Quantitative
Developer & Researcher

Engineered a sub-10µs low-latency trading system with a custom-built limit order book, FPGA market data handlers, kernel bypass (DPDK), hardware timestamping, and lock-free data structures for deterministic, microsecond-level execution.

Built a real-time portfolio optimization system using convex and non-convex optimization methods, enhancing risk-adjusted returns via adaptive asset rebalancing and multi-factor modeling, managing interest rate, FX, credit, and market risks.

Developed adaptive volatility regime switch framework dynamically selecting among passive, TWAP, and aggressive strategies. Achieved 20.0% increase in Sharpe Ratio, 6.1% transaction cost reduction, and 20.1% CVaR decrease.

As Quant Researcher at WallStreetQuants, designed and backtested a 120-day volume-momentum-based crypto portfolio strategy, yielding a 155.76% annualized return and 1.94 Sharpe Ratio (post transaction costs), significantly outperforming the Bitcoin buy-and-hold benchmark.

Local-first, autonomous agentic AI platform orchestrating LLM providers behind a unified API, with multi-step tool-calling via the Model Context Protocol (MCP), RAG and persistent semantic memory on ChromaDB, and hardware-aware deployment of quantized open-weight models with zero data leaving the host.

Cross-platform agentic AI developer platform on NixOS with declarative configuration, version-pinned dependencies, and automated health checks. Multi-agent orchestration automates isolated Git worktrees, autonomous task execution, and CI-gated shipping in one command.

Modeled asset prices & derivative strategies using Brownian Motion, GBM, Ito’s Lemma, Martingales, Girsanov’s Theorem, SDEs, Fokker–Planck and Kolmogorov equations for volatility and interest rates.

Developed the ESG Strategy, which was subsequently converted into a portfolio and embedded across all existing portfolios. Caters to the arbitrage opportunity from ESG scores on pre- and post-merger statistics.

Developed a decentralized e-commerce platform to secure and streamline retail transactions with smart contracts, currency conversion, custom hashing, and matching algorithms.

Constructed an ensemble machine learning model consisting of multiple Deep neural networks to predict QS World Ranking and provided suggestions on areas to improve.