An analytical framework for the Greek Power&Gas System Operation ("SO") and Market Exchange ("Ex") Data.

Predicted Spanish day-ahead energy demand and price with 97.5% accuracy using a range of ML and statistical time series forecasting models including XGBoost, Transformers, TFTs and SARIMA.

A comprehensive Python library for downloading Midcontinent Independent System Operator (MISO) public reports into pandas dataframes.

Algorithmic trading strategies for pairs and basket trading in cross-commodity markets

📖 An open wiki for power systems engineers, researchers, and students

Energex is a comprehensive energy derivatives analytics platform that provides data collection, analysis tools, and interactive visualizations for futures trading. Built with Python, it combines efficient data storage using DuckDB with sophisticated analysis capabilities to help traders and analysts make informed decisions.

Unofficial ESIOS API Python library. Up to date and fully equipped.

Pricing energy options (focus on German power) with MC and jump-diffusion mean-reversion model, including stochastic volatility, seasonality and regime filtering. Model parameters are calibrated on historical ENTSO-e data.

Quantitative Analysis of Strait of Hormuz Disruption on Global Capital Markets & Commodity Trades

Scenario-based analysis of global oil market supply–demand balances, OPEC+ policy sensitivity and geopolitical risk drivers, using OPEC, EIA and OEUK data.

AI x OSS reference problems for software-defined energy markets

Projects exploring energy-market forecasting, trading analytics, and quantitative modelling using Python

📘 Home Assignment for the Data Scientist Position (Curves) at Argus Media Group

Automated cross-commodity monitor linking European gas and carbon fundamentals to power risk, with daily metrics, charts, and AI-structured desk brief outputs.

End-to-end analysis of Germany electricity prices using ENTSO-E data, including time series analysis, spike detection, and machine learning prediction.

Regime-conditional volatility forecasting framework using HAR-RV as a baseline and XGBoost on either residual vol or directly on log(RV), implemented for Germany and France electricity markets. Metric: Spearman ranking. Model validation and market-neutral cross-country trading strategy.

This repository implements multiple deep learning models to forecast day-ahead electricity prices in the Germany-Luxembourg bidding zone, leveraging data from 15 European bidding zones.

Time-Series dataset combining multiple sources to explain the broader Greek energy market

Case study results for the paper "Moving from linear to conic markets for electricity"

Deep RL agent for grid-battery trading on real power markets, benchmarked against perfect-foresight and rolling-intrinsic strategies. Built on Fraunhofer SCAI's EAO framework.