§ 00 · Index

BrianBanna

Commodity markets, quantitatively. I build models that turn physical fundamentals into views on futures curves, spreads and regime shifts.

§ 01 · About
Brian Banna
Fig. 01Brian Banna

I'm currently a Quant Analyst Intern at Cargill in Geneva for the summer. In parallel, I'm pursuing my MSc in Data Science with a minor in Financial Engineering at EPFL, after a BSc in Computer Science and Engineering, also at EPFL.

My work focuses on quantitative modelling in commodity and power markets, translating physical fundamentals (S&D balances, storage, trade flows, inventories) into signals across futures curves, spreads and differentials.

Before Cargill, I completed a Quantitative Analyst internship at Pareto Economics in London, building commodity market signals from production data, refining throughputs and global trade flows to analyse supply and demand dynamics, market exposures and asset dependencies.

Previously, I served as President and Head of Sales at Junior Entreprise EPFL, Switzerland's largest student run consultancy, where I led a 35 person team. I personally closed CHF 430k in projects across finance, commodities and energy, and managed more than CHF 500k in total volume, driving 22% YoY revenue growth.

Outside of work, I'm interested in financial history and its links to global politics, classical and practical philosophy, horology and tennis.

§ 01.1 · Stack
Tools I work with.
Languages
  • 01Python
  • 02C++
  • 03R
  • 04SQL
  • 05Java
  • 06Scala
Quant / ML
  • 01NumPy
  • 02Pandas
  • 03statsmodels
  • 04arch
  • 05hmmlearn
  • 06scikit-learn
  • 07PyTorch
  • 08vectorbt
Tools / Viz
  • 01Matplotlib
  • 02Plotly
  • 03Excel (VBA)
  • 04Git
  • 05CI/CD
§ 02 · Selected Work

Selected quantitative work.

Projects across European power markets, futures curve factors, commodity spreads and market regime detection.

(01)Power Markets / Transmission/
01 / 05
Cross-Border Price Transmission in European Power Markets
01 / 05

Cross-Border Price Transmission in European Power Markets

ENTSO-E · 301k observations · 5 bidding zones
Commodity Futures Curve Modeling and Factor Trading
02 / 05

Commodity Futures Curve Modeling and Factor Trading

Adaptive Statistical Arbitrage in Commodity Spreads
03 / 05

Adaptive Statistical Arbitrage in Commodity Spreads

Market Regime Modeling for Systematic Trading
04 / 05

Market Regime Modeling for Systematic Trading

AirJav
05 / 05

AirJav

When German renewables flood the grid, which neighbouring market reprices first?

Analysed cross border price transmission in European electricity markets using ENTSO-E data, 301k observations and 5 Central European bidding zones. Built an interactive view of how German renewable surplus and negative day ahead prices move together across neighbouring markets.

Index
§ 03 · Contact

Let's talk.

Happy to talk about commodity markets, models or project work.

02
LinkedIn
/in/brianbanna
03
GitHub
/brianbanna