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

2026-03-10 09:02:56 +08:00
parent aab4477ab3
commit 396c373cba
18 changed files with 2398 additions and 634 deletions
@@ -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.
@@ -140,12 +144,12 @@ 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`         |
-| `/help`   | Get detailed instructions               | `/help`        |
+| 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`         |
+| `/help` | Get detailed instructions             | `/help`        |

 ---

@@ -161,8 +165,8 @@ Set `frontend` as the Vercel root directory for automatic CI/CD.

 ---

-**📅 Last Updated**: 2026-03-09
-**🚀 Status**: v1.1 Stable - React Dashboard Runtime in Production
+**📅 Last Updated**: 2026-03-10
+**🚀 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).
@@ -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。
@@ -140,12 +144,12 @@ 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`         |
-| `/help`   | 获取详细功能说明          | `/help`        |
+| 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`         |
+| `/help` | 获取详细功能说明          | `/help`        |

 ---

@@ -161,8 +165,8 @@ docker-compose up -d --build

 ---

-**📅 最后更新**：2026-03-09
-**🚀 状态**：v1.1 稳定版 - React 运行时已上线
+**📅 最后更新**：2026-03-10
+**🚀 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）。
@@ -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
@@ -12,11 +12,11 @@ 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.                  |
+| 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.            |

 ### 🛠️ Payment Infrastructure

@@ -92,4 +92,4 @@ graph LR

 ---

-**📅 Last Updated**: 2026-03-09
+**📅 Last Updated**: 2026-03-10
@@ -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

 ---

@@ -33,18 +34,18 @@ The core weather engine and React dashboard runtime are now stable, but product-
 | **Monolithic Bot**     | `bot_listener.py` is hard to test and evolve.       | Isolate UI interaction from business logic into `src/analysis`.  |
 | **Subscription Store** | No persistent record of who has paid.               | Migrate from in-memory user checks to **Supabase/PostgreSQL**.   |
 | **Alert Transparency** | Operators cannot easily audit "why" an alert fired. | Add an `Evidence` metadata block to all internal alert payloads. |
-| **Entitlement Guard**  | Dashboard routes are public by default.             | Add JWT/session gating in Next.js middleware + backend checks.    |
+| **Entitlement Guard**  | Dashboard routes are public by default.             | Add JWT/session gating in Next.js middleware + backend checks.   |

 ---

 ## 🟡 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. |
-| **Chart Regression Tests**| UI relies on custom Chart.js lifecycles.            | Add snapshot + interaction tests for chart datasets and legends.             |
+| 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. |
+| **Chart Regression Tests** | UI relies on custom Chart.js lifecycles.            | Add snapshot + interaction tests for chart datasets and legends.             |

 ---

@@ -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
@@ -22,38 +22,39 @@ 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。                     |
-| **Entitlement Guard** | 仪表盘路由默认仍是公开可访问。                     | 在 Next.js middleware 与后端校验中加入 JWT/会话权限守卫。        |
+| 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。              |
+| **Entitlement Guard**  | 仪表盘路由默认仍是公开可访问。             | 在 Next.js middleware 与后端校验中加入 JWT/会话权限守卫。 |

 ---

 ## 🟡 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。                         |
-| **Chart Regression Tests**| 图表依赖自定义 Chart.js 生命周期，回归风险高。    | 增加图表数据集与图例的快照测试 + 交互测试。                              |
+| 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。     |
+| **Chart Regression Tests** | 图表依赖自定义 Chart.js 生命周期，回归风险高。 | 增加图表数据集与图例的快照测试 + 交互测试。          |

 ---

 ## 🟢 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）。                         |
+| 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
@@ -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(255, 255, 255, 0.06) 50%,
-    rgba(52, 211, 153, 0.35)
+    rgba(56, 189, 248, 0.4) 0%,
+    rgba(56, 189, 248, 0.4) 30%,
+    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;
@@ -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}`
@@ -95,142 +198,141 @@ export function TemperatureChart() {
  const { locale, t } = useI18n();
  const chartData = data ? getTemperatureChartData(data, locale) : null;

-  const canvasRef = useChart(
-    () => {
-      if (!data || !chartData) {
-        return {
-          data: { datasets: [], labels: [] },
-          type: "line",
-        } satisfies ChartConfiguration<"line">;
-      }
-
-      const datasets: NonNullable<ChartConfiguration<"line">["data"]>["datasets"] = [];
-
-      if (chartData.datasets.hasMgmHourly) {
-        datasets.push({
-          backgroundColor: "rgba(234, 179, 8, 0.05)",
-          borderColor: "rgba(234, 179, 8, 0.8)",
-          borderWidth: 2,
-          data: chartData.datasets.mgmHourlyPoints,
-          fill: false,
-          label: locale === "en-US" ? "MGM Forecast" : "MGM 预报",
-          pointHoverRadius: 6,
-          pointRadius: 3,
-          spanGaps: true,
-          tension: 0.3,
-        });
-      } else {
-        datasets.push({
-          backgroundColor: "rgba(52, 211, 153, 0.05)",
-          borderColor: "rgba(52, 211, 153, 0.6)",
-          borderWidth: 1.5,
-          data: chartData.datasets.debPast,
-          fill: true,
-          label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
-          pointHoverRadius: 3,
-          pointRadius: 0,
-          tension: 0.3,
-        });
-        datasets.push({
-          borderColor: "rgba(52, 211, 153, 0.35)",
-          borderDash: [5, 3],
-          borderWidth: 1.5,
-          data: chartData.datasets.debFuture,
-          fill: false,
-          label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
-          pointRadius: 0,
-          tension: 0.3,
-        });
-      }
-
-      datasets.push({
-        backgroundColor: "#22d3ee",
-        borderColor: "#22d3ee",
-        borderWidth: 0,
-        data: chartData.datasets.metarPoints,
-        fill: false,
-        label: locale === "en-US" ? "METAR Observation" : "METAR 实测",
-        order: 0,
-        pointHoverRadius: 7,
-        pointRadius: 5,
-      });
-
-      if (chartData.datasets.mgmPoints.some((value) => value != null)) {
-        datasets.push({
-          backgroundColor: "#facc15",
-          borderColor: "#facc15",
-          borderWidth: 0,
-          data: chartData.datasets.mgmPoints,
-          fill: false,
-          label: locale === "en-US" ? "MGM Observation" : "MGM 实测",
-          order: -1,
-          pointHoverRadius: 9,
-          pointRadius: 7,
-          showLine: false,
-        });
-      }
-
-      if (
-        !chartData.datasets.hasMgmHourly &&
-        Math.abs(chartData.datasets.offset) > 0.3
-      ) {
-        datasets.push({
-          borderColor: "rgba(99, 102, 241, 0.2)",
-          borderDash: [2, 4],
-          borderWidth: 1,
-          data: chartData.datasets.temps,
-          fill: false,
-          label: locale === "en-US" ? "OM Raw" : "OM 原始",
-          pointRadius: 0,
-          tension: 0.3,
-        });
-      }
-
+  const canvasRef = useChart(() => {
+    if (!data || !chartData) {
      return {
-        data: {
-          datasets,
-          labels: chartData.times,
-        },
-        options: {
-          interaction: { intersect: false, mode: "index" },
-          maintainAspectRatio: false,
-          plugins: {
-            legend: { display: false },
-            tooltip: {
-              backgroundColor: "rgba(15, 23, 42, 0.9)",
-              borderColor: "rgba(52, 211, 153, 0.3)",
-              borderWidth: 1,
-            },
-          },
-          responsive: true,
-          scales: {
-            x: {
-              grid: { color: "rgba(255,255,255,0.04)" },
-              ticks: {
-                callback: (_value, index) =>
-                  typeof index === "number" && index % 3 === 0
-                    ? chartData.times[index]
-                    : "",
-                color: "#64748b",
-                maxRotation: 0,
-              },
-            },
-            y: {
-              grid: { color: "rgba(255,255,255,0.04)" },
-              max: chartData.max,
-              min: chartData.min,
-              ticks: {
-                callback: (value) => `${value}${data.temp_symbol || "°C"}`,
-                color: "#64748b",
-              },
-            },
-          },
-        },
+        data: { datasets: [], labels: [] },
        type: "line",
      } satisfies ChartConfiguration<"line">;
-    },
-    [data, chartData, locale],
-  );
+    }
+
+    const datasets: NonNullable<
+      ChartConfiguration<"line">["data"]
+    >["datasets"] = [];
+
+    if (chartData.datasets.hasMgmHourly) {
+      datasets.push({
+        backgroundColor: "rgba(234, 179, 8, 0.05)",
+        borderColor: "rgba(234, 179, 8, 0.8)",
+        borderWidth: 2,
+        data: chartData.datasets.mgmHourlyPoints,
+        fill: false,
+        label: locale === "en-US" ? "MGM Forecast" : "MGM 预报",
+        pointHoverRadius: 6,
+        pointRadius: 3,
+        spanGaps: true,
+        tension: 0.3,
+      });
+    } else {
+      datasets.push({
+        backgroundColor: "rgba(52, 211, 153, 0.05)",
+        borderColor: "rgba(52, 211, 153, 0.6)",
+        borderWidth: 1.5,
+        data: chartData.datasets.debPast,
+        fill: true,
+        label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
+        pointHoverRadius: 3,
+        pointRadius: 0,
+        tension: 0.3,
+      });
+      datasets.push({
+        borderColor: "rgba(52, 211, 153, 0.35)",
+        borderDash: [5, 3],
+        borderWidth: 1.5,
+        data: chartData.datasets.debFuture,
+        fill: false,
+        label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
+        pointRadius: 0,
+        tension: 0.3,
+      });
+    }
+
+    datasets.push({
+      backgroundColor: "#22d3ee",
+      borderColor: "#22d3ee",
+      borderWidth: 0,
+      data: chartData.datasets.metarPoints,
+      fill: false,
+      label: locale === "en-US" ? "METAR Observation" : "METAR 实测",
+      order: 0,
+      pointHoverRadius: 7,
+      pointRadius: 5,
+    });
+
+    if (chartData.datasets.mgmPoints.some((value) => value != null)) {
+      datasets.push({
+        backgroundColor: "#facc15",
+        borderColor: "#facc15",
+        borderWidth: 0,
+        data: chartData.datasets.mgmPoints,
+        fill: false,
+        label: locale === "en-US" ? "MGM Observation" : "MGM 实测",
+        order: -1,
+        pointHoverRadius: 9,
+        pointRadius: 7,
+        showLine: false,
+      });
+    }
+
+    if (
+      !chartData.datasets.hasMgmHourly &&
+      Math.abs(chartData.datasets.offset) > 0.3
+    ) {
+      datasets.push({
+        borderColor: "rgba(99, 102, 241, 0.2)",
+        borderDash: [2, 4],
+        borderWidth: 1,
+        data: chartData.datasets.temps,
+        fill: false,
+        label: locale === "en-US" ? "OM Raw" : "OM 原始",
+        pointRadius: 0,
+        tension: 0.3,
+      });
+    }
+
+    return {
+      data: {
+        datasets,
+        labels: chartData.times,
+      },
+      options: {
+        interaction: { intersect: false, mode: "index" },
+        maintainAspectRatio: false,
+        plugins: {
+          legend: { display: false },
+          tooltip: {
+            backgroundColor: "rgba(15, 23, 42, 0.9)",
+            borderColor: "rgba(52, 211, 153, 0.3)",
+            borderWidth: 1,
+          },
+        },
+        responsive: true,
+        scales: {
+          x: {
+            grid: { color: "rgba(255,255,255,0.04)" },
+            ticks: {
+              callback: (_value, index) =>
+                typeof index === "number" && index % 3 === 0
+                  ? chartData.times[index]
+                  : "",
+              color: "#64748b",
+              maxRotation: 0,
+            },
+          },
+          y: {
+            grid: { color: "rgba(255,255,255,0.04)" },
+            max: chartData.max,
+            min: chartData.min,
+            ticks: {
+              callback: (value) => `${value}${data.temp_symbol || "°C"}`,
+              color: "#64748b",
+            },
+          },
+        },
+      },
+      type: "line",
+    } satisfies ChartConfiguration<"line">;
+  }, [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 ? (
-          <EmptyState text={t("section.noProb")} />
-        ) : (
-          view.probabilities.slice(0, 6).map((bucket, index) => {
-            const probability = Math.round(Number(bucket.probability || 0) * 100);
+        {marketScan?.available && (topMarketBucketText || marketYesText) && (
+          <div
+            style={{
+              color: "var(--text-secondary)",
+              fontSize: "11px",
+              marginBottom: "6px",
+            }}
+          >
+            {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.label || bucket.value || index}`} className="prob-row">
-                <div className="prob-label">
-                  {bucket.label || `${bucket.value}${detail.temp_symbol}`}
-                </div>
+              <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 maxValue = comparisonValues.length ? Math.max(...comparisonValues) + 1 : 1;
+  const minValue = comparisonValues.length
+    ? 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">
@@ -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 () => {
-        if (!selectedCity || loadingState.cityDetail || loadingState.refresh) {
+      openTodayModal: async (forceRefresh?: boolean) => {
+        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 (fallback?.local_date) {
-            setFutureModalDate(fallback.local_date);
+          if (cachedDetail?.local_date) {
+            setFutureModalDate(cachedDetail.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,
@@ -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"}`;
-    const existing = pendingCityDetailRequests.get(requestKey);
-    if (existing) {
-      return existing;
+    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=false`,
+      ).finally(() => {
+        pendingCityDetailRequests.delete(requestKey);
+      });
+
+      pendingCityDetailRequests.set(requestKey, request);
+      return request;
    }

-    const request = fetchJson<CityDetail>(
-      `/api/city/${normalizeCityName(cityName)}?force_refresh=${force}`,
-    ).finally(() => {
-      pendingCityDetailRequests.delete(requestKey);
+    const params = new URLSearchParams({
+      force_refresh: "true",
+      _ts: String(Date.now()),
    });
+    return fetchJson<CityDetail>(
+      `/api/city/${normalizeCityName(cityName)}?${params.toString()}`,
+    );
+  },

-    pendingCityDetailRequests.set(requestKey, request);
-    return request;
+  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) {
@@ -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 {
@@ -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",
@@ -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;
 }
@@ -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,
@@ -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 "")
@@ -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:
@@ -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 = (
-        (primary_bucket.get("probability") / 100.0)
-        if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None
-        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 = (
+                raw_probability / 100.0 if raw_probability > 1.0 else raw_probability
+            )
+        except Exception:
+            model_probability = None
    fallback_sparkline = [
        p.get("probability", 0)
        for p in distribution[:8]