– Implemented ”Buying the Loser”, Decision Tree-based Pairs Trading, LSTM-based Trading and MVP Optimization strategies
– Coded model pipelines in Python while leading the project team of 5 graduate students to nail down an optimal structure
– Performed Variance Ratio and Push Response Tests for market behavior on 5-min price data using R and Python
– Implemented and Backtested Trend-following trading strategy with split transactional costs in Python and MATLAB
– Engineered a Sentiment based volatility predictor and trade recommender bot using optimization methods in Call/Put Options.
– Scaled to over 100 people; performed quantitative and qualitative UI/UX research by task analysis and writing user stories
– Created a time tracking and data analysis tool for better self-evaluation of productivity, physical and mental well-being.
– Performed Data Analysis, Wrangling and k-Means Clustering using R, Shiny, RStudio, and LaTeX
– Used distributed computing in Java to make accounts and transactions synchronous and thread-safe
- Used Java, Distributed computing, Threads and Locks, Object Oriented Programming, GitHub and VS Code during the development process.
– Created an app and blog in group of 3 as a part of final project of Data Science class to analyze the periodic and spatial trends of COVID-19, mental health and sentiments in the US.