I'm Amit Tomar.

I work at the intersection of machine learning, statistics, and data, building predictive models and analytical systems for real-world decision making.

Outside of research, I enjoy flying, traveling, entrepreneurship, and public speaking.

Resume Contact

About me

Hi, I'm Amit Tomar, a quantitative researcher with a background in machine learning, statistics, and finance. I graduated with a Master's degree in Financial Mathematics from the University of Illinois Urbana-Champaign. I bring over 7 years of experience spanning machine learning, software engineering, and financial markets. Before UIUC, I graduated from the Indian Institute of Technology (IIT) Bombay, and led the development of large-scale financial data and risk-analytics systems at IDfy, serving leading banks and insurers. My current work bridges quantitative research and algorithmic trading — building multi-asset predictive models, options automation systems, and AI-driven multi-agent frameworks for market prediction. I’m passionate about aviation, traveling, and public speaking, constantly striving to combine analytical rigor with creative problem-solving.

Location:Champaign, IL, USA
Interests:Traveling, Aviation, Entrepreneurship, Public Speaking
Study:University of Illinois Urbana-Champaign & IITB
Employment:UIUC, IDfy, PropertyPistol

Education

M.S. in Financial Mathematics

University of Illinois Urbana-Champaign, Gies College of Business•Dec 2025

Quantitative finance concentration focusing on algorithmic trading, machine learning, and time-series analysis. Research with Prof. Tony Zhang on DeFi markets and behavioral signals. Leadership: Toastmasters VP PR, GLOBE Ambassador.

B.Tech in Aerospace Engineering

Indian Institute of Technology (IIT) Bombay

Core engineering and mathematical training forming the foundation for quantitative and computational finance. Graduated from India's top engineering institute.

Work

Quantitative Researcher / Applied ML Developer

Personal Portfolio — Quant Research•Apr 2022 – Present

Building multi-agent and predictive systems for equities, ETFs, and derivatives. Developed dynamic backtesting, RL models, and signal scoring frameworks optimizing risk-adjusted returns.

Quantitative Research Summer Analyst

University of Illinois Urbana-Champaign•May 2025 – Aug 2025

Worked with Prof. Tony Zhang on identifying structural and behavioral drivers of crypto-asset returns using Farcaster network data. Built Python-based factor models combining on-chain activity, sentiment divergence, and centrality metrics.

Module Lead – Data & ML Systems

IDfy•Apr 2022 – Dec 2023

Led development of distributed ML and data APIs for BFSI clients. Engineered scalable KYC/AML analytics and risk-classification systems handling millions of daily requests.

Senior Machine Learning Engineer (Risk & Analytics)

IDfy•Jun 2018 – Apr 2022

Developed production-grade ML infra for real-time credit scoring and anomaly detection. Designed distributed retraining systems and live model monitoring frameworks.

Skills

Programming & Data

Python

C++

MATLAB

SQL

Quantitative & Statistical Methods

Time Series Forecasting (ARIMA, GARCH)

Machine Learning / Deep Learning

Stochastic Processes

Portfolio Optimization

Monte Carlo Simulation

Algorithmic Trading & Research

Backtesting Frameworks

Alpha Research / Strategy Design

Reinforcement Learning

Market Microstructure

Risk Modeling & Analytics

Tools & Infrastructure

Bloomberg Terminal

Docker / Linux

Git / Version Control

Zerodha API / WebSocket

TradingView / Python APIs

Spoken Languages

English

Hindi

Spanish

Japanese

Mandarin

Check out some of my work

Machine Learning-Enhanced Momentum Trading Strategy

ML-enhanced momentum strategy achieving 19.94% annualized return using ensemble models (Ridge, Random Forest, XGBoost) with 25 engineered features across multiple horizons. Demonstrates significant alpha (+6.7%) with Sharpe ratio of 0.83 over 13.5 years (2011-2024).

Predictive Power of Alternative Data in Financial Markets

Quantamental analysis using Google Trends, Glassdoor reviews, and consumer sentiment to predict stock returns. Findings show Google Trends and Glassdoor have statistically significant predictive power through panel regressions and correlation analysis.

Replicating Time-Series Momentum (Moskowitz et al. 2012)

R implementation of TSMOM strategy across 28 futures contracts (commodities, FX, bonds) from 1986-2009. Volatility-targeted trend-following delivers superior risk-adjusted returns and crisis alpha, significantly outperforming S&P 500 during market downturns.

Amit is an exceptional quantitative researcher whose innovative approach to algorithmic trading has consistently delivered impressive results. His ability to blend machine learning techniques with financial theory is truly remarkable.

-- David Quick

Amit has an extraordinary gift with numbers. His mathematical intuition and analytical precision are second to none—he can spot patterns and solve complex problems faster than anyone I've worked with.

-- Ashwin Sharma

Get in touch.

If you are interested in working together, or just want to say hi, fill out the form below or send me an email.

Email: contact@unknownamit.com
Location: Champaign IL, USA
Instagram: @unknownamit
Github: amit943c