Case Study: a hedge fund achieves higher annualized returns with EXL's automated quantitative trading algorithms

Helping hedge funds develop a hypothesis led quantitative and analytical algorithm to improve trading strategies

EXL worked with a hedge fund focused on quantitative trading in emerging markets that needed to automate trading algorithms, generate signals faster, and improve decision-making through data-driven strategy testing. The client wanted to use customized indices and periodic back-testing to evaluate opportunities while achieving higher returns, stronger automation, and more consistent performance versus benchmarks.

EXL built a hypothesis-led quantitative trading framework covering hypothesis development, back-testing, algorithm design, and performance tracking. Using historical data, C# and .NET code, and automated market-access and Bloomberg API-based signal generation, EXL created fully automated strategies with real-time portfolio risk management and dashboard reporting. The solution delivered 50% annualized returns within three months, 21% return over the index benchmark over a specific period, and performance 4.5% ahead of benchmark.