Singularity Quant ETRM — Energy Analytics Workspace

Day-ahead electricity prices on the Swiss market (Swissix) move in hourly granularity across daily, weekly and seasonal patterns. Understanding them requires more than a price chart: spreads between periods, time-band breakdowns (F1/F2/F3 tariff slots) and volatility estimates are what actually drive decisions for anyone sizing storage or flexible loads.

The challenge was to build a single interactive workspace that an engineer could open and immediately explore — KPIs at a glance, then drill-downs into hours, bands and scenarios — without writing analysis code for every question.

I built the workspace in Streamlit and Python, with pandas for the data pipeline and Plotly for the interactive charts, pulling price data from the ENTSO-E Transparency Platform.

The app is organized as an analysis workspace with 60+ tabs: an 8-KPI summary header, period-over-period comparison, configurable threshold alerts, an hourly heatmap, F1/F2/F3 band analysis, CSV export for offline work, and a Monte Carlo simulator for scenario exploration.

I run a continuous QA loop over the codebase — hourly automated checks that keep every tab green — so the workspace stays reliable as new analyses are added.

The workspace is live and free to use, embedded as an interactive terminal on this portfolio. It remains an active open-source project: tabs are added and refined regularly, and the QA cycle keeps regressions out.

It also became a reusable pattern for my other data projects: a single Streamlit app structured as a tabbed workspace, with alerts and export built in, is now the template I reach for whenever a dataset needs exploring.