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$74.5K

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$94K

How much do algorithmic trading jobs pay per year?

As of Jul 16, 2026, the average yearly pay for algorithmic trading in the United States is $85,750.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,000.00 and $91,000.00 per year, depending on experience, location, and employer.

What Is Algorithmic Trading?

Algorithmic trading involves trading in equities, currencies, or other financial instruments using computer programs. A trading program uses an algorithm to calculate current market conditions. This trading method is automated, so the program buys or sells the financial instrument when the algorithm says that the market meets all the requirements for a profitable trade. To create an algorithm, you perform mathematical and statistical analysis, also known as quantitative analysis, on an exchange or equity. After creating an algorithm with defined trading rules, you test it using historical market data. While this is primarily a technical field, you also need an understanding of the market.

How to become an algorithmic trader?

To become an algorithmic trader, you should develop strong programming skills in languages like Python or C++, gain knowledge of financial markets and trading strategies, and learn to use trading platforms and data analysis tools. A background in mathematics, statistics, or computer science is often essential, and many traders pursue certifications such as the Chartered Market Technician (CMT) or Financial Risk Manager (FRM). Experience with backtesting and risk management is also important for success in this field.

Is algo trading a good career?

Algorithmic trading is a specialized career that involves developing and implementing automated trading strategies using programming skills, data analysis, and financial knowledge. It can be lucrative and in demand in financial firms, but it requires strong technical expertise, continuous learning, and understanding of market regulations. Success in this field depends on technical proficiency, risk management, and staying updated with market trends.

Who is the richest Algo trader in the world?

Algorithmic trading is a specialized finance role involving the use of algorithms and programming skills to execute trades. While individual net worth is often private, some of the most successful algorithmic traders and quant hedge fund managers, such as Jim Simons, have accumulated significant wealth through quantitative strategies and advanced data analysis. These professionals typically work in high-frequency trading firms or hedge funds and require strong mathematical, programming, and financial skills.

What is algorithmic trading?

Algorithmic trading refers to the use of computer programs and algorithms to automatically execute trading orders in financial markets. These algorithms follow predefined rules based on factors like price, timing, and volume to optimize trading strategies and reduce human intervention. Algorithmic trading is widely used by institutional investors, hedge funds, and individual traders to increase efficiency, minimize costs, and capitalize on market opportunities. It can range from simple rule-based systems to complex strategies involving machine learning and artificial intelligence.

What is the difference between Algorithmic Trading vs Quantitative Analyst?

AspectAlgorithmic TradingQuantitative Analyst
Required CredentialsDegree in finance, computer science, or related field; programming skillsDegree in mathematics, statistics, or finance; strong analytical skills
Work EnvironmentTrading firms, hedge funds, financial institutions; fast-pacedInvestment banks, asset management firms; research-focused
Employer & Industry UsageUsed to automate trading strategiesDevelops models to inform trading decisions

While both roles involve quantitative skills and finance knowledge, Algorithmic Traders focus on implementing automated trading systems, whereas Quantitative Analysts develop models and strategies that may be used by traders or firms. The roles often overlap but differ mainly in their primary focus: execution versus modeling.

What are the main challenges faced by professionals in algorithmic trading, and how can they be addressed?

Professionals in algorithmic trading often encounter challenges such as developing strategies that remain effective in rapidly changing markets, minimizing latency for faster execution, and managing the risks associated with automated trading systems. To address these challenges, it's essential to stay updated with the latest market trends and technological advancements, conduct rigorous backtesting of algorithms, and implement robust risk management protocols. Collaboration with quantitative analysts, software engineers, and risk managers is also key to ensuring strategies are both innovative and resilient.

Can I make money with algorithmic trading?

Algorithmic trading professionals develop and implement automated trading strategies that can generate profits if the algorithms are well-designed and market conditions are favorable. Success depends on skills in programming, data analysis, and risk management, and consistent profitability is not guaranteed. Many traders experience both gains and losses, and ongoing testing and optimization are essential for potential profitability.

What are the key skills and qualifications needed to thrive as an Algorithmic Trader, and why are they important?

To thrive as an Algorithmic Trader, you need a strong background in quantitative analysis, programming (often Python, C++, or Java), and a solid understanding of financial markets, typically supported by a degree in mathematics, engineering, finance, or computer science. Familiarity with statistical modeling tools, trading platforms, and backtesting systems is essential, and certifications such as CFA or FRM can be advantageous. Superior problem-solving skills, attention to detail, and the ability to work under pressure set standout professionals apart in this field. These skills are crucial to developing, implementing, and refining trading strategies that can operate profitably and reliably in fast-moving financial environments.
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Algorithmic Trading jobs near you
Infographic showing various Algorithmic Trading job openings in the United States as of July 2026, with employment types broken down into 1% Internship, 1% As Needed, 86% Full Time, 11% Part Time, and 1% Contract. Highlights an 84% Physical, 1% Hybrid, and 15% Remote job distribution, with an average salary of $85,750 per year, or $41.2 per hour.
GBM - Public, ETF Trading, Vice President - New York

GBM - Public, ETF Trading, Vice President - New York

Goldman Sachs, Inc.

New York, NY • On-site

Full-time

Posted 7 days ago

Goldman Sachs rating

8.2

Company rating: 8.2 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

44th of 149 rated banks

Job description


About the Role: We are seeking a highly skilled and motivated Systematic ETF Trader to join our dynamic trading team. The ideal candidate will be responsible for developing, testing, and executing systematic trading strategies focused on Exchange-Traded Funds (ETFs) across various asset classes. This is an excellent opportunity for a quantitative professional with a passion for financial markets and algorithmic trading.
Key Responsibilities:
  • Strategy Development: Design, develop, and implement algorithmic trading strategies that trade ETFs across global markets.
  • Data Analysis & Modeling: Utilize historical and real-time market data to identify inefficiencies and create predictive models that inform trading decisions.
  • Backtesting & Optimization: Conduct rigorous backtesting of trading strategies to ensure robustness and optimize for risk-adjusted returns.
  • Execution: Manage and execute trades through automated systems, ensuring minimal slippage, transaction costs, and market impact.
  • Risk Management: Monitor and manage trading risks, including market risk, liquidity risk, and operational risk, ensuring compliance with risk guidelines and limits.
  • Performance Analysis: Track and analyze the performance of strategies, identify areas for improvement, and implement iterative changes to enhance profitability and performance.
  • Collaboration: Work closely with quantitative researchers, data scientists, and other traders to refine strategies and improve performance.
  • Technology Integration: Leverage advanced tools and platforms for data analysis, strategy development, and execution (e.g., Python, SQL, MATLAB, etc.).

Requirements:
  • Education: Bachelor's degree in Finance, Economics, Engineering, Computer Science, Mathematics, or a related field.
  • Experience: Proven experience (2+ years) as a quantitative trader, systematic trader, or similar role, with a strong focus on ETFs and algorithmic trading.
  • Technical Skills: Proficiency in programming languages such as Python, C++, Java, or similar, along with experience in financial modeling, data analysis, and backtesting.
  • Quantitative Skills: Strong background in statistics, econometrics, or machine learning techniques, with the ability to apply them to trading strategies.
  • Market Knowledge: Deep understanding of financial markets, especially ETFs, including structure, liquidity, and market microstructure.
  • Problem-Solving: Strong analytical and problem-solving skills with a keen ability to think critically and adapt to evolving market conditions.
  • Communication: Strong verbal and written communication skills for collaborating across teams and presenting findings to stakeholders.

The expected base salary for this New York, New York, United States-based position is $150,000-$300,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.

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About Goldman Sachs

Sourced by ZipRecruiter

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869

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