# 🌡️ PolyWeather: Intelligent Weather Quant Analysis Bot
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://deepwiki.com/yangyuan-zhen/PolyWeather)
PolyWeather is a multi-source weather analysis and quantification tool. It aggregates high-precision forecasts, real-time airport METAR observations, a math-based probability engine, and AI-driven decision support to provide deep insights for weather-related risk assessment and data-driven decision making.
📊 Live query: DEB Blended Forecast + Settlement Probability + Groq AI Decision
🗺️ Interactive Web Map: Real-time global monitoring with rich data visualization
---
## ✨ Core Features
### 1. 🌐 Interactive Web Map Dashboard
- **Global Overview**: Real-time Leaflet-based dark-themed map pinpointed to official Polymarket settlement airport coordinates.
- **Progressive Background Loading**: Intelligently fetches multi-source data across all cities without hitting API rate limits.
- **Rich Visualization**: Chart.js-powered temperature trends with METAR scatter overlay, multi-model comparison bars, Gaussian probability distribution, and dynamic risk badges.
- **Zoom-based Intelligence**: Map automatically filters minor cities (e.g., Atlanta/Ankara) and local station labels at lower zoom levels to maintain clarity, showing only major global hubs when zoomed out.
- **Technical Guide Interface**: Features an interactive on-map technical guide explaining DEB prediction curves, probability bands, and risk factors.
- **Cinematic Interaction & Sync**: City selection triggers a smooth fly-to zoom animation. The **Multi-Model Forecast** panel automatically synchronizes with the selected day in the 5-day forecast table, showing historical model performance and future projections.
- **Forced Sync & Cache Control**: For specific regions like Ankara, the dashboard supports a 60-second real-time cache TTL with a manual "Force Refresh" button to bypass global caches and fetch the absolute latest MGM/METAR data.
- **Dual-Engine Architecture**: Runs concurrently with the Telegram bot via a FastAPI backend, sharing the same data collection, analysis logic (`analyze_weather_trend`), and AI prompt pipeline.
### 2. 🧬 Dynamic Ensemble Blending (DEB Algorithm)
The system automatically tracks the historical performance of weather models (ECMWF, GFS, ICON, GEM, JMA) per city:
- **Error-Based Weighting**: Dynamically adjusts model weights based on their Mean Absolute Error (MAE) over the past 7 days. Lower error = higher weight.
- **Blended Forecast**: Provides a bias-corrected "DEB Blended High Temperature" recommendation.
- **Multi-Source Training**: Integrates official regional sources (like Turkey's MGM) into the training pipeline alongside international models (ECMWF, GFS, etc.).
- **Accuracy Tracking**: Use the `/deb` command to view DEB's historical settlement hit rate and MAE, compared against individual models.
- **Auto-Cleanup**: Only retains the last 14 days of records to prevent unbounded data growth.
### 3. 🎲 Math Probability Engine (Settlement Probability)
Automatically computes the probability for each possible settlement integer using a Gaussian distribution:
- **Reality-Anchored μ**: When actual max temperature is significantly below forecasts during/after the peak window (forecast bust), μ anchors on the observed max instead of failed predictions. Otherwise, uses a weighted average of DEB/multi-model median (70%) and ensemble median (30%).
- **Standard Deviation σ — Three-Layer Pipeline**:
1. **Ensemble Base**: σ = (P90-P10) / 2.56
2. **MAE Floor**: Uses DEB's historical MAE as σ minimum—prevents ensembles from underestimating true uncertainty
3. **Shock Score Amplifier**: σ × (1 + 0.5 × shock_score) when weather is changing rapidly
- **Time Decay**: Before peak σ×1.0 → During peak σ×0.7 → After peak σ×0.3
- **Observed Floor**: Temperatures below the current METAR max WU value are excluded
- **Dead Market Override**: When a dead market is confirmed, probability collapses to 100% at the settled value
#### 💥 Shock Score: Weather Disruption Soft Scorer (0~1)
Evaluates environmental stability from the last 4 METAR observations. Higher = more unstable = wider σ:
| Component | Weight | Trigger |
| :-------------------- | :----- | :---------------------------------------------------------------- |
| Wind Direction Change | 0~0.4 | Angle difference × wind speed amplifier (weak winds downweighted) |
| Cloud Cover Jump | 0~0.35 | Cloud code escalation (FEW→BKN, etc.) |
| Pressure Change | 0~0.25 | >2hPa change within 2 hours |
### 4. 🤖 AI Deep Analysis (Groq LLaMA 3.3 70B)
Feeds all weather data into LLaMA 70B, analyzed via a **P0→P4 Priority Chain**:
- **P0 Forecast Bust Detection** (highest priority): Graded severity (light/medium/heavy) when actual temps diverge from forecasts. Requires slope + wind/cloud verification before declaring settlement locked. "Bust ≠ locked" — still checks for second-wave warming.
- **P1 Real-Time Rhythm**: 2 consecutive METAR highs → still warming; 2 non-highs with slope ≤ 0 → dead market. Low-radiation warming → multi-factor (advection/mixing layer/heat island), no single-factor attribution.
- **P2 Inhibitors** (city-aware): Precipitation → strong suppression. High humidity + thick clouds sustained 2+ reports → possible suppression, but thresholds vary by city type (maritime vs. continental). Single factor insufficient.
- **P3 Probability Cross-Check**: References settlement probability for consistency check with P1. Contradictions explained with deviation rationale.
- **P4 Forecast Background**: DEB/forecasts for ceiling estimation; silenced when actuals significantly deviate.
- **Single Source of Truth**: Both web and Telegram bot share the same `analyze_weather_trend` function and `get_ai_analysis` prompt — identical context, identical decisions.
- **High Availability**: Auto-retry + fallback model degradation (70B → 8B). Proxy support for restricted networks.
### 5. ⏱️ Real-time Airport Observations (Zero-Cache METAR)
- **Precise Timing**: Extracts actual observation time from raw METAR text (`rawOb`), not the API's rounded `reportTime`. Accurate to the minute.
- **Live Passthrough**: Bypasses CDN caching via dynamic headers and randomized timestamps to obtain first-hand METAR/MGM reports.
- **Settlement Warning**: Automatically calculates the rounding boundary for integer-based settlement (X.5 line).
- **MGM Primary (Ankara)**: For Turkish cities like Ankara, PolyWeather uses official MGM data as a primary source for both real-time observations and 5-day hourly forecasts, ensuring maximum local accuracy.
- **Anomaly Filtering**: Automatically filters out -9999 sentinel values to prevent garbage data in output.
### 6. 📈 Historical Data Collection
- 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).
---
## ⚡ Deployment
### Requirements
- **Python 3.11+** or **Docker & Docker Compose**
- **Environment Variables**: Set parameters in your `.env` file (copy from `.env.example`).
### 🐳 Docker Deployment (Recommended)
The easiest and most stable way to deploy without system dependency conflicts.
1. **Clone and configure**
```bash
git clone https://github.com/yangyuan-zhen/PolyWeather.git
cd PolyWeather
cp .env.example .env
# Edit .env to add TELEGRAM_BOT_TOKEN, GROQ_API_KEY, etc.
nano .env
```
2. **Start the service in the background**
```bash
docker-compose up -d --build
```
3. **View live logs**
```bash
docker-compose logs -f
```
### 💻 Traditional VPS Deployment
1. Install dependencies: `pip install -r requirements.txt`
2. Configure your `.env` file.
3. Use the included `update.sh` script for one-click updates and restarts for both the Telegram Bot and the Web Map:
```bash
# Run the script to update code and restart both services in the background
./update.sh
```
_(Note: The `update.sh` script automatically fetches the latest code, kills old processes, clears ports, and launches both `bot_listener.py` and `web/app.py` via `nohup`.)_
---
## 🕹️ Bot Commands
| Command | Description |
| :------------------ | :---------------------------------------------------------------------------------------------------------------------------------- |
| `/city [city_name]` | Get weather analysis, settlement probabilities, METAR tracking, and AI insights. (Includes "Bölge/Center" detail for Ankara). |
| `/deb [city_name]` | View DEB accuracy: daily hit/miss breakdown, bias analysis (underestimate/overestimate), model MAE comparison, trading suggestions. |
| `/id` | View the Chat ID of the current conversation. |
| `/help` | Display help information. |
### Supported Cities
`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.
---
## 🏗️ Architecture
```mermaid
graph TD
User[User] -->|Query| Bot["bot_listener.py (Core Scheduler)"]
User -->|Browser| Web["web/app.py (FastAPI)"]
subgraph Data Acquisition
Bot --> Collector[WeatherDataCollector]
Web --> Collector
Collector --> OM[Open-Meteo Forecast/Ensemble]
Collector --> MM[Multi-Model ECMWF/GFS/ICON/GEM/JMA]
Collector --> METAR["Live Airport METAR (rawOb)"]
Collector --> MGM["MGM Official (Ankara)"]
end
subgraph Algorithm Layer
Collector --> Peak[Peak Hour Prediction]
Collector --> DEB[DEB Dynamic Weighting]
DEB --> DB[(daily_records Database)]
Peak --> Prob["Probability Engine (Reality-Anchored μ)"]
Collector --> Prob
METAR --> Shock[Shock Score]
Shock --> Prob
Collector --> Logic["Settlement Boundary / Dead Market"]
end
subgraph Shared Analysis
Bot --> ATF["analyze_weather_trend()"]
Web --> ATF
ATF --> AI["Groq LLaMA 70B (P0→P4)"]
end
AI -->|Market Call + Logic + Confidence| Bot
AI -->|Market Call + Logic + Confidence| Web
```
---
## 💡 Trading Tips
1. **Real-time Rhythm First**: AI analysis follows P0→P4 priority. If live METAR trends (P1) conflict with math probabilities (P3), always prioritize the live trend.
2. **Watch Settlement Probabilities**: Based on Gaussian models, direction is most certain when a temperature has > 70% probability while P1 rhythm is flat.
3. **Reference DEB Bias**: Use `/deb` to check for systematic bias. If a city is consistently "underestimated," habitually bid one WU notch higher.
4. **Identify Dead Market Signals**: When the system declares a "Dead Market," probability collapses to 100% at the settled value. Warming power is exhausted.
5. **Mind the Boundaries**: When the observed high is near X.5 (e.g., 7.50°C), be wary of rounding up due to tiny fluctuations.
6. **Forecast Bust Awareness**: When the AI reports a forecast bust (especially medium/heavy grade), all model predictions have lost reference value. Focus exclusively on METAR actuals.
---
## 🛠️ Development & Testing
Run unit tests for the core trend engine and probability models:
```bash
python -m pytest tests/test_trend_engine.py -v
```
Deploying updates to the server:
```bash
git pull
./update.sh
```
---
_Updated 2026-03-05_