Pranav Singh — Associate Consultant

I am a Quantitative Researcher who architects and validates systematic trading strategies through a rigorous, first-principles approach. My core belief is that sustainable alpha is not found by chance; it is engineered through a deep understanding of market dynamics, meticulous data handling, and an unwavering commitment to model interpretability. My process is focused on building robust, explainable, and economically-sound models before they are considered for deployment. I specialize in moving from raw, high-frequency data to actionable, validated insights. My research framework includes: Quantitative Research & Alpha Generation: Systematically testing hypotheses derived from market microstructure and economic theory. I explore the entire data-to-signal pipeline, from wrangling terabytes of granular data (L1/L2, options) to identifying predictive patterns. Advanced Feature Engineering: My primary focus. I move beyond standard factors to engineer sophisticated features that capture complex, non-linear market behaviors. This includes modeling the surface and term structure of Implied Volatility (IV) and leveraging the predictive power of option greeks, particularly second-order effects like Vanna and Charm. Machine Learning & Interpretability: Utilizing powerful models like XGBoost not as black boxes, but as investigative tools. I leverage Machine Learning Interpretability (MLI) techniques, especially SHAP analysis, to decompose every prediction. This allows me to validate a model's logic, ensure it behaves as expected across different market regimes, and build trust in its decisions. Robust Validation: Employing rigorous walk-forward backtesting and scenario analysis to stress-test strategies against overfitting and ensure their viability under real-world conditions, including transaction costs and slippage. I am now seeking a full-time Quantitative Researcher role where I can apply this validation-centric framework within a collaborative, data-driven team. My goal is to contribute to the development of profitable and resilient trading systems by ensuring that every strategy we deploy is not just high-performing, but fundamentally understood. Let's connect to discuss challenges in alpha generation and modern quantitative research.

Stackforce AI infers this person is a Quantitative Researcher specializing in Fintech and Algorithmic Trading.

Location: Gurugram, Haryana, India

Experience: 5 yrs 1 mo