• Quant-focused software engineer with interests in trading systems, derivatives, and ML
• Building reinforcement learning–based hedging strategies and backtesting systems
• Experience across full-stack development, real-time systems, and data pipelines
• Passionate about combining finance + engineering to solve complex problems

Disrupt – The FinTech Initiative at Northeastern University — Boston, MA
Quantitative Analyst | Jan 2026 – Present

• Developing quantitative trading strategies (pairs trading, mean reversion, momentum) using $50K simulated capital
• Analyzing research in equity markets (statistical arbitrage, factor investing, market microstructure)
• Building Python backtesting pipelines across 10M+ historical data points

Student Government Association (SGA) at Northeastern University — Boston, MA
Software Engineer | Jan 2026 – Present

• Developing a platform serving 5,000+ students with centralized resources and leadership information
• Architecting scalable UI using TypeScript, React, and Next.js
• Contributing through PR-based development, reviews, and feature iteration

NU Oasis — Boston, MA
Project-Series Mentor | Jan 2026 – Present

• Mentoring teams to design and deploy real-world software solutions
• Guiding API integration and full-stack development using React, Node.js, and TypeScript

Rainfall Learning – Live Tutoring — Boston, MA
Software Engineer | Nov 2025 – Present

• Leading front-end development with 15+ engineers for real-time collaborative coding platform
• Implementing CRDT-based real-time collaboration in an embedded IDE
• Improved reliability by 15% through Docker + CI/CD enhancements

Oasis — Boston, MA

• Developed a full-stack web application with a team of 5, integrating the DineOnCampus API via automated serverless cron jobs to serve real-time daily menus from Northeastern University's three dining halls
• Designed and implemented a responsive, accessible UI using React, Vite, and TailwindCSS, translating Figma prototypes into production-ready components with dietary filtering and nutrition breakdowns
• Architected a Supabase PostgreSQL backend with row-level security, managing a relational schema across locations, periods, stations, menu items, and nutrients to support efficient querying at scale
• Built a user authentication system with Supabase Auth enabling personalized features including a calorie tracker and meal voting system, while maintaining full menu access for unauthenticated users
• Deployed the application on Vercel with serverless API routes, environment-scoped secrets, and a daily cron pipeline that automatically scrapes and upserts menu data to keep content current without manual intervention

Reinforcement Learning for Derivative Hedging

• Framed dynamic option hedging as a continuous-action MDP and trained PPO and SAC agents to hedge a short European call position, benchmarked against Black-Scholes delta hedging across four market scenarios: base, high transaction cost, volatility mismatch, and regime switching
• Built a custom OpenAI Gym environment replaying 5 years of real SPY daily price data across 1,204 overlapping 30-day windows, exposing agents to the 2020 COVID crash, 2022 rate shock, and 2023–24 bull market within a single training distribution
• Designed a 6-feature normalized observation space (normalized spot price, time-to-expiry, delta, gamma exposure, current hedge position, and log-moneyness) with an asymmetric reward penalizing downside P&L variance and a terminal settlement penalty, making the agent explicitly risk-averse rather than variance-neutral
• Evaluated 5 strategies (PPO, SAC, delta hedge, no hedge, random) across all scenarios reporting 8 metrics (Sharpe, VaR 95%, CVaR 95%, mean/std P&L, max loss, % loss episodes, avg transaction cost); PPO improved Sharpe over delta by ~33% in the high-TC regime and ~37% in the volatility mismatch regime
• Built a 5-page Streamlit dashboard covering live episode animation (agent vs delta hedge step-by-step), real-time training with live learning curves for both agents, full evaluation results, a Monte Carlo scenario lab, and a live SPY options chain with implied vol surface

	patel.s15@northeastern.edu
	www.shlokpatelportfolio.live
	linkedin.com/in/-shlokpatel