2569718930@qq.com
134 lines
5.9 KiB
Markdown
134 lines
5.9 KiB
Markdown
# 🌡️ PolyWeather: Intelligent Weather Quant Analysis Bot
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PolyWeather is a weather analysis tool built for prediction markets like **Polymarket**. It aggregates multi-source forecasts, real-time airport METAR observations, a math-based probability engine, and AI-driven decision support to help users evaluate weather trading risks more scientifically.
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---
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## ✨ Core Features
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### 1. 🧬 Dynamic Ensemble Blending (DEB Algorithm)
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The system automatically tracks the historical performance of weather models (ECMWF, GFS, ICON, GEM, JMA) per city:
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- **Error-Based Weighting**: Dynamically adjusts model weights based on their Mean Absolute Error (MAE) over the past 7 days. Lower error = higher weight.
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- **Blended Forecast**: Provides a bias-corrected "DEB Blended High Temperature" recommendation.
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- **Self-Learning**: Requires at least 2 days of observations before activating weight differentiation. Uses equal-weight averaging during cold start.
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- **Concurrency Safe**: Built-in memory cache and file locking (fcntl) for high-concurrency group chat queries.
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### 2. 🎲 Math Probability Engine (Settlement Probability)
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Automatically computes the probability for each possible WU settlement integer using a Gaussian distribution fitted to the ensemble forecast:
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- **Method**: Derives standard deviation (σ) from the 51-member ensemble P10/P90, centers the distribution (μ) on a weighted average of DEB/multi-model median (70%) and ensemble median (30%).
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- **Interval Integration**: Integrates over each WU rounding interval [N-0.5, N+0.5) to compute the probability of settling at integer N.
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- **Display**: `🎲 Settlement Probability (μ=3.7): 4°C [3.5~4.5) 68% | 3°C [2.5~3.5) 32%`
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### 3. 🤖 AI Deep Analysis (Groq LLaMA 3.3 70B)
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Feeds wind speed, wind direction, cloud cover, solar radiation, and METAR trend data into LLaMA 70B:
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- **Logical Reasoning**: Uses 2-3 sentences to deeply analyze airport dynamics—whether conditions promote or inhibit warming, and whether the forecast can be reached.
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- **Market Call**: Explicitly states the expected peak time window and the specific temperature betting range. Calls "dead market" when cooling is confirmed.
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- **Confidence Score**: Quantitative 1-10 confidence rating.
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- **High Availability**: Built-in auto-retry + fallback model degradation (70B → 8B) to withstand Groq API 500/503 outages.
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### 4. ⏱️ Real-time Airport Observations (Zero-Cache METAR)
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- **Live Passthrough**: Bypasses CDN caching via dynamic headers to obtain first-hand METAR reports.
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- **Settlement Warning**: Automatically calculates the Wunderground settlement boundary (X.5 rounding line).
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### 5. 📈 Historical Data Collection
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- Includes `fetch_history.py` to retrieve up to 3 years of hourly historical weather data (temperature, humidity, radiation, pressure, 10+ dimensions), providing data foundation for future ML models (XGBoost/MOS).
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---
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## ⚡ Deployment
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### Requirements
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- **Python 3.11+**
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- Install dependencies: `pip install -r requirements.txt`
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- **Environment Variables**: Set `TELEGRAM_BOT_TOKEN` and `GROQ_API_KEY` in your `.env` file.
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### VPS Quick Deployment
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1. Clone the repository and install dependencies.
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2. Configure your `.env` file.
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3. Use the following script for one-click updates and restarts:
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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 bot_listener.py
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sleep 1
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nohup python3 bot_listener.py > bot.log 2>&1 &
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echo "✅ PolyWeather Restarted!"
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EOF
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chmod +x ~/update.sh
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```
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---
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## 🕹️ Bot Commands
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| Command | Description |
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| :------------------ | :------------------------------------------------------------------------------- |
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| `/city [city_name]` | Get weather analysis, settlement probabilities, METAR tracking, and AI insights. |
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| `/id` | View the Chat ID of the current conversation. |
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| `/help` | Display help information. |
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### Supported Cities
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`lon` (London), `par` (Paris), `ank` (Ankara), `nyc` (New York), `chi` (Chicago), `dal` (Dallas), `mia` (Miami), `atl` (Atlanta), `sea` (Seattle), `tor` (Toronto), `sel` (Seoul), `ba` (Buenos Aires), `wel` (Wellington), etc.
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---
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## 🏗️ Architecture
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```mermaid
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graph TD
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User[User] -->|Query Command| Bot[bot_listener.py Core Scheduler]
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subgraph Data Acquisition
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Bot --> Collector[WeatherDataCollector]
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Collector --> OM[Open-Meteo Forecast/Ensemble]
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Collector --> MM[Multi-Model ECMWF/GFS/ICON/GEM/JMA]
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Collector --> METAR[Live Airport METAR]
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end
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subgraph Algorithm Layer
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Collector --> DEB[DEB Dynamic Weighting]
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DEB --> DB[(daily_records Database)]
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Collector --> Prob[Gaussian Probability Engine]
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Collector --> Logic[Settlement Boundary / Trend Analysis]
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end
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subgraph AI Decision Layer
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DEB --> AI[Groq LLaMA 70B]
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Prob --> AI
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Logic --> AI
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METAR --> AI
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end
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AI -->|Market Call + Logic + Confidence| Bot
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Bot -->|DEB Blend + Probability + AI Analysis| User
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```
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---
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## 💡 Trading Tips
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1. **Watch Settlement Probability**: The probability engine is math-based and more objective than AI subjective judgment. When one temperature has > 65% probability, the direction is relatively clear.
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2. **Reference DEB Blended Value**: When models diverge, the DEB corrected value is usually more reliable than any single forecast.
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3. **Observe AI Confidence**: A score below 5 indicates high uncertainty—consider staying on the sidelines.
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4. **Watch Settlement Boundaries**: When the observed high is near X.5, be wary of rounding jumps during WU settlements.
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5. **Distribution Center μ**: The μ value shown in the probability display represents the algorithm's expected most likely actual high temperature—compare it directly with the Polymarket odds.
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---
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_Updated 2026-02-27_
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