Weather Trading¶
Energy trading signals derived from ensemble weather forecasts. Uses TIGGE and GFS data to identify profitable trading opportunities in European energy markets.
Overview¶
The core hypothesis: ensemble weather forecast spread predicts energy price volatility. When forecasts disagree, the market misprices risk. We exploit that gap.
Key Documents¶
| Document | Type | Description |
|---|---|---|
| EDG-TR-2026-003 | Technical Report | Main backtesting results and methodology |
| Big Picture | Strategy | High-level project overview and thesis |
| Backtest Plan v3 | Methodology | Current backtesting approach |
| Preliminary Study | Research | Initial signal validation |
| Literature Review | Reference | Academic references and prior work |
Team¶
| Member | Role | Responsibility |
|---|---|---|
| Takeshi Ren (ED-01) | Lead Meteorologist | Signal generation, weather analysis |
| Dr. Yang (ED-00) | Chief Advisor | Risk management, strategy |
Data Sources¶
| Source | Coverage | Format |
|---|---|---|
| ERA5 reanalysis | 2015–2019 | NetCDF |
| TIGGE ensembles | Multi-model forecasts | GRIB/NetCDF |
| GFS archive | Historical operational forecasts | GRIB |
| SMARD | German energy market data | CSV |
| AGSI | European gas storage indices | CSV |
| European prices | TTF, EEX spot and futures | CSV |
Results¶
Backtesting across multiple forecast horizons (f024–f120) with threshold-based entry signals. Results archived in the backtesting/ directory with summary visualisations.