The Role
Designs, backtests, implements, and deploys systematic trading strategies and low-latency execution algorithms for equities, derivatives, and F&O markets. Responsibilities include building backtesting and simulation frameworks, optimizing latency, integrating strategies with OMS/RMS platforms, managing risk and position sizing, analyzing market microstructure and transaction costs, monitoring performance, documenting strategies, and mentoring junior quants.
Summary Generated by Built In
About Tradelab
Tradelab builds high-performance, cloud-native trading infrastructure (OMS, RMS, low-latency execution, Algo/HFT systems) for brokers and fintechs. We power real-time trading platforms used by leading market participants and are focused on reliability, scale, and advanced algorithmic trading solutions.[tradelab]
Role overview
We are seeking a hands-on Quant Trader with 4–5 years of experience to design, develop, and deploy systematic trading strategies and execution algorithms for equities, derivatives, and F&O products. You will work closely with research, engineering, and product teams to turn quantitative ideas into production-grade algos on Tradelab’s low-latency platform. This role requires strong programming skills, solid statistics/math background, and practical market microstructure knowledge.
Key responsibilities
- Research, design, backtest, and implement systematic trading strategies for equity and derivatives markets.
- Develop and optimize low-latency execution algorithms and smart order routing logic.
- Build and maintain robust backtesting frameworks, simulation environments, and performance monitoring dashboards.
- Work with engineers to productionize strategies: profiling, latency tuning, risk controls, and integration with OMS/RMS.
- Implement risk management and position-sizing rules; ensure strategies comply with exchange and regulatory constraints.
- Analyze market microstructure, transaction costs, slippage, and market-impact to improve strategy performance.
- Maintain clear documentation of strategy logic, parameters, and trade rationales; participate in code reviews and post-trade analysis.
- Mentor junior quants and support cross-functional knowledge sharing.
Must-have qualifications
- 4–5 years experience in quantitative trading, electronic trading, or algo execution roles.
- Strong programming skills in Python; experience with C++ for low-latency components.
- Hands-on experience with backtesting libraries, time-series data handling, and vectorized computation (NumPy/Pandas/PyTorch/QuantStats/Py_Vollib/TA-Lib).
- Solid foundation in statistics, probability, and numerical methods; experience with machine learning methods relevant to trading.
- Practical understanding of market microstructure, order types, exchange APIs, and F&O trading mechanics.
- Familiarity with low-latency systems, event-driven architecture, and profiling/tuning techniques.
- Good communication skills and ability to convert research into production-ready code.
- Bachelor’s or Master’s in Mathematics, Statistics, Computer Science, Engineering, Financial Engineering, or related fields.
Preferred
- Experience integrating strategies with OMS/RMS platforms and knowledge of FIX protocol.
- Experience working at a broker, prop desk, or trading technology company.
- Familiarity with Indian exchanges (NSE/BSE/MCX) and their market data feeds.
- Prior publications, open-source contributions, or demonstrated track record of profitable strategies.
What we offer
- Opportunity to build and run production-grade strategies on a high-performance trading platform.
- Collaborative environment with experienced engineers and domain experts.
- Competitive compensation ( Between 40 - 70 LPA) and performance-linked incentives.
- Learning and growth opportunities in algorithmic trading and trading systems engineering.
Skills Required
- 4-5 years of experience in quantitative trading, electronic trading, or algorithmic execution roles
- Strong Python programming skills
- C++ experience for low-latency components
- Hands-on experience with backtesting libraries, time-series data handling, and vectorized computation
- Experience with NumPy, Pandas, PyTorch, QuantStats, Py_Vollib, or TA-Lib
- Strong foundation in statistics, probability, and numerical methods
- Experience with machine learning methods relevant to trading
- Practical understanding of market microstructure, order types, exchange APIs, and F&O trading mechanics
- Familiarity with low-latency systems, event-driven architecture, and profiling and tuning techniques
- Good communication skills and ability to convert research into production-ready code
- Bachelor’s or Master’s degree in Mathematics, Statistics, Computer Science, Engineering, Financial Engineering, or a related field
- Experience integrating strategies with OMS/RMS platforms and knowledge of FIX protocol
- Experience working at a broker, proprietary trading desk, or trading technology company
- Familiarity with Indian exchanges, including NSE, BSE, or MCX, and their market data feeds
- Prior publications, open-source contributions, or demonstrated track record of profitable strategies
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The Company
What We Do
Tradelab Technologies is a financial-technology company that develops enterprise-grade trading infrastructure for capital markets. Its cloud-native, modular solutions cover digital onboarding, order management systems, real-time pre- and post-trade risk management, execution, analytics, and scalable matching engines. The company serves institutions, brokers, exchanges, and trading firms, supporting high-volume, low-latency trading through on-premise and customized deployments, with a focus on speed, reliability, transparency, flexibility, and operational control.
