🌡️ 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.

🚀 Quick Start

Requirements

• Python 3.11+
• Dependencies: pip install -r requirements.txt
• Environment Variables: Set TELEGRAM_BOT_TOKEN in .env (required). Optionally set METEOBLUE_API_KEY for London high-precision forecasts.

VPS Deployment (Recommended)

First-time setup:

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

Create one-click update script (run once):

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):

~/update.sh

One command: pull latest code → kill old process → start new process. No branch conflict handling needed.

Local Development (Windows)

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.

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🤖 Telegram Bot Commands

Command	Description	Usage
/city [name]	Query City Weather	Get detailed forecasts, METAR & trend analysis
/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

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✨ Key Features

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

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)

The bot generates human-readable insights automatically:

• 🚨 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).

4. 📊 Risk Profiling

Every city has a data bias risk profile based on airport-to-city-center distance:

• 🔴 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

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🏗️ System Architecture

graph TD
    User[/Telegram User/] --> Bot[bot_listener.py]
    Bot --> Collector[WeatherDataCollector]

    subgraph "Data Engine"
        Collector --> OM[Open-Meteo API]
        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 --> Response[/Compact Betting Snapshot/]

• 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.

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🎯 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.

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Last updated: 2026-02-18