Bengaluru, Karnataka, India
Quantitative trader and market infrastructure engineer with experience building systematic investment strategies and institutional trading infrastructure across traditional finance and digital assets. IIT graduate and CFA Level I (Access Scholar). Started by managing approximately $1M for family and friends, delivering returns that outperformed India's broad equity benchmark by over 4% annually with significantly lower drawdowns. Later helped scale a wealth management business to $20M+ AUM in under two years by designing systematic portfolio strategies for high-net-worth investors. Built and scaled market-making infrastructure supporting 300+ spot markets and $45M+ in monthly trading volume, while developing low-latency arbitrage systems in Rust, delta-neutral funding arbitrage strategies, and tail-risk-managed options strategies. Also built a spot market-making platform from the ground up for a global digital asset exchange. Now building an institutional-grade quantitative trading and market-making firm focused on liquidity provision, systematic trading, and market infrastructure for digital assets.
Market Making & Quantitative Investment management
Crypto Spot Market Making
Crypto Market Making/Trading
1) Designed and Implemented a framework to identify best performing mutual fund categories and funds, providing 50-150 bps higher returns than the respective index and limiting the downside upto 50% 2) Optimised multi-asset portfolios based on certain risk metrics (from asset selection to allocation to instrument selection) that beats the benchmark & having 75% to 50% lesser volatility. Created a systematic rebalancing strategy to improve portfolio return by at least 50 bps. 3) Implemented a solution to calculate long/short term capital gains/losses using users transaction data and backtest an users portfolio performance if he had invested with us vs his external portfolio by cloning all of his transactions. 4) Implemented a momentum based asset rotation framework that beats the major indices by 1.3%-2.8% having 50% less volatility. 5) Automation of all important repetitive tasks using modularised Flask api, MySQL database and Docker that makes manual intervention minimal. Including but not limited to, creation of interactive dashboard to review fund/portfolio performance, overlap & asset allocation. Generation of a detailed pdf of customised investment recommendation in an automated fashion. Automated portfolio construction, rebalancing, data preprocessing and dataset generation for the use of tech team and advisors. ▪Technologies utilised: Python, Pandas, Numpy, Plotly, Streamlit, Flask, PostgresSQL, MySQL, Docker, SqlAlchemy, Excel, VS Code, Git
Part of Global Research Network(GRN) Team of WorldQuant Predictive utilising machine learning/Quant research methods. Designed new features, performed exploratory data analysis and applied feature engineering techniques on the data in SQL/Python in order to prepare a better interpretable data to feed into Machine Learning models and to improve the model performance. Experimented with bagging, boosting, linear models, regularization, model blending, and stacking etc, optimising hyper parameters using GridsearchCv, RadomizedsearchCv, Optuna etc to design models having better predictability than base models designed by senior researchers for regression and classification problems, evaluated model performance based on metrics such as Precision, Recall, Auc_roc, NDCG, MSQNE, RMSE, and MAE etc Tech Used: Python(OOPs, Pandas, Numpy, Scipy.stats, Matplotlib, Seaborn, Dask, Statsmodels), Machine Learning(Scikit-learn, Lightgbm, Xgboost etc), SQL, Analytics, Tableau