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Backtesting Jobs in New York (NOW HIRING)

Rapidtrade

Quantitative Analyst

Manhattan, NY · On-site

$150 - $250/hr

Experience with backtesting frameworks and simulation * Understanding of market microstructure and trading mechanics Nice to Have * Experience with cryptocurrency markets * Knowledge of options ...

Point72

Equity Quantitative Researcher

New York, NY · On-site

End-to-end development: alpha idea generation, data processing, strategy backtesting, optimization and production implementation * Identify and evaluate new datasets for stock return predictions

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Backtesting information

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 skills and qualifications are needed to thrive as a backtesting analyst?

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 are 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.

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Backtesting jobs near you
Infographic showing various Backtesting job openings in New York as of August 2026, with employment types broken down into 94% Full Time, 5% Part Time, and 1% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Equity Quantitative Researcher

Point72

New York, NY

Full-time

Re-posted yesterday

Job description

ROLE/RESPONSIBILITES

  • Perform rigorous and innovative research to discover systematic anomalies in equity market
  • End-to-end development: alpha idea generation, data processing, strategy backtesting, optimization and production implementation
  • Identify and evaluate new datasets for stock return predictions
  • Maintain and improve the portfolio trading in production environment

REQUIREMENTS

  • MS or PhD in physics, engineering, statistics, applied math, quantitative finance or other quantitative fields with a strong foundation in statistics
  • 1+ years of work experience in systematic alpha research in equities
  • Experience developing short term alpha signals (intraday or a few days) is a plus
  • Demonstrated proficiency in R or Python
  • Strong command of foundations of applied statistics, linear algebra, and time series models
  • Ability to quickly and efficiently scrub, format, and manipulate large, raw data sources
  • Strong knowledge of financial markets
  • Highly motivated, willing to take ownership of his/her work
  • Collaborative mindset with strong independent research ability

Apply