AmandaloveYang
170 lines
6.6 KiB
Markdown
170 lines
6.6 KiB
Markdown
# 🌡️ PolyWeather: Real-time Weather Query & Analysis Bot
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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.
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## 🚀 Quick Start
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### Requirements
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- **Python 3.11+**
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- Dependencies: `pip install -r requirements.txt`
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- **Environment Variables**: Set `TELEGRAM_BOT_TOKEN` in `.env` (required). Optionally set `METEOBLUE_API_KEY` for London high-precision forecasts.
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### VPS Deployment (Recommended)
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**First-time setup:**
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```bash
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git clone https://github.com/yangyuan-zhen/PolyWeather.git
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cd PolyWeather
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pip install -r requirements.txt
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cp .env.example .env # Edit .env with your Token and API Keys
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```
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**Create one-click update script (run once):**
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```bash
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cat > ~/update.sh << 'EOF'
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#!/bin/bash
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cd ~/PolyWeather
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git fetch origin
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git reset --hard origin/main
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pkill -f run.py
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pkill -f bot_listener.py
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sleep 1
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nohup python3 run.py > bot.log 2>&1 &
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echo "✅ Updated and restarted!"
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EOF
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chmod +x ~/update.sh
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```
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**Daily updates (after each code push):**
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```bash
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~/update.sh
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```
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> One command: pull latest code → kill old process → start new process. No branch conflict handling needed.
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### Local Development (Windows)
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```bash
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py -3.11 run.py
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```
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> 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.
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---
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## 🤖 Telegram Bot Commands
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| Command | Description | Usage |
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| :------------- | :--------------------- | :--------------------------------------------- |
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| `/city [name]` | **Query City Weather** | Get detailed forecasts, METAR & trend analysis |
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| `/id` | **Get Chat ID** | Retrieve your current Telegram Chat ID |
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| `/help` | **Help** | Display all available commands |
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### Supported Cities
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| City | Aliases | METAR Station | Extra Sources |
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|:---|:---|:---|:---|
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| London | `lon`, `伦敦` | EGLC (City Airport) | Meteoblue |
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| Paris | `par`, `巴黎` | LFPG (Charles de Gaulle) | — |
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| Ankara | `ank`, `安卡拉` | LTAC (Esenboğa) | MGM |
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| New York | `nyc`, `ny`, `纽约` | KLGA (LaGuardia) | NWS |
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| Chicago | `chi`, `芝加哥` | KORD (O'Hare) | NWS |
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| Dallas | `dal`, `达拉斯` | KDAL (Love Field) | NWS |
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| Miami | `mia`, `迈阿密` | KMIA (International) | NWS |
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| Atlanta | `atl`, `亚特兰大` | KATL (Hartsfield-Jackson) | NWS |
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| Seattle | `sea`, `西雅图` | KSEA (Sea-Tac) | NWS |
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| Toronto | `tor`, `多伦多` | CYYZ (Pearson) | — |
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| Seoul | `sel`, `首尔` | RKSI (Incheon) | — |
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| Buenos Aires | `ba`, `布宜诺斯艾利斯` | SAEZ (Ezeiza) | — |
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| Wellington | `wel`, `惠灵顿` | NZWN (Wellington) | — |
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### Example
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```
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/city 巴黎
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/city london
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/city par
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```
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---
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## ✨ Key Features
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### 1. 🏛️ Multi-Source Data Fusion
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| Source | Role | Coverage | Strength |
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| :----------------- | :---------------------- | :-------------- | :--------------------------------------------------------------------------------- |
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| **Open-Meteo** | Base Forecast | Global | 72-hour hourly temperature curves, sunrise/sunset times |
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| **Meteoblue (MB)** | **Precision Consensus** | London Only | Multi-model aggregation; excellent for microclimates |
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| **METAR** | **Settlement Standard** | Global Airports | Polymarket settlement source; real-time airport observations |
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| **NWS** | Official (US) | US Only | US National Weather Service high-fidelity forecasts |
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| **MGM** | Official (Turkey) | Ankara Only | Turkish State Met Service: pressure, cloud cover, feels-like, 24h rainfall |
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### 2. ⚡ Ultra-Fresh Data (Zero-Cache)
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- **Dynamic Timestamps**: Every API request includes a unique token to force servers to bypass CDN caches.
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- **MGM Real-time Sync**: Specialized header camouflaging and timezone correction for Turkish API.
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### 3. 🧠 Smart Trend Analysis (Plain Language)
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The bot generates human-readable insights automatically:
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- **🚨 Forecast Breakthrough Alerts**: Detects when METAR observed max exceeds all forecast highs.
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- **⏱️ Peak Window Prediction**: Identifies the exact hours when today's high is expected.
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- **🌬️ Wind Direction Cross-Validation**: Compares METAR and MGM wind data; alerts on conflicts (>90° difference).
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- **☁️ Cloud Impact Analysis**: Evaluates cloud cover's effect on warming potential.
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- **📉 Pressure Analysis**: Low pressure indicates warm/moist air passage.
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- **🌧️ Rain Detection**: Cross-validates METAR weather codes with actual rainfall data to avoid false positives.
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- **📊 Max Temperature Time Tracking**: Shows exactly when the daily high was recorded (e.g., `最高: 12°C @14:20`).
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### 4. 📊 Risk Profiling
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Every city has a data bias risk profile based on airport-to-city-center distance:
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- 🔴 **High Risk**: Seoul (48.8km), Chicago (25.3km) — large bias expected
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- 🟡 **Medium Risk**: Ankara (24.5km), Paris (25.2km), Dallas, Buenos Aires — systematic bias
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- 🟢 **Low Risk**: London (12.7km), Wellington (5.1km) — reliable data
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---
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## 🏗️ System Architecture
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```mermaid
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graph TD
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User[/Telegram User/] --> Bot[bot_listener.py]
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Bot --> Collector[WeatherDataCollector]
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subgraph "Data Engine"
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Collector --> OM[Open-Meteo API]
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Collector --> MB[Meteoblue API]
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Collector --> NOAA[METAR / NOAA]
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Collector --> MGM[Turkish MGM API]
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Collector --> NWS[US NWS API]
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end
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Collector --> Processing[Smart Analysis & Formatting]
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Processing --> Bot
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Bot --> Response[/Compact Betting Snapshot/]
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```
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- **Logic Decoupling**: `weather_sources.py` handles data fetching & parsing; `bot_listener.py` handles analysis & rendering.
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- **City Config**: `city_risk_profiles.py` contains all METAR station mappings and risk assessments.
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---
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## 🎯 Betting Strategy Tips
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1. **Check Consensus**: Compare Open-Meteo, Meteoblue (MB), and NWS/MGM forecasts.
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2. **Watch the Peak Window**: Use `/city` frequently during predicted peak hours.
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3. **Settlement Priority**: Settlement is always based on **METAR** data.
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4. **Geographic Risk**: Pay attention to bias warnings, especially for high-risk cities.
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5. **Wind Conflicts**: When METAR and MGM show opposite wind directions, expect temperature volatility.
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---
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_Last updated: 2026-02-18_
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