Daksh D.

I’m a Quant Developer and Software Engineer specializing in algorithmic trading, quantitative research, and high-performance trading systems. I help traders, funds, and fintech teams turn trading ideas into robust, backtestable, and production-ready systems. My experience spans crypto and traditional financial markets, from directional strategies and market-making algorithms to derivatives pricing, volatility modeling, and risk analytics. What I can help you build: • Algorithmic trading: strategy implementation, backtesting, execution logic, portfolio management, and risk controls. • Market making: Avellaneda–Stoikov, inventory-aware quoting, dynamic spreads, and market microstructure models. • Backtesting infrastructure: high-performance engines, realistic slippage and fee modeling, latency simulation, and event-driven systems. • Quantitative finance: options pricing, volatility surface calibration, Monte Carlo simulation, VaR/Expected Shortfall, and fixed-income analytics. • Trading integrations: broker and exchange APIs, trade replication, order management, and automated execution. • Quant development: Python, C++, and Rust, with an emphasis on performance, reliability, and maintainable architecture. Selected work: • Built a high-performance Rust backtesting engine for decentralized crypto markets, incorporating block-level latency, dynamic fees, and slippage. • Implemented an Avellaneda–Stoikov market-making strategy with volatility-based spread adjustments and inventory-aware skewing. • Developed a neural-network surrogate for rough-volatility calibration, accelerating calibration approximately 80×, from 29 seconds to 350 milliseconds. • Built a Monte Carlo VaR/Expected Shortfall engine with Greek-based P&L attribution and statistical backtesting. • Contributed to QuantLib, implementing builder classes for cross-currency swaps in C++. My broader software engineering background includes low-latency inference pipelines, distributed training and deployment systems, and full-stack applications. I value clear communication, rigorous testing, realistic performance assumptions, and maintainable code. If you have a trading strategy, quantitative model, or trading infrastructure problem, I can help you design, implement, test, and optimize it.