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.[https://tradelab.in/]
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
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Research, design, backtest, and implement systematic trading strategies for equity and derivatives markets.
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Develop and optimize low-latency execution algorithms and smart order routing logic.
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Build and maintain robust backtesting frameworks, simulation environments, and performance monitoring dashboards.
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Work with engineers to productionize strategies: profiling, latency tuning, risk controls, and integration with OMS/RMS.
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Implement risk management and position-sizing rules; ensure strategies comply with exchange and regulatory constraints.
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Analyze market microstructure, transaction costs, slippage, and market-impact to improve strategy performance.
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Maintain clear documentation of strategy logic, parameters, and trade rationales; participate in code reviews and post-trade analysis.
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Mentor junior quants and support cross-functional knowledge sharing.
Must-have qualifications
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4–5 years experience in quantitative trading, electronic trading, or algo execution roles.
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Strong programming skills in Python; experience with C++ for low-latency components.
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Hands-on experience with backtesting libraries, time-series data handling, and vectorized computation (NumPy/Pandas/PyTorch/QuantStats/Py_Vollib/TA-Lib).
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Solid foundation in statistics, probability, and numerical methods; experience with machine learning methods relevant to trading.
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Practical understanding of market microstructure, order types, exchange APIs, and F&O trading mechanics.
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Familiarity with low-latency systems, event-driven architecture, and profiling/tuning techniques.
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Good communication skills and ability to convert research into production-ready code.
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Bachelor’s or Master’s in Mathematics, Statistics, Computer Science, Engineering, Financial Engineering, or related fields.
Preferred
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Experience integrating strategies with OMS/RMS platforms and knowledge of FIX protocol.
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Experience working at a broker, prop desk, or trading technology company.
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Familiarity with Indian exchanges (NSE/BSE/MCX) and their market data feeds.
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Prior publications, open-source contributions, or demonstrated track record of profitable strategies.
What we offer
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Opportunity to build and run production-grade strategies on a high-performance trading platform.
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Collaborative environment with experienced engineers and domain experts.
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Competitive compensation ( Between 40 - 70 LPA) and performance-linked incentives.
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Learning and growth opportunities in algorithmic trading and trading systems engineering.
