1="10" y1="58" x2="286" y2="58" />
-              {trendChart.forecastPath ? (
-                <polyline points={trendChart.forecastPath} />
-              ) : null}
-              {trendChart.observationDots.map((point) => (
-                <circle key={point.key} cx={point.cx} cy={point.cy} r="3.2" />
-              ))}
-            </svg>
-            <div className="home-intraday-x-axis" aria-hidden="true">
-              {trendChart.xAxisLabels.map((axisLabel) => (
-                <span
-                  key={axisLabel.key}
-                  className="home-intraday-x-label"
-                  style={{ left: `${axisLabel.x}%` }}
-                >
-                  {axisLabel.label}
-                </span>
-              ))}
-            </div>
-          </div>
-          {trendChart.hourlyReports.length ? (
-            <div className="home-intraday-reports">
-              {trendChart.hourlyReports.map((report) => (
-                <span key={report.key}>
-                  <b>{report.label}</b>
-                  <strong>{report.temperatureText}</strong>
-                </span>
-              ))}
-            </div>
-          ) : null}
-        </div>
-      ) : null}
-
-      {forecastDays.length ? (
-        <div className="home-card-section forecast">
-          <h3>{locale === "en-US" ? "Multi-day forecast" : "多日预报"}</h3>
-          <div className="home-forecast-grid">
-            {forecastDays.map((day) => (
-              <div key={day.key} className="home-forecast-item">
-                <span>{day.label}</span>
-                <strong>{formatTemperature(day.maxTemp, symbol)}</strong>
-              </div>
-            ))}
-          </div>
-        </div>
-      ) : null}
-
-      <div className="home-card-section probability">
-        <h3>
-          {probabilityTitle} <small>{dayMaxLabel}</small>
-        </h3>
-        {displayedProbabilities.length ? (
-          <div className="home-probability-ladder">
-            {displayedProbabilities.map((bucket, index) => {
-              const probability = normalizeProbabilityValue(bucket.probability);
-              const width =
-                probability == null
-                  ? 6
-                  : Math.max(6, Math.min(100, probability * 100));
-              return (
-                <div
-                  key={`${getProbabilityLabel(bucket, symbol)}-${index}`}
-                  className="home-probability-ladder-row"
-                >
-                  <span className="home-probability-threshold">
-                    {getProbabilityLabel(bucket, symbol)}
-                  </span>
-                  <div className="home-probability-track">
-                    <i style={{ width: `${width}%` }} />
-                  </div>
-                  <strong>{formatProbability(probability)}</strong>
-                </div>
-              );
-            })}
-          </div>
-        ) : (
-          <div className="home-card-empty">
-            {isLoading
-              ? locale === "en-US"
-                ? "Loading probabilities..."
-                : "正在加载概率..."
-              : locale === "en-US"
-                ? "Probability layer pending."
-                : "概率层待加载。"}
-          </div>
-        )}
-      </div>
-
-      {tradableOpportunity ? (
-        <div className={clsx("home-card-section market", !isPro && "locked")}>
-          <div className="home-market-header">
-            <h3>
-              {marketTitle} <small>ⓘ</small>
-            </h3>
-            <span>
-              {marketScan && !tradableOpportunity
-                ? locale === "en-US"
-                  ? "Market closed"
-                  : "市场已结束"
-                : locale === "en-US"
-                  ? "Updated now"
-                  : "刚刚更新"}
-            </span>
-          </div>
-          <div className="home-market-ticket">
-            <div className="home-market-question">
-              <strong>{marketLabel}</strong>
-              <span>
-                {marketScan?.selected_date || detail?.local_date || ""}
-              </span>
-            </div>
-            <div className="home-market-prices">
-              <span className="yes">YES {formatCents(yesPrice)}</span>
-              <span className="no">NO {formatCents(noPrice)}</span>
-            </div>
-          </div>
-          <div className="home-market-metrics">
-            <span>
-              {marketEdgeLabel} <strong>{formatEdge(marketEdge)}</strong>
-            </span>
-            <span>
-              {marketImpliedLabel}{" "}
-              <strong>{formatProbability(marketProbability)}</strong>
-            </span>
-            <span>
-              {marketModelLabel}{" "}
-              <strong>{formatProbability(marketModelProbability)}</strong>
-            </span>
-            <svg viewBox="0 0 96 32" aria-hidden="true">
-              {sparklinePoints ? <polyline points={sparklinePoints} /> : null}
-            </svg>
-          </div>
-          {!isPro ? (
-            <Link href="/account" className="home-market-lock">
-              {locale === "en-US" ? "Unlock market layer" : "解锁市场层"}
-            </Link>
-          ) : null}
-        </div>
-      ) : null}
-    </aside>
-  );
-}
-
-function OpportunityStrip({
-  snapshots,
-  marketScanStatusByCity,
-  scanTargetNames,
-}: {
-  snapshots: CitySnapshot[];
-  marketScanStatusByCity: Record<string, OpportunityScanState>;
-  scanTargetNames: string[];
-}) {
-  const { locale } = useI18n();
-  const store = useDashboardStore();
-  const stripState = useMemo(() => {
-    const targetSet = new Set(scanTargetNames);
-    const totalCount = scanTargetNames.length || snapshots.length;
-    const targetedSnapshots = (
-      scanTargetNames.length
-        ? snapshots.filter((snapshot) => targetSet.has(snapshot.city.name))
-        : snapshots
-    ).slice(0, Math.max(scanTargetNames.length, 1));
-    const effectiveStatusByCity = Object.fromEntries(
-      targetedSnapshots.map((snapshot) => [
-        snapshot.city.name,
-        marketScanStatusByCity[snapshot.city.name] ||
-          (snapshot.detail?.market_scan ? "complete" : undefined),
-      ]),
-    ) as Record<string, OpportunityScanState | undefined>;
-    const completedCount = scanTargetNames.filter((cityName) => {
-      const status = effectiveStatusByCity[cityName];
-      return status === "complete" || status === "empty";
-    }).length;
-    const pendingCount = scanTargetNames.filter(
-      (cityName) => effectiveStatusByCity[cityName] === "pending",
-    ).length;
-    const errorCount = scanTargetNames.filter(
-      (cityName) => effectiveStatusByCity[cityName] === "error",
-    ).length;
-    const liveSnapshots = targetedSnapshots.filter((snapshot) =>
-      isLiveMarketScan(snapshot.detail),
-    );
-    const tradableSnapshots = targetedSnapshots
-      .filter((snapshot) => snapshot.tradableOpportunity)
-      .sort((left, right) => {
-        const leftEdge = normalizeEdgePercent(getMarketEdgeValue(left.detail));
-        const rightEdge = normalizeEdgePercent(
-          getMarketEdgeValue(right.detail),
-        );
-        return (
-          Number(rightEdge ?? Number.NEGATIVE_INFINITY) -
-            Number(leftEdge ?? Number.NEGATIVE_INFINITY) ||
-          right.score - left.score
-        );
-      });
-    const yesCount = liveSnapshots.filter(
-      (snapshot) => getBestSide(snapshot.detail) === "yes",
-    ).length;
-    const noCount = liveSnapshots.filter(
-      (snapshot) => getBestSide(snapshot.detail) === "no",
-    ).length;
-    const avgTradableEdge = tradableSnapshots.length
-      ? tradableSnapshots.reduce((sum, snapshot) => {
-          const edge = normalizeEdgePercent(
-            getMarketEdgeValue(snapshot.detail),
-          );
-          return sum + (edge ?? 0);
-        }, 0) / tradableSnapshots.length
-      : null;
-    const bestTradableSnapshot = tradableSnapshots[0] || null;
-    const bestTradableEdge = bestTradableSnapshot
-      ? normalizeEdgePercent(getMarketEdgeValue(bestTradableSnapshot.detail))
-      : null;
-    const bestTradableCityName = bestTradableSnapshot
-      ? getLocalizedCityDisplay(
-          bestTradableSnapshot.city,
-          locale,
-          bestTradableSnapshot.summary,
-          bestTradableSnapshot.detail,
-        )
-      : "--";
-    const quoteReadyCount = liveSnapshots.filter((snapshot) => {
-      const marketScan = snapshot.detail?.market_scan;
-      return Boolean(
-        marketScan?.price_analysis?.available ||
-        marketScan?.yes_buy != null ||
-        marketScan?.no_buy != null,
-      );
-    }).length;
-    const noEdgeCount = Math.max(quoteReadyCount - tradableSnapshots.length, 0);
-
-    return {
-      items: tradableSnapshots.slice(0, 5),
-      totalCount,
-      completedCount,
-      pendingCount,
-      errorCount,
-      liveCount: liveSnapshots.length,
-      tradableCount: tradableSnapshots.length,
-      noEdgeCount,
-      yesCount,
-      noCount,
-      avgTradableEdge,
-      bestTradableSnapshot,
-      bestTradableEdge,
-      bestTradableCityName,
-      summaryCards: [
-        {
-          key: "data-chain",
-          title: locale === "en-US" ? "Data Chain" : "数据链路",
-          items: [
-            {
-              label: locale === "en-US" ? "Markets" : "市场列表",
-              value: "Gamma",
-              accent: "cyan" as const,
-            },
-            {
-              label: locale === "en-US" ? "Quotes" : "盘口价格",
-              value: "CLOB",
-              accent: "green" as const,
-            },
-            {
-              label: locale === "en-US" ? "Model" : "概率模型",
-              value: "EMOS",
-              accent: "amber" as const,
-            },
-          ],
-        },
-        {
-          key: "refresh-cadence",
-          title: locale === "en-US" ? "Refresh Cadence" : "刷新节奏",
-          items: [
-            {
-              label: locale === "en-US" ? "Market list" : "市场列表",
-              value: "60s",
-              accent: "green" as const,
-            },
-            {
-              label: locale === "en-US" ? "YES/NO ask" : "YES/NO 买价",
-              value: "30s",
-              accent: "cyan" as const,
-            },
-            {
-              label: locale === "en-US" ? "Mode" : "模式",
-              value: "REST",
-            },
-          ],
-        },
-        {
-          key: "quote-filter",
-          title: locale === "en-US" ? "Quote Filter" : "报价筛选",
-          items: [
-            {
-              label: locale === "en-US" ? "Live" : "在线",
-              value: String(liveSnapshots.length),
-              accent: "green" as const,
-            },
-            {
-              label: locale === "en-US" ? "Quoted" : "已报价",
-              value: String(quoteReadyCount),
-              accent: "cyan" as const,
-            },
-            {
-              label: locale === "en-US" ? "No edge" : "无正 edge",
-              value: String(noEdgeCount),
-              accent: "amber" as const,
-            },
-          ],
-        },
-        {
-          key: "edge-decision",
-          title: locale === "en-US" ? "Decision" : "交易决策",
-          items: [
-            {
-              label: locale === "en-US" ? "Actionable" : "可买",
-              value: String(tradableSnapshots.length),
-              accent: "green" as const,
-            },
-            {
-              label: locale === "en-US" ? "Avg edge" : "平均 edge",
-              value: formatEdge(avgTradableEdge),
-              accent: avgTradableEdge != null ? ("cyan" as const) : undefined,
-            },
-            {
-              label: locale === "en-US" ? "Focus" : "当前标的",
-              value: bestTradableCityName,
-              accent: "amber" as const,
-            },
-          ],
-        },
-      ],
-      overviewKicker:
-        locale === "en-US"
-          ? "EMOS / CLOB Decision Layer"
-          : "EMOS / CLOB 交易决策层",
-      headingTitle:
-        tradableSnapshots.length > 0
-          ? locale === "en-US"
-            ? `${getTradeSideLabel(getBestSide(bestTradableSnapshot?.detail), locale)} · ${bestTradableCityName} · ${formatEdge(bestTradableEdge)}`
-            : `${getTradeSideLabel(getBestSide(bestTradableSnapshot?.detail), locale)} · ${bestTradableCityName} · ${formatEdge(bestTradableEdge)}`
-          : pendingCount > 0
-            ? locale === "en-US"
-              ? "Waiting for executable CLOB quotes"
-              : "等待可执行 CLOB 报价"
-            : locale === "en-US"
-              ? "No positive EMOS edge right now"
-              : "当前没有正 EMOS edge",
-      overviewBody:
-        tradableSnapshots.length > 0
-          ? locale === "en-US"
-            ? "Decision rule: compare the EMOS probability for the exact market bucket with the executable YES/NO ask from CLOB REST."
-            : "决策规则：把具体温度桶的 EMOS 概率和 CLOB REST 可买 YES/NO 价格相减，只展示正 edge 的买入方向。"
-          : pendingCount > 0
-            ? locale === "en-US"
-              ? "Gamma active markets are cached separately; the faster loop waits for CLOB YES/NO asks and recalculates edge locally."
-              : "Gamma 活跃市场单独缓存；更快的价格层等待 CLOB YES/NO 买价后在本地重算 edge。"
-            : locale === "en-US"
-              ? "Markets can still be live, but the current asks are not cheaper than the EMOS distribution."
-              : "盘口可以在线，但当前 ask 没有低于 EMOS 概率分布，先不提示买入。",
-      tapeStats: [
-        {
-          key: "coverage",
-          label: locale === "en-US" ? "Gamma" : "Gamma 市场",
-          value: "60s",
-          accent: "cyan" as const,
-        },
-        {
-          key: "live",
-          label: locale === "en-US" ? "CLOB asks" : "CLOB 报价",
-          value: "30s",
-          accent: "green" as const,
-        },
-        {
-          key: "tradable",
-          label: locale === "en-US" ? "Actionable" : "可买机会",
-          value: String(tradableSnapshots.length),
-          accent: "cyan" as const,
-        },
-        {
-          key: "avg-edge",
-          label: locale === "en-US" ? "Best edge" : "最佳 edge",
-          value: formatEdge(bestTradableEdge),
-          accent:
-            bestTradableEdge != null ? ("green" as const) : ("slate" as const),
-        },
-      ],
-      yesCountLabel:
-        locale === "en-US" ? `BUY YES ${yesCount}` : `买 YES ${yesCount}`,
-      noCountLabel:
-        locale === "en-US" ? `BUY NO ${noCount}` : `买 NO ${noCount}`,
-    };
-  }, [locale, marketScanStatusByCity, scanTargetNames, snapshots]);
-
-  if (!snapshots.length) return null;
-
-  const heroSnapshot = stripState.bestTradableSnapshot;
-  const heroSide = getBestSide(heroSnapshot?.detail);
-  const heroSignalLabel = getTradeSideLabel(heroSide, locale);
-  const heroSignalTone = heroSignalLabel.toLowerCase().includes("yes")
-    ? "yes"
-    : heroSignalLabel.toLowerCase().includes("no")
-      ? "no"
-      : "neutral";
-  const heroBestAsk = getBestSideAsk(heroSnapshot?.detail);
-  const heroBestProbability = getBestSideModelProbability(heroSnapshot?.detail);
-  const heroBestEdge = getExecutableEdgePercent(heroSnapshot?.detail);
-  const heroSymbol = heroSnapshot
-    ? getTempSymbol(
-        heroSnapshot.city,
-        heroSnapshot.summary,
-        heroSnapshot.detail,
-      )
-    : "°C";
-  const heroTier =
-    heroSnapshot?.city.deb_recent_tier ||
-    heroSnapshot?.city.risk_level ||
-    heroSnapshot?.summary?.risk?.level ||
-    heroSnapshot?.detail?.risk?.level;
-  const heroCityName = heroSnapshot
-    ? getLocalizedCityDisplay(
-        heroSnapshot.city,
-        locale,
-        heroSnapshot.summary,
-        heroSnapshot.detail,
-      )
-    : "--";
-  const heroMarketBucket =
-    heroSnapshot?.detail?.market_scan?.temperature_bucket;
-  const heroQuestion =
-    heroSnapshot?.detail?.market_scan?.primary_market?.question ||
-    `${getProbabilityLabel(heroMarketBucket || {}, heroSymbol)} ${
-      heroSnapshot?.detail?.market_scan?.selected_date ||
-      heroSnapshot?.detail?.local_date ||
-      ""
-    }`;
-  const heroEngine = (() => {
-    const engine = String(
-      heroSnapshot?.detail?.market_scan?.probability_engine || "EMOS",
-    ).toLowerCase();
-    if (engine.includes("emos")) return "EMOS";
-    return engine.toUpperCase();
-  })();
-  const heroSpread = heroSnapshot?.detail?.market_scan?.spread ?? null;
-  const heroQuoteSource = heroSnapshot?.detail?.market_scan?.quote_source
-    ? String(heroSnapshot.detail.market_scan.quote_source)
-        .replaceAll("_", " ")
-        .toUpperCase()
-    : "CLOB REST";
-  const heroDateLabel =
-    heroSnapshot?.detail?.market_scan?.selected_date ||
-    heroSnapshot?.detail?.local_date ||
-    "--";
-  const heroSparkline = buildSparklinePoints(
-    heroSnapshot?.detail?.market_scan?.sparkline?.length
-      ? heroSnapshot.detail.market_scan.sparkline
-      : [
-          Number(
-            heroSnapshot?.summary?.current?.temp ??
-              heroSnapshot?.detail?.current?.temp ??
-              0,
-          ),
-          Number(
-            heroSnapshot?.summary?.deb?.prediction ??
-              heroSnapshot?.detail?.deb?.prediction ??
-              0,
-          ),
-          Number(
-            heroSnapshot?.detail?.forecast?.today_high ??
-              heroSnapshot?.summary?.deb?.prediction ??
-              0,
-          ),
-        ],
-  );
-  const secondaryItems = heroSnapshot
-    ? stripState.items.filter(
-        (snapshot) => snapshot.city.name !== heroSnapshot.city.name,
-      )
-    : [];
-
-  return (
-    <section
-      className="home-opportunity-strip"
-      aria-label={locale === "en-US" ? "Opportunity strip" : "机会条"}
-    >
-      <div className="opportunity-strip-topline">
-        <div className="opportunity-strip-copy">
-          <span className="opportunity-strip-kicker">
-            {stripState.overviewKicker}
-          </span>
-          <strong>{stripState.headingTitle}</strong>
-          <p>{stripState.overviewBody}</p>
-        </div>
-        <div className="opportunity-strip-tape" aria-live="polite">
-          {stripState.tapeStats.map((item) => (
-            <div
-              key={item.key}
-              className={clsx(
-                "opportunity-tape-pill",
-                item.accent ? `accent-${item.accent}` : undefined,
-              )}
-            >
-              <span>{item.label}</span>
-              <strong>{item.value}</strong>
-            </div>
-          ))}
-          <div className="opportunity-tape-pill accent-outline">
-            <span>{stripState.yesCountLabel}</span>
-          </div>
-          <div className="opportunity-tape-pill accent-outline">
-            <span>{stripState.noCountLabel}</span>
-          </div>
-        </div>
-      </div>
-      {heroSnapshot ? (
-        <>
-          <div className="opportunity-strip-main">
-            <button
-              type="button"
-              className="opportunity-hero-card"
-              onClick={() => void store.focusCity(heroSnapshot.city.name)}
-            >
-              <div className="opportunity-hero-header">
-                <div className="opportunity-hero-copy">
-                  <span className="opportunity-hero-kicker">
-                    {locale === "en-US" ? "Current action" : "当前交易建议"}
-                  </span>
-                  <div className="opportunity-hero-title-row">
-                    <strong>{heroCityName}</strong>
-                    <span className="opportunity-hero-date">
-                      {heroDateLabel}
-                    </span>
-                  </div>
-                  <p>{heroQuestion}</p>
-                </div>
-                <div className="opportunity-hero-tags">
-                  <span className="opportunity-hero-tag engine">
-                    {heroEngine}
-                  </span>
-                  <span
-                    className={clsx(
-                      "opportunity-hero-tag",
-                      `signal-${heroSignalTone}`,
-                    )}
-                  >
-                    {heroSignalLabel}
-                  </span>
-                  <span
-                    className={clsx(
-                      "opportunity-hero-tag",
-                      "risk",
-                      String(heroTier || "other"),
-                    )}
-                  >
-                    {getRiskCopy(heroTier as RiskLevel | undefined, locale)}
-                  </span>
-                </div>
-              </div>
-
-              <div className="opportunity-hero-body">
-                <div className="opportunity-hero-edgeblock">
-                  <span>
-                    {locale === "en-US" ? "Trade now" : "现在值得买什么"}
-                  </span>
-                  <strong>
-                    {heroSide
-                      ? `${getTradeSideLabel(heroSide, locale)} ${formatCents(heroBestAsk)}`
-                      : locale === "en-US"
-                        ? "WATCH"
-                        : "观察"}
-                  </strong>
-                  <em>
-                    {getTradeFormulaLabel(heroSide, locale)} ·{" "}
-                    {locale === "en-US" ? "edge" : "优势"}{" "}
-                    {formatEdge(heroBestEdge)}
-                  </em>
-                </div>
-
-                <div className="opportunity-hero-metrics">
-                  <div className="opportunity-hero-metric">
-                    <span>
-                      {heroSide === "no"
-                        ? locale === "en-US"
-                          ? "EMOS P(NO)"
-                          : "EMOS P(NO)"
-                        : locale === "en-US"
-                          ? "EMOS P(YES)"
-                          : "EMOS P(YES)"}
-                    </span>
-                    <strong>{formatProbability(heroBestProbability)}</strong>
-                  </div>
-                  <div className="opportunity-hero-metric">
-                    <span>{locale === "en-US" ? "CLOB ask" : "CLOB 买价"}</span>
-                    <strong>{formatCents(heroBestAsk)}</strong>
-                  </div>
-                  <div className="opportunity-hero-metric">
-                    <span>{locale === "en-US" ? "Edge" : "Edge"}</span>
-                    <strong>{formatEdge(heroBestEdge)}</strong>
-                  </div>
-                  <div className="opportunity-hero-metric">
-                    <span>{locale === "en-US" ? "Spread" : "盘口点差"}</span>
-                    <strong>{formatCents(heroSpread)}</strong>
-                  </div>
-                </div>
-
-                <div className="opportunity-hero-book">
-                  <div className="opportunity-book-side yes">
-                    <span>YES</span>
-                    <strong>
-                      {formatCents(heroSnapshot.detail?.market_scan?.yes_buy)}
-                    </strong>
-                  </div>
-                  <div className="opportunity-book-side no">
-                    <span>NO</span>
-                    <strong>
-                      {formatCents(heroSnapshot.detail?.market_scan?.no_buy)}
-                    </strong>
-                  </div>
-                  <div className="opportunity-hero-sparkline-wrap">
-                    <span>
-                      {locale === "en-US" ? "Market path" : "盘口走势"}
-                    </span>
-                    <svg
-                      className="opportunity-hero-sparkline"
-                      viewBox="0 0 132 44"
-                      aria-hidden="true"
-                    >
-                      {heroSparkline ? (
-                        <polyline points={heroSparkline} />
-                      ) : null}
-                    </svg>
-                  </div>
-                </div>
-              </div>
-
-              <div className="opportunity-hero-footer">
-                <span>
-                  {heroQuoteSource} ·{" "}
-                  {locale === "en-US"
-                    ? "click for city detail"
-                    : "点击查看城市详情"}
-                </span>
-                <span>
-                  {locale === "en-US"
-                    ? `Avg edge ${formatEdge(stripState.avgTradableEdge)}`
-                    : `平均优势 ${formatEdge(stripState.avgTradableEdge)}`}
-                </span>
-              </div>
-            </button>
-
-            <div className="opportunity-side-grid">
-              {stripState.summaryCards.map((card) => (
-                <div key={card.key} className="opportunity-side-tile">
-                  <div className="opportunity-side-tile-head">
-                    <span>{card.title}</span>
-                  </div>
-                  <div className="opportunity-side-tile-body">
-                    {card.items.map((item) => (
-                      <div
-                        key={`${card.key}-${item.label}`}
-                        className="opportunity-side-stat"
-                      >
-                        <b
-                          className={
-                            item.accent ? `accent-${item.accent}` : undefined
-                          }
-                        >
-                          {item.value}
-                        </b>
-                        <span>{item.label}</span>
-                      </div>
-                    ))}
-                  </div>
-                </div>
-              ))}
-            </div>
-          </div>
-
-          {secondaryItems.length > 0 ? (
-            <div className="opportunity-mini-grid">
-              {secondaryItems.map(({ city, detail, summary }, index) => {
-                const symbol = getTempSymbol(city, summary, detail);
-                const debPrediction =
-                  summary?.deb?.prediction ?? detail?.deb?.prediction;
-                const localizedCityName = getLocalizedCityDisplay(
-                  city,
-                  locale,
-                  summary,
-                  detail,
-                );
-                const tier =
-                  city.deb_recent_tier ||
-                  city.risk_level ||
-                  summary?.risk?.level ||
-                  detail?.risk?.level;
-                const marketBucket = detail?.market_scan?.temperature_bucket;
-                const marketQuestion =
-                  detail?.market_scan?.primary_market?.question ||
-                  `${getProbabilityLabel(marketBucket || {}, symbol)} ${
-                    detail?.market_scan?.selected_date ||
-                    detail?.local_date ||
-                    ""
-                  }`;
-                const opportunitySparkline = buildSparklinePoints(
-                  detail?.market_scan?.sparkline?.length
-                    ? detail.market_scan.sparkline
-                    : [
-                        Number(
-                          summary?.current?.temp ?? detail?.current?.temp ?? 0,
-                        ),
-                        Number(debPrediction ?? 0),
-                        Number(
-                          detail?.forecast?.today_high ?? debPrediction ?? 0,
-                        ),
-                      ],
-                );
-                return (
-                  <button
-                    key={city.name}
-                    type="button"
-                    className="opportunity-mini-card"
-                    onClick={() => void store.focusCity(city.name)}
-                  >
-                    <div className="opportunity-mini-head">
-                      <div className="opportunity-mini-rank">
-                        <span>{index + 2}</span>
-                      </div>
-                      <div className="opportunity-mini-copy">
-                        <strong>{localizedCityName}</strong>
-                        <p>{marketQuestion}</p>
-                      </div>
-                      <span
-                        className={clsx(
-                          "opportunity-pill",
-                          String(tier || "other"),
-                        )}
-                      >
-                        {getRiskCopy(tier as RiskLevel | undefined, locale)}
-                      </span>
-                    </div>
-
-                    <div className="opportunity-mini-metrics">
-                      <span className="opportunity-yes">
-                        YES {formatCents(detail?.market_scan?.yes_buy)}
-                      </span>
-                      <span className="opportunity-no">
-                        NO {formatCents(detail?.market_scan?.no_buy)}
-                      </span>
-                      <span className="opportunity-edge">
-                        {formatEdge(getMarketEdgeValue(detail))}
-                      </span>
-                    </div>
-
-                    <svg
-                      className="opportunity-mini-sparkline"
-                      viewBox="0 0 96 28"
-                      aria-hidden="true"
-                    >
-                      {opportunitySparkline ? (
-                        <polyline points={opportunitySparkline} />
-                      ) : null}
-                    </svg>
-                  </button>
-                );
-              })}
-            </div>
-          ) : null}
-        </>
-      ) : (
-        <div className="opportunity-empty-state">
-          <div className="opportunity-empty-copy">
-            <strong>
-              {stripState.pendingCount > 0
-                ? locale === "en-US"
-                  ? "Scanning current market layers..."
-                  : "正在扫描当前市场层..."
-                : locale === "en-US"
-                  ? "No tradable market at the moment"
-                  : "当前没有满足条件的可交易市场"}
-            </strong>
-            <span>
-              {locale === "en-US"
-                ? `Completed ${stripState.completedCount}/${stripState.totalCount} cities, live ${stripState.liveCount}, tradable ${stripState.tradableCount}.`
-                : `已完成 ${stripState.completedCount}/${stripState.totalCount} 个城市扫描，在线盘口 ${stripState.liveCount} 个，可交易机会 ${stripState.tradableCount} 个。`}
-            </span>
-          </div>
-        </div>
-      )}
-    </section>
-  );
-}
-
-function HomeAssistantDock({ snapshots }: { snapshots: CitySnapshot[] }) {
-  const store = useDashboardStore();
-  const { locale } = useI18n();
-  const [isOpen, setIsOpen] = useState(false);
-  const [showPaywall, setShowPaywall] = useState(false);
-  const [loading, setLoading] = useState(false);
-  const [input, setInput] = useState("");
-  const [messages, setMessages] = useState<AssistantMessage[]>([]);
-  const [error, setError] = useState<string | null>(null);
-  const [isDragging, setIsDragging] = useState(false);
-  const [dockPosition, setDockPosition] =
-    useState<AssistantDockPosition | null>(null);
-  const dockRef = useRef<HTMLDivElement | null>(null);
-  const dockPositionRef = useRef<AssistantDockPosition | null>(null);
-  const dragStateRef = useRef<AssistantDockDragState | null>(null);
-  const suppressLauncherClickRef = useRef(false);
-  const messagesEndRef = useRef<HTMLDivElement | null>(null);
-
-  const selectedSnapshot = useMemo(
-    () =>
-      store.selectedCity
-        ? snapshots.find(
-            (snapshot) => snapshot.city.name === store.selectedCity,
-          ) || null
-        : null,
-    [snapshots, store.selectedCity],
-  );
-  const selectedCityName = selectedSnapshot
-    ? getLocalizedCityDisplay(
-        selectedSnapshot.city,
-        locale,
-        selectedSnapshot.summary,
-        selectedSnapshot.detail,
-      )
-    : null;
-  const assistantContext = useMemo(
-    () => buildAssistantContextPayload(snapshots, store.selectedCity, locale),
-    [locale, snapshots, store.selectedCity],
-  );
-  const starterPrompts = useMemo(() => {
-    if (locale === "en-US") {
-      return [
-        selectedCityName
-          ? `What is today's forecast high for ${selectedCityName}?`
-          : "What is today's forecast high for the focus city?",
-        "Which market is worth buying now?",
-        "Rank current opportunities by edge",
-      ];
-    }
-    return [
-      selectedCityName
-        ? `${selectedCityName} 今天预测最高温是多少？`
-        : "当前焦点城市今天预测最高温是多少？",
-      "当前有哪些值得参与的市场？",
-      "按 edge 排序",
-    ];
-  }, [locale, selectedCityName]);
-
-  useEffect(() => {
-    if (messages.length) return;
-    setMessages([
-      {
-        id: "assistant-greeting",
-        role: "assistant",
-        content: buildAssistantGreeting(locale, selectedCityName),
-      },
-    ]);
-  }, [locale, messages.length, selectedCityName]);
-
-  useEffect(() => {
-    messagesEndRef.current?.scrollIntoView({
-      behavior: loading ? "auto" : "smooth",
-      block: "end",
-    });
-  }, [loading, messages]);
-
-  useEffect(() => {
-    dockPositionRef.current = dockPosition;
-  }, [dockPosition]);
-
-  useEffect(() => {
-    if (typeof window === "undefined") return;
-
-    const syncDockPosition = () => {
-      if (window.innerWidth <= 960) {
-        setDockPosition(null);
-        return;
-      }
-      setDockPosition((current) =>
-        clampAssistantDockPosition(
-          current ||
-            readAssistantDockPosition() ||
-            getDefaultAssistantDockPosition(),
-          dockRef.current,
-        ),
-      );
-    };
-
-    syncDockPosition();
-    window.addEventListener("resize", syncDockPosition);
-    return () => {
-      window.removeEventListener("resize", syncDockPosition);
-    };
-  }, [isOpen]);
-
-  const openAssistant = () => {
-    if (suppressLauncherClickRef.current) {
-      suppressLauncherClickRef.current = false;
-      return;
-    }
-    if (!store.proAccess.subscriptionActive) {
-      setShowPaywall(true);
-      return;
-    }
-    setIsOpen(true);
-  };
-
-  const beginDockDrag = (event: ReactPointerEvent<HTMLElement>) => {
-    if (typeof window === "undefined" || window.innerWidth <= 960) return;
-    const basePosition = clampAssistantDockPosition(
-      dockPositionRef.current ||
-        readAssistantDockPosition() ||
-        getDefaultAssistantDockPosition(),
-      dockRef.current,
-    );
-    if (!basePosition) return;
-    event.preventDefault();
-    event.stopPropagation();
-    dragStateRef.current = {
-      pointerId: event.pointerId,
-      startX: event.clientX,
-      startY: event.clientY,
-      right: basePosition.right,
-      bottom: basePosition.bottom,
-      hasMoved: false,
-    };
-    event.currentTarget.setPointerCapture?.(event.pointerId);
-    dockPositionRef.current = basePosition;
-    setDockPosition(basePosition);
-    setIsDragging(true);
-  };
-
-  const updateDockDrag = (event: ReactPointerEvent<HTMLElement>) => {
-    const dragState = dragStateRef.current;
-    if (!dragState || dragState.pointerId !== event.pointerId) return;
-    event.preventDefault();
-    event.stopPropagation();
-    const deltaX = event.clientX - dragState.startX;
-    const deltaY = event.clientY - dragState.startY;
-    if (!dragState.hasMoved && Math.abs(deltaX) + Math.abs(deltaY) >= 6) {
-      dragState.hasMoved = true;
-    }
-    const nextPosition = clampAssistantDockPosition(
-      {
-        right: dragState.right - deltaX,
-        bottom: dragState.bottom - deltaY,
-      },
-      dockRef.current,
-    );
-    if (!nextPosition) return;
-    dockPositionRef.current = nextPosition;
-    setDockPosition(nextPosition);
-  };
-
-  const endDockDrag = (event: ReactPointerEvent<HTMLElement>) => {
-    const dragState = dragStateRef.current;
-    if (!dragState || dragState.pointerId !== event.pointerId) return;
-    event.preventDefault();
-    event.stopPropagation();
-    try {
-      if (event.currentTarget.hasPointerCapture?.(event.pointerId)) {
-        event.currentTarget.releasePointerCapture(event.pointerId);
-      }
-    } catch {
-      // Pointer capture may already be released by the browser.
-    }
-    const finalPosition = clampAssistantDockPosition(
-      dockPositionRef.current || {
-        right: dragState.right,
-        bottom: dragState.bottom,
-      },
-      dockRef.current,
-    );
-    if (dragState.hasMoved) {
-      suppressLauncherClickRef.current = true;
-    }
-    dockPositionRef.current = finalPosition;
-    setDockPosition(finalPosition);
-    writeAssistantDockPosition(finalPosition);
-    dragStateRef.current = null;
-    setIsDragging(false);
-  };
-
-  const sendQuestion = async (rawQuestion?: string) => {
-    const question = String(rawQuestion ?? input).trim();
-    if (!question || loading) return;
-    if (!store.proAccess.subscriptionActive) {
-      setShowPaywall(true);
-      return;
-    }
-
-    setIsOpen(true);
-    setError(null);
-    setLoading(true);
-    setMessages((current) => [
-      ...current,
-      {
-        id: `user-${Date.now()}`,
-        role: "user",
-        content: question,
-      },
-    ]);
-    setInput("");
-
-    try {
-      const reply = await dashboardClient.askAssistant({
-        question,
-        locale,
-        snapshotId: assistantContext.snapshot_id,
-        context: assistantContext,
-      });
-      setMessages((current) => [
-        ...current,
-        {
-          id: `assistant-${Date.now()}`,
-          role: "assistant",
-          content: reply.answer,
-          cached: reply.cached,
-        },
-      ]);
-    } catch (submitError) {
-      const message = String(submitError);
-      if (message.includes("HTTP 402")) {
-        setShowPaywall(true);
-      } else {
-        setError(
-          locale === "en-US"
-            ? "Assistant is temporarily unavailable."
-            : "AI 助手暂时不可用。",
-        );
-      }
-    } finally {
-      setLoading(false);
-    }
-  };
-
-  const submitForm = async (event: FormEvent<HTMLFormElement>) => {
-    event.preventDefault();
-    await sendQuestion();
-  };
-
-  return (
-    <>
-      <div
-        ref={dockRef}
-        className={clsx(
-          "home-ai-assistant",
-          !isOpen && "collapsed",
-          isDragging && "dragging",
-        )}
-        style={
-          dockPosition
-            ? {
-                right: `${dockPosition.right}px`,
-                bottom: `${dockPosition.bottom}px`,
-              }
-            : undefined
-        }
-      >
-        {!isOpen ? (
-          <button
-            type="button"
-            className="home-ai-launcher"
-            onClick={openAssistant}
-            onPointerDown={beginDockDrag}
-            onPointerMove={updateDockDrag}
-            onPointerUp={endDockDrag}
-            onPointerCancel={endDockDrag}
-            aria-label={
-              locale === "en-US" ? "Open AI assistant" : "打开 AI 助手"
-            }
-          >
-            <Image
-              src="/favicon-32x32.png"
-              alt=""
-              width={22}
-              height={22}
-              className="home-ai-launcher-icon"
-            />
-          </button>
-        ) : (
-          <section
-            className="home-ai-panel"
-            aria-label={locale === "en-US" ? "AI assistant" : "AI 助手"}
-          >
-            <div className="home-ai-header">
-              <div>
-                <strong>
-                  {locale === "en-US" ? "AI assistant" : "AI 对话助手"}
-                </strong>
-                <span>
-                  {locale === "en-US"
-                    ? "Ask about cities, forecast highs, edge, and live opportunities"
-                    : "可直接问城市、最高温预测、edge 和实时市场机会"}
-                </span>
-              </div>
-              <div className="home-ai-header-actions">
-                <span
-                  className="home-ai-drag-handle"
-                  title={locale === "en-US" ? "Move assistant" : "拖动助手"}
-                  aria-hidden="true"
-                  onPointerDown={beginDockDrag}
-                  onPointerMove={updateDockDrag}
-                  onPointerUp={endDockDrag}
-                  onPointerCancel={endDockDrag}
-                >
-                  ⋮⋮
-                </span>
-                <button
-                  type="button"
-                  className="home-ai-close"
-                  onClick={() => setIsOpen(false)}
-                  aria-label={
-                    locale === "en-US" ? "Close assistant" : "关闭 AI 助手"
-                  }
-                >
-                  ×
-                </button>
-              </div>
-            </div>
-
-            <div className="home-ai-disclaimer">
-              {locale === "en-US"
-                ? "You can ask about current temperature, today's forecast high, market opportunities, edge, and risk reasons."
-                : "可直接问当前温度、今日最高温、市场机会、edge 和风险原因。"}
-            </div>
-
-            <div className="home-ai-messages">
-              {messages.map((message) => (
-                <div
-                  key={message.id}
-                  className={clsx(
-                    "home-ai-message",
-                    message.role === "user" ? "user" : "assistant",
-                  )}
-                >
-                  <p>{message.content}</p>
-                  {message.cached ? (
-                    <small>
-                      {locale === "en-US" ? "Cache hit" : "命中缓存"}
-                    </small>
-                  ) : null}
-                </div>
-              ))}
-              {loading ? (
-                <div className="home-ai-message assistant loading">
-                  <p>
-                    {locale === "en-US" ? "Thinking..." : "正在整理答案..."}
-                  </p>
-                </div>
-              ) : null}
-              <div ref={messagesEndRef} />
-            </div>
-
-            <div className="home-ai-starters">
-              {starterPrompts.map((prompt) => (
-                <button
-                  key={prompt}
-                  type="button"
-                  className="home-ai-starter"
-                  onClick={() => void sendQuestion(prompt)}
-                >
-                  {prompt}
-                </button>
-              ))}
-            </div>
-
-            <form className="home-ai-composer" onSubmit={submitForm}>
-              <textarea
-                className="home-ai-input"
-                rows={3}
-                value={input}
-                onChange={(event) => setInput(event.target.value)}
-                placeholder={
-                  locale === "en-US"
-                    ? "Ask about a city's current temperature, today's forecast high, or market edge..."
-                    : "可以问某个城市当前温度、今日最高温，或者市场 edge..."
-                }
-              />
-              <div className="home-ai-composer-actions">
-                {error ? (
-                  <span className="home-ai-error">{error}</span>
-                ) : (
-                  <span />
-                )}
-                <button
-                  type="submit"
-                  className="home-ai-send"
-                  disabled={loading || !input.trim()}
-                >
-                  {locale === "en-US" ? "Ask" : "发送"}
-                </button>
-              </div>
-            </form>
-          </section>
-        )}
-      </div>
-
-      {showPaywall ? (
-        <div
-          className="home-ai-paywall-backdrop"
-          onClick={() => setShowPaywall(false)}
-        >
-          <div
-            className="home-ai-paywall-shell"
-            onClick={(event) => event.stopPropagation()}
-          >
-            <ProFeaturePaywall
-              feature="assistant"
-              onClose={() => setShowPaywall(false)}
-            />
-          </div>
-        </div>
-      ) : null}
-    </>
-  );
-}
-
-function DashboardScreen() {
-  const store = useDashboardStore();
-  const { t } = useI18n();
-  const didAutoFocusRef = useRef(false);
-  const marketScanInflightRef = useRef<Set<string>>(new Set());
-  const marketScanPollingRef = useRef(false);
-  const [marketScanStatusByCity, setMarketScanStatusByCity] = useState<
-    Record<string, OpportunityScanState>
-  >({});
-  const activeSummary = store.selectedCity
-    ? store.citySummariesByName[store.selectedCity] || null
-    : null;
-  const activeCityName =
-    store.selectedDetail?.display_name ||
-    activeSummary?.display_name ||
-    store.cities.find((city) => city.name === store.selectedCity)
-      ?.display_name ||
-    store.selectedCity ||
-    "";
-
-  useEffect(() => {
-    const onKeyDown = (event: KeyboardEvent) => {
-      if (event.key !== "Escape") return;
-      if (store.futureModalDate) {
-        store.closeFutureModal();
-        return;
-      }
-      if (store.historyState.isOpen) {
-        store.closeHistory();
-        return;
-      }
-      if (store.isPanelOpen) {
-        store.closePanel();
-      }
-    };
-
-    window.addEventListener("keydown", onKeyDown);
-    return () => {
-      window.removeEventListener("keydown", onKeyDown);
-    };
-  }, [store]);
-
-  // Avoid full-page flashing on initial load; only show this overlay for manual refresh.
-  const showLoading = store.loadingState.cities || store.loadingState.refresh;
-  const showCitySyncToast =
-    store.loadingState.cityDetail &&
-    activeCityName &&
-    !store.selectedDetail &&
-    !activeSummary;
-  const homepageSnapshots = useMemo(
-    () =>
-      store.cities
-        .map((city) =>
-          buildSnapshot(
-            city,
-            store.citySummariesByName[city.name] || null,
-            store.cityDetailsByName[city.name] || null,
-          ),
-        )
-        .sort(
-          (a, b) =>
-            b.score - a.score ||
-            a.city.display_name.localeCompare(b.city.display_name),
-        ),
-    [store.cities, store.cityDetailsByName, store.citySummariesByName],
-  );
-  const marketScanTargetNames = useMemo(
-    () => homepageSnapshots.map((snapshot) => snapshot.city.name),
-    [homepageSnapshots],
-  );
-  const showHomepageChrome =
-    !store.historyState.isOpen && !store.futureModalDate;
-
-  useEffect(() => {
-    if (didAutoFocusRef.current) return;
-    if (!showHomepageChrome) return;
-    if (store.selectedCity) return;
-    const topOpportunity = homepageSnapshots[0]?.city.name;
-    if (!topOpportunity) return;
-
-    didAutoFocusRef.current = true;
-    void store.focusCity(topOpportunity);
-  }, [homepageSnapshots, showHomepageChrome, store]);
-
-  useEffect(() => {
-    if (store.loadingState.refresh) {
-      marketScanInflightRef.current.clear();
-      setMarketScanStatusByCity({});
-    }
-  }, [store.loadingState.refresh]);
-
-  useEffect(() => {
-    if (store.proAccess.loading) return;
-    if (store.proAccess.authenticated) return;
-    marketScanInflightRef.current.clear();
-    setMarketScanStatusByCity({});
-  }, [store.proAccess.authenticated, store.proAccess.loading]);
-
-  useEffect(() => {
-    if (!showHomepageChrome) return;
-    if (store.proAccess.loading || !store.proAccess.authenticated) return;
-    if (!marketScanTargetNames.length) return;
-
-    let cancelled = false;
-    const queue = marketScanTargetNames.filter((cityName) => {
-      const status = marketScanStatusByCity[cityName];
-      const existingMarketScan = store.cityDetailsByName[cityName]?.market_scan;
-      return (
-        !status &&
-        !existingMarketScan &&
-        !marketScanInflightRef.current.has(cityName)
-      );
-    });
-    if (!queue.length) return;
-
-    const runWorker = async () => {
-      while (!cancelled) {
-        const cityName = queue.shift();
-        if (!cityName) return;
-        marketScanInflightRef.current.add(cityName);
-        setMarketScanStatusByCity((current) => ({
-          ...current,
-          [cityName]: "pending",
-        }));
-        try {
-          const existingDetail = store.cityDetailsByName[cityName];
-          const marketScan =
-            existingDetail?.market_scan ||
-            (await store.ensureCityMarketScan(cityName, false, { lite: true }));
-          if (cancelled) return;
-          setMarketScanStatusByCity((current) => ({
-            ...current,
-            [cityName]: marketScan ? "complete" : "empty",
-          }));
-        } catch {
-          if (cancelled) return;
-          setMarketScanStatusByCity((current) => ({
-            ...current,
-            [cityName]: "error",
-          }));
-        } finally {
-          marketScanInflightRef.current.delete(cityName);
-        }
-      }
-    };
-
-    void Promise.allSettled(
-      Array.from({ length: Math.min(2, queue.length) }, () => runWorker()),
-    );
-
-    return () => {
-      cancelled = true;
-    };
-  }, [
-    marketScanStatusByCity,
-    marketScanTargetNames,
-    showHomepageChrome,
-    store.cityDetailsByName,
-    store.ensureCityMarketScan,
-    store.proAccess.authenticated,
-    store.proAccess.loading,
-  ]);
-
-  useEffect(() => {
-    if (!showHomepageChrome) return;
-    if (store.proAccess.loading || !store.proAccess.authenticated) return;
-    if (!marketScanTargetNames.length) return;
-
-    let cancelled = false;
-    let intervalId: number | null = null;
-
-    const refreshAllMarketScans = async () => {
-      if (cancelled || marketScanPollingRef.current) return;
-      marketScanPollingRef.current = true;
-      const queue = [...marketScanTargetNames];
-      try {
-        const runWorker = async () => {
-          while (!cancelled) {
-            const cityName = queue.shift();
-            if (!cityName) return;
-            await store.ensureCityMarketScan(cityName, false, { lite: true });
-          }
-        };
-        await Promise.allSettled(
-          Array.from({ length: Math.min(3, queue.length) }, () => runWorker()),
-        );
-      } finally {
-        marketScanPollingRef.current = false;
-      }
-    };
-
-    void refreshAllMarketScans();
-    intervalId = window.setInterval(() => {
-      if (
-        typeof document !== "undefined" &&
-        document.visibilityState === "hidden"
-      ) {
-        return;
-      }
-      void refreshAllMarketScans();
-    }, HOME_OPPORTUNITY_REFRESH_MS);
-
-    return () => {
-      cancelled = true;
-      marketScanPollingRef.current = false;
-      if (intervalId != null) {
-        window.clearInterval(intervalId);
-      }
-    };
-  }, [
-    marketScanTargetNames,
-    showHomepageChrome,
-    store.ensureCityMarketScan,
-    store.proAccess.authenticated,
-    store.proAccess.loading,
-  ]);
-
-  return (
-    <div
-      className={clsx(
-        styles.root,
-        detailChromeStyles.root,
-        modalChromeStyles.root,
-      )}
-    >
-      <HeaderBar />
-      <div className="scan-terminal">
-        {/* Left — Filter Panel */}
-        <ScanFilterPanel />
-
-        {/* Center — Data Grid */}
-        <div className="scan-data-grid">
-          <div className="scan-data-grid-header">
-            <div>
-              <div className="scan-data-grid-title">
-                {t("dashboard.opportunityTitle") || "可交易机会"}
-              </div>
-              <div className="scan-data-grid-subtitle">
-                {t("dashboard.opportunitySubtitle") ||
-                  "基于当前时间、实况数据和模型预测，筛选出最具交易价值的市场"}
-              </div>
-            </div>
-          </div>
-
-          <ScanKPIBar
-            data={{
-              recommendedCount: homepageSnapshots.filter(
-                (s) => s.tradableOpportunity,
-              ).length,
-              avgEdge: (() => {
-                const tradable = homepageSnapshots.filter(
-                  (s) => s.tradableOpportunity,
-                );
-                if (!tradable.length) return null;
-                const sum = tradable.reduce((acc, s) => {
-                  const edge = normalizeEdgePercent(
-                    getMarketEdgeValue(s.detail),
-                  );
-                  return acc + (edge ?? 0);
-                }, 0);
-                return sum / tradable.length;
-              })(),
-              totalWinRate: (() => {
-                const tradable = homepageSnapshots.filter(
-                  (s) => s.tradableOpportunity,
-                );
-                if (!tradable.length) return null;
-                const modelProbs = tradable
-                  .map((s) => s.detail?.market_scan?.model_probability)
-                  .filter((v): v is number => v != null);
-                if (!modelProbs.length) return null;
-                return (
-                  (modelProbs.reduce((a, b) => a + b, 0) / modelProbs.length) *
-                  100
-                );
-              })(),
-              tradableMarkets: homepageSnapshots.filter(
-                (s) => s.tradableOpportunity,
-              ).length,
-              filteredTotal: homepageSnapshots.length,
-              totalVolume: homepageSnapshots.reduce((sum, s) => {
-                const vol =
-                  s.detail?.market_scan?.volume ??
-                  s.detail?.market_scan?.primary_market?.volume ??
-                  0;
-                return sum + vol;
-              }, 0),
-            }}
-          />
-
-          <div className="scan-view-tabs">
-            <button className="scan-view-tab active">
-              {t("dashboard.opportunityList")}
-            </button>
-            <button className="scan-view-tab">
-              {t("dashboard.distributionView")}
-            </button>
-            <button className="scan-view-tab">
-              {t("dashboard.calendarView")}
-            </button>
-          </div>
-
-          <OpportunityTable
-            rows={homepageSnapshots.map((snapshot, index) => ({
-              rank: index + 1,
-              city: snapshot.city,
-              summary: snapshot.summary,
-              detail: snapshot.detail,
-              score: snapshot.score,
-              tradable: snapshot.tradableOpportunity,
-            }))}
-          />
-        </div>
-
-        {/* Right — Scan Detail Panel */}
-        {store.selectedDetail ? (
-          <aside className="scan-detail-panel">
-            {/* City Header */}
-            <div className="scan-detail-header">
-              <span className="scan-detail-hero-placeholder" />
-              <div className="scan-detail-city-info">
-                <div className="scan-detail-city-name">{activeCityName}</div>
-                <div className="scan-detail-city-sub">
-                  {getScanStatusLabel(store.selectedDetail)}
-                  {" · "}
-                  {store.selectedDetail.temp_symbol === "°F" ? "≥ " : "≥ "}
-                  {store.selectedDetail.forecast?.today_high != null
-                    ? `${store.selectedDetail.forecast.today_high}${store.selectedDetail.temp_symbol}`
-                    : "--"}
-                </div>
-                <div className="scan-detail-volume">
-                  {formatVolume(store.selectedDetail.market_scan?.volume)} · 24h{" "}
-                  {store.selectedDetail.market_scan?.primary_market
-                    ?.liquidity != null
-                    ? `$${store.selectedDetail.market_scan.primary_market.liquidity.toLocaleString()}`
-                    : ""}
-                </div>
-              </div>
-            </div>
-
-            {/* Current Conditions */}
-            <div className="scan-detail-section">
-              <div className="scan-detail-section-title">
-                {t("section.currentConditions") || "当前概况"}
-              </div>
-              <div className="scan-conditions-table">
-                <div className="scan-condition-item">
-                  <span className="scan-condition-label">
-                    {t("detail.localTime") || "当前时间"}
-                  </span>
-                  <span className="scan-condition-value">
-                    {store.selectedDetail.local_time}
-                  </span>
-                </div>
-                <div className="scan-condition-item">
-                  <span className="scan-condition-label">
-                    {t("detail.currentTemp") || "当前温度"}
-                  </span>
-                  <span className="scan-condition-value accent-green">
-                    {store.selectedDetail.current?.temp != null
-                      ? `${store.selectedDetail.current.temp.toFixed(1)}${store.selectedDetail.temp_symbol}`
-                      : "--"}
-                  </span>
-                </div>
-                <div className="scan-condition-item">
-                  <span className="scan-condition-label">
-                    {t("detail.todayHigh") || "今日最高"}
-                  </span>
-                  <span className="scan-condition-value">
-                    {store.selectedDetail.current?.max_so_far != null
-                      ? `${store.selectedDetail.current.max_so_far.toFixed(1)}${store.selectedDetail.temp_symbol}`
-                      : "--"}
-                  </span>
-                </div>
-                <div className="scan-condition-item">
-                  <span className="scan-condition-label">
-                    {t("detail.targetTemp") || "目标温度"}
-                  </span>
-                  <span className="scan-condition-value">
-                    {store.selectedDetail.deb?.prediction != null
-                      ? `${store.selectedDetail.deb.prediction.toFixed(1)}${store.selectedDetail.temp_symbol}`
-                      : "--"}
-                  </span>
-                </div>
-                <div className="scan-condition-item">
-                  <span className="scan-condition-label">
-                    {t("detail.gap") || "距目标差距"}
-                  </span>
-                  <span
-                    className={`scan-condition-value ${
-                      store.selectedDetail.current?.temp != null &&
-                      store.selectedDetail.deb?.prediction != null
-                        ? store.selectedDetail.deb.prediction -
-                            (store.selectedDetail.current.max_so_far ??
-                              store.selectedDetail.current.temp) >
-                          0
-                          ? "accent-red"
-                          : "accent-green"
-                        : ""
-                    }`}
-                  >
-                    {store.selectedDetail.current?.temp != null &&
-                    store.selectedDetail.deb?.prediction != null
-                      ? `${(store.selectedDetail.deb.prediction - (store.selectedDetail.current.max_so_far ?? store.selectedDetail.current.temp)).toFixed(1)}${store.selectedDetail.temp_symbol}`
-                      : "--"}
-                  </span>
-                </div>
-                <div className="scan-condition-item">
-                  <span className="scan-condition-label">
-                    {t("detail.peakWindow") || "预计峰值"}
-                  </span>
-                  <span className="scan-condition-value">
-                    {store.selectedDetail.peak?.hours?.join(" - ") || "--"}
-                  </span>
-                </div>
-              </div>
-            </div>
-
-            {/* Trade Recommendation */}
-            {store.selectedDetail.market_scan?.price_analysis && (
-              <div className="scan-detail-section">
-                <div className="scan-detail-section-title">
-                  {t("section.tradeRecommendation") || "推荐交易"}
-                </div>
-                <div className="scan-trade-cards">
-                  <div className="scan-trade-card yes">
-                    <div className="scan-trade-card-title">
-                      买入 Yes{" "}
-                      {store.selectedDetail.market_scan?.temperature_bucket
-                        ?.label || ""}
-                    </div>
-                    <div className="scan-trade-card-price">
-                      {store.selectedDetail.market_scan?.price_analysis?.yes
-                        ?.ask != null
-                        ? `${(store.selectedDetail.market_scan.price_analysis.yes.ask * 100).toFixed(0)}%`
-                        : "--"}
-                      {" → "}
-                      {store.selectedDetail.market_scan?.price_analysis?.yes
-                        ?.model_probability != null
-                        ? `${(store.selectedDetail.market_scan.price_analysis.yes.model_probability * 100).toFixed(0)}%`
-                        : "--"}
-                    </div>
-                    <div
-                      className={`scan-trade-card-edge ${
-                        (store.selectedDetail.market_scan?.price_analysis?.yes
-                          ?.edge ?? 0) > 0
-                          ? "positive"
-                          : "negative"
-                      }`}
-                    >
-                      {store.selectedDetail.market_scan?.price_analysis?.yes
-                        ?.edge != null
-                        ? `${store.selectedDetail.market_scan.price_analysis.yes.edge > 0 ? "+" : ""}${(store.selectedDetail.market_scan.price_analysis.yes.edge * 100).toFixed(1)}% 边际优势`
-                        : "--"}
-                    </div>
-                  </div>
-                  <div className="scan-trade-card no">
-                    <div className="scan-trade-card-title">
-                      卖出 Yes{" "}
-                      {store.selectedDetail.market_scan?.temperature_bucket
-                        ?.label || ""}
-                    </div>
-                    <div className="scan-trade-card-price">
-                      {store.selectedDetail.market_scan?.price_analysis?.no
-                        ?.ask != null
-                        ? `${(store.selectedDetail.market_scan.price_analysis.no.ask * 100).toFixed(0)}%`
-                        : "--"}
-                      {" → "}
-                      {store.selectedDetail.market_scan?.price_analysis?.no
-                        ?.model_probability != null
-                        ? `${(1 - store.selectedDetail.market_scan.price_analysis.no.model_probability) * 100 > 0 ? `${((1 - store.selectedDetail.market_scan.price_analysis.no.model_probability) * 100).toFixed(0)}%` : "--"}`
-                        : "--"}
-                    </div>
-                    <div
-                      className={`scan-trade-card-edge ${
-                        (store.selectedDetail.market_scan?.price_analysis?.no
-                          ?.edge ?? 0) > 0
-                          ? "positive"
-                          : "negative"
-                      }`}
-                    >
-                      {store.selectedDetail.market_scan?.price_analysis?.no
-                        ?.edge != null
-                        ? `${store.selectedDetail.market_scan.price_analysis.no.edge > 0 ? "+" : ""}${(store.selectedDetail.market_scan.price_analysis.no.edge * 100).toFixed(1)}% 边际优势`
-                        : "--"}
-                    </div>
-                  </div>
-                </div>
-              </div>
-            )}
-
-            {/* Score */}
-            <div className="scan-detail-score">
-              <div>
-                <span
-                  className="scan-detail-score-big"
-                  style={{
-                    color: (() => {
-                      const snap = homepageSnapshots.find(
-                        (s) => s.city.name === store.selectedCity,
-                      );
-                      const displayScore = getDisplayScore(snap?.score ?? 0);
-                      return displayScore >= 85
-                        ? "#00E0A4"
-                        : displayScore >= 70
-                          ? "#FFB020"
-                          : "#FF4D6A";
-                    })(),
-                  }}
-                >
-                  {(() => {
-                    const snap = homepageSnapshots.find(
-                      (s) => s.city.name === store.selectedCity,
-                    );
-                    return snap ? getDisplayScore(snap.score).toFixed(1) : "--";
-                  })()}
-                </span>
-                <span className="scan-detail-score-suffix"> /100</span>
-              </div>
-              <div>
-                <div className="scan-detail-score-label">
-                  {t("detail.compositeScore") || "综合得分"}
-                </div>
-                <div className="scan-confidence-dots">
-                  {[1, 2, 3, 4, 5].map((i) => {
-                    const snap = homepageSnapshots.find(
-                      (s) => s.city.name === store.selectedCity,
-                    );
-                    const displayScore = getDisplayScore(snap?.score ?? 0);
-                    const filled = displayScore >= i * 20;
-                    return (
-                      <span
-                        key={i}
-                        className={`scan-confidence-dot ${filled ? "filled" : ""} ${displayScore < 70 ? "red" : displayScore < 85 ? "amber" : ""}`}
-                      />
-                    );
-                  })}
-                </div>
-              </div>
-            </div>
-          </aside>
-        ) : (
-          <aside className="scan-detail-panel scan-detail-empty">
-            <div className="scan-empty-state">
-              <Search size={48} />
-              <p>{t("detail.selectCity") || "← 点击左侧行查看详情"}</p>
-            </div>
-          </aside>
-        )}
-      </div>
-
-      {store.historyState.isOpen && <HistoryModal />}
-      {store.futureModalDate && <FutureForecastModal />}
-      {showLoading && (
-        <div className="loading-overlay">
-          <div className="loading-card">
-            <div className="loading-clouds" aria-hidden="true">
-              <span className="loading-cloud loading-cloud-1" />
-              <span className="loading-cloud loading-cloud-2" />
-            </div>
-            <div className="loading-radar" aria-hidden="true">
-              <div className="loading-radar-core" />
-              <div className="loading-radar-ring loading-radar-ring-1" />
-              <div className="loading-radar-ring loading-radar-ring-2" />
-              <div className="loading-radar-sweep" />
-            </div>
-            <div className="loading-copy">
-              <strong>PolyWeather</strong>
-              <span>{t("dashboard.loading")}</span>
-            </div>
-          </div>
-        </div>
-      )}
-    </div>
-  );
-}
+import { ScanTerminalDashboard } from "@/components/dashboard/ScanTerminalDashboard";
 
 export function PolyWeatherDashboard() {
-  return (
-    <I18nProvider>
-      <DashboardStoreProvider>
-        <DashboardScreen />
-      </DashboardStoreProvider>
-    </I18nProvider>
-  );
+  return <ScanTerminalDashboard />;
 }
diff --git a/frontend/components/dashboard/ScanFilterPanel.tsx b/frontend/components/dashboard/ScanFilterPanel.tsx
index 118defff..79cbe298 100644
--- a/frontend/components/dashboard/ScanFilterPanel.tsx
+++ b/frontend/components/dashboard/ScanFilterPanel.tsx
@@ -1,6 +1,6 @@
 "use client";
 
-import React, { useState } from "react";
+import React from "react";
 import {
   Crosshair,
   Clock,
@@ -10,16 +10,9 @@ import {
   Search,
 } from "lucide-react";
 import { useI18n } from "@/hooks/useI18n";
-import styles from "./Dashboard.module.css";
+import type { ScanTerminalFilters } from "@/lib/dashboard-types";
 
-interface FilterState {
-  mode: "tradable" | "early" | "touch" | "trend";
-  priceRange: [number, number];
-  minEdge: number;
-  highLiquidityOnly: boolean;
-  marketType: "maxtemp" | "all";
-  timeRange: "today" | "tomorrow" | "week";
-}
+export interface FilterState extends ScanTerminalFilters {}
 
 const SCAN_MODES = [
   {
@@ -57,29 +50,27 @@ const SCAN_MODES = [
 ] as const;
 
 export function ScanFilterPanel({
+  value,
+  onChange,
   onScan,
   isScanning,
 }: {
+  value: FilterState;
+  onChange?: (filters: FilterState) => void;
   onScan?: (filters: FilterState) => void;
   isScanning?: boolean;
 }) {
   const { locale } = useI18n();
   const isEn = locale === "en-US";
 
-  const [filters, setFilters] = useState<FilterState>({
-    mode: "tradable",
-    priceRange: [0.05, 0.95],
-    minEdge: 2,
-    highLiquidityOnly: false,
-    marketType: "maxtemp",
-    timeRange: "today",
-  });
-
   const updateFilter = <K extends keyof FilterState>(
     key: K,
-    value: FilterState[K],
+    nextValue: FilterState[K],
   ) => {
-    setFilters((prev) => ({ ...prev, [key]: value }));
+    onChange?.({
+      ...value,
+      [key]: nextValue,
+    });
   };
 
   return (
@@ -92,12 +83,12 @@ export function ScanFilterPanel({
         <div className="scan-mode-tabs">
           {SCAN_MODES.map((mode) => {
             const Icon = mode.icon;
-            const isActive = filters.mode === mode.key;
+            const isActive = value.scan_mode === mode.key;
             return (
               <button
                 key={mode.key}
                 className={`scan-mode-tab ${isActive ? "active" : ""}`}
-                onClick={() => updateFilter("mode", mode.key)}
+                onClick={() => updateFilter("scan_mode", mode.key)}
                 title={isEn ? mode.descEn : mode.descZh}
               >
                 <Icon size={16} />
@@ -124,17 +115,17 @@ export function ScanFilterPanel({
             {isEn ? "Price Range" : "价格范围"}
           </span>
           <div className="scan-range-display">
-            <span>{filters.priceRange[0].toFixed(2)}</span>
+            <span>{value.min_price.toFixed(2)}</span>
             <input
               type="range"
               min={0}
               max={100}
-              value={filters.priceRange[0] * 100}
+              value={value.min_price * 100}
               onChange={(e) =>
-                updateFilter("priceRange", [
-                  Number(e.target.value) / 100,
-                  filters.priceRange[1],
-                ])
+                updateFilter(
+                  "min_price",
+                  Math.min(Number(e.target.value) / 100, value.max_price),
+                )
               }
               className="scan-range-slider"
             />
@@ -142,16 +133,16 @@ export function ScanFilterPanel({
               type="range"
               min={0}
               max={100}
-              value={filters.priceRange[1] * 100}
+              value={value.max_price * 100}
               onChange={(e) =>
-                updateFilter("priceRange", [
-                  filters.priceRange[0],
-                  Number(e.target.value) / 100,
-                ])
+                updateFilter(
+                  "max_price",
+                  Math.max(Number(e.target.value) / 100, value.min_price),
+                )
               }
               className="scan-range-slider"
             />
-            <span>{filters.priceRange[1].toFixed(2)}</span>
+            <span>{value.max_price.toFixed(2)}</span>
           </div>
         </div>
 
@@ -161,13 +152,15 @@ export function ScanFilterPanel({
             {isEn ? "Min Edge" : "最小边际优势"}
           </span>
           <div className="scan-range-display">
-            <span>{filters.minEdge}%</span>
+            <span>{value.min_edge_pct}%</span>
             <input
               type="range"
               min={0}
               max={20}
-              value={filters.minEdge}
-              onChange={(e) => updateFilter("minEdge", Number(e.target.value))}
+              value={value.min_edge_pct}
+              onChange={(e) =>
+                updateFilter("min_edge_pct", Number(e.target.value))
+              }
               className="scan-range-slider"
             />
           </div>
@@ -179,10 +172,17 @@ export function ScanFilterPanel({
             {isEn ? "High Liquidity Only" : "只看高流动性"}
           </span>
           <button
-            className={`scan-toggle ${filters.highLiquidityOnly ? "active" : ""}`}
-            onClick={() =>
-              updateFilter("highLiquidityOnly", !filters.highLiquidityOnly)
-            }
+            className={`scan-toggle ${value.high_liquidity_only ? "active" : ""}`}
+            onClick={() => {
+              const nextValue = !value.high_liquidity_only;
+              onChange?.({
+                ...value,
+                high_liquidity_only: nextValue,
+                min_liquidity: nextValue
+                  ? Math.max(value.min_liquidity, 5000)
+                  : 500,
+              });
+            }}
           >
             <span className="scan-toggle-knob" />
           </button>
@@ -195,11 +195,11 @@ export function ScanFilterPanel({
           </span>
           <select
             className="scan-select"
-            value={filters.marketType}
+            value={value.market_type}
             onChange={(e) =>
               updateFilter(
-                "marketType",
-                e.target.value as FilterState["marketType"],
+                "market_type",
+                e.target.value as FilterState["market_type"],
               )
             }
           >
@@ -217,11 +217,11 @@ export function ScanFilterPanel({
           </span>
           <select
             className="scan-select"
-            value={filters.timeRange}
+            value={value.time_range}
             onChange={(e) =>
               updateFilter(
-                "timeRange",
-                e.target.value as FilterState["timeRange"],
+                "time_range",
+                e.target.value as FilterState["time_range"],
               )
             }
           >
@@ -235,7 +235,7 @@ export function ScanFilterPanel({
       {/* === CTA === */}
       <button
         className="scan-cta-button"
-        onClick={() => onScan?.(filters)}
+        onClick={() => onScan?.(value)}
         disabled={isScanning}
       >
         <Search size={16} />
diff --git a/frontend/components/dashboard/ScanKPIBar.tsx b/frontend/components/dashboard/ScanKPIBar.tsx
index 5608430a..710322c7 100644
--- a/frontend/components/dashboard/ScanKPIBar.tsx
+++ b/frontend/components/dashboard/ScanKPIBar.tsx
@@ -3,17 +3,9 @@
 import React from "react";
 import { TrendingUp, BarChart3, Radio, DollarSign, Target } from "lucide-react";
 import { useI18n } from "@/hooks/useI18n";
+import type { ScanTerminalResponse } from "@/lib/dashboard-types";
 
-interface KPIData {
-  recommendedCount: number;
-  recommendedDelta?: number;
-  avgEdge: number | null;
-  avgEdgeDelta?: number | null;
-  totalWinRate: number | null;
-  tradableMarkets: number;
-  filteredTotal: number;
-  totalVolume: number;
-}
+type KPIData = ScanTerminalResponse["summary"];
 
 function formatVolume(value: number): string {
   if (value >= 1_000_000) return `$${(value / 1_000_000).toFixed(1)}M`;
@@ -29,42 +21,41 @@ export function ScanKPIBar({ data }: { data: KPIData }) {
     {
       icon: Target,
       label: isEn ? "Recommended" : "推荐机会",
-      value: String(data.recommendedCount),
-      delta:
-        data.recommendedDelta != null
-          ? `${isEn ? "vs last" : "较上次"} ${data.recommendedDelta > 0 ? "+" : ""}${data.recommendedDelta}`
-          : null,
+      value: String(data.recommended_count),
+      delta: `${isEn ? "Showing" : "当前展示"} ${data.visible_count}`,
       accent: "green" as const,
     },
     {
       icon: TrendingUp,
       label: isEn ? "Avg Edge" : "平均边际优势",
-      value: data.avgEdge != null ? `+${data.avgEdge.toFixed(1)}%` : "--",
-      delta:
-        data.avgEdgeDelta != null
-          ? `${isEn ? "vs last" : "较上次"} ${data.avgEdgeDelta > 0 ? "+" : ""}${data.avgEdgeDelta.toFixed(1)}%`
-          : null,
+      value:
+        data.avg_edge_percent != null
+          ? `+${data.avg_edge_percent.toFixed(1)}%`
+          : "--",
+      delta: `${isEn ? "Candidates" : "候选总数"} ${data.candidate_total}`,
       accent: "green" as const,
     },
     {
       icon: BarChart3,
-      label: isEn ? "Total Win Rate" : "总胜率预测",
+      label: isEn ? "Avg Confidence" : "平均主信号置信度",
       value:
-        data.totalWinRate != null ? `+${data.totalWinRate.toFixed(1)}%` : "--",
-      delta: isEn ? "Model confidence" : "模型全分配",
+        data.avg_primary_confidence != null
+          ? `${data.avg_primary_confidence.toFixed(0)}`
+          : "--",
+      delta: isEn ? "Main signal score" : "主信号评分",
       accent: "green" as const,
     },
     {
       icon: Radio,
       label: isEn ? "Tradable Markets" : "可交易市场",
-      value: String(data.tradableMarkets),
-      delta: `${isEn ? "Filtered" : "过滤后"} / ${data.filteredTotal}`,
+      value: String(data.tradable_market_count),
+      delta: data.resolved_market_type || "maxtemp",
       accent: "purple" as const,
     },
     {
       icon: DollarSign,
       label: isEn ? "Total Volume" : "总成交量",
-      value: formatVolume(data.totalVolume),
+      value: formatVolume(data.total_volume),
       delta: isEn ? "Past 24h" : "过去 24 小时",
       accent: "purple" as const,
     },
diff --git a/frontend/components/dashboard/ScanTerminalDashboard.tsx b/frontend/components/dashboard/ScanTerminalDashboard.tsx
new file mode 100644
index 00000000..0f647cb7
--- /dev/null
+++ b/frontend/components/dashboard/ScanTerminalDashboard.tsx
@@ -0,0 +1,515 @@
+"use client";
+
+import clsx from "clsx";
+import {
+  startTransition,
+  useDeferredValue,
+  useEffect,
+  useEffectEvent,
+  useMemo,
+  useState,
+} from "react";
+import styles from "./Dashboard.module.css";
+import detailChromeStyles from "./DetailPanelChrome.module.css";
+import modalChromeStyles from "./ModalChrome.module.css";
+import { HeaderBar } from "@/components/dashboard/HeaderBar";
+import {
+  FilterState,
+  ScanFilterPanel,
+} from "@/components/dashboard/ScanFilterPanel";
+import { ScanKPIBar } from "@/components/dashboard/ScanKPIBar";
+import { OpportunityTable } from "@/components/dashboard/OpportunityTable";
+import { DashboardStoreProvider, useDashboardStore } from "@/hooks/useDashboardStore";
+import { I18nProvider, useI18n } from "@/hooks/useI18n";
+import { dashboardClient } from "@/lib/dashboard-client";
+import type {
+  MarketScan,
+  PrimarySignal,
+  ScanOpportunityRow,
+  ScanTerminalResponse,
+} from "@/lib/dashboard-types";
+import {
+  getLocalizedAirportName,
+  getLocalizedCityName,
+} from "@/lib/dashboard-home-copy";
+
+const DEFAULT_FILTERS: FilterState = {
+  scan_mode: "tradable",
+  min_price: 0.05,
+  max_price: 0.95,
+  min_edge_pct: 2,
+  min_liquidity: 500,
+  high_liquidity_only: false,
+  market_type: "maxtemp",
+  time_range: "today",
+  limit: 25,
+};
+
+function formatPercent(value?: number | null, signed = false) {
+  if (value == null || Number.isNaN(Number(value))) return "--";
+  const numeric = Number(value);
+  if (!signed) return `${numeric.toFixed(1)}%`;
+  return `${numeric >= 0 ? "+" : ""}${numeric.toFixed(1)}%`;
+}
+
+function formatProbability(value?: number | null) {
+  if (value == null || Number.isNaN(Number(value))) return "--";
+  return `${(Number(value) * 100).toFixed(0)}%`;
+}
+
+function formatPrice(value?: number | null) {
+  if (value == null || Number.isNaN(Number(value))) return "--";
+  return `${Math.round(Number(value) * 100)}¢`;
+}
+
+function formatVolume(value?: number | null) {
+  const numeric = Number(value || 0);
+  if (!Number.isFinite(numeric) || numeric <= 0) return "--";
+  if (numeric >= 1_000_000) return `$${(numeric / 1_000_000).toFixed(1)}M`;
+  if (numeric >= 1_000) return `$${(numeric / 1_000).toFixed(0)}K`;
+  return `$${numeric.toFixed(0)}`;
+}
+
+function formatMinutes(value?: number | null, locale = "zh-CN") {
+  if (value == null || !Number.isFinite(Number(value))) return "--";
+  const numeric = Math.max(0, Math.round(Number(value)));
+  if (locale === "en-US") return `${numeric}m`;
+  return `${numeric} 分钟`;
+}
+
+function scoreTone(score?: number | null) {
+  const numeric = Number(score || 0);
+  if (numeric >= 85) return "green";
+  if (numeric >= 70) return "amber";
+  return "red";
+}
+
+function confidenceDotCount(score?: number | null) {
+  const numeric = Number(score || 0);
+  if (numeric >= 90) return 5;
+  if (numeric >= 80) return 4;
+  if (numeric >= 70) return 3;
+  if (numeric >= 60) return 2;
+  if (numeric > 0) return 1;
+  return 0;
+}
+
+function getSideRow(
+  marketScan: MarketScan | null | undefined,
+  selectedRow: ScanOpportunityRow,
+  side: "yes" | "no",
+) {
+  const rows = Array.isArray(marketScan?.scan_rows) ? marketScan?.scan_rows : [];
+  const matched = rows.find(
+    (row) =>
+      row.market_slug === selectedRow.market_slug &&
+      row.selected_date === selectedRow.selected_date &&
+      row.side === side,
+  );
+  if (matched) return matched;
+  if (selectedRow.side === side) return selectedRow;
+  return null;
+}
+
+function DetailPanel({
+  row,
+  marketScan,
+  loading,
+}: {
+  row: ScanOpportunityRow | null;
+  marketScan?: MarketScan | null;
+  loading?: boolean;
+}) {
+  const { locale } = useI18n();
+  const isEn = locale === "en-US";
+
+  if (!row) {
+    return (
+      <aside className="scan-detail-panel">
+        <div className="scan-detail-empty">
+          {isEn ? "Select a row to inspect the main signal." : "选择一条机会，查看主信号详情。"}
+        </div>
+      </aside>
+    );
+  }
+
+  const localizedCityName = getLocalizedCityName(
+    row.city,
+    row.city_display_name || row.display_name || row.city,
+    locale,
+  );
+  const localizedAirport = getLocalizedAirportName(
+    row.city,
+    row.airport || "",
+    locale,
+  );
+  const detailSignal = marketScan?.primary_signal as PrimarySignal | null | undefined;
+  const displayRow = detailSignal && detailSignal.market_slug === row.market_slug ? detailSignal : row;
+  const distributionBias = marketScan?.distribution_bias || row.distribution_bias || null;
+  const yesRow = getSideRow(marketScan, row, "yes");
+  const noRow = getSideRow(marketScan, row, "no");
+  const tone = scoreTone(displayRow.final_score);
+  const filledDots = confidenceDotCount(displayRow.final_score);
+
+  return (
+    <aside className="scan-detail-panel">
+      <div className="scan-detail-header">
+        <div className="scan-detail-hero-placeholder" />
+        <div className="scan-detail-city-info">
+          <div className="scan-detail-city-name">{localizedCityName}</div>
+          <div className="scan-detail-city-sub">
+            {displayRow.market_question || displayRow.target_label || "--"}
+          </div>
+          <div className="scan-detail-volume">
+            {formatVolume(displayRow.volume)}{" "}
+            {isEn ? "24h volume" : "24h 成交量"}
+            {loading ? ` · ${isEn ? "loading" : "载入中"}` : ""}
+          </div>
+        </div>
+      </div>
+
+      <div className="scan-detail-section">
+        <div className="scan-detail-section-title">
+          {isEn ? "Current Context" : "当前概况"}
+        </div>
+        <div className="scan-conditions-table">
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Local Time" : "当地时间"}
+            </span>
+            <span className="scan-condition-value">{row.local_time || "--"}</span>
+          </div>
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Current Temp" : "当前温度"}
+            </span>
+            <span className="scan-condition-value">
+              {row.current_temp != null ? `${row.current_temp}${row.temp_symbol || ""}` : "--"}
+            </span>
+          </div>
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Day High (So Far)" : "今日高点（至今）"}
+            </span>
+            <span className="scan-condition-value">
+              {row.current_max_so_far != null
+                ? `${row.current_max_so_far}${row.temp_symbol || ""}`
+                : "--"}
+            </span>
+          </div>
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Target" : "目标温度"}
+            </span>
+            <span className="scan-condition-value">
+              {displayRow.target_label || "--"}
+            </span>
+          </div>
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Gap To Target" : "距目标"}
+            </span>
+            <span
+              className={clsx(
+                "scan-condition-value",
+                Number(displayRow.gap_to_target || 0) <= 0
+                  ? "accent-green"
+                  : "accent-red",
+              )}
+            >
+              {displayRow.gap_to_target != null
+                ? `${displayRow.gap_to_target >= 0 ? "+" : ""}${displayRow.gap_to_target.toFixed(1)}${row.temp_symbol || ""}`
+                : "--"}
+            </span>
+          </div>
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Window Left" : "剩余有效时间"}
+            </span>
+            <span className="scan-condition-value">
+              {formatMinutes(displayRow.remaining_window_minutes, locale)}
+            </span>
+          </div>
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Bias" : "分布偏移"}
+            </span>
+            <span className="scan-condition-value">
+              {distributionBias?.direction || "--"} ·{" "}
+              {distributionBias?.score != null
+                ? distributionBias.score.toFixed(0)
+                : "--"}
+            </span>
+          </div>
+          <div className="scan-condition-item">
+            <span className="scan-condition-label">
+              {isEn ? "Airport" : "机场锚点"}
+            </span>
+            <span className="scan-condition-value">{localizedAirport || "--"}</span>
+          </div>
+        </div>
+      </div>
+
+      <div className="scan-detail-section">
+        <div className="scan-detail-section-title">
+          {isEn ? "Recommended Trade" : "推荐交易"}
+        </div>
+        <div className="scan-trade-cards">
+          <div className="scan-trade-card yes">
+            <div className="scan-trade-card-title">BUY YES</div>
+            <div className="scan-trade-card-price">
+              {formatPrice(yesRow?.ask ?? marketScan?.yes_buy)} / {formatProbability(yesRow?.model_probability)}
+            </div>
+            <div
+              className={clsx(
+                "scan-trade-card-edge",
+                Number(yesRow?.edge_percent || 0) >= 0 ? "positive" : "negative",
+              )}
+            >
+              {formatPercent(yesRow?.edge_percent, true)}
+            </div>
+            <div className="scan-trade-card-note">
+              {isEn ? "Spread" : "点差"} {formatPrice(yesRow?.spread)} ·{" "}
+              {isEn ? "Liquidity" : "流动性"} {formatVolume(yesRow?.book_liquidity ?? yesRow?.market_liquidity)}
+            </div>
+          </div>
+          <div className="scan-trade-card no">
+            <div className="scan-trade-card-title">BUY NO</div>
+            <div className="scan-trade-card-price">
+              {formatPrice(noRow?.ask ?? marketScan?.no_buy)} / {formatProbability(noRow?.model_probability)}
+            </div>
+            <div
+              className={clsx(
+                "scan-trade-card-edge",
+                Number(noRow?.edge_percent || 0) >= 0 ? "positive" : "negative",
+              )}
+            >
+              {formatPercent(noRow?.edge_percent, true)}
+            </div>
+            <div className="scan-trade-card-note">
+              {isEn ? "Spread" : "点差"} {formatPrice(noRow?.spread)} ·{" "}
+              {isEn ? "Liquidity" : "流动性"} {formatVolume(noRow?.book_liquidity ?? noRow?.market_liquidity)}
+            </div>
+          </div>
+        </div>
+      </div>
+
+      <div className="scan-detail-score">
+        <div>
+          <div className="scan-detail-score-big">
+            {Number(displayRow.final_score || 0).toFixed(0)}
+            <span className="scan-detail-score-suffix">/100</span>
+          </div>
+          <div className="scan-detail-score-label">
+            {isEn ? "Composite signal score" : "综合主信号评分"}
+          </div>
+        </div>
+        <div className="scan-confidence-dots">
+          {Array.from({ length: 5 }).map((_, index) => {
+            const filled = index < filledDots;
+            return (
+              <span
+                key={index}
+                className={clsx(
+                  "scan-confidence-dot",
+                  filled && "filled",
+                  filled && tone === "amber" && "amber",
+                  filled && tone === "red" && "red",
+                )}
+              />
+            );
+          })}
+        </div>
+      </div>
+    </aside>
+  );
+}
+
+function ScanTerminalScreen() {
+  const store = useDashboardStore();
+  const { locale } = useI18n();
+  const isEn = locale === "en-US";
+  const [draftFilters, setDraftFilters] = useState<FilterState>(DEFAULT_FILTERS);
+  const [activeFilters, setActiveFilters] = useState<FilterState>(DEFAULT_FILTERS);
+  const [terminalData, setTerminalData] = useState<ScanTerminalResponse | null>(null);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [selectedRowId, setSelectedRowId] = useState<string | null>(null);
+  const [detailByRowId, setDetailByRowId] = useState<Record<string, MarketScan | null>>({});
+  const [detailLoadingId, setDetailLoadingId] = useState<string | null>(null);
+
+  const deferredRows = useDeferredValue(terminalData?.rows || []);
+  const selectedRow = useMemo(() => {
+    if (!deferredRows.length) return null;
+    return (
+      deferredRows.find((row) => row.id === selectedRowId) ||
+      deferredRows[0] ||
+      null
+    );
+  }, [deferredRows, selectedRowId]);
+
+  const fetchTerminal = useEffectEvent(async (filters: FilterState, force = false) => {
+    setLoading(true);
+    setError(null);
+    try {
+      const response = await dashboardClient.getScanTerminal(filters, { force });
+      startTransition(() => {
+        setTerminalData(response);
+        setActiveFilters(filters);
+        setSelectedRowId((current) => {
+          if (current && response.rows.some((row) => row.id === current)) {
+            return current;
+          }
+          return response.top_signal?.id || response.rows[0]?.id || null;
+        });
+      });
+    } catch (fetchError) {
+      setError(String(fetchError));
+    } finally {
+      setLoading(false);
+    }
+  });
+
+  const fetchDetail = useEffectEvent(async (row: ScanOpportunityRow) => {
+    if (!row.market_slug || !row.selected_date) return;
+    if (detailByRowId[row.id] !== undefined) return;
+    setDetailLoadingId(row.id);
+    try {
+      const response = await dashboardClient.getCityMarketScan(row.city, {
+        force: false,
+        marketSlug: row.market_slug,
+        targetDate: row.selected_date,
+      });
+      setDetailByRowId((current) => ({
+        ...current,
+        [row.id]: response.market_scan || null,
+      }));
+    } catch {
+      setDetailByRowId((current) => ({
+        ...current,
+        [row.id]: null,
+      }));
+    } finally {
+      setDetailLoadingId((current) => (current === row.id ? null : current));
+    }
+  });
+
+  useEffect(() => {
+    void fetchTerminal(DEFAULT_FILTERS, false);
+  }, [fetchTerminal]);
+
+  useEffect(() => {
+    const intervalId = window.setInterval(() => {
+      void fetchTerminal(activeFilters, false);
+    }, 30_000);
+    return () => {
+      window.clearInterval(intervalId);
+    };
+  }, [activeFilters, fetchTerminal]);
+
+  useEffect(() => {
+    if (!selectedRow) return;
+    void fetchDetail(selectedRow);
+  }, [selectedRow, fetchDetail]);
+
+  const selectedDetail = selectedRow ? detailByRowId[selectedRow.id] : null;
+
+  return (
+    <div
+      className={clsx(
+        styles.root,
+        detailChromeStyles.root,
+        modalChromeStyles.root,
+      )}
+    >
+      <HeaderBar
+        refreshAction={() => fetchTerminal(activeFilters, true)}
+        refreshSpinning={loading || store.loadingState.refresh}
+      />
+
+      <div className="scan-terminal">
+        <ScanFilterPanel
+          value={draftFilters}
+          onChange={setDraftFilters}
+          onScan={(filters) => {
+            setDraftFilters(filters);
+            void fetchTerminal(filters, true);
+          }}
+          isScanning={loading}
+        />
+
+        <main className="scan-data-grid">
+          <div className="scan-data-grid-header">
+            <div>
+              <div className="scan-data-grid-title">
+                {isEn ? "Tradable Opportunities" : "可交易机会"}
+              </div>
+              <div className="scan-data-grid-subtitle">
+                {isEn
+                  ? "REST-only EMOS scan with one main signal per city/date."
+                  : "基于 EMOS 分布与 CLOB REST 盘口，只输出每个城市/日期的单一主信号。"}
+              </div>
+            </div>
+            <div className="scan-data-grid-controls">
+              <span className="scan-status-badge tone-neutral">
+                {isEn ? "Updated" : "数据时间"} ·{" "}
+                {terminalData?.generated_at
+                  ? terminalData.generated_at.replace("T", " ").slice(0, 19)
+                  : "--"}
+              </span>
+            </div>
+          </div>
+
+          <ScanKPIBar
+            data={
+              terminalData?.summary || {
+                recommended_count: 0,
+                visible_count: 0,
+                candidate_total: 0,
+                avg_edge_percent: null,
+                avg_primary_confidence: null,
+                tradable_market_count: 0,
+                total_volume: 0,
+                resolved_market_type: "maxtemp",
+              }
+            }
+          />
+
+          <div className="scan-view-tabs">
+            <button type="button" className="scan-view-tab active">
+              {isEn ? "Opportunity List" : "机会列表"}
+            </button>
+          </div>
+
+          {error ? (
+            <div className="scan-detail-empty">
+              {isEn ? "Scan failed." : "扫描失败。"} {error}
+            </div>
+          ) : (
+            <OpportunityTable
+              rows={deferredRows}
+              selectedRowId={selectedRowId}
+              onSelectRow={(row) => setSelectedRowId(row.id)}
+            />
+          )}
+        </main>
+
+        <DetailPanel
+          row={selectedRow}
+          marketScan={selectedDetail}
+          loading={detailLoadingId === selectedRow?.id}
+        />
+      </div>
+    </div>
+  );
+}
+
+export function ScanTerminalDashboard() {
+  return (
+    <I18nProvider>
+      <DashboardStoreProvider>
+        <ScanTerminalScreen />
+      </DashboardStoreProvider>
+    </I18nProvider>
+  );
+}
diff --git a/frontend/lib/dashboard-client.ts b/frontend/lib/dashboard-client.ts
index 03e62710..c47d754c 100644
--- a/frontend/lib/dashboard-client.ts
+++ b/frontend/lib/dashboard-client.ts
@@ -6,6 +6,8 @@ import {
   CitySummary,
   HistoryPayload,
   MarketScan,
+  ScanTerminalFilters,
+  ScanTerminalResponse,
 } from "@/lib/dashboard-types";
 
 export type AssistantOpportunityContext = {
@@ -73,6 +75,7 @@ const pendingCityMarketScanRequests = new Map<
   }>
 >();
 const pendingAssistantRequests = new Map<string, Promise<AssistantChatResponse>>();
+const pendingScanTerminalRequests = new Map<string, Promise<ScanTerminalResponse>>();
 const PRIORITY_WARM_SESSION_KEY = "polyWeather_priority_warm_v1";
 
 type CityCacheMeta = {
@@ -350,6 +353,41 @@ export const dashboardClient = {
     return request;
   },
 
+  async getScanTerminal(
+    filters: ScanTerminalFilters,
+    options?: { force?: boolean },
+  ) {
+    const force = options?.force ?? false;
+    const params = new URLSearchParams({
+      scan_mode: String(filters.scan_mode),
+      min_price: String(filters.min_price),
+      max_price: String(filters.max_price),
+      min_edge_pct: String(filters.min_edge_pct),
+      min_liquidity: String(filters.min_liquidity),
+      high_liquidity_only: String(filters.high_liquidity_only),
+      market_type: String(filters.market_type),
+      time_range: String(filters.time_range),
+      limit: String(filters.limit),
+      force_refresh: String(force),
+    });
+    const requestKey = `${params.toString()}::${force ? "force" : "cached"}`;
+    if (!force) {
+      const existing = pendingScanTerminalRequests.get(requestKey);
+      if (existing) {
+        return existing;
+      }
+    }
+    const request = fetchJson<ScanTerminalResponse>(
+      `/api/scan/terminal?${params.toString()}`,
+    ).finally(() => {
+      pendingScanTerminalRequests.delete(requestKey);
+    });
+    if (!force) {
+      pendingScanTerminalRequests.set(requestKey, request);
+    }
+    return request;
+  },
+
   async getHistory(cityName: string, options?: { includeRecords?: boolean }) {
     const includeRecords = options?.includeRecords === true;
     const requestKey = `${normalizeCityName(cityName)}::${
diff --git a/frontend/lib/dashboard-types.ts b/frontend/lib/dashboard-types.ts
index 56af297b..584526ea 100644
--- a/frontend/lib/dashboard-types.ts
+++ b/frontend/lib/dashboard-types.ts
@@ -399,6 +399,134 @@ export interface MarketScan {
   recent_trades?: unknown[];
   scan_scope?: "lite" | "full" | string | null;
   websocket?: Record<string, unknown>;
+  distribution_bias?: DistributionBias | null;
+  window_phase?: string | null;
+  window_score?: number | null;
+  primary_signal?: PrimarySignal | null;
+  signal_status?: string | null;
+  candidate_count?: number | null;
+  scan_rows?: ScanOpportunityRow[] | null;
+  resolved_market_type?: string | null;
+}
+
+export interface DistributionBias {
+  available?: boolean;
+  value?: number | null;
+  score?: number | null;
+  direction?: "hotter" | "colder" | "balanced" | string | null;
+}
+
+export interface ScanTerminalFilters {
+  scan_mode: "tradable" | "early" | "touch" | "trend";
+  min_price: number;
+  max_price: number;
+  min_edge_pct: number;
+  min_liquidity: number;
+  high_liquidity_only: boolean;
+  market_type: "maxtemp" | "all" | string;
+  time_range: "today" | "tomorrow" | "week" | string;
+  limit: number;
+}
+
+export interface ScanOpportunityRow {
+  id: string;
+  rank?: number | null;
+  city: string;
+  city_display_name?: string | null;
+  display_name?: string | null;
+  selected_date?: string | null;
+  local_date?: string | null;
+  local_time?: string | null;
+  temp_symbol?: string | null;
+  current_temp?: number | null;
+  current_max_so_far?: number | null;
+  deb_prediction?: number | null;
+  airport?: string | null;
+  risk_level?: RiskLevel | null;
+  market_slug?: string | null;
+  market_question?: string | null;
+  market_url?: string | null;
+  market_key?: string | null;
+  side?: "yes" | "no" | string | null;
+  action?: string | null;
+  market_direction?: string | null;
+  temperature_direction?: string | null;
+  target_label?: string | null;
+  target_value?: number | null;
+  target_threshold?: number | null;
+  target_lower?: number | null;
+  target_upper?: number | null;
+  target_unit?: string | null;
+  model_probability?: number | null;
+  market_probability?: number | null;
+  model_event_probability?: number | null;
+  market_event_probability?: number | null;
+  gap?: number | null;
+  signed_gap?: number | null;
+  yes_token_id?: string | null;
+  no_token_id?: string | null;
+  yes_ask?: number | null;
+  yes_bid?: number | null;
+  no_ask?: number | null;
+  no_bid?: number | null;
+  ask?: number | null;
+  bid?: number | null;
+  midpoint?: number | null;
+  spread?: number | null;
+  book_liquidity?: number | null;
+  market_liquidity?: number | null;
+  volume?: number | null;
+  quote_source?: string | null;
+  quote_age_ms?: number | null;
+  edge?: number | null;
+  edge_percent?: number | null;
+  edge_score?: number | null;
+  bias_score?: number | null;
+  distribution_bias?: DistributionBias | null;
+  distribution_bias_direction?: string | null;
+  distribution_bias_score?: number | null;
+  distribution_bias_available?: boolean;
+  window_phase?: string | null;
+  window_score?: number | null;
+  remaining_window_minutes?: number | null;
+  liquidity_score?: number | null;
+  price_usefulness_score?: number | null;
+  spread_penalty?: number | null;
+  final_score?: number | null;
+  current_reference?: number | null;
+  gap_to_target?: number | null;
+  touch_distance?: number | null;
+  trend_alignment?: boolean;
+  tradable?: boolean;
+  active?: boolean;
+  closed?: boolean;
+  accepting_orders?: boolean;
+  enable_order_book?: boolean;
+  is_primary_market?: boolean;
+  is_primary_signal?: boolean;
+  signal_confidence?: number | null;
+  signal_status?: string | null;
+  candidate_count?: number | null;
+  resolved_market_type?: string | null;
+}
+
+export interface PrimarySignal extends ScanOpportunityRow {}
+
+export interface ScanTerminalResponse {
+  generated_at: string;
+  filters: ScanTerminalFilters;
+  summary: {
+    recommended_count: number;
+    visible_count: number;
+    candidate_total: number;
+    avg_edge_percent?: number | null;
+    avg_primary_confidence?: number | null;
+    tradable_market_count: number;
+    total_volume: number;
+    resolved_market_type?: string | null;
+  };
+  top_signal?: PrimarySignal | null;
+  rows: ScanOpportunityRow[];
 }
 
 export interface IntradayMeteorologySignal {
diff --git a/src/data_collection/polymarket_readonly.py b/src/data_collection/polymarket_readonly.py
index f7767a1f..11eda787 100644
--- a/src/data_collection/polymarket_readonly.py
+++ b/src/data_collection/polymarket_readonly.py
@@ -189,6 +189,27 @@ def _clamp_probability(value: Optional[float]) -> Optional[float]:
     return value
 
 
+def _clamp_float(value: Optional[float], lower: float, upper: float) -> Optional[float]:
+    if value is None:
+        return None
+    return max(lower, min(upper, float(value)))
+
+
+def _parse_hhmm_to_minutes(value: Any) -> Optional[int]:
+    text = str(value or "").strip()
+    if not text or ":" not in text:
+        return None
+    try:
+        hh, mm = text.split(":", 1)
+        hour = int(hh)
+        minute = int(mm[:2])
+    except Exception:
+        return None
+    if hour < 0 or hour > 23 or minute < 0 or minute > 59:
+        return None
+    return hour * 60 + minute
+
+
 def _extract_iso_date(value: Any) -> Optional[str]:
     if not value:
         return None
@@ -434,6 +455,8 @@ class PolymarketReadOnlyLayer:
         fallback_sparkline: Optional[List[float]] = None,
         forced_market_slug: Optional[str] = None,
         include_related_buckets: bool = True,
+        scan_filters: Optional[Dict[str, Any]] = None,
+        scan_context: Optional[Dict[str, Any]] = None,
     ) -> Dict[str, Any]:
         date_str = _extract_iso_date(target_date) or str(target_date or "")
         city_key = _normalize_city_key(city)
@@ -478,6 +501,14 @@ class PolymarketReadOnlyLayer:
             "all_buckets": [],
             "recent_trades": [],
             "scan_scope": "full" if include_related_buckets else "lite",
+            "distribution_bias": None,
+            "window_phase": None,
+            "window_score": None,
+            "primary_signal": None,
+            "signal_status": "no_market",
+            "candidate_count": 0,
+            "scan_rows": [],
+            "resolved_market_type": "maxtemp",
             "websocket": {
                 "enabled": False,
                 "status": "disabled_rest_only",
@@ -713,6 +744,28 @@ class PolymarketReadOnlyLayer:
             no_buy=no_buy,
             no_sell=no_sell,
         )
+        distribution_scan = self._build_distribution_scan_pack(
+            city_key=market_city_key,
+            target_date=date_str,
+            primary_market=market,
+            probability_distribution=probability_distribution,
+            temp_symbol=temp_symbol,
+            scan_context=scan_context,
+            scan_filters=scan_filters,
+        )
+        primary_signal = distribution_scan.get("primary_signal")
+        if isinstance(primary_signal, dict):
+            signal_label = str(primary_signal.get("action") or signal_label or "").strip() or signal_label
+            signal_score = _safe_float(primary_signal.get("final_score"))
+            if signal_score is not None and signal_score >= 85.0:
+                confidence = "high"
+            elif signal_score is not None and signal_score >= 70.0:
+                confidence = "medium"
+            elif signal_score is not None:
+                confidence = "low"
+            signal_edge = _safe_float(primary_signal.get("edge_percent"))
+            if signal_edge is not None:
+                edge_percent = signal_edge
 
         sparkline_values: List[float] = []
         for candidate in (
@@ -761,6 +814,14 @@ class PolymarketReadOnlyLayer:
                 "sparkline": sparkline_values,
                 "top_buckets": top_buckets,
                 "all_buckets": all_buckets,
+                "distribution_bias": distribution_scan.get("distribution_bias"),
+                "window_phase": distribution_scan.get("window_phase"),
+                "window_score": distribution_scan.get("window_score"),
+                "primary_signal": primary_signal,
+                "signal_status": distribution_scan.get("signal_status"),
+                "candidate_count": distribution_scan.get("candidate_count"),
+                "scan_rows": distribution_scan.get("rows") or [],
+                "resolved_market_type": distribution_scan.get("resolved_market_type") or "maxtemp",
                 "websocket": {
                     "enabled": False,
                     "status": "disabled_rest_only",
@@ -1682,8 +1743,8 @@ class PolymarketReadOnlyLayer:
 
     def _fetch_token_market_data(self, token_id: str) -> Dict[str, Any]:
         # REST-only path: CLOB public endpoints.
-        buy = _extract_price(self._clob_get("/price", {"token_id": token_id, "side": "BUY"}))
-        sell = _extract_price(
+        bid = _extract_price(self._clob_get("/price", {"token_id": token_id, "side": "BUY"}))
+        ask = _extract_price(
             self._clob_get("/price", {"token_id": token_id, "side": "SELL"})
         )
         midpoint = _extract_price(self._clob_get("/midpoint", {"token_id": token_id}))
@@ -1692,7 +1753,7 @@ class PolymarketReadOnlyLayer:
         )
         orderbook_raw = self._clob_get("/book", {"token_id": token_id})
         book, book_liquidity = self._normalize_orderbook(orderbook_raw)
-        buy, sell = self._resolve_trade_prices(buy=buy, sell=sell, book=book)
+        buy, sell = self._resolve_trade_prices(buy=ask, sell=bid, book=book)
         return {
             "buy": buy,
             "sell": sell,
@@ -2120,3 +2181,897 @@ class PolymarketReadOnlyLayer:
         ):
             return "below"
         return "exact"
+
+    def _clob_post(self, path: str, payload: Any) -> Any:
+        url = f"{self.clob_url}{path}"
+        try:
+            resp = self._session.post(url, json=payload, timeout=self.http_timeout)
+            resp.raise_for_status()
+            return resp.json()
+        except Exception:
+            return None
+
+    def _batch_chunks(self, values: List[str], size: int = 200) -> List[List[str]]:
+        if not values:
+            return []
+        chunk_size = max(1, min(int(size or 200), 500))
+        return [values[index : index + chunk_size] for index in range(0, len(values), chunk_size)]
+
+    def _extract_payload_token_id(self, payload: Any) -> Optional[str]:
+        item = _to_plain_dict(payload)
+        if not item and isinstance(payload, dict):
+            item = payload
+        if not item:
+            return None
+        token_id = str(
+            item.get("asset_id")
+            or item.get("assetId")
+            or item.get("token_id")
+            or item.get("tokenId")
+            or item.get("id")
+            or ""
+        ).strip()
+        return token_id or None
+
+    def _extract_batch_scalar_map(self, payload: Any) -> Dict[str, float]:
+        if not payload:
+            return {}
+        data = payload
+        if isinstance(data, dict):
+            for key in ("data", "midpoints", "spreads", "items", "results"):
+                nested = data.get(key)
+                if isinstance(nested, (dict, list)):
+                    data = nested
+                    break
+        result: Dict[str, float] = {}
+        if isinstance(data, dict):
+            for key, value in data.items():
+                numeric = _extract_price(value)
+                if numeric is None:
+                    continue
+                result[str(key).strip()] = numeric
+            return result
+        if isinstance(data, list):
+            for item in data:
+                token_id = self._extract_payload_token_id(item)
+                if not token_id:
+                    continue
+                item_dict = _to_plain_dict(item)
+                numeric = _extract_price(
+                    item_dict.get("midpoint")
+                    or item_dict.get("mid_price")
+                    or item_dict.get("spread")
+                    or item_dict.get("price")
+                    or item_dict.get("last_trade_price")
+                    or item_dict.get("value")
+                )
+                if numeric is None:
+                    continue
+                result[token_id] = numeric
+        return result
+
+    def _extract_batch_price_map(self, payload: Any, side: str) -> Dict[str, float]:
+        if not payload:
+            return {}
+        data = payload.get("data") if isinstance(payload, dict) and isinstance(payload.get("data"), dict) else payload
+        result: Dict[str, float] = {}
+        if not isinstance(data, dict):
+            return result
+        for token_id, side_map in data.items():
+            token_key = str(token_id).strip()
+            if not token_key:
+                continue
+            item = _to_plain_dict(side_map)
+            if not item and isinstance(side_map, dict):
+                item = side_map
+            numeric = _extract_price(item.get(side) if item else side_map)
+            if numeric is None:
+                continue
+            result[token_key] = numeric
+        return result
+
+    def _extract_batch_book_map(self, payload: Any) -> Dict[str, Dict[str, Any]]:
+        if not payload:
+            return {}
+        data = payload
+        if isinstance(data, dict):
+            for key in ("data", "books", "items", "results"):
+                nested = data.get(key)
+                if isinstance(nested, (dict, list)):
+                    data = nested
+                    break
+        result: Dict[str, Dict[str, Any]] = {}
+        if isinstance(data, dict):
+            for token_id, book in data.items():
+                token_key = str(token_id).strip()
+                book_dict = _to_plain_dict(book)
+                if not token_key or not book_dict:
+                    continue
+                result[token_key] = book_dict
+            return result
+        if isinstance(data, list):
+            for item in data:
+                token_id = self._extract_payload_token_id(item)
+                book_dict = _to_plain_dict(item)
+                if not token_id or not book_dict:
+                    continue
+                result[token_id] = book_dict
+        return result
+
+    def _batch_get_token_market_data(
+        self,
+        token_ids: List[str],
+        *,
+        include_books: bool = False,
+    ) -> Dict[str, Dict[str, Any]]:
+        unique_tokens = []
+        seen = set()
+        for token_id in token_ids:
+            normalized = str(token_id or "").strip()
+            if not normalized or normalized in seen:
+                continue
+            seen.add(normalized)
+            unique_tokens.append(normalized)
+
+        if not unique_tokens:
+            return {}
+
+        now = time.time()
+        results: Dict[str, Dict[str, Any]] = {}
+        missing: List[str] = []
+        with self._lock:
+            for token_id in unique_tokens:
+                cached = self._price_cache.get(token_id)
+                if not cached or now - cached.get("t", 0) >= self.price_cache_ttl:
+                    missing.append(token_id)
+                    continue
+                cached_data = cached.get("data", {}) or {}
+                if include_books and not cached_data.get("book") and cached_data.get("book_liquidity") is None:
+                    missing.append(token_id)
+                    continue
+                results[token_id] = dict(cached_data)
+
+        if not missing:
+            return results
+
+        ask_map: Dict[str, float] = {}
+        bid_map: Dict[str, float] = {}
+        midpoint_map: Dict[str, float] = {}
+        spread_map: Dict[str, float] = {}
+        last_trade_map: Dict[str, float] = {}
+        book_map: Dict[str, Dict[str, Any]] = {}
+
+        for chunk in self._batch_chunks(missing):
+            sell_payload = self._clob_post(
+                "/prices",
+                [{"token_id": token_id, "side": "SELL"} for token_id in chunk],
+            )
+            buy_payload = self._clob_post(
+                "/prices",
+                [{"token_id": token_id, "side": "BUY"} for token_id in chunk],
+            )
+            midpoint_payload = self._clob_post(
+                "/midpoints",
+                [{"token_id": token_id} for token_id in chunk],
+            )
+            spread_payload = self._clob_post(
+                "/spreads",
+                [{"token_id": token_id} for token_id in chunk],
+            )
+            last_trade_payload = self._clob_post(
+                "/last-trade-prices",
+                [{"token_id": token_id} for token_id in chunk],
+            )
+            books_payload = (
+                self._clob_post(
+                    "/books",
+                    [{"token_id": token_id} for token_id in chunk],
+                )
+                if include_books
+                else None
+            )
+            ask_map.update(self._extract_batch_price_map(sell_payload, "SELL"))
+            bid_map.update(self._extract_batch_price_map(buy_payload, "BUY"))
+            midpoint_map.update(self._extract_batch_scalar_map(midpoint_payload))
+            spread_map.update(self._extract_batch_scalar_map(spread_payload))
+            last_trade_map.update(self._extract_batch_scalar_map(last_trade_payload))
+            if include_books:
+                book_map.update(self._extract_batch_book_map(books_payload))
+
+        fetched: Dict[str, Dict[str, Any]] = {}
+        unresolved: List[str] = []
+        for token_id in missing:
+            raw_book = book_map.get(token_id)
+            book, book_liquidity = self._normalize_orderbook(raw_book)
+            ask = _extract_price(ask_map.get(token_id))
+            bid = _extract_price(bid_map.get(token_id))
+            midpoint = _extract_price(midpoint_map.get(token_id))
+            spread = _extract_price(spread_map.get(token_id))
+            last_trade = _extract_price(last_trade_map.get(token_id))
+
+            buy, sell = self._resolve_trade_prices(buy=ask, sell=bid, book=book)
+            if midpoint is None and buy is not None and sell is not None:
+                midpoint = (buy + sell) / 2.0
+            if midpoint is None:
+                midpoint = _extract_price(raw_book.get("last_trade_price") if isinstance(raw_book, dict) else None)
+            if spread is None and buy is not None and sell is not None:
+                spread = max(0.0, float(buy) - float(sell))
+            if spread is not None and midpoint is not None:
+                midpoint = _clamp_probability(midpoint)
+            if buy is None and midpoint is not None and spread is not None:
+                buy = _clamp_probability(midpoint + spread / 2.0)
+            if sell is None and midpoint is not None and spread is not None:
+                sell = _clamp_probability(midpoint - spread / 2.0)
+
+            if (
+                buy is None
+                and sell is None
+                and midpoint is None
+                and last_trade is None
+                and book is None
+            ):
+                unresolved.append(token_id)
+                continue
+
+            fetched[token_id] = {
+                "buy": buy,
+                "sell": sell,
+                "midpoint": midpoint,
+                "spread": spread,
+                "last_trade_price": last_trade,
+                "quote_source": "polymarket_clob_rest_batch",
+                "quote_age_ms": 0,
+                "book": book,
+                "book_liquidity": book_liquidity,
+            }
+
+        for token_id in unresolved:
+            fetched[token_id] = self._fetch_token_market_data(token_id)
+
+        with self._lock:
+            for token_id, data in fetched.items():
+                self._price_cache[token_id] = {"data": data, "t": now}
+
+        results.update(fetched)
+        return results
+
+    def _normalize_scan_filters(self, scan_filters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        raw = scan_filters if isinstance(scan_filters, dict) else {}
+        min_price = _clamp_float(_safe_float(raw.get("min_price")), 0.0, 1.0)
+        max_price = _clamp_float(_safe_float(raw.get("max_price")), 0.0, 1.0)
+        if min_price is None:
+            min_price = 0.05
+        if max_price is None:
+            max_price = 0.95
+        if min_price > max_price:
+            min_price, max_price = max_price, min_price
+
+        high_liquidity_only = bool(_safe_bool(raw.get("high_liquidity_only")))
+        min_liquidity = _safe_float(raw.get("min_liquidity"))
+        if min_liquidity is None:
+            min_liquidity = 5000.0 if high_liquidity_only else float(self.min_liquidity_for_signal or 500.0)
+        if high_liquidity_only:
+            min_liquidity = max(min_liquidity, 5000.0)
+
+        return {
+            "scan_mode": str(raw.get("scan_mode") or "tradable").strip().lower() or "tradable",
+            "min_price": float(min_price),
+            "max_price": float(max_price),
+            "min_edge_pct": max(0.0, _safe_float(raw.get("min_edge_pct")) or float(self.edge_threshold or 2.0)),
+            "min_liquidity": max(0.0, float(min_liquidity)),
+            "high_liquidity_only": high_liquidity_only,
+            "market_type": str(raw.get("market_type") or "maxtemp").strip().lower() or "maxtemp",
+            "time_range": str(raw.get("time_range") or "today").strip().lower() or "today",
+            "limit": max(1, _safe_int(raw.get("limit"), 60)),
+            "max_spread": max(0.0, _safe_float(raw.get("max_spread")) or 0.03),
+        }
+
+    def _build_window_meta(
+        self,
+        target_date: str,
+        scan_context: Optional[Dict[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        context = scan_context if isinstance(scan_context, dict) else {}
+        local_date = _extract_iso_date(context.get("local_date")) or _extract_iso_date(target_date)
+        local_time = context.get("local_time")
+        peak = context.get("peak") if isinstance(context.get("peak"), dict) else {}
+        first_h = int(_safe_float(peak.get("first_h")) or 13)
+        last_h = int(_safe_float(peak.get("last_h")) or 15)
+        target_iso = _extract_iso_date(target_date)
+        if not local_date or not target_iso:
+            return {
+                "phase": "today_default",
+                "score": 0.65,
+                "remaining_minutes": None,
+                "same_day": True,
+            }
+
+        try:
+            diff_days = (
+                datetime.fromisoformat(target_iso).date()
+                - datetime.fromisoformat(local_date).date()
+            ).days
+        except Exception:
+            diff_days = 0
+
+        if diff_days >= 2:
+            return {
+                "phase": "week_ahead",
+                "score": 0.45,
+                "remaining_minutes": None,
+                "same_day": False,
+            }
+        if diff_days == 1:
+            return {
+                "phase": "tomorrow",
+                "score": 0.60,
+                "remaining_minutes": None,
+                "same_day": False,
+            }
+        if diff_days < 0:
+            return {
+                "phase": "past",
+                "score": 0.0,
+                "remaining_minutes": None,
+                "same_day": False,
+            }
+
+        now_minutes = _parse_hhmm_to_minutes(local_time)
+        if now_minutes is None:
+            return {
+                "phase": "today_default",
+                "score": 0.65,
+                "remaining_minutes": None,
+                "same_day": True,
+            }
+
+        first_minutes = max(0, first_h * 60)
+        last_minutes = min(23 * 60 + 59, last_h * 60)
+        if now_minutes > last_minutes + 120:
+            return {
+                "phase": "post_peak",
+                "score": 0.50,
+                "remaining_minutes": 0,
+                "same_day": True,
+            }
+        if first_minutes <= now_minutes <= last_minutes + 120:
+            return {
+                "phase": "active_peak",
+                "score": 1.00,
+                "remaining_minutes": max(0, last_minutes + 120 - now_minutes),
+                "same_day": True,
+            }
+        if first_minutes - 180 <= now_minutes < first_minutes:
+            return {
+                "phase": "setup_today",
+                "score": 0.85,
+                "remaining_minutes": max(0, last_minutes + 120 - now_minutes),
+                "same_day": True,
+            }
+        return {
+            "phase": "early_today",
+            "score": 0.70,
+            "remaining_minutes": max(0, first_minutes - now_minutes),
+            "same_day": True,
+        }
+
+    def _resolve_market_target_threshold(
+        self,
+        market_direction: str,
+        bucket_range: Optional[Tuple[float, Optional[float], str]],
+        bucket_temp: Optional[float],
+    ) -> Optional[float]:
+        if not bucket_range:
+            return bucket_temp
+        lower, upper, _unit = bucket_range
+        if market_direction in {"above", "below"}:
+            return lower
+        if upper is not None:
+            return (lower + upper) / 2.0
+        return lower
+
+    def _resolve_temperature_direction(
+        self,
+        *,
+        side: str,
+        market_direction: str,
+        target_threshold: Optional[float],
+        current_reference: Optional[float],
+    ) -> str:
+        if market_direction == "above":
+            return "hotter" if side == "yes" else "colder"
+        if market_direction == "below":
+            return "colder" if side == "yes" else "hotter"
+        hotter_bias = True
+        if target_threshold is not None and current_reference is not None:
+            hotter_bias = target_threshold >= current_reference
+        if side == "yes":
+            return "hotter" if hotter_bias else "colder"
+        return "colder" if hotter_bias else "hotter"
+
+    def _is_trend_aligned(
+        self,
+        *,
+        temperature_direction: str,
+        trend_info: Optional[Dict[str, Any]],
+        network_lead_signal: Optional[Dict[str, Any]],
+    ) -> bool:
+        trend = trend_info if isinstance(trend_info, dict) else {}
+        network = network_lead_signal if isinstance(network_lead_signal, dict) else {}
+        trend_direction = _normalize_text(trend.get("direction"))
+        if temperature_direction == "hotter" and trend_direction == "rising":
+            return True
+        if temperature_direction == "colder" and trend_direction in {"falling", "stagnant"}:
+            return True
+        lead_delta = _safe_float(network.get("delta"))
+        if lead_delta is None:
+            return False
+        if temperature_direction == "hotter":
+            return lead_delta > 0
+        return lead_delta < 0
+
+    def _build_distribution_scan_pack(
+        self,
+        *,
+        city_key: str,
+        target_date: str,
+        primary_market: Dict[str, Any],
+        probability_distribution: Optional[List[Dict[str, Any]]] = None,
+        temp_symbol: Optional[str] = None,
+        scan_context: Optional[Dict[str, Any]] = None,
+        scan_filters: Optional[Dict[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        filters = self._normalize_scan_filters(scan_filters)
+        window_meta = self._build_window_meta(target_date, scan_context)
+        rows: List[Dict[str, Any]] = []
+        related_markets = self._collect_related_temperature_markets(
+            city_key=city_key,
+            target_date=target_date,
+            primary_market=primary_market,
+        )
+        if not related_markets:
+            return {
+                "rows": [],
+                "distribution_bias": {
+                    "available": False,
+                    "value": None,
+                    "direction": "balanced",
+                    "score": 0.0,
+                    "valid_markets": 0,
+                },
+                "primary_signal": None,
+                "signal_status": "no_market",
+                "candidate_count": 0,
+                "window_phase": window_meta.get("phase"),
+                "window_score": window_meta.get("score"),
+                "resolved_market_type": "maxtemp",
+            }
+
+        market_entries: List[Dict[str, Any]] = []
+        token_ids: List[str] = []
+        for market in related_markets:
+            tokens = self._extract_market_tokens(market)
+            yes_token, no_token = self._resolve_yes_no_tokens(tokens)
+            if not yes_token or not no_token:
+                continue
+            yes_token_id = str(yes_token.get("token_id") or "").strip()
+            no_token_id = str(no_token.get("token_id") or "").strip()
+            if not yes_token_id or not no_token_id:
+                continue
+            bucket_range = self._extract_market_bucket_range(market)
+            bucket_temp = self._extract_market_bucket_temp(market)
+            raw_direction = self._extract_market_bucket_direction(market)
+            market_direction = "range" if bucket_range and bucket_range[1] is not None else raw_direction
+            model_event_probability = self._aggregate_distribution_probability_for_market(
+                market=market,
+                probability_distribution=probability_distribution,
+                temp_symbol=temp_symbol,
+            )
+            token_ids.extend([yes_token_id, no_token_id])
+            market_entries.append(
+                {
+                    "market": market,
+                    "yes_token": yes_token,
+                    "no_token": no_token,
+                    "yes_token_id": yes_token_id,
+                    "no_token_id": no_token_id,
+                    "bucket_range": bucket_range,
+                    "bucket_temp": bucket_temp,
+                    "market_direction": market_direction,
+                    "target_threshold": self._resolve_market_target_threshold(
+                        market_direction,
+                        bucket_range,
+                        bucket_temp,
+                    ),
+                    "target_label": self._extract_market_bucket_label(market, bucket_temp),
+                    "model_event_probability": model_event_probability,
+                    "market_liquidity": _extract_price(
+                        market.get("liquidityNum")
+                        or market.get("liquidity")
+                        or market.get("liquidityClob")
+                    ),
+                    "volume": _extract_price(
+                        market.get("volumeNum")
+                        or market.get("volume")
+                        or market.get("volume24hr")
+                    ),
+                    "trade_state": self._market_trade_state(market),
+                    "enable_order_book": bool(
+                        market.get("enableOrderBook", market.get("enable_order_book", False))
+                    ),
+                }
+            )
+
+        broad_quotes = self._batch_get_token_market_data(token_ids, include_books=False)
+        bias_inputs: List[Tuple[float, float]] = []
+        for entry in market_entries:
+            yes_quote = broad_quotes.get(entry["yes_token_id"], {})
+            no_quote = broad_quotes.get(entry["no_token_id"], {})
+            market_event_probability = (
+                _extract_price(yes_quote.get("midpoint"))
+                or _extract_price(yes_quote.get("buy"))
+                or _extract_price(yes_quote.get("sell"))
+                or _extract_price(entry["yes_token"].get("implied_probability"))
+            )
+            if market_event_probability is not None:
+                market_event_probability = _clamp_probability(market_event_probability)
+            yes_ask = _extract_price(yes_quote.get("buy"))
+            yes_bid = _extract_price(yes_quote.get("sell"))
+            no_ask = _extract_price(no_quote.get("buy"))
+            no_bid = _extract_price(no_quote.get("sell"))
+            if no_ask is None and yes_ask is not None:
+                no_ask = _clamp_probability(1.0 - yes_bid) if yes_bid is not None else None
+            if no_bid is None and yes_bid is not None:
+                no_bid = _clamp_probability(1.0 - yes_ask) if yes_ask is not None else None
+            spread = _extract_price(yes_quote.get("spread"))
+            if spread is None and yes_ask is not None and yes_bid is not None:
+                spread = max(0.0, yes_ask - yes_bid)
+
+            entry["market_event_probability"] = market_event_probability
+            entry["yes_ask"] = yes_ask
+            entry["yes_bid"] = yes_bid
+            entry["no_ask"] = no_ask
+            entry["no_bid"] = no_bid
+            entry["midpoint"] = _extract_price(yes_quote.get("midpoint")) or market_event_probability
+            entry["spread"] = spread
+            entry["yes_book_liquidity"] = _extract_price(yes_quote.get("book_liquidity"))
+            entry["no_book_liquidity"] = _extract_price(no_quote.get("book_liquidity"))
+            entry["quote_source"] = yes_quote.get("quote_source") or no_quote.get("quote_source")
+            entry["quote_age_ms"] = _safe_int(
+                yes_quote.get("quote_age_ms") if yes_quote.get("quote_age_ms") is not None else no_quote.get("quote_age_ms"),
+                0,
+            )
+
+            model_event_probability = _clamp_probability(_safe_float(entry.get("model_event_probability")))
+            if (
+                model_event_probability is not None
+                and market_event_probability is not None
+                and entry["market_direction"] in {"above", "below"}
+            ):
+                gap = model_event_probability - market_event_probability
+                signed_gap = -gap if entry["market_direction"] == "below" else gap
+                bias_inputs.append((max(model_event_probability, 0.08), signed_gap))
+
+        distribution_bias_value = None
+        distribution_bias_score = 0.0
+        distribution_bias_direction = "balanced"
+        if len(bias_inputs) >= 3:
+            total_weight = sum(weight for weight, _signed_gap in bias_inputs)
+            if total_weight > 0:
+                distribution_bias_value = sum(weight * signed_gap for weight, signed_gap in bias_inputs) / total_weight
+                distribution_bias_score = max(0.0, min(abs(distribution_bias_value) / 0.08, 1.0)) * 100.0
+                if distribution_bias_value >= 0.015:
+                    distribution_bias_direction = "hotter"
+                elif distribution_bias_value <= -0.015:
+                    distribution_bias_direction = "colder"
+
+        distribution_bias = {
+            "available": len(bias_inputs) >= 3 and distribution_bias_value is not None,
+            "value": distribution_bias_value,
+            "direction": distribution_bias_direction,
+            "score": distribution_bias_score,
+            "valid_markets": len(bias_inputs),
+        }
+
+        current_reference_raw = _safe_float(
+            (scan_context or {}).get("current_max_so_far")
+            or (scan_context or {}).get("current_temp")
+        )
+
+        def _row_from_entry(entry: Dict[str, Any], side: str) -> Optional[Dict[str, Any]]:
+            model_event_probability = _clamp_probability(_safe_float(entry.get("model_event_probability")))
+            market_event_probability = _clamp_probability(_safe_float(entry.get("market_event_probability")))
+            ask = _clamp_probability(_safe_float(entry.get("yes_ask") if side == "yes" else entry.get("no_ask")))
+            bid = _clamp_probability(_safe_float(entry.get("yes_bid") if side == "yes" else entry.get("no_bid")))
+            if model_event_probability is None or ask is None:
+                return None
+
+            model_probability = (
+                model_event_probability
+                if side == "yes"
+                else _clamp_probability(1.0 - model_event_probability)
+            )
+            market_probability = (
+                market_event_probability
+                if side == "yes"
+                else _clamp_probability(1.0 - market_event_probability)
+            )
+            if model_probability is None:
+                return None
+
+            market = entry["market"]
+            target_threshold = _safe_float(entry.get("target_threshold"))
+            bucket_range = entry.get("bucket_range")
+            market_unit = bucket_range[2] if bucket_range else ("F" if self._is_fahrenheit_symbol(temp_symbol) else "C")
+            current_reference = self._convert_temp_to_market_unit(
+                current_reference_raw,
+                source_symbol=temp_symbol,
+                market_unit=market_unit,
+            )
+            gap_to_target = (
+                target_threshold - current_reference
+                if target_threshold is not None and current_reference is not None
+                else None
+            )
+            temperature_direction = self._resolve_temperature_direction(
+                side=side,
+                market_direction=str(entry.get("market_direction") or "exact"),
+                target_threshold=target_threshold,
+                current_reference=current_reference,
+            )
+            trend_alignment = self._is_trend_aligned(
+                temperature_direction=temperature_direction,
+                trend_info=(scan_context or {}).get("trend"),
+                network_lead_signal=(scan_context or {}).get("network_lead_signal"),
+            )
+            edge = model_probability - ask
+            edge_percent = edge * 100.0
+            liquidity_reference = max(
+                _safe_float(
+                    entry.get("yes_book_liquidity") if side == "yes" else entry.get("no_book_liquidity")
+                ) or 0.0,
+                _safe_float(entry.get("market_liquidity")) or 0.0,
+            )
+            if liquidity_reference >= 10000:
+                liquidity_score = 1.0
+            elif liquidity_reference >= 5000:
+                liquidity_score = 0.8
+            elif liquidity_reference >= 1000:
+                liquidity_score = 0.6
+            else:
+                liquidity_score = 0.4
+            if 0.10 <= ask <= 0.90:
+                price_usefulness_score = 1.0
+            elif 0.05 <= ask < 0.10 or 0.90 < ask <= 0.95:
+                price_usefulness_score = 0.7
+            else:
+                price_usefulness_score = 0.0
+            bias_score = 0.0
+            if distribution_bias["available"]:
+                if distribution_bias_direction == "balanced" or distribution_bias_direction == temperature_direction:
+                    bias_score = distribution_bias_score / 100.0
+            spread = _safe_float(entry.get("spread"))
+            spread_penalty = max(
+                0.0,
+                min(((spread or 0.0) - 0.01) / 0.02, 1.0),
+            ) * 15.0
+            edge_score = max(0.0, min(edge_percent / 12.0, 1.0))
+            final_score = 100.0 * (
+                0.35 * edge_score
+                + 0.25 * bias_score
+                + 0.20 * float(window_meta.get("score") or 0.0)
+                + 0.10 * liquidity_score
+                + 0.10 * price_usefulness_score
+            ) - spread_penalty
+            market_slug = str(market.get("slug") or "").strip()
+            target_label = str(entry.get("target_label") or "").strip()
+            action = f"BUY {'YES' if side == 'yes' else 'NO'}"
+            if target_label:
+                action = f"{action} {target_label}"
+            return {
+                "id": f"{city_key}|{target_date}|{market_slug}|{side}",
+                "city": city_key,
+                "selected_date": target_date,
+                "market_slug": market_slug or None,
+                "market_question": market.get("question") or market.get("title"),
+                "market_url": self._build_market_url(market),
+                "side": side,
+                "action": action,
+                "market_direction": entry.get("market_direction"),
+                "temperature_direction": temperature_direction,
+                "target_label": entry.get("target_label"),
+                "target_value": entry.get("bucket_temp"),
+                "target_threshold": target_threshold,
+                "target_lower": bucket_range[0] if bucket_range else None,
+                "target_upper": bucket_range[1] if bucket_range else None,
+                "target_unit": market_unit,
+                "model_probability": model_probability,
+                "market_probability": market_probability,
+                "model_event_probability": model_event_probability,
+                "market_event_probability": market_event_probability,
+                "gap": (
+                    model_event_probability - market_event_probability
+                    if model_event_probability is not None and market_event_probability is not None
+                    else None
+                ),
+                "signed_gap": (
+                    -1.0 * (model_event_probability - market_event_probability)
+                    if model_event_probability is not None
+                    and market_event_probability is not None
+                    and entry.get("market_direction") == "below"
+                    else (
+                        model_event_probability - market_event_probability
+                        if model_event_probability is not None and market_event_probability is not None
+                        else None
+                    )
+                ),
+                "yes_token_id": entry.get("yes_token_id"),
+                "no_token_id": entry.get("no_token_id"),
+                "yes_ask": entry.get("yes_ask"),
+                "yes_bid": entry.get("yes_bid"),
+                "no_ask": entry.get("no_ask"),
+                "no_bid": entry.get("no_bid"),
+                "ask": ask,
+                "bid": bid,
+                "midpoint": entry.get("midpoint"),
+                "spread": spread,
+                "book_liquidity": _safe_float(
+                    entry.get("yes_book_liquidity") if side == "yes" else entry.get("no_book_liquidity")
+                ),
+                "market_liquidity": entry.get("market_liquidity"),
+                "volume": entry.get("volume"),
+                "quote_source": entry.get("quote_source"),
+                "quote_age_ms": entry.get("quote_age_ms"),
+                "edge": edge,
+                "edge_percent": edge_percent,
+                "edge_score": edge_score,
+                "bias_score": bias_score,
+                "window_phase": window_meta.get("phase"),
+                "window_score": window_meta.get("score"),
+                "remaining_window_minutes": window_meta.get("remaining_minutes"),
+                "liquidity_score": liquidity_score,
+                "price_usefulness_score": price_usefulness_score,
+                "spread_penalty": spread_penalty,
+                "final_score": final_score,
+                "distribution_bias_direction": distribution_bias_direction,
+                "distribution_bias_score": distribution_bias_score,
+                "distribution_bias_available": distribution_bias["available"],
+                "current_reference": current_reference,
+                "gap_to_target": gap_to_target,
+                "touch_distance": abs(gap_to_target) if gap_to_target is not None else None,
+                "trend_alignment": trend_alignment,
+                "tradable": bool(entry["trade_state"].get("tradable")),
+                "active": entry["trade_state"].get("active"),
+                "closed": entry["trade_state"].get("closed"),
+                "accepting_orders": entry["trade_state"].get("accepting_orders"),
+                "enable_order_book": entry.get("enable_order_book"),
+                "is_primary_market": bool(
+                    str(primary_market.get("slug") or "").strip().lower()
+                    and market_slug
+                    and str(primary_market.get("slug") or "").strip().lower() == market_slug.lower()
+                ),
+            }
+
+        preliminary_rows: List[Dict[str, Any]] = []
+        for entry in market_entries:
+            row_yes = _row_from_entry(entry, "yes")
+            row_no = _row_from_entry(entry, "no")
+            if row_yes:
+                preliminary_rows.append(row_yes)
+            if row_no:
+                preliminary_rows.append(row_no)
+
+        preliminary_rows.sort(key=lambda row: float(row.get("final_score") or 0.0), reverse=True)
+        shortlist_market_slugs = []
+        seen_slugs = set()
+        for row in preliminary_rows:
+            market_slug = str(row.get("market_slug") or "").strip()
+            if not market_slug or market_slug in seen_slugs:
+                continue
+            seen_slugs.add(market_slug)
+            shortlist_market_slugs.append(market_slug)
+            if len(shortlist_market_slugs) >= 10:
+                break
+
+        shortlisted_tokens: List[str] = []
+        for entry in market_entries:
+            market_slug = str(entry["market"].get("slug") or "").strip()
+            if market_slug not in seen_slugs:
+                continue
+            shortlisted_tokens.extend([entry["yes_token_id"], entry["no_token_id"]])
+
+        precise_quotes = self._batch_get_token_market_data(shortlisted_tokens, include_books=True)
+        for entry in market_entries:
+            market_slug = str(entry["market"].get("slug") or "").strip()
+            if market_slug not in seen_slugs:
+                continue
+            yes_quote = precise_quotes.get(entry["yes_token_id"], {})
+            no_quote = precise_quotes.get(entry["no_token_id"], {})
+            if yes_quote:
+                entry["yes_ask"] = _extract_price(yes_quote.get("buy")) or entry.get("yes_ask")
+                entry["yes_bid"] = _extract_price(yes_quote.get("sell")) or entry.get("yes_bid")
+                entry["midpoint"] = _extract_price(yes_quote.get("midpoint")) or entry.get("midpoint")
+                entry["spread"] = _extract_price(yes_quote.get("spread")) or entry.get("spread")
+                entry["yes_book_liquidity"] = _extract_price(yes_quote.get("book_liquidity")) or entry.get("yes_book_liquidity")
+                entry["quote_source"] = yes_quote.get("quote_source") or entry.get("quote_source")
+            if no_quote:
+                entry["no_ask"] = _extract_price(no_quote.get("buy")) or entry.get("no_ask")
+                entry["no_bid"] = _extract_price(no_quote.get("sell")) or entry.get("no_bid")
+                entry["no_book_liquidity"] = _extract_price(no_quote.get("book_liquidity")) or entry.get("no_book_liquidity")
+                entry["quote_source"] = no_quote.get("quote_source") or entry.get("quote_source")
+            if entry.get("spread") is None and entry.get("yes_ask") is not None and entry.get("yes_bid") is not None:
+                entry["spread"] = max(0.0, float(entry["yes_ask"]) - float(entry["yes_bid"]))
+
+        final_rows: List[Dict[str, Any]] = []
+        for entry in market_entries:
+            for side in ("yes", "no"):
+                row = _row_from_entry(entry, side)
+                if row:
+                    final_rows.append(row)
+
+        def _passes_hard_filters(row: Dict[str, Any]) -> bool:
+            ask = _safe_float(row.get("ask"))
+            edge_percent = _safe_float(row.get("edge_percent"))
+            spread = _safe_float(row.get("spread"))
+            liquidity = max(
+                _safe_float(row.get("book_liquidity")) or 0.0,
+                _safe_float(row.get("market_liquidity")) or 0.0,
+            )
+            if ask is None or edge_percent is None:
+                return False
+            if not row.get("tradable") or row.get("accepting_orders") is False:
+                return False
+            if row.get("enable_order_book") is False:
+                return False
+            if ask < filters["min_price"] or ask > filters["max_price"]:
+                return False
+            if edge_percent < filters["min_edge_pct"]:
+                return False
+            if spread is None or spread > filters["max_spread"]:
+                return False
+            if liquidity < filters["min_liquidity"]:
+                return False
+            return True
+
+        def _passes_mode_filters(row: Dict[str, Any]) -> bool:
+            scan_mode = filters["scan_mode"]
+            if scan_mode == "tradable":
+                return float(row.get("window_score") or 0.0) >= 0.65
+            if scan_mode == "early":
+                return str(row.get("window_phase") or "") in {"tomorrow", "week_ahead", "early_today"}
+            if scan_mode == "touch":
+                return (
+                    bool(window_meta.get("same_day"))
+                    and str(row.get("window_phase") or "") in {"setup_today", "active_peak"}
+                    and (_safe_float(row.get("touch_distance")) is not None)
+                    and float(row.get("touch_distance")) <= 2.0
+                )
+            if scan_mode == "trend":
+                return bool(row.get("trend_alignment"))
+            return True
+
+        filtered_rows = [
+            row
+            for row in final_rows
+            if _passes_hard_filters(row) and _passes_mode_filters(row)
+        ]
+        filtered_rows.sort(
+            key=lambda row: (
+                float(row.get("final_score") or 0.0),
+                float(row.get("edge_percent") or 0.0),
+            ),
+            reverse=True,
+        )
+
+        primary_signal = filtered_rows[0] if filtered_rows else None
+        signal_status = "ready" if primary_signal else "no_signal"
+        return {
+            "rows": filtered_rows[: filters["limit"]],
+            "distribution_bias": distribution_bias,
+            "primary_signal": primary_signal,
+            "signal_status": signal_status,
+            "candidate_count": len(filtered_rows),
+            "window_phase": window_meta.get("phase"),
+            "window_score": window_meta.get("score"),
+            "resolved_market_type": "maxtemp",
+        }
diff --git a/tests/test_polymarket_readonly.py b/tests/test_polymarket_readonly.py
index 42540359..297a2e26 100644
--- a/tests/test_polymarket_readonly.py
+++ b/tests/test_polymarket_readonly.py
@@ -484,3 +484,193 @@ def test_find_primary_market_prefers_preferred_temperature_and_cache_key():
     assert reason_22 is None
     assert selected_27["slug"] == "highest-temperature-in-madrid-on-april-23-2026-27c"
     assert selected_22["slug"] == "highest-temperature-in-madrid-on-april-23-2026-22corbelow"
+
+
+def _build_scan_test_layer():
+    layer = PolymarketReadOnlyLayer()
+    markets = [
+        {
+            "id": "m-above-14",
+            "slug": "highest-temperature-in-wellington-on-april-24-2026-14c-or-higher",
+            "question": "Will the highest temperature in Wellington be 14C or higher on April 24?",
+            "active": True,
+            "closed": False,
+            "acceptingOrders": True,
+            "enableOrderBook": True,
+            "liquidityNum": 12000,
+            "volumeNum": 4000,
+            "_model_prob": 0.60,
+        },
+        {
+            "id": "m-below-16",
+            "slug": "highest-temperature-in-wellington-on-april-24-2026-16c-or-lower",
+            "question": "Will the highest temperature in Wellington be 16C or lower on April 24?",
+            "active": True,
+            "closed": False,
+            "acceptingOrders": True,
+            "enableOrderBook": True,
+            "liquidityNum": 9000,
+            "volumeNum": 3500,
+            "_model_prob": 0.30,
+        },
+        {
+            "id": "m-above-17",
+            "slug": "highest-temperature-in-wellington-on-april-24-2026-17c-or-higher",
+            "question": "Will the highest temperature in Wellington be 17C or higher on April 24?",
+            "active": True,
+            "closed": False,
+            "acceptingOrders": True,
+            "enableOrderBook": True,
+            "liquidityNum": 7000,
+            "volumeNum": 2800,
+            "_model_prob": 0.20,
+        },
+    ]
+    token_map = {
+        "m-above-14": {"yes": "yes-14", "no": "no-14"},
+        "m-below-16": {"yes": "yes-16", "no": "no-16"},
+        "m-above-17": {"yes": "yes-17", "no": "no-17"},
+    }
+    quote_map = {
+        "yes-14": {"buy": 0.48, "sell": 0.46, "midpoint": 0.40, "spread": 0.02, "book_liquidity": 14000},
+        "no-14": {"buy": 0.54, "sell": 0.52, "midpoint": 0.60, "spread": 0.02, "book_liquidity": 14000},
+        "yes-16": {"buy": 0.42, "sell": 0.40, "midpoint": 0.50, "spread": 0.02, "book_liquidity": 9000},
+        "no-16": {"buy": 0.56, "sell": 0.54, "midpoint": 0.50, "spread": 0.02, "book_liquidity": 9000},
+        "yes-17": {"buy": 0.08, "sell": 0.07, "midpoint": 0.10, "spread": 0.01, "book_liquidity": 7500},
+        "no-17": {"buy": 0.92, "sell": 0.91, "midpoint": 0.90, "spread": 0.01, "book_liquidity": 7500},
+    }
+
+    layer._collect_related_temperature_markets = lambda **_kwargs: markets
+    layer._aggregate_distribution_probability_for_market = (
+        lambda market, **_kwargs: market.get("_model_prob")
+    )
+    layer._extract_market_tokens = lambda market: [
+        {"outcome": "Yes", "token_id": token_map[market["id"]]["yes"]},
+        {"outcome": "No", "token_id": token_map[market["id"]]["no"]},
+    ]
+    layer._batch_get_token_market_data = (
+        lambda token_ids, include_books=False: {
+            token_id: dict(quote_map[token_id])
+            for token_id in token_ids
+            if token_id in quote_map
+        }
+    )
+    return layer, markets
+
+
+def test_distribution_scan_bias_flips_below_markets_into_hotter_signal():
+    layer, markets = _build_scan_test_layer()
+
+    scan = layer._build_distribution_scan_pack(
+        city_key="wellington",
+        target_date="2026-04-24",
+        primary_market=markets[0],
+        probability_distribution=[],
+        temp_symbol="°C",
+        scan_context={
+            "local_date": "2026-04-24",
+            "local_time": "13:10",
+            "peak": {"first_h": 14, "last_h": 16},
+            "current_max_so_far": 13.4,
+            "current_temp": 13.0,
+            "trend": {"recent": []},
+            "network_lead_signal": {},
+        },
+        scan_filters={"limit": 10},
+    )
+
+    bias = scan["distribution_bias"]
+    assert bias["available"] is True
+    assert bias["direction"] == "hotter"
+    assert bias["score"] > 0
+
+
+def test_distribution_scan_returns_single_primary_signal_from_yes_no_mix():
+    layer, markets = _build_scan_test_layer()
+
+    scan = layer._build_distribution_scan_pack(
+        city_key="wellington",
+        target_date="2026-04-24",
+        primary_market=markets[0],
+        probability_distribution=[],
+        temp_symbol="°C",
+        scan_context={
+            "local_date": "2026-04-24",
+            "local_time": "13:10",
+            "peak": {"first_h": 14, "last_h": 16},
+            "current_max_so_far": 13.6,
+            "current_temp": 13.2,
+            "trend": {"recent": []},
+            "network_lead_signal": {},
+        },
+        scan_filters={"limit": 10, "min_edge_pct": 2},
+    )
+
+    assert scan["candidate_count"] >= 2
+    assert isinstance(scan["primary_signal"], dict)
+    assert scan["primary_signal"]["side"] == "yes"
+    assert scan["primary_signal"]["id"] == scan["rows"][0]["id"]
+    assert scan["signal_status"] == "ready"
+
+
+def test_distribution_scan_hard_filters_block_unusable_extreme_quotes():
+    layer, markets = _build_scan_test_layer()
+    layer._batch_get_token_market_data = (
+        lambda token_ids, include_books=False: {
+            token_id: {
+                "buy": 0.99 if token_id.startswith("yes") else 0.01,
+                "sell": 0.95 if token_id.startswith("yes") else 0.0,
+                "midpoint": 0.97 if token_id.startswith("yes") else 0.03,
+                "spread": 0.04,
+                "book_liquidity": 100,
+            }
+            for token_id in token_ids
+        }
+    )
+
+    scan = layer._build_distribution_scan_pack(
+        city_key="wellington",
+        target_date="2026-04-24",
+        primary_market=markets[0],
+        probability_distribution=[],
+        temp_symbol="°C",
+        scan_context={
+            "local_date": "2026-04-24",
+            "local_time": "13:10",
+            "peak": {"first_h": 14, "last_h": 16},
+            "current_max_so_far": 13.6,
+            "current_temp": 13.2,
+            "trend": {"recent": []},
+            "network_lead_signal": {},
+        },
+        scan_filters={"limit": 10},
+    )
+
+    assert scan["signal_status"] == "no_signal"
+    assert scan["candidate_count"] == 0
+    assert scan["rows"] == []
+
+
+def test_batch_token_market_data_falls_back_to_single_fetch_when_batch_fails():
+    layer = PolymarketReadOnlyLayer()
+    layer._clob_post = lambda *_args, **_kwargs: None
+    layer._fetch_token_market_data = lambda token_id: {
+        "buy": 0.33,
+        "sell": 0.31,
+        "midpoint": 0.32,
+        "quote_source": f"fallback:{token_id}",
+    }
+
+    result = layer._batch_get_token_market_data(["token-a", "token-b"])
+
+    assert result["token-a"]["midpoint"] == 0.32
+    assert result["token-b"]["quote_source"] == "fallback:token-b"
+
+
+def test_normalize_scan_filters_raises_liquidity_floor_when_high_liquidity_only():
+    layer = PolymarketReadOnlyLayer()
+
+    filters = layer._normalize_scan_filters({"high_liquidity_only": True})
+
+    assert filters["high_liquidity_only"] is True
+    assert filters["min_liquidity"] >= 5000
diff --git a/tests/test_web_observability.py b/tests/test_web_observability.py
index 6f835aaa..8f18a6f7 100644
--- a/tests/test_web_observability.py
+++ b/tests/test_web_observability.py
@@ -3,6 +3,7 @@ from fastapi.testclient import TestClient
 
 from web.app import app
 import web.routes as routes
+from web.scan_terminal_service import _scan_terminal_cache_key
 from src.database.db_manager import DBManager
 from src.database.runtime_state import TruthRecordRepository, TrainingFeatureRecordRepository
 
@@ -260,6 +261,69 @@ def test_ops_truth_history_returns_filtered_rows(monkeypatch):
     assert payload["items"][0]["settlement_station_code"] == "RCSS"
 
 
+def test_scan_terminal_endpoint_forwards_filters(monkeypatch):
+    monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
+
+    captured = {}
+
+    def _fake_build_scan_terminal_payload(filters, *, force_refresh=False):
+        captured["filters"] = dict(filters)
+        captured["force_refresh"] = force_refresh
+        return {
+            "generated_at": "2026-04-23T00:00:00Z",
+            "filters": filters,
+            "summary": {
+                "recommended_count": 1,
+                "visible_count": 1,
+                "candidate_total": 3,
+                "avg_edge_percent": 4.2,
+                "avg_primary_confidence": 88.0,
+                "tradable_market_count": 1,
+                "total_volume": 1500,
+                "resolved_market_type": "maxtemp",
+            },
+            "top_signal": None,
+            "rows": [],
+        }
+
+    monkeypatch.setattr(routes, "build_scan_terminal_payload", _fake_build_scan_terminal_payload)
+
+    response = client.get(
+        "/api/scan/terminal?scan_mode=trend&min_price=0.1&max_price=0.8&min_edge_pct=3"
+        "&min_liquidity=700&high_liquidity_only=true&market_type=all&time_range=week&limit=12&force_refresh=true"
+    )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["summary"]["recommended_count"] == 1
+    assert captured["force_refresh"] is True
+    assert captured["filters"]["scan_mode"] == "trend"
+    assert captured["filters"]["market_type"] == "all"
+    assert captured["filters"]["time_range"] == "week"
+    assert captured["filters"]["limit"] == 12
+
+
+def test_scan_terminal_cache_key_includes_filter_dimensions():
+    first = _scan_terminal_cache_key(
+        {
+            "scan_mode": "tradable",
+            "time_range": "today",
+            "limit": 25,
+        }
+    )
+    second = _scan_terminal_cache_key(
+        {
+            "scan_mode": "trend",
+            "time_range": "week",
+            "limit": 10,
+        }
+    )
+
+    assert first != second
+    assert "trend" in second
+    assert "week" in second
+
+
 def test_city_history_is_read_only_and_uses_sqlite_truth_and_features(monkeypatch):
     monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
 
diff --git a/web/analysis_service.py b/web/analysis_service.py
index b1d02028..2fafb065 100644
--- a/web/analysis_service.py
+++ b/web/analysis_service.py
@@ -2805,6 +2805,7 @@ def _build_city_market_scan_payload(
     market_slug: Optional[str] = None,
     target_date: Optional[str] = None,
     lite: bool = False,
+    scan_filters: Optional[Dict[str, Any]] = None,
 ) -> Dict[str, Any]:
     city = str(data.get("name") or "").strip().lower()
     local_date = str(data.get("local_date") or "").strip()
@@ -2878,6 +2879,16 @@ def _build_city_market_scan_payload(
         for p in distribution_all[:8]
         if isinstance(p, dict)
     ]
+    current = data.get("current") or {}
+    scan_context = {
+        "local_date": data.get("local_date"),
+        "local_time": data.get("local_time"),
+        "peak": data.get("peak") or {},
+        "current_max_so_far": current.get("max_so_far"),
+        "current_temp": current.get("temp"),
+        "trend": data.get("trend") or {},
+        "network_lead_signal": data.get("network_lead_signal") or {},
+    }
     market_scan = _market_layer.build_market_scan(
         city=data.get("name"),
         target_date=selected_date or data.get("local_date"),
@@ -2888,6 +2899,8 @@ def _build_city_market_scan_payload(
         fallback_sparkline=fallback_sparkline,
         forced_market_slug=market_slug,
         include_related_buckets=not lite,
+        scan_filters=scan_filters,
+        scan_context=scan_context,
     )
     if isinstance(market_scan, dict):
         market_scan["anchor_model"] = anchor_model
diff --git a/web/routes.py b/web/routes.py
index 86d51463..a0ac0553 100644
--- a/web/routes.py
+++ b/web/routes.py
@@ -32,6 +32,7 @@ from web.analysis_service import (
     _build_city_market_scan_payload,
     _build_city_summary_payload,
 )
+from web.scan_terminal_service import build_scan_terminal_payload
 from web.core import (
     AnalyticsEventRequest,
     CITIES,
@@ -1694,3 +1695,36 @@ async def city_market_scan(
         lite,
     )
 
+
+@router.get("/api/scan/terminal")
+async def scan_terminal(
+    request: Request,
+    scan_mode: str = "tradable",
+    min_price: float = 0.05,
+    max_price: float = 0.95,
+    min_edge_pct: float = 2.0,
+    min_liquidity: float = 500.0,
+    high_liquidity_only: bool = False,
+    market_type: str = "maxtemp",
+    time_range: str = "today",
+    limit: int = 25,
+    force_refresh: bool = False,
+):
+    _assert_entitlement(request)
+    filters = {
+        "scan_mode": scan_mode,
+        "min_price": min_price,
+        "max_price": max_price,
+        "min_edge_pct": min_edge_pct,
+        "min_liquidity": min_liquidity,
+        "high_liquidity_only": high_liquidity_only,
+        "market_type": market_type,
+        "time_range": time_range,
+        "limit": limit,
+    }
+    return await run_in_threadpool(
+        build_scan_terminal_payload,
+        filters,
+        force_refresh=force_refresh,
+    )
+
diff --git a/web/scan_terminal_service.py b/web/scan_terminal_service.py
new file mode 100644
index 00000000..cd5ecc7e
--- /dev/null
+++ b/web/scan_terminal_service.py
@@ -0,0 +1,358 @@
+from __future__ import annotations
+
+import json
+import os
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional
+
+from loguru import logger
+
+from web.analysis_service import _analyze, _build_city_market_scan_payload
+from web.core import CITIES
+
+_SCAN_TERMINAL_CACHE_LOCK = threading.Lock()
+_SCAN_TERMINAL_CACHE: Dict[str, Dict[str, Any]] = {}
+SCAN_TERMINAL_PAYLOAD_TTL_SEC = max(
+    5,
+    int(os.getenv("POLYWEATHER_SCAN_TERMINAL_PAYLOAD_TTL_SEC", "30")),
+)
+
+
+def _safe_float(value: Any) -> Optional[float]:
+    try:
+        if value is None or value == "":
+            return None
+        return float(value)
+    except Exception:
+        return None
+
+
+def _safe_int(value: Any, default: int) -> int:
+    try:
+        return int(value)
+    except Exception:
+        return int(default)
+
+
+def _normalize_scan_terminal_filters(
+    raw_filters: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    raw = raw_filters if isinstance(raw_filters, dict) else {}
+    min_price = _safe_float(raw.get("min_price"))
+    max_price = _safe_float(raw.get("max_price"))
+    if min_price is None:
+        min_price = 0.05
+    if max_price is None:
+        max_price = 0.95
+    min_price = max(0.0, min(1.0, min_price))
+    max_price = max(0.0, min(1.0, max_price))
+    if min_price > max_price:
+        min_price, max_price = max_price, min_price
+
+    high_liquidity_only = bool(raw.get("high_liquidity_only"))
+    min_liquidity = _safe_float(raw.get("min_liquidity"))
+    if min_liquidity is None:
+        min_liquidity = 5000.0 if high_liquidity_only else 500.0
+    if high_liquidity_only:
+        min_liquidity = max(min_liquidity, 5000.0)
+
+    return {
+        "scan_mode": str(raw.get("scan_mode") or "tradable").strip().lower()
+        or "tradable",
+        "min_price": float(min_price),
+        "max_price": float(max_price),
+        "min_edge_pct": max(0.0, _safe_float(raw.get("min_edge_pct")) or 2.0),
+        "min_liquidity": max(0.0, float(min_liquidity)),
+        "high_liquidity_only": high_liquidity_only,
+        "market_type": str(raw.get("market_type") or "maxtemp").strip().lower()
+        or "maxtemp",
+        "time_range": str(raw.get("time_range") or "today").strip().lower()
+        or "today",
+        "limit": max(1, min(_safe_int(raw.get("limit"), 25), 100)),
+        "max_spread": max(0.0, _safe_float(raw.get("max_spread")) or 0.03),
+    }
+
+
+def _scan_terminal_cache_key(filters: Dict[str, Any]) -> str:
+    normalized = _normalize_scan_terminal_filters(filters)
+    return json.dumps(normalized, ensure_ascii=True, sort_keys=True)
+
+
+def _get_cached_scan_terminal_payload(
+    filters: Dict[str, Any],
+) -> Optional[Dict[str, Any]]:
+    cache_key = _scan_terminal_cache_key(filters)
+    now = time.time()
+    with _SCAN_TERMINAL_CACHE_LOCK:
+        cached = _SCAN_TERMINAL_CACHE.get(cache_key)
+        if not cached:
+            return None
+        cached_at = float(cached.get("t") or 0.0)
+        if now - cached_at >= float(SCAN_TERMINAL_PAYLOAD_TTL_SEC):
+            return None
+        payload = cached.get("payload")
+        if not isinstance(payload, dict):
+            return None
+        return dict(payload)
+
+
+def _set_cached_scan_terminal_payload(
+    filters: Dict[str, Any],
+    payload: Dict[str, Any],
+) -> None:
+    cache_key = _scan_terminal_cache_key(filters)
+    with _SCAN_TERMINAL_CACHE_LOCK:
+        _SCAN_TERMINAL_CACHE[cache_key] = {
+            "t": time.time(),
+            "payload": dict(payload),
+        }
+
+
+def _resolve_time_range_dates(data: Dict[str, Any], time_range: str) -> List[str]:
+    local_date = str(data.get("local_date") or "").strip()
+    multi_model_daily = data.get("multi_model_daily") or {}
+    available_dates = sorted(
+        str(date_key).strip()
+        for date_key in (multi_model_daily.keys() if isinstance(multi_model_daily, dict) else [])
+        if str(date_key).strip()
+    )
+
+    if not local_date:
+        return available_dates[:1]
+    if time_range == "today":
+        return [local_date]
+
+    try:
+        local_dt = datetime.fromisoformat(local_date)
+    except Exception:
+        return available_dates[:7] if time_range == "week" else available_dates[:1]
+
+    if time_range == "tomorrow":
+        target = (local_dt + timedelta(days=1)).strftime("%Y-%m-%d")
+        if target in available_dates:
+            return [target]
+        future_dates = [date_key for date_key in available_dates if date_key > local_date]
+        return future_dates[:1]
+
+    if time_range == "week":
+        target_dates = [date_key for date_key in available_dates if date_key >= local_date]
+        if local_date not in target_dates:
+            target_dates.insert(0, local_date)
+        deduped: List[str] = []
+        for date_key in target_dates:
+            if date_key not in deduped:
+                deduped.append(date_key)
+            if len(deduped) >= 7:
+                break
+        return deduped
+
+    return [local_date]
+
+
+def _build_terminal_row(
+    *,
+    city: str,
+    data: Dict[str, Any],
+    scan: Dict[str, Any],
+    row: Dict[str, Any],
+) -> Dict[str, Any]:
+    current = data.get("current") or {}
+    multi_model_daily = data.get("multi_model_daily") or {}
+    selected_date = str(row.get("selected_date") or scan.get("selected_date") or data.get("local_date") or "").strip()
+    daily_entry = multi_model_daily.get(selected_date) if isinstance(multi_model_daily, dict) else {}
+    if not isinstance(daily_entry, dict):
+        daily_entry = {}
+
+    display_name = str(data.get("display_name") or city).strip() or city
+    market_slug = str(row.get("market_slug") or "").strip()
+    side = str(row.get("side") or "").strip().lower()
+    edge_percent = _safe_float(row.get("edge_percent"))
+    final_score = _safe_float(row.get("final_score"))
+    volume = _safe_float(row.get("volume")) or 0.0
+    primary_signal = scan.get("primary_signal") or {}
+
+    return {
+        **row,
+        "id": str(row.get("id") or f"{city}|{selected_date}|{market_slug}|{side}"),
+        "city": city,
+        "city_display_name": display_name,
+        "selected_date": selected_date or None,
+        "local_date": data.get("local_date"),
+        "local_time": data.get("local_time"),
+        "temp_symbol": data.get("temp_symbol"),
+        "current_temp": current.get("temp"),
+        "current_max_so_far": current.get("max_so_far"),
+        "deb_prediction": ((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None)
+        or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None),
+        "display_name": display_name,
+        "airport": ((data.get("risk") or {}).get("airport") if isinstance(data.get("risk"), dict) else None),
+        "risk_level": ((data.get("risk") or {}).get("level") if isinstance(data.get("risk"), dict) else None),
+        "distribution_bias": scan.get("distribution_bias"),
+        "window_phase": row.get("window_phase") or scan.get("window_phase"),
+        "window_score": row.get("window_score") if row.get("window_score") is not None else scan.get("window_score"),
+        "signal_status": scan.get("signal_status"),
+        "candidate_count": scan.get("candidate_count"),
+        "resolved_market_type": scan.get("resolved_market_type") or "maxtemp",
+        "market_key": f"{city}|{selected_date}|{market_slug}",
+        "is_primary_signal": bool(primary_signal and primary_signal.get("id") == row.get("id")),
+        "signal_confidence": final_score,
+        "edge_percent": edge_percent,
+        "final_score": final_score,
+        "volume": volume,
+    }
+
+
+def _scan_city_terminal_rows(
+    city: str,
+    filters: Dict[str, Any],
+    *,
+    force_refresh: bool = False,
+) -> Dict[str, Any]:
+    data = _analyze(
+        city,
+        force_refresh=force_refresh,
+        include_llm_commentary=False,
+        detail_mode="market",
+    )
+    target_dates = _resolve_time_range_dates(data, filters["time_range"])
+    rows: List[Dict[str, Any]] = []
+    primary_scores: List[float] = []
+    candidate_total = 0
+
+    for target_date in target_dates:
+        payload = _build_city_market_scan_payload(
+            data,
+            market_slug=None,
+            target_date=target_date,
+            lite=True,
+            scan_filters=filters,
+        )
+        scan = payload.get("market_scan") or {}
+        candidate_total += int(scan.get("candidate_count") or 0)
+        primary_signal = scan.get("primary_signal")
+        if not isinstance(primary_signal, dict) or not primary_signal:
+            continue
+        row = _build_terminal_row(
+            city=city,
+            data=data,
+            scan=scan,
+            row=primary_signal,
+        )
+        rows.append(row)
+        score = _safe_float(row.get("final_score"))
+        if score is not None:
+            primary_scores.append(score)
+
+    return {
+        "city": city,
+        "rows": rows,
+        "candidate_total": candidate_total,
+        "primary_scores": primary_scores,
+    }
+
+
+def build_scan_terminal_payload(
+    raw_filters: Optional[Dict[str, Any]] = None,
+    *,
+    force_refresh: bool = False,
+) -> Dict[str, Any]:
+    filters = _normalize_scan_terminal_filters(raw_filters)
+    if not force_refresh:
+        cached = _get_cached_scan_terminal_payload(filters)
+        if cached is not None:
+            return cached
+
+    city_names = list(CITIES.keys())
+    max_workers = max(1, min(6, len(city_names)))
+    city_results: List[Dict[str, Any]] = []
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        future_map = {
+            executor.submit(
+                _scan_city_terminal_rows,
+                city_name,
+                filters,
+                force_refresh=force_refresh,
+            ): city_name
+            for city_name in city_names
+        }
+        for future in as_completed(future_map):
+            city_name = future_map[future]
+            try:
+                city_results.append(future.result())
+            except Exception as exc:
+                logger.warning("scan terminal city failed city={}: {}", city_name, exc)
+
+    primary_rows: List[Dict[str, Any]] = []
+    primary_scores: List[float] = []
+    candidate_total = 0
+
+    for result in city_results:
+        candidate_total += int(result.get("candidate_total") or 0)
+        primary_rows.extend(result.get("rows") or [])
+        primary_scores.extend(result.get("primary_scores") or [])
+
+    primary_rows.sort(
+        key=lambda row: (
+            float(row.get("final_score") or 0.0),
+            float(row.get("edge_percent") or 0.0),
+        ),
+        reverse=True,
+    )
+
+    ranked_rows: List[Dict[str, Any]] = []
+    for index, row in enumerate(primary_rows[: filters["limit"]], start=1):
+        ranked_rows.append(
+            {
+                **row,
+                "rank": index,
+            }
+        )
+
+    unique_market_volume: Dict[str, float] = {}
+    for row in primary_rows:
+        market_key = str(row.get("market_key") or row.get("id") or "").strip()
+        if not market_key:
+            continue
+        unique_market_volume[market_key] = max(
+            unique_market_volume.get(market_key, 0.0),
+            float(row.get("volume") or 0.0),
+        )
+
+    avg_edge = None
+    if primary_rows:
+        edge_values = [
+            float(row.get("edge_percent") or 0.0)
+            for row in primary_rows
+            if _safe_float(row.get("edge_percent")) is not None
+        ]
+        if edge_values:
+            avg_edge = sum(edge_values) / len(edge_values)
+
+    avg_confidence = None
+    if primary_scores:
+        avg_confidence = sum(primary_scores) / len(primary_scores)
+
+    top_signal = ranked_rows[0] if ranked_rows else None
+    payload = {
+        "generated_at": datetime.utcnow().isoformat() + "Z",
+        "filters": filters,
+        "summary": {
+            "recommended_count": len(primary_rows),
+            "visible_count": len(ranked_rows),
+            "candidate_total": candidate_total,
+            "avg_edge_percent": avg_edge,
+            "avg_primary_confidence": avg_confidence,
+            "tradable_market_count": len(unique_market_volume),
+            "total_volume": sum(unique_market_volume.values()),
+            "resolved_market_type": "maxtemp",
+        },
+        "top_signal": top_signal,
+        "rows": ranked_rows,
+    }
+
+    _set_cached_scan_terminal_payload(filters, payload)
+    return payload