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Nilay Tiwari

MS Student, Computational Finance · Carnegie Mellon University (Tepper School of Business)

I am an MS student in Computational Finance at CMU Tepper. My research interests include quantitative trading, stochastic methods, and machine learning applied to financial markets. Before CMU, I spent three years as a quantitative strategist at QuadEye Securities, building and deploying systematic volatility strategies across U.S. and Indian equity options markets.

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Publications

arXiv
2022
Multi-Agent Reinforcement Learning with Mean-Field Equilibrium
Nilay Tiwari, et al.
Algorithms, MDPI · 2022 · Purdue University, CLAN Labs

Formulated novel RL algorithms for joint decision making among multiple agents and proved convergence to Mean-Field Equilibrium (MFE).

[arXiv] [Paper]
Thesis
2018
Composite Stochastic Optimization with Constraints
Nilay Tiwari
Undergraduate Thesis · IIT Kanpur · 2018

Devised novel algorithms for Non-Consensus Multi-Agent Optimization; simulated in Python for two-agent systems.

SURGE
2018
Testing Okun's Law via Panel Econometric Models
Nilay Tiwari
SURGE Research Grant · IIT Kanpur · 2018

Investigated violation of Okun's Law; validated Fixed Effects model as the superior estimator using panel regression.

Experience

QuadEye Securities Mar 2022 – Apr 2025 · Gurgaon, India
Quantitative Strategist, U.S. Markets (Jul 2024 – Apr 2025)
  • Led systematic volatility strategy across NYSE equity options with multi-million USD notional exposure
  • Productionized low-latency execution in C++; built backtesting infrastructure in R
  • Integrated market-implied vol signals to improve earnings regime and boost Sharpe ratio
Quantitative Strategist, Indian Markets (Mar 2022 – Jul 2024)
  • Developed cross-sectional volatility arb strategy in NSE options — Sharpe ratio of 10
  • Captured 10%+ market share in key symbols; multi-million USD daily turnover
  • Designed Genetic Algorithms for optimization, cutting R&D iteration to under one minute
Silverleaf Capital Services Oct 2020 – Feb 2022 · Mumbai, India
High Frequency Trading Analyst
  • Built market-making strategies with multi-level order book quoting logic for NSE equities
  • Implemented RNN models for directional alpha in index options and cash–futures arbitrage
Purdue University, CLAN Labs May – Jul 2019 · Remote
Research Intern
  • Developed multi-agent RL algorithms; work published in Algorithms (2022)

Education

Carnegie Mellon University, Tepper School of Business
MS in Computational Finance · Expected Dec 2026
GRE Quant: 170/170 · Distinguished Merit Scholarship ($15,000 USD)
Indian Institute of Technology Kanpur
B.Tech in Electrical Engineering · Jul 2020
GPA: 9.1/10.0 · Academic Excellence Award 2017–18 · SURGE Research Grant 2018

Skills

C++ Python R Bash Git / SVN Stochastic Calculus Reinforcement Learning Genetic Algorithms Options Pricing Econometrics FPGA Systems HFT Infrastructure

Misc