🌡️ PolyWeather: Intelligent Weather Quant Analysis Bot

PolyWeather is a weather analysis tool specifically designed for prediction markets like Polymarket. It aggregates multi-source forecasts, real-time airport METAR observations, and incorporates AI-driven decision support to help users evaluate weather-related risks more scientifically.

✨ Core Features

1. 🧬 Dynamic Ensemble Blending (DEB Algorithm)

The system automatically tracks the historical performance of various weather models (ECMWF, GFS, ICON, GEM, JMA) in specific cities:

Error-Based Weighting: Dynamically adjusts weights for each model based on their Mean Absolute Error (MAE) over the past 7 days.
Blended Forecast: Provides a "Blended High Temperature" recommendation corrected for historical biases.
Concurrency Optimization: Built-in singleton cache and file locking mechanism to support high-concurrency queries and ensure data safety.

2. 🤖 AI Intelligent Analysis (Groq LLaMA 3.3)

Integrates the LLaMA 70B model to interpret rapidly changing meteorological data:

Logical Deduction: Considers dynamic factors such as wind speed, wind direction, cloud cover, and solar radiation to judge temperature trends.
Confidence Scoring: Provides a confidence score from 1-10 for the current market conditions.
Automatic Cooldown Determination: When temperature drop is observed or the forecast peak has passed, the AI provides a definitive market conclusion.

3. ⏱️ Real-time Airport Observations (Zero-Cache METAR)

Live Passthrough: Bypasses CDN caching via dynamic headers to obtain first-hand METAR reports from airports.
Settlement Warning: Automatically calculates the Wunderground settlement boundary (X.5 rounding line) to warn of potential volatility.

4. 📈 Historical Data Collection

Includes fetch_history.py to retrieve up to 3 years of hourly historical weather data for any city, supporting future algorithm development.

⚡ Deployment

Requirements

Python 3.11+
Install dependencies: pip install -r requirements.txt
Environment Variables: Set TELEGRAM_BOT_TOKEN and GROQ_API_KEY in your .env file.

VPS Quick Deployment

Clone the repository and install dependencies.
Configure your .env file.
Use the following script for one-click updates and restarts:

cat > ~/update.sh << 'EOF'
#!/bin/bash
cd ~/PolyWeather
git fetch origin
git reset --hard origin/main
pkill -f bot_listener.py
sleep 1
nohup python3 bot_listener.py > bot.log 2>&1 &
echo "✅ PolyWeather Restarted!"
EOF
chmod +x ~/update.sh

🕹️ Bot Commands

Command	Description
`/city [city_name]`	Get in-depth weather analysis, live tracking, and AI-driven insights.
`/id`	View the Chat ID of the current conversation.
`/help`	Display help information.

Supported City Examples

lon (London), par (Paris), ank (Ankara), nyc (New York), chi (Chicago), ba (Buenos Aires), etc.

🏗️ Architecture

graph TD
    User[User / Signal Receiver] -->|Query Command| Bot[bot_listener.py Core Scheduler]

    subgraph Data Acquisition
        Bot --> Collector[WeatherDataCollector]
        Collector --> OM[Open-Meteo Live/Forecast]
        Collector --> MM[Multi-Model Predictors ECMWF/GFS etc.]
        Collector --> METAR[Live Airport Observations]
    end

    subgraph Logic Processing
        Collector --> DEB[DEB Dynamic Weighting]
        DEB --> DB[(daily_records JSON Database)]
        Collector --> Logic[Settlement Analysis / Trend Detection]
    end

    subgraph AI Decision Layer
        DEB --> AIAnalyzer[Groq/LLaMA 3.3 AI Model]
        Logic --> AIAnalyzer
        METAR --> AIAnalyzer
    end

    AIAnalyzer -->|Generates: Spread+Logic+Confidence| Bot
    Bot -->|Returns Analysis Snapshot| User

💡 Trading Tips

Reference DEB Blended Value: When models diverge, the DEB corrected value is usually more reliable than a single forecast.
Observe AI Confidence: A confidence score below 5 indicates high uncertainty in the current meteorological environment.
Watch Settlement Boundaries: When the observed high is near X.5, be wary of rounding jumps during Wunderground settlements.

Updated 2026

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