Divyansh Jain

Architected and deployed a Long-Short Momentum intraday strategy for US Equities, achieving 30% CAGR and a 3.8 Sharpe Ratio by leveraging SOTA momentum signal research and HFT infrastructure.

Engineered a systematic Long-Only strategy for Indian markets using microstructure research into production-grade features, delivering 25% CAGR and a 3.5 Sharpe Ratio.

Developed a positional US Equity Options strategy, leveraging ML-driven volatility forecasting and options pricing research, achieving 25% CAGR out-of-sample with active risk management.

Applied statistical techniques and Python engines to validate alpha signals across US and Global Equities & Derivatives, ensuring robustness against overfitting and regime changes.

Gained hands-on experience in photolithography, utilizing high-precision tools like lithography mask aligners and vacuum chambers.

Engaged with leading professors and industry experts on topics such as high-performance computing (HPC), AI & ML applications in chip design, and quantum computing and Quantum Finance.

Researched from platforms like Bloomberg to construct financial models and dynamic asset allocation models across Equities, Commodities, Fixed Income, Crypto, and REITs, tailored to various market scenarios.

Developed a predictive model using ML to forecast market trends, improving investment strategy accuracy by 15%.

Collaborated with team members to validate model performance using industry-standard tools and metrics, contributing to research for FPGA & HPC semiconductor chips for quantitative finance.

Developed systematic statistical arbitrage strategies (alphas) for long-short, market-neutral portfolios across US, EUR, ASIA, and Global financial markets.

Researched and optimized over 200 alpha models using alternative datasets and research papers, achieving Sharpe ratios exceeding 3.00, with a highest of 5.

Backtested and simulated alpha models to ensure their robustness and consistency across various market regimes.

Strengthened cybersecurity by implementing password protection protocols, reducing security breaches by 25%.

Proposed strategic solutions and implemented safeguards, leading to a 20% decrease in system vulnerabilities.

Utilized ML in the Workshastra project, resulting in a 30% improvement in candidate behavior analysis accuracy.

Conducted thorough data cleansing & preprocessing, achieving a 95% data quality assurance rate.