Systematic quant investment firm building ML-driven multi-asset trading signals
Trexquant is a Stamford-based quantitative investment partnership running systematic, machine-learning-driven strategies across asset classes. Its stack pairs a Python research core (NumPy, Pandas, PyTorch) with C++ execution, Spark/Arrow/Parquet data tooling and multi-cloud infrastructure on AWS, GCP and Azure. Hiring is research- and engineering-heavy but decelerating, with open Chief Operating Officer and Quantitative Research Head seats signaling a leadership build-out.
Notable leadership hires: Chief Operating Officer, Quantitative Research Head
Trexquant Investment LP is a quantitative investment firm founded in 2012 and headquartered in Stamford, Connecticut. It builds multi-asset portfolios using machine learning models that turn large sets of data variables into systematic trading signals. The firm operates with 51–200 employees and recruits across the United States, China, India and Canada. Its teams are concentrated in quantitative research, engineering and data, supported by finance, legal and operations functions.
Trexquant uses Python, NumPy, Pandas, PyTorch and C++ for research and trading, Apache Spark, Arrow and Parquet for data, plus AWS, GCP, Azure, Kubernetes, Docker, GitLab CI/CD, Prometheus and Grafana.
Trexquant Investment LP is headquartered in Stamford, Connecticut, and also hires in China, India and Canada.
Current work includes market-neutral signal development, systematic ETF trading strategies, integrating backtesting with live trading, new data pipelines and data source integration, and trading cost model updates.
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Trexquant Investment LP's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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