Trexquant is a systematic hedge fund where we use thousands of statistical algorithms to trade equity, futures and other markets globally. Starting with many data sets, we develop large sets of features and use various machine learning methods to discover trading signals and effectively combine them into market-neutral portfolios. We are looking for data scientists, physicists, engineers, economists and programmers to develop the next generation of machine learning strategies that can accurately predict the future movements of liquid financial assets. Our Quantitative Researchers are embedded within the following teams:Alpha Researcher:As a member of the Alpha Researcher team, you will be involved in developing market-neutral signals, parsing and analyzing large data sets and collaborating with the Data and Strategy Research team to build a diverse set of predictive models.Strategy Researcher （Machine Learning track）:As a member of the Strategy team, you will be developing systematic strategies based on a variety of machine learning and statistical methods. The data you train and validate comes from actual market trading When you apply for a Quantitative Researcher role at Trexquant, we will first assess you on the core skills required for the Quantitative Researcher. During the interview process, we will be able to get to know you better, learn about your strengths and match you to the best research team that closely aligns with your skills and preference.Responsibilities Develop market-neutral, medium-frequency signals that predict future stock returns 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 Requirements BS/MS/PhD degree in any stem field Passion for machine learning Fluent with programming languages like Python Strong problem-solving skills Ability to work effectively both as an individual and a team player Knowledge of financial accounting is a plus Background in quantitative finance is a plus but not necessary Benefits 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 Comprehensive benefits including healthcare and insurance Trexquant is an Equal Opportunity Employer
Trexquant is a systematic hedge fund where we use thousands of statistical algorithms to trade equity, futures and other markets globally. Starting with many data sets, we develop large sets of features and use various machine learning methods to discover trading signals and effectively combine them into market-neutral portfolios. We are looking for data scientists, physicists, engineers, economists and programmers to develop the next generation of machine learning strategies that can accurately predict the future movements of liquid financial assets.
Our Quantitative Researchers are embedded within the following teams:
Alpha Researcher:
As a member of the Alpha Researcher team, you will be involved in developing market-neutral signals, parsing and analyzing large data sets and collaborating with the Data and Strategy Research team to build a diverse set of predictive models.
Strategy Researcher （Machine Learning track）:
As a member of the Strategy team, you will be developing systematic strategies based on a variety of machine learning and statistical methods. The data you train and validate comes from actual market trading
When you apply for a Quantitative Researcher role at Trexquant, we will first assess you on the core skills required for the Quantitative Researcher. During the interview process, we will be able to get to know you better, learn about your strengths and match you to the best research team that closely aligns with your skills and preference.
Responsibilities
- Develop market-neutral, medium-frequency signals that predict future stock returns
- 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
Requirements
- BS/MS/PhD degree in any stem field
- Passion for machine learning
- Fluent with programming languages like Python
- Strong problem-solving skills
- Ability to work effectively both as an individual and a team player
- Knowledge of financial accounting is a plus
- Background in quantitative finance is a plus but not necessary
Benefits
- 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
- Comprehensive benefits including healthcare and insurance
Trexquant is an Equal Opportunity Employer
Trexquant applies quantitative methods to systematically build optimized global market-neutral equity portfolios in liquid markets. Trading signals (Alphas) are developed from thousands of data variables and extensively tested. Strategies dynamically adjust allocations to Alphas depending on recent performance. Thousands of strategies using tens of thousands of signals currently drive our live production, and our talented team of researchers from some of the best schools in the world inject new ideas into our system on an ongoing basis. Capital is managed across thousands of equity positions in the United States, Europe, Japan, Australia, and Canada.
Trexquant applies quantitative methods to systematically build optimized global market-neutral equity portfolios in liquid markets. Trading signals (Alphas) are developed from thousands of data variables and extensively tested. Strategies dynamically adjust allocations to Alphas depending on recent performance. Thousands of strategies using tens of thousands of signals currently drive our live production, and our talented team of researchers from some of the best schools in the world inject new ideas into our system on an ongoing basis. Capital is managed across thousands of equity positions in the United States, Europe, Japan, Australia, and Canada.
