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Dixit Gajjar

Dixit Gajjar

Quantitative researcher in algorithmic trading and risk modeling

dixit-gajjar
Hoboken, New Jersey, United States
Joined March 2026
Favicon imagedixitmgajjar.github.io
Favicon imagegithub.com

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Summary

Applied quantitative research focused on algorithmic trading: Dixit's public projects center on algorithmic trading architectures (Momentum Transformer re-implementation), automated execution engines (AlpacaBot), and high-performance backtesting, showing an emphasis on reproducing and engineering trading models for practical use. github+2
Engineering-first approach to research: Projects emphasize software engineering adaptations—upgrading models to TensorFlow 2.x, building data pipelines from Yahoo Finance, optimizing training for local hardware, and deploying serverless paper-trading workflows—indicating strong applied engineering skills alongside quantitative work. github+2
Expertise in stochastic simulation and risk modelling: Work includes Monte Carlo simulators for Vasicek/CIR interest-rate models, Value at Risk / CVaR portfolio risk analysis, and implementation of Longstaff–Schwartz for American option pricing, showing a focus on model-driven risk analytics. streamlit+2
Practical experience through internships and mentorship: Publicly available internship documents and affiliation with Stevens Institute (M.S. candidate) show a mix of industry internship experience and academic/peer-mentoring roles during graduate studies. scribd+2

Work

Education

Projects

Writing

Analyzing Tesla Trade and Quote Data: Liquidity, Volatility, and PIN

Report analyzing Tesla TAQ data to estimate liquidity metrics, volatility measures, and Probability of Informed Trading (PIN).

Favicon imagedrive.google.com

Momentum Transformer — Performance and Engineering Report

Technical report accompanying the Momentum Transformer re-implementation, including backtest performance metrics (1990–2025) and engineering notes on data pipeline and model adaptations.

Favicon imagegithub.com

Longstaff-Schwartz (LSM) Technical Report — American Options Pricing

Technical write-up implementing the Longstaff–Schwartz Least Squares Monte Carlo method to price American options.

Favicon imagegithub.com

Hobbies

Builds interactive data visualizations, including an India birth-rate map. github