Abhishek Kumar

Quantitative trader and researcher specializing in multi-asset arbitrage and systematic strategies across equities, derivatives, commodities, and crypto. Experienced in deploying and scaling market-neutral strategies with institutional capital ($5M–$8M per strategy), delivering consistent double-digit returns with sub-2% drawdowns. Strong focus on execution efficiency, market microstructure, and capital optimization, with production-grade systems in C++ and Python.

Quantitative Researcher and Trader - Lighthouse Canton - Singapore

(2025-06)

• Deployed and managed onshore–offshore index futures arbitrage across India, Japan, and Asia, allocating ~$5M and generating ~12% annualized returns with <1.5% drawdown
• Designed and executed options arbitrage strategies (box spreads) across India and Taiwan, managing ~$8M with 9–11% returns and ~1% drawdowns
• Built and traded commodity spread strategies (~$5M capital), capturing cross-exchange and calendar inefficiencies with stable, low-volatility returns
• Executed crypto basis arbitrage, systematically capturing funding rate and spot–futures dislocations across exchanges
• Managed a continuously deployed arbitrage book (10–20 day capital cycles), dynamically reallocating capital based on opportunity and margin efficiency
• Optimized collateral and margin utilization, improving capital efficiency and return on deployed capital

Quantitative Researcher and Trader - Alpha Alternatives - Mumbai, India

(2023-07 - 2025-06)

• Developed and deployed systematic trading strategies across index derivatives and equities in Indian markets
• Built a low-latency execution engine reducing execution slippage by ~30% through improved order placement logic and microstructure-aware routing
• Designed risk, margin, and clearing frameworks, enabling scalable multi-strategy portfolio management
• Developed data pipelines and analytics systems in Python for real-time market data ingestion, execution tracking, and performance attribution
• Improved execution reliability and monitoring through integrated logging, validation, and post-trade analytics

Quantitative Trader - SSPL Wealth - Chennai, India

(2022-03 - 2023-06)

• Developed and deployed algorithmic trading strategies on tick-level market data using Python
• Performed backtesting and Monte Carlo simulations to evaluate strategy robustness and risk characteristics
• Built and productionized execution and analytics tools, improving strategy reliability and performance monitoring

MSc in Financial Engineering - Financial Engineering - WorldQuant University (2022-07 - 2024-04)

Bachelor of Technology - Electrical Engineering - National Institute of Technology, Patna (2015-07 - 2019-05)