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Backtesting Jobs in Illinois (NOW HIRING)

Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints * Define, compute, and curate features across instruments, regimes, and ...

Quantitative Developer - Python

Chicago, IL ยท On-site

$200K - $225K/yr

Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints * Define, compute, and curate features across instruments, regimes, and ...

Quantitative Developer - Python

Chicago, IL ยท On-site

$200K - $225K/yr

Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints * Define, compute, and curate features across instruments, regimes, and ...

Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints * Define, compute, and curate features across instruments, regimes, and ...

Machine Learning Engineer

Mundelein, IL ยท On-site

$200K - $250K/yr

Design simulation & backtesting frameworks before deployment * Implement observability tools to monitor performance and drift What They're Looking For: * 3+ years building and deploying production ML ...

New

Manage all aspects of the research process, including methodology selection, data collection and analysis, prototyping, backtesting, and performance monitoring * Maintain and clean datasets with an ...

Manage all aspects of the research process, including idea generation, data analysis, hypothesis development and testing, alpha discovery, trading strategy generation, backtesting and portfolio ...

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Backtesting information

What are the key skills and qualifications needed to thrive as a Backtesting Analyst, and why are they important?

To thrive as a Backtesting Analyst, you need a strong background in quantitative analysis, statistics, programming (typically in Python or R), and familiarity with financial markets, usually supported by a degree in mathematics, finance, or a related field. Proficiency with backtesting platforms (such as QuantConnect or Zipline), data analysis tools, and version control systems like Git is often required. Attention to detail, critical thinking, and strong problem-solving abilities are key soft skills that help ensure robust model evaluation and development. These skills are vital for accurately assessing trading strategies and minimizing risk in real-world financial applications.

What is backtesting?

Backtesting is the process of evaluating a trading strategy or investment model by applying it to historical market data. This helps traders and analysts see how the strategy would have performed in the past, which can provide insights into its potential effectiveness and risks. While backtesting can help identify strengths and weaknesses, it's important to remember that past performance is not always indicative of future results. The reliability of backtesting depends on data quality, strategy design, and how well it simulates real trading conditions.

What are some common challenges faced when backtesting trading strategies, and how can they be managed?

One common challenge in backtesting trading strategies is the risk of overfitting, where a model performs exceptionally well on historical data but fails in live markets. Data quality and availability can also pose issues, as incomplete or inaccurate data may skew results. To manage these challenges, it's important to use out-of-sample testing, robust data cleaning processes, and to validate strategies on multiple datasets. Collaborating with quantitative analysts and developers can also help ensure the backtesting process is thorough and reliable.

What is the difference between Backtesting vs Quantitative Analyst?

AspectBacktestingQuantitative Analyst
Primary RoleTesting trading strategies using historical dataDeveloping and implementing quantitative models for investment decisions
Required SkillsData analysis, programming, finance knowledgeMathematics, programming, financial theory
Work EnvironmentTrading firms, hedge funds, financial institutionsAsset management firms, hedge funds, banks
CertificationsOften none required, but CFA or CQF helpfulCFA, CQF, or advanced degrees common

Backtesting focuses on evaluating trading strategies with historical data, while a Quantitative Analyst develops models to inform investment decisions. Both roles require strong analytical skills and finance knowledge but differ in scope and responsibilities.

What job categories do people searching Backtesting jobs in Illinois look for? The top searched job categories for Backtesting jobs in Illinois are:

Quantitative Developer - Python

IMC

Chicago, IL โ€ข On-site

Other

Posted 16 days ago


Job description

IMC is looking for a Quantitative Developer to own the full path from research to production. This role blends research and engineering, with tight feedback loops from ideation to live trading. You will build the systems that turn quantitative insights into measurable edge, with direct visibility into how your work creates impact.

Your Core Responsibilities

  • Build and maintain systems that span research and production, enabling rapid iteration from idea to production
  • Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints
  • Define, compute, and curate features across instruments, regimes, and time horizons
  • Own feature and signal pipelines, ensuring clean, consistent delivery from research to execution
  • Contribute to strategy optimization,ย balancing expected performance with real-world constraints
  • Debug issues end-to-end across research and execution

Your Skills and Experience

  • 3-7 years of experience in quantitative software development, preferably at a trading firm or systematic fund
  • Strong production experience in Python, including data analysis workflows (pandas, polars, or similar)
  • Strong grounding in probability, statistics, and time series analysis; familiarity with backtesting and simulation frameworks
  • Solid understanding of ML concepts as applied to systematic strategies, from research through production
  • Experience with low-latency systems is valuable
  • Ability to work fluidly across research and engineering teams