Trexquant is a systematic hedge fund where we use thousands of statistical algorithms to trade equity and futures markets globally. Starting with many data sets, we develop a large set of features and use various machine learning methods to discover trading signals and effectively combine them into market-neutral portfolios. We are looking for scientists, engineers, economists, and programmers to develop the next generation of machine learning strategies that can accurately predict the future movements of liquid financial assets.
Responsibilities
- Design, implement, and optimize various machine learning models aimed at predicting liquid assets using a wide set of financial data and a vast library of trading signals
- Parse data sets to be used for future alpha(strategy) development
- Investigate and implement state-of-the-art academic research in the field of quantitative finance
- Collaborate with experienced and resourceful quantitative researchers to carry out experiments and test hypothesis using simulations
- BS/MS/PhD degree in any stem field
- Passion for machine learning and quantitative finance
- Strong problem-solving skills
- Ability to work effectively both as an individual and a team player
- Fluent with programming languages like Python
- Knowledge of financial accounting is a plus
- Experience between 2 years to 15 years
- Competitive compensation with bonus tied to the performance of algorithms you develop
- Work in a collaborative and friendly environment, participate in decision-making process for research direction, and have opportunity to lead on new ideas
Top Skills
What We Do
Being a quantitative finance firm that uses Machine Learning (ML) to create multi-asset portfolios and seek profit from the market, Trexquant has continuously improved its investment and research platform since starting operations, leveraging new and emerging technologies. Trexquant uses rigorous quantitative methods to create multi-asset portfolios in global markets. To do this, Trexquant develops trading signals using its vast and continuously growing collection of data variables used as inputs for more complex trading models called Strategies. The result is an ever-growing and adapting engine built from thousands of intricate models and tens of thousands of signals, tailor-made with the goal to outperform the market during any condition. Capital is managed across 5,500+ cash equity positions across the United States, Europe, Japan, Australia, and Canada.
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