Graduate Program - Quantitative Trading

Founded in 2011, Fasanara Capital is a London-headquartered global asset manager and technology platform managing approximately USD 6 billion in assets, with a team of 120+ professionals globally.

We combine investment expertise with advanced data, analytics and proprietary technology to originate, analyse and manage complex portfolios at scale.

The Graduate Programme – Fasanara Talent Grand Prix 🏎️
The Graduate Talent Grand Prix is a structured, multi-stage selection process designed to identify the strongest STEM graduates in Italy.

With the opening of our Ferrara office, we are creating a direct bridge between local talent and global opportunities.

High performers are everywhere — and deserve a chance.
Excellence knows no geography. Opportunity often does.
The gap between the two is where inequality lives — and where real alpha hides.

At Fasanara, we focus on identifying overlooked talent and connecting it to capital, technology and a global platform. Because when opportunity catches up with talent, the results are outsized.

This programme is designed to give high-potential graduates direct exposure to a global investment platform through hands-on experience, real projects, and close collaboration with experienced teams.

Overview of the Role

As part of the Quantitative Trading department, you will support the trading team across: strategy research, quantitative model development, portfolio analysis, and data-driven decision making.

During the programme, you will gain exposure to a range of activities across algorithmic trading, signal research, risk management, and execution analytics. You may contribute to the development and backtesting of trading strategies, performance attribution, research initiatives, and analytical outputs used by both senior portfolio managers and external stakeholders.

You will assist in improving trading system efficiency, ensuring data integrity, and delivering high-quality quantitative research within a fast-paced trading environment.

This stream is suited to candidates with a strong quantitative background, a passion for financial markets, and the ability to apply statistical and computational methods within a systematic trading framework.