Shreyans Nagori

Business Analyst

Pittsburgh, Pennsylvania, United States3 yrs 1 mo experience

AI ML PractitionerAI Enabled

Key Highlights

Masters in Computational Finance with a focus on quantitative trading.
Developed high-frequency trading systems for Indian options markets.
Research paper accepted at WWW 2023 on fair allocation methods.

Stackforce AI infers this person is a Quantitative Researcher with expertise in Fintech and Machine Learning applications.

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Skills

Core Skills

Quantitative ResearchLow Latency TradingMachine LearningStatistical Research

Other Skills

Algorithm designApproximation AlgorithmsArtificial Intelligence (AI)BadmintonC++Collaborative filteringCompetitive ProgrammingComputer VisionDeep learningDistributed AlgorithmsGame TheoryGraph Neural Networks (GNNs)Graph theoryGurobiHigh frequency trading

About

I am a Masters in Computational Finance student passionate about developing quantitative trading strategies. Before joining the program, I worked as a quantitative researcher, devising high frequency trading systems for options in NSE and BSE markets. Using tick data, I’ve developed predictive signals in the equity and vol space for better pricing. With a Bachelor’s in Computer Science from IIT Delhi, I developed a good background in using statistical and numerical methods for working with financial data, and developing optimised execution algorithm. My research interests span derivatives, market microstructure and using machine learning in trading algorithms. I'm eager to connect with fellow quants, researchers, and finance professionals for collaborations and actively seeking Summer quant internship opportunities. Feel free to reach out at snagori@andrew.cmu.edu. I am a Badminton enthusiast and played it at state level back in my school days. When not playing badminton, I like to read up on history, watch comedy and tennis.

Experience

Nk securities research

Quantitative Researcher

May 2022 – Jun 2025 · 3 yrs 1 mo · Gurugram, Haryana, India

High frequency trading on Indian options.

High frequency tradingQuantitative researchQuantitative ResearchLow Latency Trading

Flipkart

Research Intern

Jun 2021 – Aug 2021 · 2 mos

Implemented a deep learning based collaborative filtering method for finding user similarity
Used GNNs to extract features from user-item graph representations.
Created robin round algorithm for fair distribution of item and a strategy to maximise nash objective.
Our paper Towards Fair Allocation in Social Commerce Platforms got accepted in WWW 2023

Deep learningCollaborative filteringGraph Neural Networks (GNNs)Algorithm designMachine LearningStatistical Research

Indian institute of science (iisc)

Research Intern

May 2020 – Jul 2020 · 2 mos · Bengaluru, Karnataka, India

Under Prof. Arindam Khan
Trying to improve bounds of proper coloring of edge weighted bipartite graphs and using primal dual analysis on ranking algorithms.

Graph theoryPrimal dual analysisRanking algorithms