Chirag Kapoor

Quantitative Researcher

Edelweiss•  July 2022 - Present•  Mumbai

- Commanding 1/3rd of the book's exposure across diverse strategies with a 2024’s Sharpe above 2.5 and a Calmar of 3. - Exceeded FY 2023/24 revenue target by 33% and surpassed OS/IS benchmarks of 0.7, ensuring robust performance. - Developed short-term momentum strategies that safeguarded the book during a challenging phase in 2023. - Enhanced the reversion basket using analyst dataset, stabilising the book with crucial net-offs in existing strategies. - Implemented a fundamental strategy that performed strongly during FY2023/24 and in the recent election phase. - Accelerated alpha research speed and automated strategy monitoring, significantly boosting productivity and scalability. - Developed new risk management techniques that reduced volatility and improved the Calmar ratio, ensuring robust OS. - Provided actionable insights and played a crucial role in data monitoring, process oversight, and trade execution.

Summer Intern

Akshat S. and Amol N. SRC•  June 2021 - August 2021•  Singapore

- Designed a custom dashboard using Dash and Plotly to facilitate the analysis and backtesting of multiple investment models, enabling efficient visualisation of key performance metrics and informing investment decision-making. - Developed a trading model based on the Fractal Market Hypothesis to identify imminent trend reversals in FX crosses. - Short-Term Equilibrium Exchange Rate Model – Developed a STEER model based on 5 economic factors for a universe of 841 FX pairs - each pair was backtested and optimised. A portfolio of best-performing currencies was selected. - Mean-Reverting FX basket strategy – Developed a host of mean-reverting FX baskets using dimensionality reduction techniques. Each basket was individually optimised, and the best-performing bets were sized into an overall portfolio. - Generic Backtesting Toolkit – Engineered an overarching backtesting platform that could be used to study any trading strategy. - Also architected a database (SQLite) to structure the massive stream of daily data.

Data Analyst

Freightwalla•  May 2020 - July 2020•  Mumbai

- Sailing Schedule Timeliness – Enhanced the search data to include the performance of carriers based on heuristics and Machine Learning models so that clients can make smarter booking choices. Identified anomalous behaviour with the system data using statistical modelling and formulated a pipeline for the same. - Personalised Search Results – Studied the customer data and engineered a scoring system based on statistical analysis. - Customer Retention Prediction – Won best hackathon idea for building a time-series model to get a forecast of client's booking habits, including data on key metrics such as number of bookings, average freight rate, and preferred commodity.