From 0333717bdbbef1a44f3417a37339ea8c24bcaf24 Mon Sep 17 00:00:00 2001 From: AmandaloveYang <2569718930@qq.com> Date: Sat, 21 Feb 2026 10:43:13 +0800 Subject: [PATCH] feat: introduce model consensus scoring, ensemble forecast spread, and entry timing signals with enhanced trend analysis and updated documentation. --- README.md | 92 +++++++++++++++++++++++++++++++++++++--------- README_ZH.md | 98 +++++++++++++++++++++++++++++++++++++++---------- bot_listener.py | 4 +- 3 files changed, 154 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index 50d7c98a..bb2b7d89 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 🌡️ PolyWeather: Real-time Weather Query & Analysis Bot -An intelligent weather bot for prediction markets and professional weather betting. Fetches ultra-fresh data directly from global weather stations, bypassing CDN caches, and provides automated trend analysis in plain language. +An intelligent weather bot for prediction markets and professional weather betting. Fetches ultra-fresh data directly from global weather stations, bypassing CDN caches, and provides automated trend analysis with **model consensus scoring** and **entry timing signals** in plain language. ## 🚀 Quick Start @@ -96,20 +96,62 @@ py -3.11 run.py ### 1. 🏛️ Multi-Source Data Fusion -| Source | Role | Coverage | Strength | -| :----------------- | :---------------------- | :-------------- | :--------------------------------------------------------------------------------- | -| **Open-Meteo** | Base Forecast | Global | 72-hour hourly temperature curves, sunrise/sunset times | -| **Meteoblue (MB)** | **Precision Consensus** | London Only | Multi-model aggregation; excellent for microclimates | -| **METAR** | **Settlement Standard** | Global Airports | Polymarket settlement source; real-time airport observations | -| **NWS** | Official (US) | US Only | US National Weather Service high-fidelity forecasts | -| **MGM** | Official (Turkey) | Ankara Only | Turkish State Met Service: pressure, cloud cover, feels-like, 24h rainfall | +| Source | Role | Coverage | Strength | +| :---------------------- | :---------------------- | :-------------- | :-------------------------------------------------------------------------- | +| **Open-Meteo** | Base Forecast | Global | 72h hourly curves, sunrise/sunset, **sunshine duration**, **shortwave radiation** | +| **Open-Meteo Ensemble** | **Uncertainty Range** | Global | 51-member ensemble: median, P10, P90 spread for confidence assessment | +| **Meteoblue (MB)** | **Precision Consensus** | London Only | Multi-model aggregation; excellent for microclimates | +| **METAR** | **Settlement Standard** | Global Airports | Polymarket settlement source; real-time airport observations | +| **NWS** | Official (US) | US Only | US National Weather Service high-fidelity forecasts | +| **MGM** | Official (Turkey) | Ankara Only | Turkish State Met Service: pressure, cloud cover, feels-like, 24h rainfall | ### 2. ⚡ Ultra-Fresh Data (Zero-Cache) - **Dynamic Timestamps**: Every API request includes a unique token to force servers to bypass CDN caches. - **MGM Real-time Sync**: Specialized header camouflaging and timezone correction for Turkish API. -### 3. 🧠 Smart Trend Analysis (Plain Language) +### 3. 🎯 Model Consensus Scoring (NEW) + +The bot automatically rates how well different forecast sources agree, using a three-tier system: + +| Level | Condition (°C / °F) | Meaning | +|:---|:---|:---| +| 🎯 **High** | Spread ≤ 0.8°C / 1.5°F | All models converge — high confidence, low risk | +| ⚖️ **Medium** | Spread ≤ 1.5°C / 3.0°F | Minor disagreement — moderate confidence | +| ⚠️ **Low** | Spread > 1.5°C / 3.0°F | Major divergence — high uncertainty, wait for more data | + +Sources compared: Open-Meteo (OM), Meteoblue (MB), NWS, MGM — only **independent** forecast sources. Ensemble median is deliberately excluded to avoid double-counting with Open-Meteo. + +### 4. 📊 Ensemble Forecast Spread (NEW) + +Fetches 51-member ensemble forecasts from Open-Meteo to quantify prediction uncertainty: + +> 📊 **Ensemble**: Median 10.8°C, 90% range [9.5°C - 12.1°C], spread 2.6°. + +A tight range = high confidence in the forecast. A wide range = the atmosphere is chaotic, higher risk. + +### 5. ⏰ Entry Timing Signal (NEW) + +A composite score combining three factors to advise on betting timing: + +| Factor | Score | +|:---|:---| +| Peak already passed | +3 | +| ≤ 2h to peak | +2 | +| ≤ 4h to peak | +1 | +| Model consensus: High | +2 | +| Model consensus: Medium | +1 | +| Actual ≈ Forecast (gap ≤ 0.5°) | +2 | +| Actual close to Forecast (gap ≤ 1.5°) | +1 | + +| Total ≥ | Signal | Advice | +|:---|:---|:---| +| 5 | ⏰ **Ideal** | Low uncertainty — good to bet | +| 3 | ⏰ **Good** | Consider small positions | +| 2 | ⏰ **Cautious** | Keep observing | +| <2 | ⏰ **Not Recommended** | High uncertainty — wait | + +### 6. 🧠 Smart Trend Analysis (Plain Language) The bot generates human-readable insights automatically: @@ -120,8 +162,11 @@ The bot generates human-readable insights automatically: - **📉 Pressure Analysis**: Low pressure indicates warm/moist air passage. - **🌧️ Rain Detection**: Cross-validates METAR weather codes with actual rainfall data to avoid false positives. - **📊 Max Temperature Time Tracking**: Shows exactly when the daily high was recorded (e.g., `最高: 12°C @14:20`). +- **☀️ Weather Condition Summary**: Synthesizes METAR phenomena + cloud cover into a single glanceable icon + text (e.g., `⛅ Partly Cloudy`). +- **🌤️ Solar Radiation Analysis**: Tracks cumulative shortwave radiation vs. daily total; warns when clouds severely block sunlight. +- **🌙 Warm Advection Detection**: Identifies when peak temperature occurred during zero-radiation hours (e.g., 3 AM), proving the high was driven by warm air mass rather than solar heating. -### 4. 📊 Risk Profiling +### 7. 📊 Risk Profiling Every city has a data bias risk profile based on airport-to-city-center distance: @@ -129,6 +174,12 @@ Every city has a data bias risk profile based on airport-to-city-center distance - 🟡 **Medium Risk**: Ankara (24.5km), Paris (25.2km), Dallas, Buenos Aires — systematic bias - 🟢 **Low Risk**: London (12.7km), Wellington (5.1km) — reliable data +### 8. 🌅 Enhanced Display + +- **Sunrise/Sunset + Sunshine Hours**: `🌅 07:34 | 🌇 18:29 | ☀️ 9.9h` +- **Weather Condition at a Glance**: `✈️ 实测 (METAR): 9°C | ⛅ Partly Cloudy | 15:00` +- **WU Settlement Preview**: Shows the Wunderground-rounded value for settlement reference. + --- ## 🏗️ System Architecture @@ -140,30 +191,35 @@ graph TD subgraph "Data Engine" Collector --> OM[Open-Meteo API] + Collector --> ENS[Open-Meteo Ensemble] Collector --> MB[Meteoblue API] Collector --> NOAA[METAR / NOAA] Collector --> MGM[Turkish MGM API] Collector --> NWS[US NWS API] end - Collector --> Processing[Smart Analysis & Formatting] + Collector --> Processing[Consensus Scoring & Trend Analysis] Processing --> Bot - Bot --> Response[/Compact Betting Snapshot/] + Bot --> Response[/Betting Snapshot with Entry Signal/] ``` - **Logic Decoupling**: `weather_sources.py` handles data fetching & parsing; `bot_listener.py` handles analysis & rendering. - **City Config**: `city_risk_profiles.py` contains all METAR station mappings and risk assessments. +- **Ensemble Integration**: 51-member ensemble contributes to consensus scoring and provides P10/P90 uncertainty bands. --- ## 🎯 Betting Strategy Tips -1. **Check Consensus**: Compare Open-Meteo, Meteoblue (MB), and NWS/MGM forecasts. -2. **Watch the Peak Window**: Use `/city` frequently during predicted peak hours. -3. **Settlement Priority**: Settlement is always based on **METAR** data. -4. **Geographic Risk**: Pay attention to bias warnings, especially for high-risk cities. -5. **Wind Conflicts**: When METAR and MGM show opposite wind directions, expect temperature volatility. +1. **Check Model Consensus**: The 🎯/⚖️/⚠️ rating tells you immediately if the forecast is reliable. +2. **Use the Entry Signal**: Wait for ⏰ **Ideal** or **Good** timing before placing bets. Don't bet early when uncertainty is high. +3. **Watch Ensemble Spread**: A tight 90% band (< 2°) means model confidence is high — this is where edges live. +4. **Watch the Peak Window**: Use `/city` frequently during predicted peak hours. +5. **Settlement Priority**: Settlement is always based on **METAR** data, rounded to integer via Wunderground. +6. **Geographic Risk**: Pay attention to bias warnings, especially for high-risk cities like Seoul and Chicago. +7. **Solar Radiation Clues**: If the bot reports "warm advection driven" 🌙, the temperature was pushed by warm air, not sunlight — this pattern often breaks model predictions. +8. **Wind Conflicts**: When METAR and MGM show opposite wind directions, expect temperature volatility. --- -_Last updated: 2026-02-18_ +_Last updated: 2026-02-21_ diff --git a/README_ZH.md b/README_ZH.md index f24cd2d6..f7b8c4f4 100644 --- a/README_ZH.md +++ b/README_ZH.md @@ -1,6 +1,6 @@ # 🌡️ PolyWeather: 实时天气查询与分析机器人 -专为预测市场和天气博弈设计的智能天气机器人。通过绕过 CDN 缓存直接从全球气象站获取最新数据，并提供通俗易懂的自动趋势分析。 +专为预测市场和天气博弈设计的智能天气机器人。通过绕过 CDN 缓存直接从全球气象站获取最新数据，并提供**模型共识评分**和**入场时机信号**等通俗易懂的自动趋势分析。 ## 🚀 快速开始 @@ -96,20 +96,66 @@ py -3.11 run.py ### 1. 🏛️ 多源数据融合 -| 数据源 | 数据角色 | 覆盖范围 | 优势 | -| :----------------- | :------------- | :--------- | :--------------------------------------------------- | -| **Open-Meteo** | 基础预测 | 全球 | 72 小时逐小时温度曲线、日出日落时间 | -| **Meteoblue (MB)** | **高精度共识** | 仅限伦敦 | 聚合多家模型，对微气候处理极佳 | -| **METAR** | **结算标准** | 全球机场 | Polymarket 结算参考的绝对真理，实时机场观测 | -| **NWS** | 官方预测(美) | 仅限美国 | 美国国家气象局高精度预报 | -| **MGM** | 官方预测(土) | 仅限安卡拉 | 土耳其气象局：气压、云量、体感温度、24h 降水 | +| 数据源 | 数据角色 | 覆盖范围 | 优势 | +| :---------------------- | :------------- | :--------- | :----------------------------------------------------------- | +| **Open-Meteo** | 基础预测 | 全球 | 72h 逐小时温度曲线、日出日落、**日照时长**、**短波辐射** | +| **Open-Meteo Ensemble** | **不确定性区间** | 全球 | 51 成员集合预报：中位数、P10、P90 散度用于置信度评估 | +| **Meteoblue (MB)** | **高精度共识** | 仅限伦敦 | 聚合多家模型，对微气候处理极佳 | +| **METAR** | **结算标准** | 全球机场 | Polymarket 结算参考的绝对真理，实时机场观测 | +| **NWS** | 官方预测(美) | 仅限美国 | 美国国家气象局高精度预报 | +| **MGM** | 官方预测(土) | 仅限安卡拉 | 土耳其气象局：气压、云量、体感温度、24h 降水 | ### 2. ⚡ 超新鲜数据 (零缓存) - **动态时间戳**：每个 API 请求都附带唯一令牌，强制服务器绕过 CDN 缓存。 - **MGM 实时同步**：针对土耳其 MGM API 做了专门的 Header 伪装和时区校正。 -### 3. 🧠 智能趋势分析（通俗语言） +### 3. 🎯 模型共识评分（新功能） + +机器人自动评估各预报源的一致程度，分为三个等级： + +| 等级 | 条件（摄氏/华氏） | 含义 | +|:---|:---|:---| +| 🎯 **高共识** | 极差 ≤ 0.8°C / 1.5°F | 所有模型高度收敛 — 高置信，低风险 | +| ⚖️ **中共识** | 极差 ≤ 1.5°C / 3.0°F | 轻微分歧 — 中等置信 | +| ⚠️ **低共识** | 极差 > 1.5°C / 3.0°F | 模型严重分歧 — 不确定性大，建议观察 | + +参与评分的数据源：Open-Meteo (OM)、Meteoblue (MB)、NWS、MGM — 仅限**独立**预报源。集合预报中位数不参与共识评分，避免与 Open-Meteo 确定性预报双重计数。 + +**核心逻辑**：当 3 个及以上模型在温度区间上高度收敛，而市场定价尚未反映时，这就是典型的**结构性定价错误**——低风险套利的黄金机会。 + +### 4. 📊 集合预报散度（新功能） + +从 Open-Meteo 获取 51 成员集合预报，量化预测不确定性： + +> 📊 **集合预报**：中位数 10.8°C，90% 区间 [9.5°C - 12.1°C]，波动幅度 2.6°。 + +区间窄 = 大气状态明确，预报可信。区间宽 = 大气混沌，风险高。 + +### 5. ⏰ 入场时机信号（新功能） + +综合三个因子打分，给出入场建议： + +| 因子 | 分值 | +|:---|:---| +| 最热已过 | +3 | +| 距峰值 ≤ 2h | +2 | +| 距峰值 ≤ 4h | +1 | +| 模型高共识 | +2 | +| 模型中共识 | +1 | +| 实测 ≈ 预报（差 ≤ 0.5°）| +2 | +| 实测接近预报（差 ≤ 1.5°）| +1 | + +| 总分 ≥ | 信号 | 建议 | +|:---|:---|:---| +| 5 | ⏰ **理想** | 不确定性低，适合下注 | +| 3 | ⏰ **较好** | 可以考虑小仓位入场 | +| 2 | ⏰ **谨慎** | 建议继续观察 | +| <2 | ⏰ **不建议** | 不确定性大，等更多数据 | + +**核心理念**：拒绝过早布局，选择接近解析时刻、波动率压缩时晚入场，降低不确定性风险。 + +### 6. 🧠 智能趋势分析（通俗语言） 机器人自动生成人类可读的分析洞察： @@ -121,8 +167,11 @@ py -3.11 run.py - **📉 气压分析**：低气压意味着暖湿气流过境，有利升温。 - **🌧️ 降雨检测**：交叉验证 METAR 天气代码和实际降水量，避免误报。 - **📊 最高温时间追踪**：精确显示每日最高温出现的时间（如 `最高: 12°C @14:20`）。 +- **☀️ 天气状况一览**：综合 METAR 天气现象 + 云量，生成一目了然的天气图标 + 文字（如 `⛅ 晴间多云`）。 +- **🌤️ 太阳辐射分析**：追踪累计短波辐射 vs 全天总量；当云层严重遮挡阳光时发出预警。 +- **🌙 暖平流检测**：当最高温出现在太阳辐射为零的时段（如凌晨 3 点），自动识别并标注"气温由暖空气推高，而非太阳晒热"。 -### 4. 📊 风险等级 +### 7. 📊 风险等级 每个城市都有基于机场-市区距离的数据偏差风险档案： @@ -130,6 +179,12 @@ py -3.11 run.py - 🟡 **中危**：安卡拉 (24.5km)、巴黎 (25.2km)、达拉斯、布宜诺斯艾利斯 — 有系统偏差 - 🟢 **低危**：伦敦 (12.7km)、惠灵顿 (5.1km) — 数据靠谱 +### 8. 🌅 增强显示 + +- **日出日落 + 日照时长**：`🌅 07:34 | 🌇 18:29 | ☀️ 9.9h` +- **天气状况一目了然**：`✈️ 实测 (METAR): 9°C | ⛅ 晴间多云 | 15:00` +- **WU 结算预览**：显示 Wunderground 四舍五入后的值，方便结算参考。 + --- ## 🏗️ 系统架构 @@ -139,32 +194,37 @@ graph TD User[/Telegram User/] --> Bot[bot_listener.py] Bot --> Collector[WeatherDataCollector] - subgraph "Data Engine" + subgraph "数据引擎" Collector --> OM[Open-Meteo API] + Collector --> ENS[Open-Meteo Ensemble] Collector --> MB[Meteoblue API] Collector --> NOAA[METAR / NOAA] Collector --> MGM[Turkish MGM API] Collector --> NWS[US NWS API] end - Collector --> Processing[智能分析 & 格式化] + Collector --> Processing[共识评分 & 趋势分析] Processing --> Bot - Bot --> Response[/天气分析快照/] + Bot --> Response[/附带入场信号的天气快照/] ``` - **逻辑解耦**：`weather_sources.py` 负责数据获取与解析；`bot_listener.py` 负责分析与渲染。 - **城市配置**：`city_risk_profiles.py` 包含所有 METAR 机场映射和风险评估。 +- **集合预报集成**：51 成员集合预报参与共识评分，并提供 P10/P90 不确定性区间。 --- ## 🎯 博弈策略提示 -1. **检查模型共识**：对比 Open-Meteo、Meteoblue (MB) 和 NWS/MGM 的预报。 -2. **关注峰值窗口**：在预测的峰值时段频繁使用 `/city` 刷新。 -3. **结算优先级**：结算永远以 **METAR** 数据为准。 -4. **地理风险**：重点关注高危城市的偏差警告。 -5. **风向冲突**：METAR 和 MGM 风向相反时，温度波动风险增大。 +1. **看模型共识**：🎯/⚖️/⚠️ 评级让你一眼判断预报是否可靠。高共识 + 市场低定价 = 套利机会。 +2. **用入场信号**：等 ⏰ **理想** 或 **较好** 时机再下注。不确定性高时绝不提前入场。 +3. **关注集合散度**：90% 区间越窄（< 2°），模型置信越高 — 这才是 edge 所在。 +4. **紧盯峰值窗口**：在预测的峰值时段频繁使用 `/city` 刷新。 +5. **结算优先级**：结算永远以 **METAR** 数据为准，通过 Wunderground 四舍五入到整数。 +6. **地理风险**：重点关注高危城市（如首尔、芝加哥）的偏差警告。 +7. **太阳辐射线索**：如果机器人报告"暖平流驱动" 🌙，说明温度由暖空气推高 — 这种模式经常打破模型预测。 +8. **风向冲突**：METAR 和 MGM 风向相反时，温度波动风险增大。 --- -_最后更新: 2026-02-18_ +_最后更新: 2026-02-21_ diff --git a/bot_listener.py b/bot_listener.py index fadd9d6b..1c226010 100644 --- a/bot_listener.py +++ b/bot_listener.py @@ -68,11 +68,9 @@ def analyze_weather_trend(weather_data, temp_symbol): labeled_forecasts.append(("NWS", nws["today_high"])) if mgm.get("today_high") is not None: labeled_forecasts.append(("MGM", mgm["today_high"])) - # 集合预报中位数 (如果有) + # 集合预报数据 (仅用于不确定性区间展示，不参与共识评分，避免与 OM 双重计数) ensemble = weather_data.get("ensemble", {}) ens_median = ensemble.get("median") - if ens_median is not None: - labeled_forecasts.append(("ENS", ens_median)) consensus_level = "unknown" consensus_spread = None