Quant Trader

CPS, Inc.; Chicago (IL); On-site; USD 150,000 - 230,000; Full time

Job summary

Cps4jobs in Chicago is hiring a Quantitative Trader to develop and improve systematic strategies in highly liquid futures markets. You will mix hands-on research with live trading oversight, collaborating with software and hardware engineers to translate signals into executable decisions.

The role demands experience with equity index futures, strong Python and ML skills, and the ability to understand C++ implementations for latency-sensitive systems.

Qualifications

• Two or more years of quantitative trading or research experience in equity index futures.
• Experience with CME markets (ES and/or NQ).
• Experience analyzing granular exchange data and reconstructing order books.
• Track record of predictive signals or execution models in live, low-latency trading.
• Understanding of passive execution, liquidity provision, signal decay, and adverse selection.
• Strong Python, statistical and ML methods, and large datasets experience.
• Ability to understand C++ implementations and work with latency-sensitive infra.
• Hardware-accelerated trading systems experience is a plus.
• Knowledge of equity futures, cash markets and index derivatives.
• Experience with exchange connectivity, network performance, or precise timestamps.

Responsibilities

• Identify short-term trading opportunities through analysis of order activity and liquidity changes.
• Take research from hypothesis through data prep, modeling, evaluation, implementation and live monitoring.
• Improve automated quoting strategies by evaluating execution probability and trading costs.
• Study exchange behavior and order execution patterns to inform strategy decisions.
• Assess how system speed, data delivery, and connectivity affect trading results.
• Collaborate with engineers to address performance limitations.
• Translate research findings into production requirements and verify adherence to strategy.
• Investigate unexpected outcomes, data inconsistencies, and model performance changes.

Skills

Quant trading

Python

Low-latency

ML techniques

Data analysis

C++ basics

Tools

CME data feeds

Order book reconstruction

Job description

A Chicago-based high-frequency trading firm is hiring a Quantitative Trader to develop and improve systematic strategies in highly liquid futures markets. The role combines hands-on research with responsibility for live trading performance, working closely with software and hardware engineers. This opportunity suits someone who understands equity index futures market structure, has built signals from detailed exchange data, and enjoys turning research into executable trading decisions.

What You'll Do

• Identify short-term trading opportunities through analysis of order activity, transaction data, liquidity changes, and relationships between connected markets.
• Take research from initial hypothesis through data preparation, statistical modeling, historical evaluation, implementation, and live performance monitoring.
• Improve automated quoting strategies by evaluating execution probability, queue dynamics, trading costs, and the risk of unfavorable price movements.
• Study exchange behavior and order execution patterns to strengthen strategy decisions and execution quality.
• Assess how system speed, market data delivery, and connectivity affect trading results.
• Work with engineers to identify and address performance limitations.
• Translate research findings into implementation requirements and verify that production behavior matches the intended strategy.
• Investigate unexpected trading outcomes, data inconsistencies, and model performance changes.

What You'll Bring

• Two or more years of quantitative trading or research experience involving equity index futures within a proprietary trading or HFT environment.
• Direct experience with CME markets, ideally including ES and/or NQ.
• Experience analyzing granular exchange data, reconstructing order books, and evaluating order-level activity.
• A track record of contributing predictive signals or execution models to live, low-latency trading strategies.
• Understanding of passive execution, order priority, liquidity provision, signal decay, and adverse selection.
• Strong Python research skills, familiarity with statistical and machine learning methods, and experience working with large datasets.
• Ability to understand C++ implementations and collaborate with engineers responsible for latency-sensitive trading infrastructure.
• Helpful Additional Experience Hardware-accelerated trading systems.
• Relationships between equity futures, cash markets, and index derivatives.
• Exchange connectivity, network performance, or precision timestamp analysis.

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