🌡️ 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 with model consensus scoring and entry timing signals in plain language.

🚀 Quick Start

Requirements

• Python 3.11+
• Dependencies: pip install -r requirements.txt <<<<<<< HEAD

• 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.

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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.

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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
Multi-Model (5 NWP)	Consensus Scoring	Global	ECMWF, GFS, ICON, GEM, JMA — 5 fully independent NWP models via Open-Meteo
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	Observations (Turkey)	Ankara Only	Turkish State Met Service: pressure, cloud cover, feels-like, 24h rainfall

⚠️ All NWP model queries use airport coordinates (matching METAR station), not city center. This eliminates systematic bias between forecast and settlement locations.

Open-Meteo API Architecture: Three API calls go through the same platform, each serving a different purpose:

                    Open-Meteo (API Platform)
                          │
            ┌─────────────┼─────────────┐
            │             │             │
     ┌──────┴──────┐      │      ┌──────┴──────┐
     │ /forecast   │      │      │ /forecast   │
     │ (default)   │      │      │ ?models=... │
     │ = best_match│      │      │ = multi-model│
     └──────┬──────┘      │      └──────┬──────┘
            │             │             │
            ▼             │             ▼
     Auto-selects best    │      Returns each model
     model (≈ ECMWF)      │      ECMWF / GFS / ICON
     → Hourly curves      │      GEM / JMA
     → Sunrise/sunset     │      → Consensus scoring
     → Sunshine/radiation │
                   ┌──────┴──────┐
                   │  /ensemble  │
                   │  51 members │
                   └──────┬──────┘
                          │
                          ▼
                   Median / P10 / P90
                   → Uncertainty range

💡 The OM default forecast is essentially one of the 5 models (auto-selected), so it is excluded from consensus scoring to avoid double-counting.

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. 🎯 Multi-Model Consensus Scoring

The bot queries 5 independent NWP models (ECMWF, GFS, ICON, GEM, JMA) to rate forecast agreement:

Level	Condition (°C / °F)	Meaning
🎯 High	Spread ≤ 0.8°C / 1.5°F	All 5 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

Primary models: ECMWF IFS (Europe), GFS (US NOAA), ICON (Germany DWD), GEM (Canada), JMA (Japan). Plus Meteoblue (London) and NWS (US) when available. Ensemble median is excluded to avoid double-counting.

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.

Deterministic vs Ensemble Divergence Detection: When the OM deterministic forecast exceeds the ensemble P90 or falls below P10, the bot flags it. If actual observations later verify the forecast, the warning upgrades to a ✅ Forecast Verified message.

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:

• 🚨 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).
• ☀️ 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.

7. 📊 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

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.

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🏗️ System Architecture

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

    subgraph "Data Engine"
        Collector --> MM[Multi-Model API<br/>ECMWF/GFS/ICON/GEM/JMA]
        Collector --> OM[Open-Meteo Forecast]
        Collector --> ENS[Open-Meteo Ensemble]
        Collector --> MB[Meteoblue API]
        Collector --> NOAA[METAR / NOAA]
        Collector --> MGM[MGM Observations]
        Collector --> NWS[US NWS API]
    end

    Collector --> Processing[Consensus Scoring & Trend Analysis]
    Processing --> Bot
    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.
• Multi-Model Consensus: 5 independent NWP models (ECMWF, GFS, ICON, GEM, JMA) for robust consensus scoring.
• Ensemble Integration: 51-member ensemble provides P10/P90 uncertainty bands and divergence detection.
• Airport-Aligned Coordinates: All NWP queries target METAR station coordinates, not city centers.

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🎯 Betting Strategy Tips

<<<<<<< HEAD

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."

5. Check Model Consensus: The 🎯/⚖️/⚠️ rating tells you immediately if the forecast is reliable.
6. Use the Entry Signal: Wait for ⏰ Ideal or Good timing before placing bets. Don't bet early when uncertainty is high.
7. Watch Ensemble Spread: A tight 90% band (< 2°) means model confidence is high — this is where edges live.
8. Watch the Peak Window: Use /city frequently during predicted peak hours.
9. Settlement Priority: Settlement is always based on METAR data, rounded to integer via Wunderground.
10. Geographic Risk: Pay attention to bias warnings, especially for high-risk cities like Seoul and Chicago.
11. 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.
12. Wind Conflicts: When METAR and MGM show opposite wind directions, expect temperature volatility.

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Last updated: 2026-02-22

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