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

- 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 explicit 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.
- Solid 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.

📌 Quant Trader (Bengaluru)
🏢 Tradelab
📍 Bengaluru