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docs: comprehensive update of all documentation - Paris, wind analysis, VPS deploy, supported cities table

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MARKET_DISCOVERY.md
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# Polymarket Weather Market Discovery Technical Documentation
> ⚠️ **Current Status: Suspended**
> The automated market discovery and monitoring engine described here has been commented out in `run.py`. The system currently operates in "passive query mode" — weather analysis is only triggered by the `/city` command.
This document explains the technical implementation of how PolyWeather identifies and tracks weather markets on Polymarket.
## 1. Data Sources
README.md
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# 🌡️ PolyWeather: Real-time Weather Query & Analysis Bot
An intelligent weather information bot designed to provide ultra-fast, live meteorological data, high-fidelity forecasts, and smart trend analysis. Built for speed and accuracy, it bypasses network caching to deliver the most up-to-date reports from global weather stations.
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.
## 🚀 Quick Start
@@ -8,19 +8,53 @@ An intelligent weather information bot designed to provide ultra-fast, live mete
- **Python 3.11+**
- Dependencies: `pip install -r requirements.txt`
- **Environment**: Configure `METEOBLUE_API_KEY` in `.env` to enable high-precision London forecasts.
- **Environment Variables**: Set `TELEGRAM_BOT_TOKEN` in `.env` (required). Optionally set `METEOBLUE_API_KEY` for London high-precision forecasts.
### Running Locally (Windows/Linux)
### VPS Deployment (Recommended)
**First-time setup:**
```bash
# Windows
py -3.11 run.py
# Linux/VPS
python3 run.py
git clone https://github.com/yangyuan-zhen/PolyWeather.git
cd PolyWeather
pip install -r requirements.txt
cp .env.example .env # Edit .env with your Token and API Keys
```
_Note: The system is currently in **Weather Query Mode**. Legacy active market monitoring and automated trading modules are suspended._
**Create one-click update script (run once):**
```bash
cat > ~/update.sh << 'EOF'
#!/bin/bash
cd ~/PolyWeather
git fetch origin
git reset --hard origin/main
pkill -f run.py
pkill -f bot_listener.py
sleep 1
nohup python3 run.py > bot.log 2>&1 &
echo "✅ Updated and restarted!"
EOF
chmod +x ~/update.sh
```
**Daily updates (after each code push):**
```bash
~/update.sh
```
> One command: pull latest code → kill old process → start new process. No branch conflict handling needed.
### Local Development (Windows)
```bash
py -3.11 run.py
```
> Local machine is for editing code and Git push only. IDE import errors are expected (dependencies not installed locally) and do not affect VPS operation.
_Note: The system is currently in **Weather Query Mode**. Legacy market monitoring and automated trading modules are suspended._
---
@@ -32,50 +66,75 @@ _Note: The system is currently in **Weather Query Mode**. Legacy active market m
| `/id` | **Get Chat ID** | Retrieve your current Telegram Chat ID |
| `/help` | **Help** | Display all available commands |
### Supported Cities
| City | Aliases | METAR Station | Extra Sources |
|:---|:---|:---|:---|
| London | `lon`, `伦敦` | EGLC (City Airport) | Meteoblue |
| Paris | `par`, `巴黎` | LFPG (Charles de Gaulle) | — |
| Ankara | `ank`, `安卡拉` | LTAC (Esenboğa) | MGM |
| New York | `nyc`, `ny`, `纽约` | KLGA (LaGuardia) | NWS |
| Chicago | `chi`, `芝加哥` | KORD (O'Hare) | NWS |
| Dallas | `dal`, `达拉斯` | KDAL (Love Field) | NWS |
| Miami | `mia`, `迈阿密` | KMIA (International) | NWS |
| Atlanta | `atl`, `亚特兰大` | KATL (Hartsfield-Jackson) | NWS |
| Seattle | `sea`, `西雅图` | KSEA (Sea-Tac) | NWS |
| Toronto | `tor`, `多伦多` | CYYZ (Pearson) | — |
| Seoul | `sel`, `首尔` | RKSI (Incheon) | — |
| Buenos Aires | `ba`, `布宜诺斯艾利斯` | SAEZ (Ezeiza) | — |
| Wellington | `wel`, `惠灵顿` | NZWN (Wellington) | — |
### Example
```
/city 巴黎
/city london
/city par
```
---
## ✨ Key Features
### 1. 🏛️ Multi-Source Data Fusion (High-Fidelity)
The bot aggregates data from multiple authoritative sources, layered by reliability:
### 1. 🏛️ Multi-Source Data Fusion
| Source | Role | Coverage | Strength |
| :----------------- | :---------------------- | :-------------- | :--------------------------------------------------------------------------------- |
| **Open-Meteo** | Base Forecast | Global | Provides detailed 72-hour temperature curves for all cities. |
| **Meteoblue (MB)** | **Precision Consensus** | London Only | **Traders' choice**. Aggregates multiple models; excellent for microclimates. |
| **METAR** | **Settlement Standard** | Global Airports | The absolute truth for Polymarket settlement; real-time station data. |
| **NWS** | Official (US) | US Only | High-fidelity forecasts for US cities, critical for extreme weather events. |
| **MGM** | Official (Turkey) | Ankara | Direct access to Turkish State Meteorological Service for local official accuracy. |
| **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 |
### 2. ⚡ Ultra-Fresh Data (Cache-Busting)
### 2. ⚡ Ultra-Fresh Data (Zero-Cache)
To counter second-by-second variations in weather betting, we implemented **Zero-Cache Technology**:
- **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.
- **Micro-timestamp Tokens**: Every request includes a dynamic token to force servers to bypass CDN caches.
- **MGM Real-time Sync**: Specialized header camouflaging to bypass local Turkish API anti-crawling for Ankara.
### 3. 🧠 Smart Trend Analysis (Plain Language)
### 3. ⏱️ Automated Trend Analysis
The bot generates human-readable insights automatically:
The bot doesn't just fetch data; it interprets it:
- **🚨 Forecast Breakthrough Alerts**: Detects when METAR observed max exceeds all forecast highs.
- **⏱️ Peak Window Prediction**: Identifies the exact hours when today's high is expected.
- **🌬️ Wind Direction Cross-Validation**: Compares METAR and MGM wind data; alerts on conflicts (>90° difference).
- **☁️ Cloud Impact Analysis**: Evaluates cloud cover's effect on warming potential.
- **📉 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`).
- **Peak Window Prediction**: Automatically identifies the timeframe when today's record is most likely to be hit.
- **Risk Profiling**: Assigns risk levels based on geographic traits (e.g., Ankara high-altitude swings, London coastal microclimates).
- **Source Attribution**: Every data point is clearly labeled ([MGM], [METAR], [MB]) to help you weigh the data.
### 4. 📊 Risk Profiling
### 4. 📊 Smart Max-Temp Tracking
Every city has a data bias risk profile based on airport-to-city-center distance:
Optimized for Polymarket settlement logic:
- **Local Day Filtering**: Uses city UTC offsets to strictly count observations after 00:00 local time.
- **Multi-dimension Monitoring**: Includes "Feels Like" temperatures and 24h precipitation to assist in nuanced trade decisions.
- 🔴 **High Risk**: Seoul (48.8km), Chicago (25.3km) — large bias expected
- 🟡 **Medium Risk**: Ankara (24.5km), Paris (25.2km), Dallas, Buenos Aires — systematic bias
- 🟢 **Low Risk**: London (12.7km), Wellington (5.1km) — reliable data
---
## 🏗️ System Architecture
PolyWeather uses a **Lightweight, Plugin-based** architecture for millisecond responses.
```mermaid
graph TD
User[/Telegram User/] --> Bot[bot_listener.py]
@@ -83,25 +142,30 @@ graph TD
subgraph "Data Engine"
Collector --> OM[Open-Meteo API]
Collector --> MB[Meteoblue Weather API]
Collector --> NOAA[METAR Data Center]
Collector --> MB[Meteoblue API]
Collector --> NOAA[METAR / NOAA]
Collector --> MGM[Turkish MGM API]
Collector --> NWS[US NWS API]
end
Collector --> Processing[Smart Analysis & Formatting]
Processing --> Bot
Bot --> Reponse[/Compact Betting Snapshot/]
Bot --> Response[/Compact Betting Snapshot/]
```
- **Logic Decoupling**: `weather_sources.py` handles parsing; `bot_listener.py` handles rendering.
- **Legacy Modules**: `main.py` contains the old automated trading engine. Focus has shifted to "assisted manual decision-making."
- **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.
---
## 🎯 Betting Strategy Tips
1. **Check Consensus**: Compare Open-Meteo and Meteoblue (MB). Consensus usually implies higher probability.
2. **Watch the Peak**: Use `/city` frequently during predicted peak windows to catch momentum.
3. **Weighting Hierarchy**: Settlement is **METAR**; high-accuracy trend is **MB** (London); Official (NWS/MGM) is the "anchor."
4. **Geographic Risk**: Pay close attention to cities where "Bias will significantly amplify."
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.
---
_Last updated: 2026-02-18_
README_ZH.md
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# 🌡️ PolyWeather: 实时天气查询与分析机器人
一个智能天气信息机器人，专为提供超快、实时的气象数据、高保真预报和智能趋势分析而设计。通过绕过网络缓存，直接从全球气象站获取最新数据。
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。
## 🚀 快速开始
@@ -8,7 +8,7 @@
- **Python 3.11+**
- 依赖安装: `pip install -r requirements.txt`
- **环境变量**: 需在 `.env` 中配置 `METEOBLUE_API_KEY` 以激活伦敦高精度预报。
- **环境变量**: 在 `.env` 中设置 `TELEGRAM_BOT_TOKEN`（必需）。可选设置 `METEOBLUE_API_KEY` 以激活伦敦高精度预报。
### VPS 部署 (推荐)
@@ -52,7 +52,7 @@ chmod +x ~/update.sh
py -3.11 run.py
```
> 本地笔记本**不需要安装 Python**，只用来编辑代码和 Git 推送。IDE 的 import 报错是因为本地没装依赖，不影响 VPS 运行。
> 本地笔记本**不需要安装依赖**，只用来编辑代码和 Git 推送。IDE 的 import 报错是因为本地没装依赖，不影响 VPS 运行。
_注意：系统当前处于 **天气查询模式**。主动市场监控和自动交易模块已暂停。_
@@ -66,56 +66,76 @@ _注意：系统当前处于 **天气查询模式**。主动市场监控和自
| `/id` | **获取 Chat ID** | 获取当前 Telegram 聊天 ID |
| `/help` | **帮助** | 显示所有可用指令 |
### /city 指令示例
### 支持的城市
| 城市 | 缩写/别名 | METAR 机场 | 额外数据源 |
|:---|:---|:---|:---|
| 伦敦 London | `lon`, `伦敦` | EGLC (City Airport) | Meteoblue |
| 巴黎 Paris | `par`, `巴黎` | LFPG (Charles de Gaulle) | — |
| 安卡拉 Ankara | `ank`, `安卡拉` | LTAC (Esenboğa) | MGM |
| 纽约 New York | `nyc`, `ny`, `纽约` | KLGA (LaGuardia) | NWS |
| 芝加哥 Chicago | `chi`, `芝加哥` | KORD (O'Hare) | NWS |
| 达拉斯 Dallas | `dal`, `达拉斯` | KDAL (Love Field) | NWS |
| 迈阿密 Miami | `mia`, `迈阿密` | KMIA (International) | NWS |
| 亚特兰大 Atlanta | `atl`, `亚特兰大` | KATL (Hartsfield-Jackson) | NWS |
| 西雅图 Seattle | `sea`, `西雅图` | KSEA (Sea-Tac) | NWS |
| 多伦多 Toronto | `tor`, `多伦多` | CYYZ (Pearson) | — |
| 首尔 Seoul | `sel`, `首尔` | RKSI (Incheon) | — |
| 布宜诺斯艾利斯 Buenos Aires | `ba`, `布宜诺斯艾利斯` | SAEZ (Ezeiza) | — |
| 惠灵顿 Wellington | `wel`, `惠灵顿` | NZWN (Wellington) | — |
### 使用示例
```
/city 伦敦
/city 巴黎
/city london
/city par
```
---
## ✨ 核心功能
### 1. 🏛️ 多源数据融合 (Multi-Source Fusion)
### 1. 🏛️ 多源数据融合
机器人聚合了全球最权威的几个数据源，并按权重进行分层：
| 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 | 提供所有城市的 72 小时精细化温度曲线 |
| **Meteoblue (MB)** | **高精度共识** | 仅限伦敦 | **交易员首选**。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，对美国城市的极端天气预判准确 |
| **MGM** | 官方预测(土) | 仅限安卡拉 | 土耳其气象局，提供安卡拉 Esenboğa 机场的官方数据 |
### 2. ⚡ 超新鲜数据 (零缓存)
### 2. ⚡ 超新鲜数据 (Cache-Busting)
为了应对气象博弈中秒级的变化，我们实现了 **0 缓存技术**：
- **微秒级令牌**：每个 API 请求都附带动态时间戳，强制气象服务器绕过 CDN 缓存返回最新值。
- **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. ⏱️ 自动态势分析
### 3. 🧠 智能趋势分析（通俗语言）
机器人不仅仅搬运数据，它还会进行逻辑加工：
机器人自动生成人类可读的分析洞察：
- **峰值时刻预测**：自动计算今天气温最高点出现的概率窗口（如：14:00 - 15:00）。
- **风险等级 (Risk-Profile)**：根据地理特征（如安卡拉的高原温差、伦敦的近海微气候）自动分配风险等级。
- **数据溯源**：报表明确标注每个数字的来源（[MGM], [METAR], [MB]）。
- **🚨 预报击穿预警**：当 METAR 实测最高温超过所有预报时自动警报。
- **⏱️ 峰值时段预测**：精确预测当日最高温出现的时间窗口。
- **🌬️ 风向交叉验证**：同时对比 METAR 和 MGM 风向数据，差异超 90° 自动告警。
- **🍃 风速分析**：标注风速并结合风向判断对温度的影响。
- **☁️ 云层遮挡分析**：评估云量对升温潜力的影响（晴天/多云/阴天）。
- **📉 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`）。
### 4. 📊 智能最高温追踪
### 4. 📊 风险等级
针对 Polymarket 的结算逻辑进行优化：
每个城市都有基于机场-市区距离的数据偏差风险档案：
- **当地日历日过滤**：基于城市 UTC 偏移，严格统计当地时间 00:00 之后的实测最高温。
- **多维度监测**：集成体感温度 (`feels_like`) 和 24h 累计降雨量，辅助多维度判断。
- 🔴 **高危**：首尔 (48.8km)、芝加哥 (25.3km) — 偏差大
- 🟡 **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
---
## 🏗️ 系统架构
本项目采用 **“轻量化、插件式”** 架构，旨在实现毫秒级响应。
```mermaid
graph TD
User[/Telegram User/] --> Bot[bot_listener.py]
@@ -123,25 +143,30 @@ graph TD
subgraph "Data Engine"
Collector --> OM[Open-Meteo API]
Collector --> MB[Meteoblue Weather API]
Collector --> NOAA[METAR Data Center]
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]
Processing --> Bot
Bot --> Reponse[/精简版博弈快照/]
Bot --> Response[/天气分析快照/]
```
- **逻辑解耦**：`weather_sources.py` 负责外部数据的解析；`bot_listener.py` 负责消息模板渲染。
- **遗留模块说明**：项目根目录下的 `main.py` 包含旧版本的自动交易引擎。目前重心在“手动辅助决策”，如需开启请查阅 [MARKET_DISCOVERY_ZH.md](./MARKET_DISCOVERY_ZH.md)。
- **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。
---
## 🎯 博弈策略提示
1. **检查模型共识**：查看 Open-Meteo 和 Meteoblue (MB) 是否达成共识。
2. **关注峰值窗口**：在预测的峰值时段多次使用 `/city` 刷新。
3. **数据权重优先级**：结算以 **METAR** 为准，趋势预测以 **MB** 为准（仅限伦敦）。
4. **地理风险评估**：重点关注提示中的“偏差会显著放大”警告（如安卡拉、伦敦）。
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。
---
_最后更新: 2026-02-18_