Pratyush Baliarsingh

Pratyush Baliarsingh

Aspiring Quantitative Finance & Deep Learning Engineer

Mumbai, IN.

About

Highly analytical Computational Mathematics student with a strong foundation in Deep Learning, Quantitative Finance, and AI/ML. Proven ability to develop advanced models for financial markets, optimize portfolios, and detect anomalies, as demonstrated by achieving a +2.6% better return than S&P 500 and improving model accuracy by 20%. Seeking to leverage expertise in data science, algorithmic trading, and financial modeling to drive impactful solutions in a dynamic quantitative role.

Education

National Institute of Technology, Agartala

Agartala, Tripura, India
→
Bachelor of Technology Computational Mathematics Grade: CGPA: 9.08

Work

Indian Institute of Technology, Guwahati

|

Research Intern

Guwahati, Assam, India

→

Summary

Conducted advanced research in quantitative finance, developing novel Deep Learning approaches for financial option pricing and market behavior analysis.

Highlights

Researched and developed novel Deep Learning models for pricing vanilla and exotic financial options under the guidance of Professor N. Selvaraju.

Engineered, trained, and evaluated complex neural architectures to accurately capture path-dependent and non-linear payoffs, alongside volatility behavior in financial markets.

Benchmarked Deep Learning models against classical financial models, including Black-Scholes-Merton (BSM), Heston, SABR, Longstaff-Schwartz, and Monte Carlo Simulations.

Co-authored a research paper based on this work, currently under peer review, contributing to cutting-edge financial research.

Projects

Fintrix - Reinforcement Learning-Based Portfolio Optimization Framework

Summary

Developed a full-stack reinforcement learning system for universal market portfolio optimization.

Anomaly Detection in Stock Prices Using an LSTM Autoencoder

Summary

Implemented an LSTM Autoencoder for monitoring stock price data and identifying anomalies.

Achievements

Max 1252 Rated

Codeforces

Achieved a competitive programming rating of Max 1252 on Codeforces, demonstrating strong algorithmic problem-solving skills.

Solved 400+ Questions

LeetCode

Successfully solved over 400 algorithmic problems on LeetCode, showcasing proficiency in data structures and algorithms.

Certificates

Time Series Analysis and Forecasting with Python

Not Specified

Google Advanced Data Analytics Specialization

Google

Positions of Responsibility

Quantitative Finance Lead

|

Data Science and Artificial Intelligence Club (DSAI), NIT-A

→

Led and organized quantitative finance projects within the Data Science and Artificial Intelligence Club at NIT-A.

Highlights

Spearheaded the development and execution of several quantitative finance projects, fostering member engagement and skill development within the club.

Skills

Programming Languages

C++, Python, Rust, R, SQL.

Data Science & ML Libraries

NumPy, Pandas, Matplotlib, Seaborn, scikit-learn, TensorFlow, PyTorch, Ray, Gymnasium, Amazon Chronos, QuantLib.

Computer Science Fundamentals

Data Structures and Algorithms (DSA), Object-Oriented Programming (OOPS), Database Management Systems.

Development Tools

Git, GitHub, MATLAB, VSCode, Jupyter Notebooks.

Languages

English

Fluent

Hindi

Native

German

Conversational