12 Algorithmic Trading jobs in India

• Develop, test, and deploy quantitative trading algorithms and strategies for equities, futures, options, and FX markets.
• Conduct in-depth market research and data analysis to identify trading opportunities and patterns.
• Build and maintain robust backtesting frameworks and simulation environments.
• Collaborate with traders and portfolio managers to refine strategy performance and risk management.
• Implement and optimize trading models using statistical, machine learning, and econometrics techniques.
• Analyze trade execution performance and identify areas for improvement.
• Develop and maintain high-quality, production-ready code in languages such as Python, C++, or R.
• Manage large datasets, ensuring data integrity and efficient processing.
• Stay current with the latest advancements in quantitative finance, machine learning, and financial technology.
• Contribute to the team's research pipeline and generate innovative trading ideas.
• Assess and mitigate risks associated with algorithmic trading strategies.
• Communicate complex quantitative concepts and findings to both technical and non-technical stakeholders.

• Master's or Ph.D. in a quantitative field such as Finance, Mathematics, Statistics, Computer Science, Physics, or Economics.
• Minimum of 5 years of experience in quantitative analysis, algorithmic trading, or statistical arbitrage.
• Advanced proficiency in programming languages commonly used in quantitative finance, such as Python (with libraries like NumPy, Pandas, SciPy, scikit-learn), C++, or R.
• Strong understanding of financial markets, asset classes, and trading strategies.
• Experience with financial data providers (e.g., Bloomberg, Refinitiv) and order management systems.
• Expertise in statistical modeling, time series analysis, machine learning, and econometrics.
• Proven ability to develop and validate complex quantitative models.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong communication and collaboration skills, essential for a remote team environment.
• Experience with high-frequency trading (HFT) strategies is a plus.
• Familiarity with cloud computing platforms is advantageous.

• Design, develop, backtest, and implement quantitative trading strategies across various asset classes (equities, futures, options, FX).
• Conduct in-depth research into market microstructure, statistical arbitrage, and predictive modeling.
• Analyze large volumes of historical and real-time market data to identify trading opportunities and patterns.
• Develop and maintain high-frequency trading models, ensuring robustness and performance.
• Collaborate with portfolio managers and traders to refine strategies and implement new ideas.
• Build and optimize low-latency execution algorithms.
• Monitor and analyze the performance of live trading strategies, identifying areas for improvement.
• Develop and implement risk management frameworks for trading strategies.
• Utilize statistical and machine learning techniques to enhance model accuracy and predictive power.
• Stay current with financial market trends, technological advancements, and academic research in quantitative finance.
• Mentor junior quantitative analysts and contribute to the team's knowledge base.
• Ensure compliance with all relevant regulations and internal policies.

• Master's or Ph.D. in a quantitative field such as Mathematics, Statistics, Physics, Computer Science, or Financial Engineering.
• Minimum of 5 years of experience in quantitative analysis, algorithmic trading, or related roles within the financial industry.
• Proven track record of developing and successfully deploying profitable trading strategies.
• Strong programming skills in languages such as Python, C++, or Java, with experience in data analysis libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., Scikit-learn, TensorFlow).
• Expertise in statistical modeling, time series analysis, econometrics, and machine learning techniques.
• Deep understanding of financial markets, derivatives, and trading execution mechanisms.
• Experience with financial data providers (e.g., Bloomberg, Refinitiv) and low-latency data feeds.
• Excellent analytical, problem-solving, and critical thinking skills.
• Ability to thrive in a fast-paced, highly collaborative, and remote work environment.
• Strong communication skills for presenting complex findings to diverse audiences.

• Research, develop, and backtest quantitative trading strategies across various asset classes (equities, futures, forex, options).
• Analyze large datasets to identify market inefficiencies and trading opportunities.
• Implement and optimize trading algorithms, ensuring high performance and low latency.
• Collaborate with portfolio managers and traders to refine strategy parameters and execution.
• Monitor the performance of live trading strategies and identify areas for improvement.
• Develop statistical models for risk management and portfolio optimization.
• Utilize programming languages such as Python, R, C++, and MATLAB for research and implementation.
• Stay current with the latest academic research and industry trends in quantitative finance.
• Contribute to the development of trading infrastructure and tools.
• Evaluate and integrate new data sources to enhance trading models.
• Document research findings, methodologies, and implementation details thoroughly.
• Mentor junior quantitative analysts and contribute to team knowledge sharing.

• Master's or Ph.D. in a quantitative field such as Mathematics, Statistics, Physics, Computer Science, or Financial Engineering.
• Minimum of 5 years of experience in quantitative research or algorithmic trading strategy development.
• Proven track record of successfully developing and implementing profitable trading strategies.
• Expertise in statistical modeling, time series analysis, machine learning, and data mining techniques.
• Proficiency in programming languages such as Python (with libraries like NumPy, Pandas, SciPy, scikit-learn) and C++.
• Strong understanding of financial markets, derivatives, and trading mechanisms.
• Experience with high-frequency trading (HFT) concepts and technologies is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Ability to work independently and collaboratively in a fast-paced, demanding environment.
• Strong communication skills to articulate complex quantitative concepts.
• Experience with large-scale data analysis and distributed computing frameworks (e.g., Spark) is beneficial.

BestEx Research is a financial technology and research firm specializing in building sophisticated execution algorithms and transaction cost modeling tools servicing multiple asset classes. The firm provides high-performance algorithmic execution services to hedge funds, CTAs, asset managers, and banks through a traditional electronic broker and in a broker-neutral Software as a Service (SaaS) model.

Its cloud-based platform, Algo Management System (AMS), is the first end-to-end algorithmic trading solution for equities and futures that delivers an entire ecosystem around execution algorithms, including transaction cost analysis (TCA), an algorithm customization tool called Strategy Studio, a trading dashboard, and pre-trade analytics in a single platform. The platform is currently live for U.S., Europe, and Canadian equities and global futures trading.

BestEx Research is disrupting a $100 billion industry by challenging the status quo of stale, black-box solutions from banks and offering next-generation execution algorithms that combine performance improvement with transparency and customization. BestEx Research uses leading-edge technology to support its low-latency, highly scalable research and trading systems, with its backend in C++, research libraries in C++/Python and R, and web-based technologies for delivering its front-end platforms.

BestEx Research’s mission is to become the leader in automation and measurement of execution across asset classes globally and significantly reduce transaction costs for our clients.

Visit bestexresearch.com for more information about our mission, products, research, and services.

BestEx Research’s Bangalore office is not an “offshore center.” It’s a core engineering and research hub—working on the exact same problems and projects as our U.S. team. You’ll be part of the global brain trust, solving some of the hardest problems in trading, systems, and data science.

What You’ll Love:

·    Zero bureaucracy, zero silos—engineers directly collaborate with traders, researchers, and the management team.

·    Direct ownership of subsystems.

·    Daily opportunity to learn from pioneers in HFT, low-latency systems, and algo trading.

·    A high-trust environment, where performance speaks louder than hierarchy.

·    Competitive compensation in India, including equity and cash bonuses.

·    Ability to transition between various teams and projects, whether related to trading systems, algorithmic trading or system tooling

·    5-week structured training program:

o   Market microstructure and trading mechanics

o   Algorithmic execution and strategy design

o   Exchange simulators and performance testing

o   Market data systems and real-time analytics

o   Hands-on exercises using production research data

·    Continuous professional development, with refresher courses, advanced sessions, and on-demand training tailored to your growth.

·    Global exposure: Opportunities to collaborate with, visit, or relocate to our U.S., Singapore, or London offices, based on performance and business needs.

You’ll be part of a lean, focused team building ultra-low-latency trading systems, real-time exchange simulators, execution algorithms, and alpha forecasting models. Our engineers own the stack—from C++ nanosecond-sensitive infrastructure to Python-based research platforms.

This role is ideal for someone who thrives at the intersection of research and engineering—comfortable building systems, testing hypotheses, and working directly with data that moves markets.

Designing and developing system architecture :

·    Core trading systems and algorithmic trading frameworks

·    High-performance execution algorithms for global markets

·    Exchange simulators and tick-level backtesting frameworks

·    Market data adaptors and exchange connectivity adaptors

Building from scratch :

·    Trading systems for new asset classes and execution algorithms

·    Performance optimizations for low-latency, high-throughput systems

Enhancing existing systems :

·    Add support for new features and improve scalability and reliability

Collaborating closely with :

·    A global team of quants, traders, and senior engineers to design cutting-edge solutions

Analyzing and optimizing system performance :

·    Across application, OS, and hardware layers to achieve industry-leading performance

·    Work primarily with C++17 in a high-impact, low-bureaucracy environment

Requirements

·    Bachelor’s or Master’s from a top-tier CS or Engineering program (IIT/NIT/BITS preferred but not required)

·    At least 5+ years software development experience with C++

·    Expert-level knowledge of C++ and excellent software design skills

·    Expert-level knowledge of TCP/IP and multithreading

·    Excellent problem solving and analytical skills

·    Excellent communication skills and ability to articulate ideas

·    An attitude of constant curiosity and growth, as well as the ability and desire to mentor others

·    While domain experience is a plus, we welcome strong engineers from other industries

·    Database optimization and experience with research or market data pipelines

·    Prior work on trading platforms, FIX engines, or exchange protocols

·    Contributions to open-source C++/Python performance tools, ability to work with large datasets  

·    Prior experience developing trading systems or execution algorithms

·    Knowledge of relational databases, database optimizations, and SQL.

·    Experience with Python/R

#LI-DM1

Job Title: Quant Developer / Algorithmic Developer – Crypto Trading

Location: Remote / Hybrid

Type: Full-time

Experience: 3 to 7 Years

Compensation: USDT Only

We are looking for a highly skilled Quantitative Developer / Algorithmic Developer to join our team in building advanced, production-grade crypto trading systems. This role is ideal for someone who excels at the intersection of strategy development, high-performance Python programming, and system design — with a deep interest in cryptocurrency markets.

You will be responsible for developing and optimizing algorithmic trading strategies, designing robust infrastructure, integrating with exchanges, and playing a critical role in converting research ideas into actionable and scalable trading logic.

If you’re passionate about crypto markets and want to shape the next generation of intelligent trading systems, we’d love to connect.

• Design, build, and deploy crypto trading strategies across spot, futures, and derivatives.
• Translate strategy logic into efficient, high-quality Python code, with well-structured modules and reusable components.
• Work on data ingestion, labeling, and pattern recognition logic using real-time and historical market data.
• Integrate and customize machine learning/statistical models for signal generation, filtering, and risk control.
• Interface with exchange APIs (REST/WebSocket) for order placement, live market data, and account management.
• Manage data storage and retrieval using PostgreSQL, Redis, and other relevant tools.
• Optimize for latency, slippage, execution cost, and strategy robustness under varying market conditions.
• Develop and maintain FastAPI-based internal tools for monitoring, research, and strategy deployment.
• Collaborate closely with quant researchers, infra engineers, and traders to iterate on model improvements.
• Conduct post-trade analysis and continuous strategy performance evaluation.

• Strong hands-on programming experience in Python (required); additional experience with C++ is a plus.
• Solid understanding of algorithmic trading systems, including signal development, execution logic, and risk constraints.
• Experience with data engineering, especially data labeling, pattern detection, and feature pipeline construction.
• Proficiency in working with PostgreSQL, Redis, and FastAPI.
• Good knowledge of crypto exchange APIs (WebSockets & REST), order types, and real-time data handling.
• Familiarity with quantitative strategy concepts (PnL attribution, Sharpe ratio, drawdowns, position sizing).
• Ability to build clean, maintainable code with attention to detail and performance.
• Prior experience with strategy development or backtesting frameworks.
• Deep curiosity about crypto markets, with a strong drive to build practical, alpha-generating systems.

• Experience in systematic trading or algorithmic strategy implementation in crypto or traditional markets.
• Knowledge of pattern detection systems (e.g., SMC, market structure, candlestick/chart patterns).
• Exposure to machine learning models (supervised, ensemble, reinforcement learning) applied to financial data.
• Understanding of portfolio optimization, risk modeling, and capital allocation logic.

Job Title: Quant Developer / Algorithmic Developer – Crypto Trading

Location: Remote / Hybrid

Type: Full-time

Experience: 3 to 7 Years

Compensation: USDT Only

We are looking for a highly skilled Quantitative Developer / Algorithmic Developer to join our team in building advanced, production-grade crypto trading systems. This role is ideal for someone who excels at the intersection of strategy development, high-performance Python programming, and system design — with a deep interest in cryptocurrency markets.

You will be responsible for developing and optimizing algorithmic trading strategies, designing robust infrastructure, integrating with exchanges, and playing a critical role in converting research ideas into actionable and scalable trading logic.

If you’re passionate about crypto markets and want to shape the next generation of intelligent trading systems, we’d love to connect.

• Design, build, and deploy crypto trading strategies across spot, futures, and derivatives.
• Translate strategy logic into efficient, high-quality Python code, with well-structured modules and reusable components.
• Work on data ingestion, labeling, and pattern recognition logic using real-time and historical market data.
• Integrate and customize machine learning/statistical models for signal generation, filtering, and risk control.
• Interface with exchange APIs (REST/WebSocket) for order placement, live market data, and account management.
• Manage data storage and retrieval using PostgreSQL, Redis, and other relevant tools.
• Optimize for latency, slippage, execution cost, and strategy robustness under varying market conditions.
• Develop and maintain FastAPI-based internal tools for monitoring, research, and strategy deployment.
• Collaborate closely with quant researchers, infra engineers, and traders to iterate on model improvements.
• Conduct post-trade analysis and continuous strategy performance evaluation.

• Strong hands-on programming experience in Python (required); additional experience with C++ is a plus.
• Solid understanding of algorithmic trading systems, including signal development, execution logic, and risk constraints.
• Experience with data engineering, especially data labeling, pattern detection, and feature pipeline construction.
• Proficiency in working with PostgreSQL, Redis, and FastAPI.
• Good knowledge of crypto exchange APIs (WebSockets & REST), order types, and real-time data handling.
• Familiarity with quantitative strategy concepts (PnL attribution, Sharpe ratio, drawdowns, position sizing).
• Ability to build clean, maintainable code with attention to detail and performance.
• Prior experience with strategy development or backtesting frameworks.
• Deep curiosity about crypto markets, with a strong drive to build practical, alpha-generating systems.

• Experience in systematic trading or algorithmic strategy implementation in crypto or traditional markets.
• Knowledge of pattern detection systems (e.g., SMC, market structure, candlestick/chart patterns).
• Exposure to machine learning models (supervised, ensemble, reinforcement learning) applied to financial data.
• Understanding of portfolio optimization, risk modeling, and capital allocation logic.