feat: implement PolyWeather dashboard with map UI, data collection, analysis, and comprehensive documentation.

README.md

@@ -46,9 +46,13 @@ This project uses a decoupled production setup for reliability and iteration spe
   - **City Specialization**: `17130` (`Ankara (Bölge/Center)`) remains the Ankara lead station without replacing LTAC settlement observation.
 - **⚖️ DEB Smart Blending**
   - Dynamic weighting based on city-level performance and current model spread.
-- **🧩 React Dashboard Runtime**
+- **📈 Market Data Integration**
+  - Live Polymarket quotes, probabilities, and dynamic settlement bucket tracking.
+  - Automatic Market Edge and Spread calculation comparing DEB vs Market.
+- **🧩 React Dashboard Runtime (v1.2)**
+  - Fully internationalized (i18n) with English and Simplified Chinese support.
+  - Premium UI: Glassmorphism overlays, dynamic "Sonar Pulse" markers, and fluid loading states.
   - Typed store + typed API client + Leaflet/Chart.js lifecycle wrappers.
-  - City click workflow: map focus + right panel open + nearby stations render.
   - Today analysis workflow: open modal + freeze map motion.
 - **🔔 Alert Engine**
   - **Momentum Spike**: Captures rapid short-window temperature slope changes.
@@ -141,7 +145,7 @@ Set `frontend` as the Vercel root directory for automatic CI/CD.
 ## 💬 Bot Commands
 
 | Command | Description                           | Example        |
-| :-------- | :-------------------------------------- | :------------- |
+| :------ | :------------------------------------ | :------------- |
 | `/city` | Query real-time analysis for a city   | `/city ankara` |
 | `/deb`  | View historical accuracy of DEB model | `/deb london`  |
 | `/top`  | View activity leaderboard             | `/top`         |
@@ -161,8 +165,8 @@ Set `frontend` as the Vercel root directory for automatic CI/CD.
 
 ---
 
-**📅 Last Updated**: 2026-03-09
+**📅 Last Updated**: 2026-03-10
-**🚀 Status**: v1.1 Stable - React Dashboard Runtime in Production
+**🚀 Status**: v1.2 Stable - React Dashboard with i18n & Polymarket Integration in Production
 
 > [!TIP]
-> **Production Note**: The UI layout and visual contract remain unchanged while data flow, map lifecycle, and modal interaction are now managed by typed React modules.
+> **Production Note**: The UI layout remains consistent while introducing full internationalization, market quote integration, and premium visual feedback (glassmorphism overlays, sonar markers).

README_ZH.md

@@ -46,9 +46,13 @@ PolyWeather 是一套专为 **Polymarket** 深度博弈者设计的实时情报
   - **城市特化**：安卡拉保留 `17130`（`Ankara (Bölge/Center)`）领先站逻辑，不替代 LTAC 结算主站。
 - **⚖️ DEB 智能融合**
   - 基于城市历史表现与当前模型分歧动态调整权重。
-- **🧩 React 仪表盘运行时**
+- **📈 市场数据深度整合**
+  - 实时接入 Polymarket 报价、结算概率及动态“最热温度桶”追踪。
+  - 自动对比 DEB 与市场差值，计算 Edge 与点差。
+- **🧩 React 仪表盘运行时 (v1.2)**
+  - 全面支持双语国际化 (i18n)：英文与简体中文无缝切换。
+  - 尊享版 UI：新增毛玻璃效果、动态 Sonar 脉冲标记及流畅的全屏加载动效。
   - 类型化 Store + 类型化 Data Client + Leaflet/Chart.js 生命周期封装。
-  - 点击城市：地图聚焦 + 右侧卡片打开 + 周边站点展示。
   - 点击“今日日内分析”：打开模态框并冻结地图动画。
 - **🔔 异动预警系统**
   - **动量突变**：捕捉短窗口温度斜率变化。
@@ -141,7 +145,7 @@ docker-compose up -d --build
 ## 💬 机器人指令
 
 | 命令    | 说明                      | 示例           |
-| :-------- | :------------------------ | :------------- |
+| :------ | :------------------------ | :------------- |
 | `/city` | 查询指定城市实时分析      | `/city ankara` |
 | `/deb`  | 查看 DEB 模型的历史准确率 | `/deb london`  |
 | `/top`  | 查看活跃积分排行榜        | `/top`         |
@@ -161,8 +165,8 @@ docker-compose up -d --build
 
 ---
 
-**📅 最后更新**：2026-03-09
+**📅 最后更新**：2026-03-10
-**🚀 状态**：v1.1 稳定版 - React 运行时已上线
+**🚀 状态**：v1.2 稳定版 - 国际化及 Polymarket 市场层融合已上线
 
 > [!TIP]
-> **生产提示**：在不改变既有 UI 布局与视觉层级的前提下，数据流、地图联动和模态行为已迁移到类型化 React 组件体系。
+> **生产提示**：在不改变既有 UI 布局的前提下，前端已全面引入国际化、市场报价集成及进阶视觉效果（如动态雷达标记、高级加载与毛玻璃控件）。

docs/API_ZH.md

@@ -160,9 +160,10 @@
   - `primary_market`
   - `selected_condition_id`
   - `yes_token` / `no_token`
-  - `yes_buy` / `yes_sell` / `no_buy` / `no_sell`
+  - `yes_buy` / `yes_sell` / `no_buy` / `no_sell` / `model_probability`
   - `market_price`（优先 midpoint）
   - `edge_percent`（`(model_probability - market_price) * 100`）
+  - `temperature_bucket` / `top_buckets` （结算温度桶及市场概率）
   - `signal_label`（`BUY YES` / `BUY NO` / `MONITOR`）
   - `websocket.asset_ids` / `websocket.condition_ids`（仅用于订阅标识，P0 不下单）
 
@@ -249,4 +250,4 @@
 
 ---
 
-**最后更新**: 2026-03-09
+**最后更新**: 2026-03-10

docs/COMMERCIALIZATION.md

@@ -13,7 +13,7 @@ PolyWeather is positioned as a **premium intelligence service** for weather-driv
 ## 💰 Pricing & Monetization
 
 | Tier                 | Price         | Primary Value Proposition                               |
-| :------------------- | :------------ | :------------------------------------------------------------ |
+| :------------------- | :------------ | :------------------------------------------------------ |
 | **Telegram Channel** | **$1 / mo**   | High-fidelity proactive alerts, low noise.              |
 | **Web Dashboard**    | **$5 / mo**   | Full multi-model context + historical DEB benchmarking. |
 | **VIP Bundle**       | **$5.5 / mo** | Unified access to dashboard + signal stream.            |
@@ -92,4 +92,4 @@ graph LR
 
 ---
 
-**📅 Last Updated**: 2026-03-09
+**📅 Last Updated**: 2026-03-10

docs/TECH_DEBT.md

@@ -22,7 +22,8 @@ The core weather engine and React dashboard runtime are now stable, but product-
 - [x] DEB Blending Algorithm
 - [x] Proactive Telegram Alert Engine
 - [x] Vercel Dashboard Infrastructure
-- [x] React component-driven dashboard runtime (replacing legacy `public/static/app.js` rendering path)
+- [x] React component-driven dashboard runtime
+- [x] Internationalization (i18n) & Polymarket P0 Data Merge
 
 ---
 
@@ -40,7 +41,7 @@ The core weather engine and React dashboard runtime are now stable, but product-
 ## 🟡 Medium Priority: Quality of Life
 
 | Debt Item                  | Impact                                              | Suggested Fix                                                                |
-| :------------------------ | :-------------------------------------------------- | :--------------------------------------------------------------------------- |
+| :------------------------- | :-------------------------------------------------- | :--------------------------------------------------------------------------- |
 | **Hard-coded Thresholds**  | Modification requires code changes (e.g., 5s CD).   | Extract all business constants into a structured `config.yaml`.              |
 | **Simulation Harness**     | No way to "replay" a rainy day to test alert logic. | Build a `ReplayEngine` using `data/daily_records.json`.                      |
 | **Backend Naming**         | Artifacts of "market price" logic remain in naming. | Systematic refactor of variable names to reflect weather-intelligence focus. |
@@ -66,4 +67,4 @@ The core weather engine and React dashboard runtime are now stable, but product-
 
 ---
 
-**📅 Last Updated**: 2026-03-09
+**📅 Last Updated**: 2026-03-10

docs/TECH_DEBT_ZH.md

@@ -22,14 +22,15 @@ pie title 系统健康度与技术债
 - [x] DEB 融合算法
 - [x] 主动式 Telegram 预警引擎
 - [x] Vercel 仪表盘基础设施
-- [x] React 组件驱动仪表盘运行时（已替换 legacy `public/static/app.js` 渲染路径）
+- [x] React 组件驱动仪表盘运行时
+- [x] 国际化 (i18n) 与前端市场数据集成 (Polymarket)
 
 ---
 
 ## 🔴 高优先级：立即处理
 
 | 债务项                 | 影响                                       | 建议修复                                                  |
-| :-------------------- | :------------------------------------------------- | :--------------------------------------------------------------- |
+| :--------------------- | :----------------------------------------- | :-------------------------------------------------------- |
 | **Monolithic Bot**     | `bot_listener.py` 可测试性差，演进成本高。 | 将 UI 交互与业务逻辑解耦，沉入 `src/analysis`。           |
 | **Subscription Store** | 付费用户缺少持久化记录。                   | 从内存校验迁移到 **Supabase/PostgreSQL**。                |
 | **Alert Transparency** | 运维侧难以审计“告警为何触发”。             | 为所有内部告警载荷增加 `Evidence` 元数据块。              |
@@ -40,7 +41,7 @@ pie title 系统健康度与技术债
 ## 🟡 中优先级：体验与效率
 
 | 债务项                     | 影响                                           | 建议修复                                             |
-| :---------------------- | :------------------------------------------------- | :---------------------------------------------------------------------- |
+| :------------------------- | :--------------------------------------------- | :--------------------------------------------------- |
 | **Hard-coded Thresholds**  | 阈值修改需要改代码（如 5s 冷却）。             | 将业务常量统一抽离到结构化 `config.yaml`。           |
 | **Simulation Harness**     | 无法“回放历史天气日”验证告警逻辑。             | 基于 `data/daily_records.json` 构建 `ReplayEngine`。 |
 | **Backend Naming**         | 仍有“市场价格时代”的命名残留。                 | 系统化重命名，统一为 weather-intelligence 语义。     |
@@ -51,7 +52,7 @@ pie title 系统健康度与技术债
 ## 🟢 低优先级：性能优化
 
 | 债务项                     | 影响                           | 建议修复                                 |
-| :----------------------- | :------------------------------------------------- | :--------------------------------------------------------------- |
+| :------------------------- | :----------------------------- | :--------------------------------------- |
 | **Serverless Cold Starts** | Vercel 首次 API 调用可能偏慢。 | 为主要城市接口增加边缘缓存或预热任务。   |
 | **Local SQLite Files**     | 与 Vercel 短暂文件系统不兼容。 | 全面迁移到远程数据库（Supabase/Redis）。 |
 
@@ -66,4 +67,4 @@ pie title 系统健康度与技术债
 
 ---
 
-**📅 最后更新**：2026-03-09
+**📅 最后更新**：2026-03-10

frontend/components/dashboard/Dashboard.module.css

@@ -33,7 +33,7 @@
   /* Spacing */
   --panel-width: 560px;
   --header-height: 56px;
-  --sidebar-width: 280px;
+  --sidebar-width: 260px;
 
   /* Effects */
   --glass-blur: 20px;
@@ -328,6 +328,7 @@
 }
 
 .root :global(.city-item) {
+  width: 100%;
   display: flex;
   flex-direction: column;
   gap: 4px;
@@ -336,6 +337,10 @@
   cursor: pointer;
   transition: var(--transition);
   border: 1px solid transparent;
+  background: transparent;
+  color: inherit;
+  font-family: inherit;
+  text-align: left;
 }
 .root :global(.city-item:hover) {
   background: rgba(99, 102, 241, 0.08);
@@ -722,6 +727,29 @@
   background: rgba(99, 102, 241, 0.15);
 }
 
+.root :global(.prob-market-inline) {
+  min-width: 120px;
+  text-align: right;
+  font-size: 12px;
+  font-weight: 700;
+  border-radius: 999px;
+  padding: 4px 10px;
+  letter-spacing: 0.02em;
+  font-variant-numeric: tabular-nums;
+}
+
+.root :global(.prob-market-inline.yes) {
+  color: #4ade80;
+  background: rgba(74, 222, 128, 0.12);
+  border: 1px solid rgba(74, 222, 128, 0.3);
+}
+
+.root :global(.prob-market-inline.no) {
+  color: #fb7185;
+  background: rgba(251, 113, 133, 0.12);
+  border: 1px solid rgba(251, 113, 133, 0.26);
+}
+
 /* ── Model Bars ── */
 .root :global(.model-bars) {
   display: flex;
@@ -1386,6 +1414,41 @@
   color: var(--text-primary);
   margin: 0;
 }
+.root :global(.future-modal-title-with-actions) {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+}
+
+.root :global(.future-refresh-btn) {
+  background: transparent;
+  border: none;
+  cursor: pointer;
+  color: var(--text-muted);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: 6px;
+  border-radius: 6px;
+  transition: all 0.2s ease;
+}
+
+.root :global(.future-refresh-btn:hover) {
+  color: var(--accent-cyan);
+  background: rgba(34, 211, 238, 0.1);
+}
+
+.root :global(.future-refresh-btn.spinning svg) {
+  animation: spin 1s linear infinite;
+  color: var(--accent-cyan);
+}
+
+@keyframes spin {
+  100% {
+    transform: rotate(360deg);
+  }
+}
+
 .root :global(.modal-close) {
   background: none;
   border: none;
@@ -1435,6 +1498,14 @@
   color: var(--text-primary);
 }
 
+.root :global(.h-stat-card .h-stat-note) {
+  display: block;
+  margin-top: 6px;
+  font-size: 11px;
+  color: var(--text-muted);
+  line-height: 1.35;
+}
+
 .root :global(.history-chart-wrapper) {
   position: relative;
   height: 300px;
@@ -1551,6 +1622,301 @@
   padding-right: 14px;
 }
 
+.root :global(.future-v2-layout) {
+  display: grid;
+  grid-template-columns: 360px minmax(0, 1fr);
+  gap: 14px;
+  align-items: start;
+}
+
+.root :global(.future-v2-left) {
+  display: grid;
+  gap: 12px;
+}
+
+.root :global(.future-v2-right) {
+  display: grid;
+  gap: 14px;
+}
+
+.root :global(.future-v2-card) {
+  background: rgba(255, 255, 255, 0.02);
+  border: 1px solid var(--border-subtle);
+  border-radius: 14px;
+  padding: 14px;
+}
+
+.root :global(.future-v2-hero-card) {
+  background: linear-gradient(
+    180deg,
+    rgba(99, 102, 241, 0.12) 0%,
+    rgba(255, 255, 255, 0.02) 100%
+  );
+}
+
+.root :global(.future-v2-hero-title) {
+  margin: 0;
+  font-size: 18px;
+  font-weight: 700;
+  color: var(--text-primary);
+  letter-spacing: -0.01em;
+}
+
+.root :global(.future-v2-hero-main) {
+  display: flex;
+  align-items: center;
+  gap: 14px;
+  margin-top: 14px;
+}
+
+.root :global(.future-v2-hero-temp) {
+  font-size: 56px;
+  font-weight: 800;
+  letter-spacing: -0.04em;
+  line-height: 1;
+  color: #f8fafc;
+  text-shadow: 0 10px 30px rgba(34, 211, 238, 0.14);
+}
+
+.root :global(.future-v2-hero-divider) {
+  width: 1px;
+  height: 56px;
+  background: rgba(255, 255, 255, 0.12);
+}
+
+.root :global(.future-v2-hero-weather) {
+  display: grid;
+  gap: 4px;
+  font-size: 15px;
+  color: var(--text-secondary);
+}
+
+.root :global(.future-v2-hero-icon) {
+  display: inline-flex;
+  align-items: center;
+  line-height: 1;
+}
+
+.root :global(.future-v2-hero-obs) {
+  margin-top: 10px;
+  color: var(--text-secondary);
+  font-size: 26px;
+  font-weight: 500;
+  font-variant-numeric: tabular-nums;
+}
+
+.root :global(.future-v2-mini-grid) {
+  margin-top: 14px;
+  display: grid;
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+  gap: 10px;
+}
+
+.root :global(.future-v2-mini-grid.future-v2-mini-grid-tight) {
+  margin-top: 8px;
+}
+
+.root :global(.future-v2-mini-item) {
+  border-radius: 10px;
+  border: 1px solid var(--border-subtle);
+  background: rgba(255, 255, 255, 0.025);
+  padding: 10px;
+  display: grid;
+  gap: 4px;
+}
+
+.root :global(.future-v2-mini-item span) {
+  color: var(--text-muted);
+  font-size: 11px;
+}
+
+.root :global(.future-v2-mini-item strong) {
+  color: var(--text-primary);
+  font-size: 23px;
+  line-height: 1.15;
+  font-weight: 700;
+}
+
+.root :global(.future-v2-card-title) {
+  margin: 0;
+  color: var(--text-primary);
+  font-size: 13px;
+  font-weight: 700;
+  letter-spacing: 0.02em;
+}
+
+.root :global(.future-v2-market-v3) {
+  margin-top: 16px;
+  display: flex;
+  flex-direction: column;
+  gap: 16px;
+  position: relative;
+}
+
+.root :global(.market-layer-loading-overlay) {
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: rgba(15, 23, 42, 0.6);
+  backdrop-filter: blur(2px);
+  z-index: 10;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  border-radius: 8px;
+  color: var(--accent-cyan);
+  font-size: 13px;
+  font-weight: 500;
+}
+
+.root :global(.market-sub-title) {
+  color: var(--text-secondary);
+  font-size: 11px;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  margin-bottom: 8px;
+  font-weight: 600;
+}
+
+.root :global(.market-layer-target) {
+  background: rgba(34, 211, 238, 0.04);
+  border: 1px solid rgba(34, 211, 238, 0.15);
+  border-radius: 8px;
+  padding: 12px;
+}
+
+.root :global(.market-target-header) {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 12px;
+  font-size: 13px;
+  color: var(--text-secondary);
+}
+
+.root :global(.market-target-bucket) {
+  font-size: 16px;
+  color: var(--text-primary);
+  font-weight: 700;
+}
+
+.root :global(.market-edge-box) {
+  background: rgba(15, 23, 42, 0.4);
+  border-radius: 6px;
+  padding: 10px;
+}
+
+.root :global(.market-edge-header) {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 8px;
+  font-size: 12px;
+  color: var(--text-primary);
+  font-weight: 600;
+}
+
+.root :global(.market-edge-val) {
+  font-size: 15px;
+  font-weight: 700;
+}
+
+.root :global(.market-edge-val.positive) {
+  color: var(--accent-green);
+}
+
+.root :global(.market-edge-val.negative) {
+  color: var(--text-secondary);
+}
+
+.root :global(.market-edge-compare) {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 8px;
+  border-top: 1px solid rgba(255, 255, 255, 0.06);
+  padding-top: 8px;
+}
+
+.root :global(.edge-stat) {
+  display: flex;
+  flex-direction: column;
+  gap: 2px;
+}
+
+.root :global(.edge-label) {
+  font-size: 10px;
+  color: var(--text-muted);
+}
+
+.root :global(.edge-value) {
+  font-size: 13px;
+  color: var(--text-secondary);
+  font-weight: 600;
+}
+
+.root :global(.market-layer-book),
+.root :global(.market-layer-context) {
+  padding: 0 4px;
+}
+
+.root :global(.market-book-row),
+.root :global(.market-context-row) {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  font-size: 13px;
+  padding: 6px 0;
+  border-bottom: 1px dashed rgba(255, 255, 255, 0.05);
+}
+
+.root :global(.market-book-row:last-child),
+.root :global(.market-context-row:last-child) {
+  border-bottom: none;
+}
+
+.root :global(.book-label),
+.root :global(.market-context-row span) {
+  color: var(--text-secondary);
+}
+
+.root :global(.book-quote strong),
+.root :global(.market-context-row strong) {
+  color: var(--text-primary);
+  font-weight: 600;
+}
+
+.root :global(.book-quote span) {
+  color: var(--text-muted);
+  font-size: 11px;
+}
+
+.root :global(.book-spread) {
+  color: var(--text-muted);
+  font-size: 11px;
+}
+
+.root :global(.mt-3) {
+  margin-top: 12px;
+}
+
+.root :global(.future-v2-market-signal) {
+  margin-top: 10px;
+  padding: 8px 10px;
+  border-radius: 10px;
+  background: rgba(34, 211, 238, 0.08);
+  border: 1px solid rgba(34, 211, 238, 0.22);
+  color: var(--accent-cyan);
+  font-size: 12px;
+  font-weight: 600;
+}
+
+.root :global(.future-v2-main-chart) {
+  min-height: 340px;
+}
+
 .root :global(.future-modal-section) {
   background: rgba(255, 255, 255, 0.02);
   border: 1px solid var(--border-subtle);
@@ -1660,9 +2026,12 @@
   border-radius: 999px;
   background: linear-gradient(
     90deg,
-    rgba(245, 158, 11, 0.35),
+    rgba(56, 189, 248, 0.4) 0%,
-    rgba(255, 255, 255, 0.06) 50%,
+    rgba(56, 189, 248, 0.4) 30%,
-    rgba(52, 211, 153, 0.35)
+    rgba(255, 255, 255, 0.06) 45%,
+    rgba(255, 255, 255, 0.06) 55%,
+    rgba(245, 158, 11, 0.4) 70%,
+    rgba(245, 158, 11, 0.4) 100%
   );
   overflow: hidden;
 }
@@ -1675,9 +2044,11 @@
   width: 14px;
   height: 14px;
   border-radius: 999px;
-  background: var(--accent-cyan);
+  background: var(--text-primary);
   transform: translate(-50%, -50%);
-  box-shadow: 0 0 0 4px rgba(34, 211, 238, 0.14);
+  box-shadow: 0 0 0 3px rgba(255, 255, 255, 0.15);
+  z-index: 2;
+  transition: left 0.5s ease;
 }
 
 .root :global(.future-front-meta) {
@@ -1699,6 +2070,14 @@
 }
 
 @media (max-width: 960px) {
+  .root :global(.future-v2-layout) {
+    grid-template-columns: 1fr;
+  }
+
+  .root :global(.future-v2-mini-item strong) {
+    font-size: 18px;
+  }
+
   .root :global(.future-modal-grid),
   .root :global(.future-trend-grid) {
     grid-template-columns: 1fr;

frontend/components/dashboard/FutureForecastModal.tsx

@@ -1,5 +1,17 @@
 "use client";
 
+import {
+  Cloud,
+  CloudFog,
+  CloudLightning,
+  CloudRain,
+  CloudSnow,
+  CloudSun,
+  Search,
+  Sun,
+  Wind,
+} from "lucide-react";
+
 import { ChartConfiguration } from "chart.js/auto";
 import clsx from "clsx";
 import { CSSProperties } from "react";
@@ -20,6 +32,155 @@ import {
   parseAiAnalysis,
 } from "@/lib/dashboard-utils";
 
+function normalizeMarketValue(value?: number | null) {
+  const numeric = Number(value);
+  if (!Number.isFinite(numeric)) return null;
+  if (numeric > 1) return Math.max(0, Math.min(1, numeric / 100));
+  return Math.max(0, Math.min(1, numeric));
+}
+
+function WeatherIcon({ emoji, size = 32 }: { emoji: string; size?: number }) {
+  if (emoji === "☀️") return <Sun size={size} color="#facc15" />;
+  if (emoji === "⛅" || emoji === "🌤️")
+    return <CloudSun size={size} color="#38bdf8" />;
+  if (emoji === "☁️") return <Cloud size={size} color="#94a3b8" />;
+  if (emoji === "🌧️" || emoji === "🌦️")
+    return <CloudRain size={size} color="#60a5fa" />;
+  if (emoji === "⛈️") return <CloudLightning size={size} color="#c084fc" />;
+  if (emoji === "❄️" || emoji === "🌨️")
+    return <CloudSnow size={size} color="#7dd3fc" />;
+  if (emoji === "🌫️") return <CloudFog size={size} color="#a1a1aa" />;
+  if (emoji === "💨") return <Wind size={size} color="#cbd5e1" />;
+  return <Search size={size} color="#64748b" />;
+}
+
+function formatMarketPercent(value?: number | null) {
+  const normalized = normalizeMarketValue(value);
+  if (normalized == null) return "--";
+  return `${(normalized * 100).toFixed(1)}%`;
+}
+
+function formatMarketPriceCents(value?: number | null) {
+  const normalized = normalizeMarketValue(value);
+  if (normalized == null) return "--";
+  const cents = normalized * 100;
+  const rounded = Math.round(cents * 10) / 10;
+  return `${Number.isInteger(rounded) ? rounded.toFixed(0) : rounded.toFixed(1)}¢`;
+}
+
+function formatSignedPercent(value?: number | null) {
+  const numeric = Number(value);
+  if (!Number.isFinite(numeric)) return "--";
+  const sign = numeric > 0 ? "+" : "";
+  return `${sign}${numeric.toFixed(1)}%`;
+}
+
+function formatSpreadPercent(low?: number | null, high?: number | null) {
+  const a = normalizeMarketValue(low);
+  const b = normalizeMarketValue(high);
+  if (a == null || b == null) return "--";
+  const spreadCents = Math.abs((b - a) * 100);
+  const rounded = Math.round(spreadCents * 10) / 10;
+  return `${Number.isInteger(rounded) ? rounded.toFixed(0) : rounded.toFixed(1)}¢`;
+}
+
+function resolveCounterPrice(
+  directValue?: number | null,
+  mirrorValue?: number | null,
+) {
+  const direct = normalizeMarketValue(directValue);
+  if (direct != null) return direct;
+  const mirror = normalizeMarketValue(mirrorValue);
+  if (mirror == null) return null;
+  return Math.max(0, Math.min(1, 1 - mirror));
+}
+
+function formatBucketLabel(
+  bucket?: {
+    label?: string | null;
+    bucket?: string | null;
+    range?: string | null;
+    value?: number | null;
+    temp?: number | null;
+  } | null,
+) {
+  if (!bucket) return "--";
+  const direct =
+    String(bucket.label || "").trim() ||
+    String(bucket.bucket || "").trim() ||
+    String(bucket.range || "").trim();
+  if (direct) {
+    let str = direct.toUpperCase().replace(/\s+/g, "");
+    str = str.replace(/°?C($|\+|-)/g, "℃$1");
+    if (!str.includes("℃") && /[0-9]/.test(str)) {
+      str += "℃";
+    }
+    return str;
+  }
+
+  const temp = Number(bucket.value ?? bucket.temp);
+  if (Number.isFinite(temp)) {
+    return `${Math.round(temp)}℃`;
+  }
+  return "--";
+}
+
+function parseMetarSignedInt(token: string) {
+  if (!token) return null;
+  const normalized = token.toUpperCase();
+  if (!/^[M]?\d{2}$/.test(normalized)) return null;
+  const value = Number(normalized.replace("M", ""));
+  if (!Number.isFinite(value)) return null;
+  return normalized.startsWith("M") ? -value : value;
+}
+
+function parseMetarTempDew(rawMetar?: string | null) {
+  const text = String(rawMetar || "").toUpperCase();
+  if (!text)
+    return { tempC: null as number | null, dewC: null as number | null };
+  const match = text.match(/\s(M?\d{2})\/(M?\d{2})(?:\s|$)/);
+  if (!match)
+    return { tempC: null as number | null, dewC: null as number | null };
+  return {
+    tempC: parseMetarSignedInt(match[1]),
+    dewC: parseMetarSignedInt(match[2]),
+  };
+}
+
+function estimateHumidityFromTempDew(
+  tempC?: number | null,
+  dewC?: number | null,
+) {
+  const t = Number(tempC);
+  const d = Number(dewC);
+  if (!Number.isFinite(t) || !Number.isFinite(d)) return null;
+  const es = Math.exp((17.625 * t) / (243.04 + t));
+  const ed = Math.exp((17.625 * d) / (243.04 + d));
+  const rh = (ed / es) * 100;
+  if (!Number.isFinite(rh)) return null;
+  return Math.max(0, Math.min(100, rh));
+}
+
+function parseVisibilityText(
+  rawMetar?: string | null,
+  visibilityMi?: number | null,
+) {
+  const direct = Number(visibilityMi);
+  if (Number.isFinite(direct)) {
+    return `${direct} mi`;
+  }
+
+  const text = String(rawMetar || "").toUpperCase();
+  if (!text) return "--";
+  if (text.includes("CAVOK")) return ">=6 mi";
+
+  const sm = text.match(/\s(\d{1,2}(?:\/\d)?)SM(?:\s|$)/);
+  if (sm) {
+    return `${sm[1]} mi`;
+  }
+  return "--";
+}
+
 function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
   const store = useDashboardStore();
   const { locale, t } = useI18n();
@@ -29,8 +190,7 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
   const todayChartData =
     detail && isToday ? getTemperatureChartData(detail, locale) : null;
 
-  const canvasRef = useChart(
+  const canvasRef = useChart(() => {
-    () => {
     if (!detail || !view) {
       return {
         data: { datasets: [], labels: [] },
@@ -39,7 +199,9 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
     }
 
     if (isToday && todayChartData) {
-        const datasets: NonNullable<ChartConfiguration<"line">["data"]>["datasets"] = [];
+      const datasets: NonNullable<
+        ChartConfiguration<"line">["data"]
+      >["datasets"] = [];
 
       if (todayChartData.datasets.hasMgmHourly) {
         datasets.push({
@@ -48,7 +210,7 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
           borderWidth: 2,
           data: todayChartData.datasets.mgmHourlyPoints,
           fill: false,
-            label: locale === "en-US" ? "MGM Forecast" : "MGM 预报",
+          label: locale === "en-US" ? "MGM Forecast" : "MGM 预测",
           pointHoverRadius: 6,
           pointRadius: 3,
           spanGaps: true,
@@ -61,7 +223,7 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
           borderWidth: 1.5,
           data: todayChartData.datasets.debPast,
           fill: true,
-            label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
+          label: locale === "en-US" ? "DEB Forecast" : "DEB 预测",
           pointHoverRadius: 3,
           pointRadius: 0,
           tension: 0.3,
@@ -72,7 +234,7 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
           borderWidth: 1.5,
           data: todayChartData.datasets.debFuture,
           fill: false,
-            label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
+          label: locale === "en-US" ? "DEB Forecast" : "DEB 预测",
           pointRadius: 0,
           tension: 0.3,
         });
@@ -138,8 +300,8 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
                   if (!text) return false;
                   if (!text.includes("DEB")) return true;
 
-                    const firstDebIndex = (chartData.datasets || []).findIndex((dataset) =>
+                  const firstDebIndex = (chartData.datasets || []).findIndex(
-                      String(dataset.label || "").includes("DEB"),
+                    (dataset) => String(dataset.label || "").includes("DEB"),
                   );
                   return legendItem.datasetIndex === firstDebIndex;
                 },
@@ -193,7 +355,8 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
             borderColor: "#22d3ee",
             data: view.slice.map((point) => point.temp),
             fill: false,
-              label: locale === "en-US" ? "Open-Meteo Temperature" : "Open-Meteo 温度",
+            label:
+              locale === "en-US" ? "Open-Meteo Temperature" : "Open-Meteo 温度",
             pointRadius: 2,
             tension: 0.28,
           },
@@ -252,9 +415,7 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
       },
       type: "line",
     } satisfies ChartConfiguration<"line">;
-    },
+  }, [detail, isToday, locale, todayChartData, view]);
-    [detail, isToday, locale, todayChartData, view],
-  );
 
   return (
     <>
@@ -274,6 +435,7 @@ export function FutureForecastModal() {
   const store = useDashboardStore();
   const { locale, t } = useI18n();
   const detail = store.selectedDetail;
+  const marketScan = store.selectedMarketScan;
   const dateStr = store.futureModalDate;
 
   if (!detail || !dateStr) return null;
@@ -289,6 +451,88 @@ export function FutureForecastModal() {
     "--score-position": scorePosition,
   } as CSSProperties & { "--score-position": string };
   const weatherSummary = getWeatherSummary(detail, locale);
+  const marketMidpoint = formatMarketPercent(
+    marketScan?.market_price ?? marketScan?.yes_token?.implied_probability,
+  );
+  const modelProbability = formatMarketPercent(marketScan?.model_probability);
+  const marketYesBuy = formatMarketPriceCents(marketScan?.yes_buy);
+  const marketYesSell = formatMarketPriceCents(marketScan?.yes_sell);
+  const marketNoBuy = formatMarketPriceCents(
+    resolveCounterPrice(marketScan?.no_buy, marketScan?.yes_buy),
+  );
+  const marketNoSell = formatMarketPriceCents(
+    resolveCounterPrice(marketScan?.no_sell, marketScan?.yes_sell),
+  );
+  const marketEdge = formatSignedPercent(marketScan?.edge_percent);
+  const marketSpread = formatSpreadPercent(
+    marketScan?.yes_buy,
+    marketScan?.yes_sell,
+  );
+  const topBucket = Array.isArray(marketScan?.top_buckets)
+    ? [...marketScan.top_buckets]
+        .map((item) => ({
+          ...item,
+          probability: normalizeMarketValue(item?.probability),
+        }))
+        .filter(
+          (
+            item,
+          ): item is {
+            label?: string | null;
+            bucket?: string | null;
+            range?: string | null;
+            value?: number | null;
+            temp?: number | null;
+            probability: number;
+          } => item.probability != null,
+        )
+        .sort((a, b) => b.probability - a.probability)[0]
+    : null;
+  const settlementBucketLabel = formatBucketLabel(
+    marketScan?.temperature_bucket,
+  );
+  const hottestBucketLabel = formatBucketLabel(topBucket);
+  const hottestBucketProb = formatMarketPercent(topBucket?.probability);
+  const marketSignal = marketScan?.signal_label
+    ? `${marketScan.signal_label}${
+        marketScan.confidence ? ` / ${marketScan.confidence}` : ""
+      }`
+    : "--";
+  const metarParsed = parseMetarTempDew(detail.current?.raw_metar);
+  const fallbackDewpoint =
+    detail.current?.dewpoint ??
+    metarParsed.dewC ??
+    (Array.isArray(detail.hourly_next_48h?.dew_point)
+      ? detail.hourly_next_48h?.dew_point?.[0]
+      : null);
+  const fallbackHumidity =
+    detail.current?.humidity ??
+    estimateHumidityFromTempDew(
+      detail.current?.temp ?? metarParsed.tempC,
+      fallbackDewpoint,
+    );
+  const topObservedTemp =
+    detail.current?.max_so_far != null
+      ? detail.current.max_so_far
+      : detail.current?.temp;
+  const currentTempText =
+    detail.current?.temp != null
+      ? `${detail.current.temp}${detail.temp_symbol}`
+      : "--";
+  const humidityText =
+    fallbackHumidity != null ? `${Math.round(fallbackHumidity)}%` : "--";
+  const dewpointText =
+    fallbackDewpoint != null
+      ? `${fallbackDewpoint}${detail.temp_symbol}`
+      : "--";
+  const windText =
+    detail.current?.wind_speed_kt != null
+      ? `${detail.current.wind_speed_kt} kt`
+      : "--";
+  const visibilityText = parseVisibilityText(
+    detail.current?.raw_metar,
+    detail.current?.visibility_mi,
+  );
 
   return (
     <div
@@ -304,7 +548,11 @@ export function FutureForecastModal() {
     >
       <div className="modal-content large future-modal">
         <div className="modal-header">
-          <h2 id="future-modal-title">
+          <h2
+            id="future-modal-title"
+            className="future-modal-title-with-actions"
+          >
+            <span>
               {isToday
                 ? t("future.todayTitle", {
                     city: detail.display_name.toUpperCase(),
@@ -313,11 +561,36 @@ export function FutureForecastModal() {
                     city: detail.display_name.toUpperCase(),
                     date: dateStr,
                   })}
+            </span>
+            <button
+              className={clsx(
+                "future-refresh-btn",
+                store.loadingState.marketScan && "spinning",
+              )}
+              onClick={() => store.openTodayModal(true)}
+              title={locale === "en-US" ? "Refresh Data" : "刷新数据"}
+            >
+              <svg
+                width="14"
+                height="14"
+                viewBox="0 0 24 24"
+                fill="none"
+                stroke="currentColor"
+                strokeWidth="2.5"
+                strokeLinecap="round"
+                strokeLinejoin="round"
+              >
+                <path d="M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8" />
+                <path d="M3 3v5h5" />
+              </svg>
+            </button>
           </h2>
           <button
             type="button"
             className="modal-close"
-            aria-label={isToday ? t("future.closeTodayAria") : t("future.closeDateAria")}
+            aria-label={
+              isToday ? t("future.closeTodayAria") : t("future.closeDateAria")
+            }
             onClick={store.closeFutureModal}
           >
             ×
@@ -325,117 +598,282 @@ export function FutureForecastModal() {
         </div>
 
         <div className="modal-body future-modal-body">
-          <div className="history-stats">
+          {isToday ? (
-            {isToday && (
+            <div className="future-v2-layout">
-              <>
+              <aside className="future-v2-left">
-                <div className="h-stat-card">
+                <section className="future-v2-card future-v2-hero-card">
-                  <span className="label">{t("future.currentObs")}</span>
+                  <h3 className="future-v2-hero-title">
-                  <span className="val">
+                    {locale === "en-US"
-                    {detail.current?.temp ?? "--"}
+                      ? "Current Conditions"
-                    {detail.temp_symbol} @{detail.current?.obs_time || "--"}
+                      : "实况与气象特征"}
+                  </h3>
+                  <div className="future-v2-hero-main">
+                    <div className="future-v2-hero-temp">{currentTempText}</div>
+                    <div className="future-v2-hero-divider" />
+                    <div className="future-v2-hero-weather">
+                      <span className="future-v2-hero-icon">
+                        <WeatherIcon
+                          emoji={weatherSummary.weatherIcon}
+                          size={42}
+                        />
                       </span>
+                      <span>{weatherSummary.weatherText}</span>
                     </div>
-                <div className="h-stat-card">
+                  </div>
-                  <span className="label">{t("future.currentWeather")}</span>
+                  <div className="future-v2-hero-obs">
-                  <span className="val">
+                    @{detail.current?.obs_time || "--"}
-                    {weatherSummary.weatherIcon} {weatherSummary.weatherText}
+                  </div>
+                  <div className="future-v2-mini-grid">
+                    <div className="future-v2-mini-item">
+                      <span>
+                        {locale === "en-US" ? "High So Far" : "目前最高温"}
                       </span>
-                </div>
+                      <strong>
-                <div className="h-stat-card">
+                        {topObservedTemp ?? "--"}
-                  <span className="label">{t("future.wuRef")}</span>
-                  <span className="val">
-                    {detail.current?.wu_settlement ?? "--"}
                         {detail.temp_symbol}
+                      </strong>
+                    </div>
+                    <div className="future-v2-mini-item">
+                      <span>{locale === "en-US" ? "Sunrise" : "日出时间"}</span>
+                      <strong>{detail.forecast?.sunrise || "--"}</strong>
+                    </div>
+                    <div className="future-v2-mini-item">
+                      <span>{locale === "en-US" ? "Sunset" : "日落时间"}</span>
+                      <strong>{detail.forecast?.sunset || "--"}</strong>
+                    </div>
+                    <div className="future-v2-mini-item">
+                      <span>
+                        {locale === "en-US" ? "Sunshine" : "日照时长"}
                       </span>
-                </div>
+                      <strong>
-                <div className="h-stat-card">
-                  <span className="label">{t("future.sunrise")}</span>
-                  <span className="val">{detail.forecast?.sunrise || "--"}</span>
-                </div>
-                <div className="h-stat-card">
-                  <span className="label">{t("future.sunset")}</span>
-                  <span className="val">{detail.forecast?.sunset || "--"}</span>
-                </div>
-                <div className="h-stat-card">
-                  <span className="label">{t("future.sunshine")}</span>
-                  <span className="val">
                         {detail.forecast?.sunshine_hours != null
                           ? `${detail.forecast.sunshine_hours}h`
                           : "--"}
+                      </strong>
+                    </div>
+                  </div>
+                </section>
+
+                <section className="future-v2-card">
+                  <h4 className="future-v2-card-title">
+                    {locale === "en-US" ? "Current Metrics" : "当前指标"}
+                  </h4>
+                  <div className="future-v2-mini-grid future-v2-mini-grid-tight">
+                    <div className="future-v2-mini-item">
+                      <span>{locale === "en-US" ? "Humidity" : "湿度"}</span>
+                      <strong>{humidityText}</strong>
+                    </div>
+                    <div className="future-v2-mini-item">
+                      <span>{locale === "en-US" ? "Dew Point" : "露点"}</span>
+                      <strong>{dewpointText}</strong>
+                    </div>
+                    <div className="future-v2-mini-item">
+                      <span>{locale === "en-US" ? "Wind" : "风速"}</span>
+                      <strong>{windText}</strong>
+                    </div>
+                    <div className="future-v2-mini-item">
+                      <span>
+                        {locale === "en-US" ? "Visibility" : "能见度"}
                       </span>
+                      <strong>{visibilityText}</strong>
+                    </div>
+                  </div>
+                </section>
+
+                <section className="future-v2-card">
+                  <h4 className="future-v2-card-title">
+                    {locale === "en-US" ? "Market Alignment" : "市场对照"}
+                  </h4>
+                  <div className="future-v2-market-v3">
+                    {/* Loading Overlay */}
+                    {store.loadingState.marketScan && (
+                      <div className="market-layer-loading-overlay">
+                        <div
+                          className="loading-spinner"
+                          style={{
+                            marginBottom: "8px",
+                            width: "24px",
+                            height: "24px",
+                            borderWidth: "2px",
+                          }}
+                        />
+                        {locale === "en-US"
+                          ? "Crunching Polymarket Edges..."
+                          : "正在计算市场对手盘..."}
                       </div>
-              </>
                     )}
 
-            <div className="h-stat-card">
+                    {/* Layer 1: Target & Edge */}
-              <span className="label">
+                    <div className="market-layer-target">
-                {isToday ? t("future.todayForecastHigh") : t("future.targetForecast")}
+                      <div className="market-target-header">
+                        <span>
+                          {locale === "en-US"
+                            ? "Target Bucket:"
+                            : "结算温度区间："}
                         </span>
-              <span className="val">
+                        <strong className="market-target-bucket">
-                {view.forecastEntry?.max_temp ?? "--"}
+                          {settlementBucketLabel}
-                {detail.temp_symbol}
+                        </strong>
+                      </div>
+
+                      <div className="market-edge-box">
+                        <div className="market-edge-header">
+                          <span>
+                            📊 {locale === "en-US" ? "Core Edge" : "核心测算"}
+                          </span>
+                          <strong
+                            className={clsx(
+                              "market-edge-val",
+                              Number(marketScan?.edge_percent) > 0
+                                ? "positive"
+                                : "negative",
+                            )}
+                          >
+                            ({marketEdge})
+                          </strong>
+                        </div>
+                        <div className="market-edge-compare">
+                          <div className="edge-stat">
+                            <span className="edge-label">
+                              {locale === "en-US" ? "Model Prob:" : "模型概率:"}
+                            </span>
+                            <strong className="edge-value">
+                              {modelProbability}
+                            </strong>
+                          </div>
+                          <div className="edge-stat">
+                            <span className="edge-label">
+                              {locale === "en-US"
+                                ? "Market Prob:"
+                                : "市场概率:"}
+                            </span>
+                            <strong className="edge-value">
+                              {marketMidpoint}
+                            </strong>
+                          </div>
+                        </div>
+                      </div>
+                    </div>
+
+                    {/* Layer 2: Order Book */}
+                    <div className="market-layer-book">
+                      <div className="market-sub-title">
+                        📈 {locale === "en-US" ? "Order Book" : "订单簿报价"}
+                      </div>
+                      <div className="market-book-row">
+                        <span className="book-label">YES:</span>
+                        <span className="book-quote">
+                          <strong>{marketYesBuy}</strong> /{" "}
+                          <strong>{marketYesSell}</strong>
+                        </span>
+                        <span className="book-spread">
+                          ({locale === "en-US" ? "Spread" : "价差"}{" "}
+                          {marketSpread})
                         </span>
                       </div>
-            <div className="h-stat-card">
+                      <div className="market-book-row">
-              <span className="label">{t("future.deb")}</span>
+                        <span className="book-label">NO:</span>
-              <span className="val">
+                        <span className="book-quote">
-                {view.deb ?? "--"}
+                          <strong>{marketNoBuy}</strong> /{" "}
-                {detail.temp_symbol}
+                          <strong>{marketNoSell}</strong>
-              </span>
-            </div>
-            <div className="h-stat-card">
-              <span className="label">{t("future.mu")}</span>
-              <span className="val">
-                {view.mu != null ? `${view.mu.toFixed(1)}${detail.temp_symbol}` : "--"}
-              </span>
-            </div>
-            <div className="h-stat-card">
-              <span className="label">{t("future.score")}</span>
-              <span className="val">
-                {view.front.score > 0 ? "+" : ""}
-                {view.front.score}
                         </span>
                       </div>
                     </div>
 
-          <section className="future-modal-section">
+                    {/* Layer 3: Context */}
-            <h3>{isToday ? t("future.todayTempTrend") : t("future.targetTempTrend")}</h3>
+                    <div className="market-layer-context">
+                      <div className="market-sub-title">
+                        👀 {locale === "en-US" ? "Market Radar" : "情绪雷达"}
+                      </div>
+                      <div className="market-context-row">
+                        <span>
+                          {locale === "en-US"
+                            ? "Top Volume Bucket:"
+                            : "市场当前押注最热:"}
+                        </span>
+                        <strong>
+                          {hottestBucketLabel}{" "}
+                          {hottestBucketProb !== "--"
+                            ? `(${hottestBucketProb})`
+                            : ""}
+                        </strong>
+                      </div>
+                    </div>
+                  </div>
+                  <div className="future-v2-market-signal mt-3">
+                    {locale === "en-US" ? "Signal" : "信号"}:{" "}
+                    <strong>{marketSignal}</strong>
+                  </div>
+                </section>
+              </aside>
+
+              <main className="future-v2-right">
+                <section className="future-modal-section future-v2-main-chart">
+                  <h3>
+                    {locale === "en-US"
+                      ? "Today's temperature forecast (obs + market)"
+                      : "今日气温预测（观测 + 市场）"}
+                  </h3>
                   <DailyTemperatureChart dateStr={dateStr} />
                 </section>
 
                 <div className="future-modal-grid">
                   <section className="future-modal-section">
                     <h3>{t("future.probability")}</h3>
-              <ProbabilityDistribution detail={detail} targetDate={dateStr} hideTitle />
+                    <div style={{ position: "relative", minHeight: "120px" }}>
+                      {/* Loading Overlay */}
+                      {store.loadingState.marketScan && (
+                        <div className="market-layer-loading-overlay">
+                          <div
+                            className="loading-spinner"
+                            style={{
+                              marginBottom: "8px",
+                              width: "24px",
+                              height: "24px",
+                              borderWidth: "2px",
+                            }}
+                          />
+                          {locale === "en-US"
+                            ? "Crunching Polymarket Edges..."
+                            : "正在同步市场挂单..."}
+                        </div>
+                      )}
+                      <ProbabilityDistribution
+                        detail={detail}
+                        targetDate={dateStr}
+                        hideTitle
+                        marketScan={marketScan}
+                      />
+                    </div>
                   </section>
                   <section className="future-modal-section">
                     <h3>{t("future.models")}</h3>
-              <ModelForecast detail={detail} targetDate={dateStr} hideTitle />
+                    <ModelForecast
+                      detail={detail}
+                      targetDate={dateStr}
+                      hideTitle
+                    />
                   </section>
                 </div>
 
                 <div className="future-modal-grid">
                   <section className="future-modal-section">
-              <h3>
+                    <h3>{t("future.structureToday")}</h3>
-                <span className="section-inline-icon" aria-hidden="true">
-                  <svg
-                    viewBox="0 0 24 24"
-                    fill="none"
-                    stroke="currentColor"
-                    strokeWidth="1.9"
-                    strokeLinecap="round"
-                    strokeLinejoin="round"
-                  >
-                    <path d="M4 19V5" />
-                    <path d="M10 19V10" />
-                    <path d="M16 19V7" />
-                    <path d="M22 19V13" />
-                  </svg>
-                </span>
-                {isToday ? t("future.structureToday") : t("future.structureDate")}
-              </h3>
                     <div className="future-front-score">
-                <div className="future-front-bar" style={barStyle} />
+                      <div className="future-front-bar" style={barStyle}>
+                        <div
+                          style={{
+                            position: "absolute",
+                            top: 0,
+                            bottom: 0,
+                            left: "50%",
+                            width: "2px",
+                            background: "rgba(255, 255, 255, 0.2)",
+                            transform: "translateX(-50%)",
+                            zIndex: 1,
+                          }}
+                        />
+                      </div>
                       <div className="future-front-meta">
                         <span className="future-front-pill">
                           {t("future.judgement")}: {view.front.label}
@@ -445,15 +883,17 @@ export function FutureForecastModal() {
                           {t(`confidence.${view.front.confidence}`)}
                         </span>
                         <span className="future-front-pill">
-                    {t("future.maxPrecip")}: {Math.round(view.front.precipMax)}%
+                          {t("future.maxPrecip")}:{" "}
+                          {Math.round(view.front.precipMax)}%
                         </span>
                       </div>
-                <div className="future-text-block">{view.front.summary}</div>
                     </div>
                     <div className="future-trend-grid">
-                {view.front.metrics.map((metric) => (
+                      {view.front.metrics.slice(0, 6).map((metric) => (
                         <div key={metric.label} className="future-trend-card">
-                    <div className="future-trend-label">{metric.label}</div>
+                          <div className="future-trend-label">
+                            {metric.label}
+                          </div>
                           <div
                             className={clsx(
                               "future-trend-value",
@@ -472,70 +912,105 @@ export function FutureForecastModal() {
                   <section className="future-modal-section">
                     <h3>{t("future.ai")}</h3>
                     <div className="future-text-block">
-                {ai.summary ? <div>{ai.summary}</div> : null}
+                      {ai.summary ? (
-
+                        <div>{ai.summary}</div>
+                      ) : (
+                        <div>{t("future.noAi")}</div>
+                      )}
                       {ai.bullets.length > 0 && (
-                  <div style={{ marginTop: ai.summary ? "10px" : 0 }}>
+                        <div style={{ marginTop: "10px" }}>
-                    {ai.bullets.map((item) => (
+                          {ai.bullets.slice(0, 3).map((item) => (
                             <div key={item}>{item}</div>
                           ))}
                         </div>
                       )}
-
+                      <div style={{ marginTop: "12px" }}>
-                {!ai.summary && ai.bullets.length === 0 && (
+                        {nowcastRows.slice(0, 4).map(([label, value]) => (
-                  <div>{t("future.noAi")}</div>
-                )}
-
-                <div style={{ marginTop: "14px" }}>
-                  {nowcastRows.map(([label, value]) => (
                           <div key={label}>
                             <strong>{label}: </strong>
                             {value}
                           </div>
                         ))}
                       </div>
-
+                      {riskLines.length > 0 && (
-                {view.front.weatherGovPeriods.length > 0 && (
+                        <div style={{ marginTop: "12px" }}>
-                  <div style={{ marginTop: "10px" }}>
+                          <strong>
-                    <strong>{t("future.weatherGov")}: </strong>
+                            {locale === "en-US" ? "Risk" : "风险"}:{" "}
-                    {view.front.weatherGovPeriods
+                          </strong>
-                      .map((period) => period.short_forecast || period.detailed_forecast)
+                          {riskLines[0]}
-                      .filter(Boolean)
+                        </div>
-                      .join(" / ")}
+                      )}
+                      {climateDrivers.length > 0 && (
+                        <div style={{ marginTop: "8px" }}>
+                          <strong>
+                            {locale === "en-US" ? "Climate" : "气候"}:{" "}
+                          </strong>
+                          {climateDrivers[0].text}
                         </div>
                       )}
                     </div>
                   </section>
                 </div>
+              </main>
+            </div>
+          ) : (
+            <>
+              <div className="history-stats">
+                <div className="h-stat-card">
+                  <span className="label">{t("future.targetForecast")}</span>
+                  <span className="val">
+                    {view.forecastEntry?.max_temp ?? "--"}
+                    {detail.temp_symbol}
+                  </span>
+                </div>
+                <div className="h-stat-card">
+                  <span className="label">{t("future.deb")}</span>
+                  <span className="val">
+                    {view.deb ?? "--"}
+                    {detail.temp_symbol}
+                  </span>
+                </div>
+                <div className="h-stat-card">
+                  <span className="label">{t("future.mu")}</span>
+                  <span className="val">
+                    {view.mu != null
+                      ? `${view.mu.toFixed(1)}${detail.temp_symbol}`
+                      : "--"}
+                  </span>
+                </div>
+                <div className="h-stat-card">
+                  <span className="label">{t("future.score")}</span>
+                  <span className="val">
+                    {view.front.score > 0 ? "+" : ""}
+                    {view.front.score}
+                  </span>
+                </div>
+              </div>
+
+              <section className="future-modal-section">
+                <h3>{t("future.targetTempTrend")}</h3>
+                <DailyTemperatureChart dateStr={dateStr} />
+              </section>
 
-          {isToday && (
               <div className="future-modal-grid">
                 <section className="future-modal-section">
-                <h3>{t("future.risk")}</h3>
+                  <h3>{t("future.probability")}</h3>
-                <div className="risk-info">
+                  <ProbabilityDistribution
-                  {riskLines.map((line) => (
+                    detail={detail}
-                    <div key={line} className="risk-row">
+                    targetDate={dateStr}
-                      <span style={{ color: "var(--accent-cyan)", opacity: 0.6 }}>
+                    hideTitle
-                        •
+                  />
-                      </span>
-                      <span>{line}</span>
-                    </div>
-                  ))}
-                </div>
                 </section>
-
                 <section className="future-modal-section">
-                <h3>{t("future.climate")}</h3>
+                  <h3>{t("future.models")}</h3>
-                <div className="insight-list">
+                  <ModelForecast
-                  {climateDrivers.map((driver) => (
+                    detail={detail}
-                    <div key={driver.label} className="insight-item">
+                    targetDate={dateStr}
-                      <div className="insight-title">{driver.label}</div>
+                    hideTitle
-                      <div className="insight-text">{driver.text}</div>
+                  />
-                    </div>
-                  ))}
-                </div>
                 </section>
               </div>
+            </>
           )}
         </div>
       </div>

frontend/components/dashboard/PanelSections.tsx

@@ -5,7 +5,12 @@ import clsx from "clsx";
 import { useChart } from "@/hooks/useChart";
 import { useCityData, useDashboardStore } from "@/hooks/useDashboardStore";
 import { useI18n } from "@/hooks/useI18n";
-import { CityDetail } from "@/lib/dashboard-types";
+import {
+  CityDetail,
+  MarketScan,
+  MarketTopBucket,
+  ProbabilityBucket,
+} from "@/lib/dashboard-types";
 import {
   getHeroMetaItems,
   getModelView,
@@ -17,7 +22,101 @@ import {
 } from "@/lib/dashboard-utils";
 
 function EmptyState({ text }: { text: string }) {
-  return <div style={{ color: "var(--text-muted)", fontSize: "13px" }}>{text}</div>;
+  return (
+    <div style={{ color: "var(--text-muted)", fontSize: "13px" }}>{text}</div>
+  );
+}
+
+function toPercent(value?: number | null) {
+  const numeric = Number(value);
+  if (!Number.isFinite(numeric)) return null;
+  return `${(numeric * 100).toFixed(1)}%`;
+}
+
+function toPriceCents(value?: number | null) {
+  const numeric = Number(value);
+  if (!Number.isFinite(numeric)) return null;
+  const normalized = numeric > 1 ? numeric / 100 : numeric;
+  const cents = normalized * 100;
+  const rounded = Math.round(cents * 10) / 10;
+  const text = Number.isInteger(rounded)
+    ? String(rounded.toFixed(0))
+    : String(rounded);
+  return `${text}c`;
+}
+
+function parseTempFromText(value: unknown) {
+  const text = String(value || "");
+  const match = text.match(/(-?\d+(?:\.\d+)?)/);
+  if (!match) return null;
+  const numeric = Number(match[1]);
+  return Number.isFinite(numeric) ? numeric : null;
+}
+
+function getBucketTemp(bucket: ProbabilityBucket) {
+  const byValue = Number(bucket.value);
+  if (Number.isFinite(byValue)) return byValue;
+  return parseTempFromText(bucket.label || bucket.bucket || bucket.range);
+}
+
+function getMarketBucketTemp(scan?: MarketScan | null) {
+  if (!scan) return null;
+
+  const byBucketValue = Number(scan.temperature_bucket?.value);
+  if (Number.isFinite(byBucketValue)) return byBucketValue;
+
+  const byBucketLabel = parseTempFromText(
+    scan.temperature_bucket?.label ||
+      scan.temperature_bucket?.bucket ||
+      scan.temperature_bucket?.range,
+  );
+  if (byBucketLabel != null) return byBucketLabel;
+
+  const slug = String(scan.selected_slug || scan.primary_market?.slug || "");
+  const slugMatch = slug.match(/-(-?\d+(?:\.\d+)?)c(?:$|[^a-z0-9])/i);
+  if (slugMatch) {
+    const numeric = Number(slugMatch[1]);
+    if (Number.isFinite(numeric)) return numeric;
+  }
+
+  return parseTempFromText(scan.primary_market?.question);
+}
+
+function getMarketYesPrice(scan?: MarketScan | null) {
+  const preferred = Number(scan?.market_price);
+  if (Number.isFinite(preferred)) return preferred;
+  const implied = Number(scan?.yes_token?.implied_probability);
+  return Number.isFinite(implied) ? implied : null;
+}
+
+function getMarketNoPrice(scan?: MarketScan | null) {
+  const direct = Number(scan?.no_buy);
+  if (Number.isFinite(direct)) return direct;
+  const marketYes = getMarketYesPrice(scan);
+  if (marketYes != null) return Math.max(0, Math.min(1, 1 - marketYes));
+  return null;
+}
+
+function normalizeMarketProbability(value?: number | null) {
+  const numeric = Number(value);
+  if (!Number.isFinite(numeric)) return null;
+  if (numeric > 1) return Math.max(0, Math.min(1, numeric / 100));
+  return Math.max(0, Math.min(1, numeric));
+}
+
+function getMarketTopBuckets(scan?: MarketScan | null) {
+  const buckets = Array.isArray(scan?.top_buckets) ? scan.top_buckets : [];
+  if (!buckets.length) return [];
+
+  return buckets
+    .map((item) => ({
+      ...item,
+      probability: normalizeMarketProbability(item.probability),
+    }))
+    .filter(
+      (item): item is MarketTopBucket & { probability: number } =>
+        item.probability != null,
+    );
 }
 
 export function HeroSummary() {
@@ -55,7 +154,9 @@ export function HeroSummary() {
       </div>
       <div className="hero-details">
         <div className="hero-item">
-          <span className="label">{locale === "en-US" ? "Current Obs" : "当前实测"}</span>
+          <span className="label">
+            {locale === "en-US" ? "Current Obs" : "当前实测"}
+          </span>
           <span className="value">
             {current.temp != null
               ? `${current.temp}${data.temp_symbol} @${current.obs_time || "--"}`
@@ -73,7 +174,9 @@ export function HeroSummary() {
           </span>
         </div>
         <div className="hero-item">
-          <span className="label">{locale === "en-US" ? "DEB Forecast" : "DEB 预测"}</span>
+          <span className="label">
+            {locale === "en-US" ? "DEB Forecast" : "DEB 预测"}
+          </span>
           <span className="value">
             {data.deb?.prediction != null
               ? `${data.deb.prediction}${data.temp_symbol}`
@@ -95,8 +198,7 @@ export function TemperatureChart() {
   const { locale, t } = useI18n();
   const chartData = data ? getTemperatureChartData(data, locale) : null;
 
-  const canvasRef = useChart(
+  const canvasRef = useChart(() => {
-    () => {
     if (!data || !chartData) {
       return {
         data: { datasets: [], labels: [] },
@@ -104,7 +206,9 @@ export function TemperatureChart() {
       } satisfies ChartConfiguration<"line">;
     }
 
-      const datasets: NonNullable<ChartConfiguration<"line">["data"]>["datasets"] = [];
+    const datasets: NonNullable<
+      ChartConfiguration<"line">["data"]
+    >["datasets"] = [];
 
     if (chartData.datasets.hasMgmHourly) {
       datasets.push({
@@ -228,9 +332,7 @@ export function TemperatureChart() {
       },
       type: "line",
     } satisfies ChartConfiguration<"line">;
-    },
+  }, [data, chartData, locale]);
-    [data, chartData, locale],
-  );
 
   return (
     <section className="chart-section">
@@ -238,7 +340,9 @@ export function TemperatureChart() {
       <div className="chart-wrapper">
         <canvas ref={canvasRef} />
       </div>
-      <div className="chart-legend">{chartData?.legendText || t("section.chartEmpty")}</div>
+      <div className="chart-legend">
+        {chartData?.legendText || t("section.chartEmpty")}
+      </div>
     </section>
   );
 }
@@ -247,13 +351,28 @@ export function ProbabilityDistribution({
   detail,
   hideTitle = false,
   targetDate,
+  marketScan,
 }: {
   detail: CityDetail;
   hideTitle?: boolean;
   targetDate?: string | null;
+  marketScan?: MarketScan | null;
 }) {
-  const { t } = useI18n();
+  const { locale, t } = useI18n();
   const view = getProbabilityView(detail, targetDate);
+  const marketBucketTemp = getMarketBucketTemp(marketScan);
+  const marketYesPrice = getMarketYesPrice(marketScan);
+  const marketNoPrice = getMarketNoPrice(marketScan);
+  const marketYesText = toPercent(marketYesPrice);
+  const marketNoText = toPercent(marketNoPrice);
+  const isToday = !targetDate || targetDate === detail.local_date;
+  const marketTopBuckets = isToday ? getMarketTopBuckets(marketScan) : [];
+  const sortedMarketTopBuckets = [...marketTopBuckets]
+    .sort((a, b) => Number(b.probability || 0) - Number(a.probability || 0))
+    .slice(0, 4);
+  const useMarketTopBuckets =
+    marketScan?.available && sortedMarketTopBuckets.length > 0;
+  const topMarketBucketText = toPercent(sortedMarketTopBuckets[0]?.probability);
 
   return (
     <section className="prob-section">
@@ -261,7 +380,11 @@ export function ProbabilityDistribution({
       <div className="prob-bars">
         {view.mu != null && (
           <div
-            style={{ color: "var(--text-muted)", fontSize: "11px", marginBottom: "6px" }}
+            style={{
+              color: "var(--text-muted)",
+              fontSize: "11px",
+              marginBottom: "6px",
+            }}
           >
             {t("section.mu", {
               unit: detail.temp_symbol || "",
@@ -269,16 +392,62 @@ export function ProbabilityDistribution({
             })}
           </div>
         )}
-        {view.probabilities.length === 0 ? (
+        {marketScan?.available && (topMarketBucketText || marketYesText) && (
-          <EmptyState text={t("section.noProb")} />
+          <div
-        ) : (
+            style={{
-          view.probabilities.slice(0, 6).map((bucket, index) => {
+              color: "var(--text-secondary)",
-            const probability = Math.round(Number(bucket.probability || 0) * 100);
+              fontSize: "11px",
-            return (
+              marginBottom: "6px",
-              <div key={`${bucket.label || bucket.value || index}`} className="prob-row">
+            }}
-                <div className="prob-label">
+          >
-                  {bucket.label || `${bucket.value}${detail.temp_symbol}`}
+            {useMarketTopBuckets
+              ? locale === "en-US"
+                ? `Market top-4 buckets (top): ${topMarketBucketText}`
+                : `市场概率（前4温度桶）：最高 ${topMarketBucketText}`
+              : locale === "en-US"
+                ? `Market probability (this bucket): ${marketYesText}`
+                : `市场概率（该温度桶）: ${marketYesText}`}
           </div>
+        )}
+        {useMarketTopBuckets ? (
+          sortedMarketTopBuckets.map((bucket, index) => {
+            const probability = Math.round(
+              Number(bucket.probability || 0) * 100,
+            );
+            let bucketLabel =
+              bucket.label ||
+              (bucket.value != null
+                ? `${bucket.value}${detail.temp_symbol}`
+                : `${bucket.temp ?? "--"}${detail.temp_symbol}`);
+
+            if (bucketLabel) {
+              let str = String(bucketLabel).toUpperCase().replace(/\s+/g, "");
+              str = str.replace(/°?C($|\+|-)/g, "℃$1");
+              if (!str.includes("℃") && /[0-9]/.test(str)) {
+                str += "℃";
+              }
+              bucketLabel = str;
+            }
+            const buyYesText = toPriceCents(
+              bucket.yes_buy ?? bucket.market_price ?? bucket.probability,
+            );
+            const buyNoText = toPriceCents(bucket.no_buy);
+            const marketTag = buyYesText
+              ? locale === "en-US"
+                ? `Buy Yes: ${buyYesText}`
+                : `买 Yes: ${buyYesText}`
+              : buyNoText
+                ? locale === "en-US"
+                  ? `Buy No: ${buyNoText}`
+                  : `买 No: ${buyNoText}`
+                : null;
+
+            return (
+              <div
+                key={`${bucket.slug || bucket.label || index}`}
+                className="prob-row"
+              >
+                <div className="prob-label">{bucketLabel}</div>
                 <div className="prob-bar-track">
                   <div
                     className={clsx("prob-bar-fill", `rank-${index}`)}
@@ -287,6 +456,82 @@ export function ProbabilityDistribution({
                     {probability}%
                   </div>
                 </div>
+                {marketTag && (
+                  <div className={clsx("prob-market-inline", "yes")}>
+                    {marketTag}
+                  </div>
+                )}
+              </div>
+            );
+          })
+        ) : view.probabilities.length === 0 ? (
+          <EmptyState text={t("section.noProb")} />
+        ) : (
+          view.probabilities.slice(0, 6).map((bucket, index) => {
+            const probability = Math.round(
+              Number(bucket.probability || 0) * 100,
+            );
+            const bucketTemp = getBucketTemp(bucket);
+            const isMarketBucket =
+              marketYesText != null &&
+              marketBucketTemp != null &&
+              bucketTemp != null &&
+              Math.abs(bucketTemp - marketBucketTemp) < 0.26;
+            const marketTag = isMarketBucket
+              ? locale === "en-US"
+                ? `Buy Yes: ${marketYesText || "--"}`
+                : `买 Yes: ${marketYesText || "--"}`
+              : marketNoText
+                ? locale === "en-US"
+                  ? `Buy No: ${marketNoText}`
+                  : `买 No: ${marketNoText}`
+                : null;
+            const yesPriceText = toPriceCents(marketYesPrice);
+            const noPriceText = toPriceCents(marketNoPrice);
+            const marketTagFinal = isMarketBucket
+              ? locale === "en-US"
+                ? `Buy Yes: ${yesPriceText || "--"}`
+                : `买 Yes: ${yesPriceText || "--"}`
+              : noPriceText
+                ? locale === "en-US"
+                  ? `Buy No: ${noPriceText}`
+                  : `买 No: ${noPriceText}`
+                : marketTag;
+            let bucketLabel =
+              bucket.label || `${bucket.value}${detail.temp_symbol}`;
+            if (bucketLabel) {
+              let str = String(bucketLabel).toUpperCase().replace(/\s+/g, "");
+              str = str.replace(/°?C($|\+|-)/g, "℃$1");
+              if (!str.includes("℃") && /[0-9]/.test(str)) {
+                str += "℃";
+              }
+              bucketLabel = str;
+            }
+
+            return (
+              <div
+                key={`${bucket.label || bucket.value || index}`}
+                className="prob-row"
+              >
+                <div className="prob-label">{bucketLabel}</div>
+                <div className="prob-bar-track">
+                  <div
+                    className={clsx("prob-bar-fill", `rank-${index}`)}
+                    style={{ width: `${Math.max(probability, 8)}%` }}
+                  >
+                    {probability}%
+                  </div>
+                </div>
+                {marketTagFinal && (
+                  <div
+                    className={clsx(
+                      "prob-market-inline",
+                      isMarketBucket ? "yes" : "no",
+                    )}
+                  >
+                    {marketTagFinal}
+                  </div>
+                )}
               </div>
             );
           })
@@ -313,8 +558,12 @@ export function ModelForecast({
   const numericValues = modelEntries.map(([, value]) => Number(value));
   const comparisonValues =
     view.deb != null ? [...numericValues, Number(view.deb)] : numericValues;
-  const minValue = comparisonValues.length ? Math.min(...comparisonValues) - 1 : 0;
+  const minValue = comparisonValues.length
-  const maxValue = comparisonValues.length ? Math.max(...comparisonValues) + 1 : 1;
+    ? Math.min(...comparisonValues) - 1
+    : 0;
+  const maxValue = comparisonValues.length
+    ? Math.max(...comparisonValues) + 1
+    : 1;
   const range = Math.max(maxValue - minValue, 1);
 
   return (
@@ -341,12 +590,18 @@ export function ModelForecast({
                       {name}
                     </div>
                     <div className="model-bar-track">
-                      <div className="model-bar-fill" style={{ width: `${width}%` }}>
+                      <div
+                        className="model-bar-fill"
+                        style={{ width: `${width}%` }}
+                      >
                         {numeric}
                         {detail.temp_symbol}
                       </div>
                       {debLine != null && (
-                        <div className="model-deb-line" style={{ left: `${debLine}%` }} />
+                        <div
+                          className="model-deb-line"
+                          style={{ left: `${debLine}%` }}
+                        />
                       )}
                     </div>
                   </div>
@@ -410,13 +665,19 @@ export function ForecastTable() {
               <button
                 key={day.date}
                 type="button"
-                className={clsx("forecast-day", isToday && "today", isSelected && "selected")}
+                className={clsx(
+                  "forecast-day",
+                  isToday && "today",
+                  isSelected && "selected",
+                )}
                 onClick={() => {
                   store.openFutureModal(day.date);
                 }}
               >
                 <div className="f-date">
-                  {isToday ? t("forecast.today") : day.date.substring(5).replace("-", "/")}
+                  {isToday
+                    ? t("forecast.today")
+                    : day.date.substring(5).replace("-", "/")}
                 </div>
                 <div className="f-temp">
                   {day.max_temp}
@@ -471,7 +732,9 @@ export function RiskInfo() {
       <h3>{t("section.risk")}</h3>
       <div className="risk-info">
         {!risk.airport ? (
-          <span style={{ color: "var(--text-muted)" }}>{t("section.noRiskProfile")}</span>
+          <span style={{ color: "var(--text-muted)" }}>
+            {t("section.noRiskProfile")}
+          </span>
         ) : (
           <>
             <div className="risk-row">

frontend/hooks/useDashboardStore.tsx

@@ -21,6 +21,7 @@ import {
   HistoryPoint,
   HistoryState,
   LoadingState,
+  MarketScan,
 } from "@/lib/dashboard-types";
 
 interface DashboardStoreValue extends DashboardState {
@@ -35,13 +36,15 @@ interface DashboardStoreValue extends DashboardState {
   openFutureModal: (dateStr: string) => void;
   openGuide: () => void;
   openHistory: () => Promise<void>;
-  openTodayModal: () => Promise<void>;
+  openTodayModal: (forceRefresh?: boolean) => Promise<void>;
   registerMapStopMotion: (stopMotion: () => void) => void;
   refreshAll: () => Promise<void>;
   refreshSelectedCity: () => Promise<void>;
+  selectedMarketScan: MarketScan | null;
   selectedDetail: CityDetail | null;
   selectCity: (cityName: string) => Promise<void>;
   setForecastDate: (dateStr: string | null) => void;
+  marketScanByCityName: Record<string, MarketScan>;
 }
 
 const DashboardStoreContext = createContext<DashboardStoreValue | null>(null);
@@ -52,6 +55,7 @@ function getInitialLoadingState(): LoadingState {
     cityDetail: false,
     history: false,
     refresh: false,
+    marketScan: false,
   };
 }
 
@@ -177,6 +181,9 @@ export function DashboardStoreProvider({
   const [cityDetailMetaByName, setCityDetailMetaByName] = useState<
     Record<string, { cachedAt: number; revision: string }>
   >(() => initialCache.meta);
+  const [marketScanByCityName, setMarketScanByCityName] = useState<
+    Record<string, MarketScan>
+  >({});
   const [selectedCity, setSelectedCity] = useState<string | null>(null);
   const [isPanelOpen, setIsPanelOpen] = useState(false);
   const [selectedForecastDate, setSelectedForecastDate] = useState<
@@ -203,6 +210,9 @@ export function DashboardStoreProvider({
   const selectedDetail = selectedCity
     ? cityDetailsByName[selectedCity] || null
     : null;
+  const selectedMarketScan = selectedCity
+    ? marketScanByCityName[selectedCity] || null
+    : null;
 
   useEffect(() => {
     dashboardClient.writeCityDetailCacheBundle(
@@ -241,7 +251,9 @@ export function DashboardStoreProvider({
       }
     }
 
-    const latestDetail = await dashboardClient.getCityDetail(cityName, { force });
+    const latestDetail = await dashboardClient.getCityDetail(cityName, {
+      force,
+    });
     const detail = mergeAiAnalysisIfStable(cached, latestDetail);
     setCityDetailsByName((current) => ({
       ...current,
@@ -261,6 +273,29 @@ export function DashboardStoreProvider({
     return detail;
   };
 
+  const ensureCityMarketScan = async (
+    cityName: string,
+    force = false,
+    marketSlug?: string | null,
+  ) => {
+    const cached = marketScanByCityName[cityName];
+    if (!force && cached && !marketSlug) {
+      return cached;
+    }
+
+    const latestScan = await dashboardClient.getCityMarketScan(cityName, {
+      force,
+      marketSlug,
+    });
+    if (latestScan) {
+      setMarketScanByCityName((current) => ({
+        ...current,
+        [cityName]: latestScan,
+      }));
+    }
+    return latestScan;
+  };
+
   const loadCities = async () => {
     setLoadingState((current) => ({ ...current, cities: true }));
     try {
@@ -330,6 +365,8 @@ export function DashboardStoreProvider({
     try {
       const detail = await ensureCityDetail(cityName);
       setSelectedForecastDate(detail.local_date);
+      // 预热市场数据，不做 await 阻塞，后台静默拉取
+      void ensureCityMarketScan(cityName, false).catch(() => {});
     } finally {
       setLoadingState((current) => ({ ...current, cityDetail: false }));
     }
@@ -433,24 +470,50 @@ export function DashboardStoreProvider({
       },
       openGuide: () => setIsGuideOpen(true),
       openHistory,
-      openTodayModal: async () => {
+      openTodayModal: async (forceRefresh?: boolean) => {
-        if (!selectedCity || loadingState.cityDetail || loadingState.refresh) {
+        if (!selectedCity || loadingState.cityDetail) {
           return;
         }
 
         mapStopMotionRef.current();
-        setLoadingState((current) => ({ ...current, refresh: true }));
+        const cachedDetail = cityDetailsByName[selectedCity];
+
+        // 乐观 UI: 有缓存则立刻秒开 modal，不阻塞显示
+        if (cachedDetail?.local_date) {
+          setSelectedForecastDate(cachedDetail.local_date);
+          setFutureModalDate(cachedDetail.local_date);
+          setLoadingState((current) => ({ ...current, marketScan: true }));
+        } else {
+          setLoadingState((current) => ({
+            ...current,
+            refresh: true,
+            marketScan: true,
+          }));
+        }
+
+        // 异步静默拉取最新气象与市场数据
         try {
           const detail = await ensureCityDetail(selectedCity, true);
           setSelectedForecastDate(detail.local_date);
           setFutureModalDate(detail.local_date);
+
+          try {
+            // 如果缓存里没有或者想要强制刷新，则拉取最新市场数据
+            await ensureCityMarketScan(
+              selectedCity,
+              forceRefresh || !marketScanByCityName[selectedCity],
+            );
+          } catch {}
         } catch {
-          const fallback = cityDetailsByName[selectedCity];
+          if (cachedDetail?.local_date) {
-          if (fallback?.local_date) {
+            setFutureModalDate(cachedDetail.local_date);
-            setFutureModalDate(fallback.local_date);
           }
         } finally {
-          setLoadingState((current) => ({ ...current, refresh: false }));
+          setLoadingState((current) => ({
+            ...current,
+            refresh: false,
+            marketScan: false,
+          }));
         }
       },
       registerMapStopMotion: (stopMotion: () => void) => {
@@ -458,12 +521,14 @@ export function DashboardStoreProvider({
       },
       refreshAll,
       refreshSelectedCity,
+      selectedMarketScan,
       selectedCity,
       selectedDetail,
       selectedForecastDate,
       selectCity,
       setForecastDate: (dateStr: string | null) =>
         setSelectedForecastDate(dateStr),
+      marketScanByCityName,
     }),
     [
       cities,
@@ -474,6 +539,8 @@ export function DashboardStoreProvider({
       isPanelOpen,
       isGuideOpen,
       loadingState,
+      marketScanByCityName,
+      selectedMarketScan,
       selectedCity,
       selectedDetail,
       selectedForecastDate,

frontend/lib/dashboard-client.ts

@@ -3,6 +3,7 @@
 import {
   CityDetail,
   CityListItem,
+  MarketScan,
   CitySummary,
   HistoryPoint,
 } from "@/lib/dashboard-types";
@@ -12,6 +13,7 @@ const CACHE_TTL_MS = 5 * 60 * 1000;
 const pendingCityDetailRequests = new Map<string, Promise<CityDetail>>();
 const pendingHistoryRequests = new Map<string, Promise<HistoryPoint[]>>();
 const pendingCitySummaryRequests = new Map<string, Promise<CitySummary>>();
+const pendingMarketScanRequests = new Map<string, Promise<MarketScan | null>>();
 
 type CityCacheMeta = {
   cachedAt: number;
@@ -134,20 +136,75 @@ export const dashboardClient = {
 
   async getCityDetail(cityName: string, options?: { force?: boolean }) {
     const force = options?.force ?? false;
-    const requestKey = `${cityName}::${force ? "force" : "cached"}`;
+    if (!force) {
+      const requestKey = `${cityName}::cached`;
       const existing = pendingCityDetailRequests.get(requestKey);
       if (existing) {
         return existing;
       }
 
       const request = fetchJson<CityDetail>(
-      `/api/city/${normalizeCityName(cityName)}?force_refresh=${force}`,
+        `/api/city/${normalizeCityName(cityName)}?force_refresh=false`,
       ).finally(() => {
         pendingCityDetailRequests.delete(requestKey);
       });
 
       pendingCityDetailRequests.set(requestKey, request);
       return request;
+    }
+
+    const params = new URLSearchParams({
+      force_refresh: "true",
+      _ts: String(Date.now()),
+    });
+    return fetchJson<CityDetail>(
+      `/api/city/${normalizeCityName(cityName)}?${params.toString()}`,
+    );
+  },
+
+  async getCityMarketScan(
+    cityName: string,
+    options?: { force?: boolean; marketSlug?: string | null },
+  ) {
+    const force = options?.force ?? false;
+    const marketSlug = options?.marketSlug || null;
+    if (!force) {
+      const requestKey = `${cityName}::cached::${marketSlug || "-"}`;
+      const existing = pendingMarketScanRequests.get(requestKey);
+      if (existing) {
+        return existing;
+      }
+
+      const params = new URLSearchParams({
+        force_refresh: "false",
+      });
+      if (marketSlug) {
+        params.set("market_slug", marketSlug);
+      }
+
+      const request = fetchJson<{ market_scan?: MarketScan }>(
+        `/api/city/${normalizeCityName(cityName)}/detail?${params.toString()}`,
+      )
+        .then((data) => data.market_scan || null)
+        .finally(() => {
+          pendingMarketScanRequests.delete(requestKey);
+        });
+
+      pendingMarketScanRequests.set(requestKey, request);
+      return request;
+    }
+
+    const params = new URLSearchParams({
+      force_refresh: "true",
+      _ts: String(Date.now()),
+    });
+    if (marketSlug) {
+      params.set("market_slug", marketSlug);
+    }
+
+    return fetchJson<{ market_scan?: MarketScan }>(
+      `/api/city/${normalizeCityName(cityName)}/detail?${params.toString()}`,
+    ).then((data) => data.market_scan || null);
   },
 
   async getHistory(cityName: string) {

frontend/lib/dashboard-types.ts

@@ -181,6 +181,71 @@ export interface DailyModelForecast {
   probabilities?: ProbabilityBucket[];
 }
 
+export interface MarketToken {
+  outcome?: string | null;
+  token_id?: string | null;
+  implied_probability?: number | null;
+  buy_price?: number | null;
+  sell_price?: number | null;
+  midpoint?: number | null;
+  last_trade_price?: number | null;
+}
+
+export interface MarketPrimary {
+  id?: string | null;
+  question?: string | null;
+  slug?: string | null;
+  condition_id?: string | null;
+  end_date?: string | null;
+  active?: boolean;
+  closed?: boolean;
+  liquidity?: number | null;
+  volume?: number | null;
+}
+
+export interface MarketTopBucket {
+  label?: string | null;
+  value?: number | null;
+  temp?: number | null;
+  probability?: number | null;
+  market_price?: number | null;
+  yes_buy?: number | null;
+  yes_sell?: number | null;
+  no_buy?: number | null;
+  no_sell?: number | null;
+  slug?: string | null;
+  question?: string | null;
+  is_primary?: boolean;
+}
+
+export interface MarketScan {
+  available?: boolean;
+  reason?: string | null;
+  primary_market?: MarketPrimary | null;
+  selected_date?: string | null;
+  selected_condition_id?: string | null;
+  selected_slug?: string | null;
+  temperature_bucket?: ProbabilityBucket | null;
+  model_probability?: number | null;
+  market_price?: number | null;
+  edge_percent?: number | null;
+  signal_label?: string | null;
+  confidence?: string | null;
+  yes_token?: MarketToken | null;
+  no_token?: MarketToken | null;
+  yes_buy?: number | null;
+  yes_sell?: number | null;
+  no_buy?: number | null;
+  no_sell?: number | null;
+  last_trade_price?: number | null;
+  liquidity?: number | null;
+  volume?: number | null;
+  sparkline?: number[];
+  top_buckets?: MarketTopBucket[] | null;
+  recent_trades?: unknown[];
+  websocket?: Record<string, unknown>;
+}
+
 export interface AiAnalysisStructured {
   summary?: string | null;
   text?: string | null;
@@ -227,6 +292,7 @@ export interface CityDetail {
   updated_at?: string;
   multi_model_daily?: Record<string, DailyModelForecast>;
   source_forecasts?: SourceForecasts;
+  market_scan?: MarketScan;
 }
 
 export interface HistoryPoint {
@@ -241,6 +307,7 @@ export interface LoadingState {
   cityDetail: boolean;
   refresh: boolean;
   history: boolean;
+  marketScan?: boolean;
 }
 
 export interface HistoryState {

frontend/lib/i18n.ts

@@ -71,7 +71,7 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
     "future.score": "趋势评分",
     "future.todayTempTrend": "今日温度走势",
     "future.targetTempTrend": "目标日小时走势",
-    "future.probability": "结算概率分布",
+    "future.probability": "模型结算概率分布",
     "future.models": "多模型预报",
     "future.structureToday": "今日日内结构信号",
     "future.structureDate": "未来 6-48 小时趋势",
@@ -91,7 +91,7 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
 
     "section.todayTempTrend": "今日温度走势",
     "section.chartEmpty": "暂无小时级数据",
-    "section.probability": "结算概率分布",
+    "section.probability": "模型结算概率分布",
     "section.mu": "动态分布中心 μ = {value}{unit}",
     "section.noProb": "暂无概率数据",
     "section.models": "多模型预报",
@@ -133,7 +133,8 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
       "No scenery image matched. You can still review station and observation profile below.",
     "detail.profile": "City Profile",
     "detail.todayMiniTrend": "Today's Intraday Trend (Compact)",
-    "detail.chartLegendEmpty": "No hourly observations or forecast curve available.",
+    "detail.chartLegendEmpty":
+      "No hourly observations or forecast curve available.",
 
     "forecast.title": "Multi-day Forecast",
     "forecast.empty": "No multi-day forecast available",
@@ -171,7 +172,7 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
     "future.score": "Trend Score",
     "future.todayTempTrend": "Today's Temperature Trend",
     "future.targetTempTrend": "Target-day Hourly Trend",
-    "future.probability": "Settlement Probability Distribution",
+    "future.probability": "Model Settlement Probabilities",
     "future.models": "Multi-model Forecast",
     "future.structureToday": "Intraday Structural Signal",
     "future.structureDate": "6-48h Structural Trend",
@@ -179,11 +180,13 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
     "future.confidence": "Confidence",
     "future.maxPrecip": "Max Precip Probability",
     "future.ai": "AI Deep Analysis",
-    "future.noAi": "No AI analysis available. Structured meteorological and model data are used as baseline.",
+    "future.noAi":
+      "No AI analysis available. Structured meteorological and model data are used as baseline.",
     "future.weatherGov": "weather.gov text",
     "future.risk": "Settlement & Deviation Risk",
     "future.climate": "What Mainly Drives Local Climate",
-    "future.chartLegendEmpty": "No METAR bulletin or hourly observations available",
+    "future.chartLegendEmpty":
+      "No METAR bulletin or hourly observations available",
 
     "confidence.high": "High",
     "confidence.medium": "Medium",
@@ -191,13 +194,14 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
 
     "section.todayTempTrend": "Today's Temperature Trend",
     "section.chartEmpty": "No hourly data available",
-    "section.probability": "Settlement Probability Distribution",
+    "section.probability": "Model Settlement Probabilities",
     "section.mu": "Dynamic center μ = {value}{unit}",
     "section.noProb": "No probability data available",
     "section.models": "Multi-model Forecast",
     "section.noModels": "No multi-model forecast available",
     "section.ai": "AI Deep Analysis",
-    "section.aiEmpty": "No AI analysis available. Structured meteorological and model data are currently used.",
+    "section.aiEmpty":
+      "No AI analysis available. Structured meteorological and model data are currently used.",
     "section.risk": "Data Deviation Risk",
     "section.noRiskProfile": "No risk profile available",
     "section.airport": "Airport",

frontend/lib/types.ts

@@ -292,6 +292,20 @@ export interface MarketScan {
   liquidity: number | null;
   volume: number | null;
   sparkline: number[];
+  top_buckets?: Array<{
+    label?: string | null;
+    value?: number | null;
+    temp?: number | null;
+    probability?: number | null;
+    market_price?: number | null;
+    yes_buy?: number | null;
+    yes_sell?: number | null;
+    no_buy?: number | null;
+    no_sell?: number | null;
+    slug?: string | null;
+    question?: string | null;
+    is_primary?: boolean;
+  }>;
   recent_trades: Trade[];
   websocket: any;
 }

src/analysis/market_alert_engine.py

@@ -421,6 +421,111 @@ def _join_trigger_types_cn(rules: Dict[str, Dict[str, Any]]) -> str:
     return " + ".join(parts)
 
 
+def _norm_probability(v: Any) -> Optional[float]:
+    n = _sf(v)
+    if n is None:
+        return None
+    if n > 1.0:
+        n = n / 100.0
+    return max(0.0, min(1.0, n))
+
+
+def _fmt_percent(v: Any) -> str:
+    n = _norm_probability(v)
+    if n is None:
+        return "--"
+    return f"{n * 100:.1f}%"
+
+
+def _fmt_cents(v: Any) -> str:
+    n = _norm_probability(v)
+    if n is None:
+        return "--"
+    cents = n * 100.0
+    return f"{cents:.1f}c"
+
+
+def _bucket_label(bucket: Any) -> Optional[str]:
+    if not isinstance(bucket, dict):
+        return None
+    direct = (
+        str(bucket.get("label") or "").strip()
+        or str(bucket.get("bucket") or "").strip()
+        or str(bucket.get("range") or "").strip()
+    )
+    if direct:
+        return direct
+    value = _sf(bucket.get("value"))
+    if value is not None:
+        return f"{round(value)}C"
+    temp = _sf(bucket.get("temp"))
+    if temp is not None:
+        return f"{round(temp)}C"
+    return None
+
+
+def _extract_market_snapshot(city_weather: Dict[str, Any]) -> Dict[str, Any]:
+    scan = city_weather.get("market_scan") or {}
+    if not isinstance(scan, dict):
+        return {"available": False}
+    if not scan.get("available"):
+        return {"available": False}
+
+    yes_buy = _norm_probability(scan.get("yes_buy"))
+    yes_sell = _norm_probability(scan.get("yes_sell"))
+    market_prob = _norm_probability(
+        scan.get("market_price")
+        or ((scan.get("yes_token") or {}).get("implied_probability"))
+    )
+    model_prob = _norm_probability(scan.get("model_probability"))
+    spread = None
+    if yes_buy is not None and yes_sell is not None:
+        spread = abs(yes_sell - yes_buy)
+
+    top_bucket = None
+    top_bucket_rows: List[Dict[str, Any]] = []
+    top_buckets = scan.get("top_buckets") or []
+    if isinstance(top_buckets, list):
+        normalized = []
+        for row in top_buckets:
+            if not isinstance(row, dict):
+                continue
+            p = _norm_probability(row.get("probability"))
+            if p is None:
+                continue
+            normalized.append((p, row))
+        if normalized:
+            normalized.sort(key=lambda x: x[0], reverse=True)
+            top_bucket = normalized[0][1]
+            for p, row in normalized[:4]:
+                top_bucket_rows.append(
+                    {
+                        "label": _bucket_label(row),
+                        "probability": p,
+                        "yes_buy": _norm_probability(row.get("yes_buy")),
+                        "yes_sell": _norm_probability(row.get("yes_sell")),
+                    }
+                )
+
+    return {
+        "available": True,
+        "selected_bucket": _bucket_label(scan.get("temperature_bucket")),
+        "top_bucket": _bucket_label(top_bucket) if isinstance(top_bucket, dict) else None,
+        "top_bucket_prob": _norm_probability(
+            top_bucket.get("probability") if isinstance(top_bucket, dict) else None
+        ),
+        "market_prob": market_prob,
+        "model_prob": model_prob,
+        "yes_buy": yes_buy,
+        "yes_sell": yes_sell,
+        "spread": spread,
+        "edge_percent": _sf(scan.get("edge_percent")),
+        "signal_label": scan.get("signal_label"),
+        "confidence": scan.get("confidence"),
+        "top_bucket_rows": top_bucket_rows,
+    }
+
+
 def _build_advice_cn(
     rules: Dict[str, Dict[str, Any]],
     temp_symbol: str,
@@ -472,6 +577,7 @@ def _build_telegram_messages(
     city_weather: Dict[str, Any],
     rules: Dict[str, Dict[str, Any]],
     map_url: Optional[str],
+    market_snapshot: Optional[Dict[str, Any]] = None,
     suppression: Optional[Dict[str, Any]] = None,
 ) -> Dict[str, str]:
     temp_symbol = city_weather.get("temp_symbol", "°C")
@@ -482,6 +588,7 @@ def _build_telegram_messages(
     center_deb = rules.get("ankara_center_deb_hit", {})
     momentum = rules.get("momentum_spike", {})
     advection = rules.get("advection", {})
+    market_snapshot = market_snapshot or _extract_market_snapshot(city_weather)
 
     if current_temp is None:
         return {"zh": "", "en": ""}
@@ -559,6 +666,30 @@ def _build_telegram_messages(
         lines_zh.append(peak_line)
     if lead_line:
         lines_zh.append(lead_line)
+    if market_snapshot.get("available") and market_snapshot.get("top_bucket_rows"):
+        lines_zh.append("市场结算概率分布（Top4）：")
+        for row in (market_snapshot.get("top_bucket_rows") or [])[:4]:
+            label = row.get("label") or "--"
+            prob_text = _fmt_percent(row.get("probability"))
+            yes_buy_text = _fmt_cents(row.get("yes_buy"))
+            lines_zh.append(f"{label} {prob_text} | 买Yes: {yes_buy_text}")
+    if market_snapshot.get("available") and not market_snapshot.get("top_bucket_rows"):
+        market_edge = _sf(market_snapshot.get("edge_percent"))
+        market_edge_text = f"{market_edge:+.1f}%" if market_edge is not None else "--"
+        lines_zh.append(
+            "市场联动：同桶 "
+            f"模型 {_fmt_percent(market_snapshot.get('model_prob'))} vs "
+            f"市场 {_fmt_percent(market_snapshot.get('market_prob'))} | "
+            f"Yes {_fmt_cents(market_snapshot.get('yes_buy'))}/{_fmt_cents(market_snapshot.get('yes_sell'))} | "
+            f"点差 {_fmt_cents(market_snapshot.get('spread'))} | "
+            f"偏差 {market_edge_text} | "
+            f"信号 {market_snapshot.get('signal_label') or '--'}/{market_snapshot.get('confidence') or '--'}"
+        )
+        if market_snapshot.get("top_bucket"):
+            lines_zh.append(
+                f"市场最热桶：{market_snapshot.get('top_bucket')} "
+                f"({_fmt_percent(market_snapshot.get('top_bucket_prob'))})"
+            )
     lines_zh.append(f"AI 建议：{advice}")
     lines_zh.append(f"点击查看实时地图：{final_map}")
 
@@ -602,6 +733,30 @@ def _build_telegram_messages(
                 f"Peak state: intraday high {max_so_far:.1f}{temp_symbol} at {max_temp_time}, "
                 f"now off by {rollback:.1f}{temp_symbol}"
             )
+    if market_snapshot.get("available") and market_snapshot.get("top_bucket_rows"):
+        lines_en.append("Settlement distribution (Top4):")
+        for row in (market_snapshot.get("top_bucket_rows") or [])[:4]:
+            label = row.get("label") or "--"
+            prob_text = _fmt_percent(row.get("probability"))
+            yes_buy_text = _fmt_cents(row.get("yes_buy"))
+            lines_en.append(f"{label} {prob_text} | Buy Yes: {yes_buy_text}")
+    if market_snapshot.get("available") and not market_snapshot.get("top_bucket_rows"):
+        market_edge = _sf(market_snapshot.get("edge_percent"))
+        market_edge_text = f"{market_edge:+.1f}%" if market_edge is not None else "--"
+        lines_en.append(
+            "Market: same-bucket "
+            f"model {_fmt_percent(market_snapshot.get('model_prob'))} vs "
+            f"market {_fmt_percent(market_snapshot.get('market_prob'))} | "
+            f"Yes {_fmt_cents(market_snapshot.get('yes_buy'))}/{_fmt_cents(market_snapshot.get('yes_sell'))} | "
+            f"spread {_fmt_cents(market_snapshot.get('spread'))} | "
+            f"edge {market_edge_text} | "
+            f"signal {market_snapshot.get('signal_label') or '--'}/{market_snapshot.get('confidence') or '--'}"
+        )
+        if market_snapshot.get("top_bucket"):
+            lines_en.append(
+                f"Top market bucket: {market_snapshot.get('top_bucket')} "
+                f"({_fmt_percent(market_snapshot.get('top_bucket_prob'))})"
+            )
     lines_en.append(f"Action: {advice}")
     lines_en.append(f"Map: {final_map}")
 
@@ -618,6 +773,7 @@ def build_trading_alerts(
     temp_symbol = city_weather.get("temp_symbol", "°C")
     city = city_weather.get("name", "")
     now = datetime.now(timezone.utc).isoformat()
+    market_snapshot = _extract_market_snapshot(city_weather)
 
     rules: Dict[str, Dict[str, Any]] = {
         "ankara_center_deb_hit": _calc_ankara_center_deb_alert(city_weather, temp_symbol),
@@ -658,6 +814,7 @@ def build_trading_alerts(
         city_weather=city_weather,
         rules=rules,
         map_url=map_url,
+        market_snapshot=market_snapshot,
         suppression=suppression,
     )
 
@@ -668,6 +825,7 @@ def build_trading_alerts(
         "severity": severity,
         "trigger_count": len(triggered),
         "rules": rules,
+        "market_snapshot": market_snapshot,
         "suppression": suppression,
         "triggered_alerts": triggered,
         "telegram": telegram,

src/data_collection/polymarket_readonly.py

@@ -394,6 +394,7 @@ class PolymarketReadOnlyLayer:
             "liquidity": None,
             "volume": None,
             "sparkline": fallback_sparkline or [],
+            "top_buckets": [],
             "recent_trades": [],
             "websocket": {},
         }
@@ -485,6 +486,13 @@ class PolymarketReadOnlyLayer:
 
         signal_label, confidence = self._derive_signal(edge_percent, liquidity)
 
+        top_buckets = self._build_top_temperature_buckets(
+            city_key=city_key,
+            target_date=date_str,
+            primary_market=market,
+            limit=4,
+        )
+
         yes_payload = {
             "outcome": yes_token.get("outcome") or "Yes",
             "token_id": yes_token.get("token_id"),
@@ -551,6 +559,7 @@ class PolymarketReadOnlyLayer:
                 "liquidity": liquidity,
                 "volume": volume,
                 "sparkline": sparkline_values,
+                "top_buckets": top_buckets,
                 "websocket": {
                     "market_url": market_url,
                     "asset_ids": [
@@ -1174,3 +1183,232 @@ class PolymarketReadOnlyLayer:
         if slug:
             return f"https://polymarket.com/market/{slug}"
         return None
+
+    def _build_top_temperature_buckets(
+        self,
+        city_key: str,
+        target_date: str,
+        primary_market: Dict[str, Any],
+        limit: int = 4,
+    ) -> List[Dict[str, Any]]:
+        candidate_markets = self._collect_related_temperature_markets(
+            city_key=city_key,
+            target_date=target_date,
+            primary_market=primary_market,
+        )
+        if not candidate_markets:
+            return []
+
+        ranked: List[
+            Tuple[
+                float,
+                float,
+                Dict[str, Any],
+                Dict[str, Any],
+                Dict[str, Any],
+                Dict[str, Any],
+                Dict[str, Any],
+            ]
+        ] = []
+        for market in candidate_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()
+            yes_prices = self._get_token_market_data(yes_token_id) if yes_token_id else {}
+            no_prices = self._get_token_market_data(no_token_id) if no_token_id else {}
+
+            yes_midpoint = _extract_price(yes_prices.get("midpoint"))
+            yes_implied = _extract_price(yes_token.get("implied_probability"))
+            no_implied = _extract_price(no_token.get("implied_probability"))
+            market_prob = (
+                yes_midpoint
+                if yes_midpoint is not None
+                else (
+                    yes_implied
+                    if yes_implied is not None
+                    else (1.0 - no_implied if no_implied is not None else None)
+                )
+            )
+            if market_prob is None:
+                continue
+
+            market_prob = max(0.0, min(1.0, float(market_prob)))
+            volume = (
+                _extract_price(
+                    market.get("volumeNum")
+                    or market.get("volume")
+                    or market.get("volume24hr")
+                )
+                or 0.0
+            )
+            ranked.append(
+                (
+                    market_prob,
+                    volume,
+                    market,
+                    yes_token,
+                    no_token,
+                    yes_prices,
+                    no_prices,
+                )
+            )
+
+        if not ranked:
+            return []
+
+        ranked.sort(key=lambda item: (item[0], item[1]), reverse=True)
+        top_rows: List[Dict[str, Any]] = []
+        max_items = max(1, int(limit or 4))
+        primary_slug = str(primary_market.get("slug") or "").strip().lower()
+
+        for market_prob, _volume, market, yes_token, no_token, yes_prices, no_prices in ranked[
+            :max_items
+        ]:
+            yes_buy = _extract_price(yes_prices.get("buy"))
+            yes_sell = _extract_price(yes_prices.get("sell"))
+            yes_midpoint = _extract_price(yes_prices.get("midpoint")) or market_prob
+            no_buy = _extract_price(no_prices.get("buy"))
+            no_sell = _extract_price(no_prices.get("sell"))
+
+            if no_buy is None and yes_buy is not None:
+                no_buy = max(0.0, min(1.0, 1.0 - yes_buy))
+            if no_sell is None and yes_sell is not None:
+                no_sell = max(0.0, min(1.0, 1.0 - yes_sell))
+
+            bucket_temp = self._extract_market_bucket_temp(market)
+            market_slug = str(market.get("slug") or "").strip()
+
+            top_rows.append(
+                {
+                    "label": self._extract_market_bucket_label(market, bucket_temp),
+                    "value": bucket_temp,
+                    "temp": bucket_temp,
+                    "probability": market_prob,
+                    "market_price": yes_midpoint,
+                    "yes_buy": yes_buy,
+                    "yes_sell": yes_sell,
+                    "no_buy": no_buy,
+                    "no_sell": no_sell,
+                    "slug": market_slug or None,
+                    "question": market.get("question") or market.get("title"),
+                    "is_primary": bool(
+                        primary_slug
+                        and market_slug
+                        and primary_slug == market_slug.strip().lower()
+                    ),
+                }
+            )
+
+        return top_rows
+
+    def _collect_related_temperature_markets(
+        self,
+        city_key: str,
+        target_date: str,
+        primary_market: Dict[str, Any],
+    ) -> List[Dict[str, Any]]:
+        related: List[Dict[str, Any]] = []
+        canonical_event_slug = self._build_weather_event_slug(city_key, target_date)
+        if canonical_event_slug:
+            related.extend(self._load_event_markets(canonical_event_slug))
+
+        event_slug = self._extract_event_slug(primary_market)
+        if event_slug and event_slug != canonical_event_slug:
+            related.extend(self._load_event_markets(event_slug))
+
+        if not related:
+            for market in self._load_markets(active_only=True):
+                if self._score_market(city_key, target_date, market) <= 0:
+                    continue
+                if self._extract_market_bucket_temp(market) is None:
+                    continue
+                related.append(market)
+
+        related.append(primary_market)
+
+        unique: List[Dict[str, Any]] = []
+        seen = set()
+        for market in related:
+            if not isinstance(market, dict):
+                continue
+            dedupe_key = str(
+                market.get("id")
+                or market.get("slug")
+                or market.get("conditionId")
+                or ""
+            ).strip()
+            if not dedupe_key:
+                continue
+            if dedupe_key in seen:
+                continue
+            seen.add(dedupe_key)
+            unique.append(market)
+        return unique
+
+    def _extract_event_slug(self, market: Dict[str, Any]) -> Optional[str]:
+        event_slug = str(market.get("eventSlug") or "").strip().lower()
+        if event_slug:
+            return event_slug
+
+        slug = str(market.get("slug") or "").strip().lower()
+        if not slug:
+            return None
+
+        trimmed = re.sub(
+            r"-(?:m)?\d+(?:-\d+)?c(?:-or-(?:higher|lower|above|below))?$",
+            "",
+            slug,
+        )
+        trimmed = trimmed.strip("-")
+        return trimmed or None
+
+    def _load_event_markets(self, event_slug: str) -> List[Dict[str, Any]]:
+        normalized_slug = str(event_slug or "").strip().lower()
+        if not normalized_slug:
+            return []
+
+        try:
+            resp = self._session.get(
+                f"{self.gamma_url}/events",
+                params={"slug": normalized_slug, "limit": 5},
+                timeout=self.http_timeout,
+            )
+            resp.raise_for_status()
+            payload = resp.json()
+        except Exception:
+            return []
+
+        events = payload if isinstance(payload, list) else []
+        out: List[Dict[str, Any]] = []
+        for event in events:
+            if not isinstance(event, dict):
+                continue
+            event_item_slug = str(event.get("slug") or "").strip().lower()
+            if event_item_slug and event_item_slug != normalized_slug:
+                continue
+            for market in event.get("markets") or []:
+                if not isinstance(market, dict):
+                    continue
+                market["eventSlug"] = market.get("eventSlug") or event_item_slug
+                market["eventTitle"] = market.get("eventTitle") or event.get("title")
+                out.append(market)
+        return out
+
+    def _extract_market_bucket_label(
+        self,
+        market: Dict[str, Any],
+        bucket_temp: Optional[float],
+    ) -> str:
+        question = str(market.get("question") or market.get("title") or "").strip()
+        text = question.lower()
+        if bucket_temp is not None:
+            if "or higher" in text or "or above" in text or "and above" in text:
+                return f"{bucket_temp:g}C+"
+            if "or lower" in text or "or below" in text or "and below" in text:
+                return f"<={bucket_temp:g}C"
+            return f"{bucket_temp:g}C"
+        return question or str(market.get("slug") or "")

src/utils/telegram_push.py

@@ -117,6 +117,12 @@ def _trigger_type_key(alert_payload: Dict[str, Any]) -> str:
         for alert in (alert_payload.get("triggered_alerts") or [])
         if alert.get("type")
     )
+    market = alert_payload.get("market_snapshot") or {}
+    if isinstance(market, dict) and market.get("available"):
+        signal = str(market.get("signal_label") or "").strip()
+        bucket = str(market.get("selected_bucket") or "").strip()
+        if signal:
+            trigger_types.append(f"mkt:{signal}:{bucket}")
     return "|".join(trigger_types)
 
 
@@ -127,6 +133,7 @@ def _alert_signature(alert_payload: Dict[str, Any]) -> str:
     breakthrough = rules.get("forecast_breakthrough") or {}
     advection = rules.get("advection") or {}
     suppression = alert_payload.get("suppression") or {}
+    market = alert_payload.get("market_snapshot") or {}
 
     signature_payload = {
         "city": alert_payload.get("city"),
@@ -149,6 +156,18 @@ def _alert_signature(alert_payload: Dict[str, Any]) -> str:
         "suppression_reason": suppression.get("reason"),
         "suppression_peak_time": suppression.get("max_temp_time"),
         "suppression_rollback": round(float(suppression.get("rollback") or 0.0), 1),
+        "market_available": bool(market.get("available")),
+        "market_bucket": market.get("selected_bucket"),
+        "market_top_bucket": market.get("top_bucket"),
+        "market_top_bucket_prob": round(float(market.get("top_bucket_prob") or 0.0), 3),
+        "market_prob": round(float(market.get("market_prob") or 0.0), 3),
+        "model_prob": round(float(market.get("model_prob") or 0.0), 3),
+        "market_yes_buy": round(float(market.get("yes_buy") or 0.0), 3),
+        "market_yes_sell": round(float(market.get("yes_sell") or 0.0), 3),
+        "market_spread": round(float(market.get("spread") or 0.0), 3),
+        "market_edge_percent": round(float(market.get("edge_percent") or 0.0), 2),
+        "market_signal": market.get("signal_label"),
+        "market_confidence": market.get("confidence"),
     }
     raw = json.dumps(signature_payload, sort_keys=True, ensure_ascii=True)
     return hashlib.sha1(raw.encode("utf-8")).hexdigest()
@@ -160,10 +179,18 @@ def build_trade_alert_for_city(
     force_refresh: bool = False,
     target_date: Optional[str] = None,
 ) -> Dict[str, Any]:
-    from web.app import _analyze
+    from web.app import _analyze, _build_city_detail_payload
     from src.analysis.market_alert_engine import build_trading_alerts
 
     city_weather = _analyze(city, force_refresh=force_refresh)
+    try:
+        aggregate_detail = _build_city_detail_payload(city_weather)
+        market_scan = aggregate_detail.get("market_scan")
+        if isinstance(market_scan, dict):
+            city_weather = {**city_weather, "market_scan": market_scan}
+    except Exception as exc:
+        logger.debug(f"market scan attach skipped city={city}: {exc}")
+
     resolved_target_date = target_date or city_weather.get("local_date")
     if resolved_target_date:
         datetime.strptime(resolved_target_date, "%Y-%m-%d")
@@ -212,7 +239,7 @@ def _maybe_send_alert(
     last_sig_ts = int((state.get("by_signature") or {}).get(signature) or 0)
     last_city_active = bool(last_city.get("active"))
 
-    if last_city_active and last_city_key == trigger_key:
+    if last_city_active and last_city_key == trigger_key and last_city_sig == signature:
         return False
 
     if last_city_ts and now_ts - last_city_ts < cooldown_sec:

web/app.py

@@ -681,11 +681,15 @@ def _build_city_detail_payload(
 ) -> Dict[str, Any]:
     distribution = data.get("probabilities", {}).get("distribution", []) or []
     primary_bucket = distribution[0] if distribution else None
+    model_probability = None
+    if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None:
+        try:
+            raw_probability = float(primary_bucket.get("probability"))
             model_probability = (
-        (primary_bucket.get("probability") / 100.0)
+                raw_probability / 100.0 if raw_probability > 1.0 else raw_probability
-        if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None
-        else None
             )
+        except Exception:
+            model_probability = None
     fallback_sparkline = [
         p.get("probability", 0)
         for p in distribution[:8]