Jun 2016 – Dec 2017 · 1 yr 6 mos · Gurugram, Haryana, India · On-site

• High-Frequency Trading (HFT)
• Designed, improved, and implemented high-frequency trading strategies in commodity markets in India, leveraging C++ for development and creating HFT signals.
• Developed market-making strategies for equity markets in India and South Africa, focusing on high-frequency trading techniques.
• Researched on high-frequency trading strategies for USD/INR currency options markets in India.
• Engaged in cutting-edge research on high-frequency TBT data to develop trading signals for short-term price movements for HFT strategies. Developed an improved version of Order Flow Imbalance Indicator and fine-tuned it for several asset classes in Indian market like equities,equities futures,index options etc
• Inter-Day Algorithmic Trading
• Researched and designed alphas for market-neutral inter-day algorithmic trading strategies in the Indian futures market, achieving exceptional annualized Sharpe ratios (>4.0), minimal drawdowns, and high returns.
• Analyzed extensive datasets, including inter/intraday price-volume data, options prices, open interest, and analyst recommendations, to identify market inefficiencies and create profitable trading signals.
• Played a key role as one of the top-performing quantitative researchers, contributing alphas with total weight age over 25% to the long-short market-neutral portfolio valued at $30 million, which achieved more than 80% annualized return on capital.
• Provided real-time analytical support to experienced traders, enhancing decision-making as a part of the quant research team.

May 2014 – Jul 2014 · 2 mos · Lausanne Area, Switzerland

• I worked as a Visiting Student Researcher at LIONS, focusing on incorporating shearlets as a representation basis for the sparse recovery of high-dimensional signals from compressed measurements.
• Shearlets are a recently introduced multi-scale framework that provides optimal sparse approximations (in terms of L2 error) for encoding anisotropic features in multivariate signals, such as images. During my internship, I aimed to enhance the reconstruction performance of the L1-minimization convex programming algorithm for recovering sparse high-dimensional images from linear, non-adaptive measurements by leveraging the properties of shearlets.
• Additionally, I delved deeper into the underlying sparsity structures of shearlets to further improve reconstruction performance. This led to the development of a Hierarchical-Structured Sparsity Model for shearlets, which significantly enhanced signal recovery during sparse reconstruction.

May 2013 – Jul 2013 · 2 mos · Mumbai

• Designed and developed a predictive tool to optimize BTS ( Base Transceiver Stations ) operations by forecasting 24-hour load profiles. Analyzed factors like day of the week, public holidays, and festivals to create a robust mathematical model. Implemented machine learning algorithms, including Naive Bayes, Decision Trees, and KNN, alongside time-series techniques like Auto-Regressive (AR) and Moving Average (MA) models. This solution enhanced load prediction accuracy and addressed power inefficiencies by enabling data-driven decision-making for BTS capacity management.