With over 17 years of experience in enterprise technology, I pursue quantitative finance as a personal passion and continuous learning journey. In my spare time, I enjoy researching market behavior, developing algorithmic trading tools, and experimenting with data-driven strategies to deepen my understanding of financial markets.
Driven by a personal interest in data-driven investing and systematic trading, I actively explore quantitative finance and algorithmic trading as a hobby and continuous learning journey. In my spare time, I design and experiment with analytical tools and models to better understand financial market behaviour using statistics, data analysis, and computational methods.
Current areas of exploration:
All projects are developed independently using Python, open-source libraries, and publicly available market data — primarily as a way to deepen my understanding of quantitative modelling, financial statistics, and algorithmic trading systems.
Completed the EPAT certification focused on:
This programme complements my technical background and supports my ongoing research in systematic trading and quantitative finance.
17+ years of experience in enterprise technology, working across globally recognised IT services and product companies on large-scale software systems, data platforms, and customer-facing digital solutions.
Key areas of experience:
Working extensively with large datasets, event-driven systems, and scalable architectures strengthened my interest in data-driven systems and quantitative analysis, which led me to explore algorithmic trading and financial modelling as a personal passion.
| Employment | Senior IT Professional (Full-Time) |
| Industry | Global IT Services & Technology |
| IT Experience | 17+ Years |
| Quant Learning | ~1 Year (Self-Taught + QuantInsti) |
| Certification | QuantInsti EPAT – Dec 2025 |
| Focus Markets | NSE / BSE (Indian Equities) |
| Primary Language | Python 3.10+ |
| Location | India |
Enterprise IT
Quant & Finance (Personal)
This platform documents my personal exploration of quantitative finance, algorithmic trading, and data-driven market analysis. It brings together a set of tools, models, and experiments built while studying financial markets through programming, statistics, and systematic strategy development.
Each module was developed from scratch using Python and publicly available market data, focusing on understanding how quantitative ideas translate into practical, executable systems — such as backtesting engines, factor models, and market analysis tools.
The goal is to continuously learn, experiment, and refine approaches to systematic investing and quantitative research.
Personal Project & Educational Use Only. This website and all associated code is a personal, non-commercial project created solely for educational purposes, self-development, and career portfolio demonstration.
All code, tools, and research showcased here were developed entirely independently using only personal equipment, publicly available open-source libraries, and publicly accessible market data.
The strategies and tools presented are for research and demonstration purposes only and do not constitute financial advice. Past backtesting results do not guarantee future performance. This platform does not manage any third-party funds and is not a registered investment service.