I am a quantitative researcher and economist with a background in international and financial economics, financial engineering, and professional accounting. My work spans quantitative finance, systematic trading, and economic research, combining statistical modeling, computational methods, and applied policy analysis. In quantitative finance, my research focuses on derivatives, systematic trading strategies, portfolio construction, and risk analytics. I build robust and interpretable quantitative models that translate empirical research into practical trading and risk-management systems. Alongside financial research, I conduct economic research focused on the Global South, particularly on economic development, institutional reforms, and evidence-based economic policy design. My research interests include financial market development, macroeconomic stability, industrial growth, and policy frameworks that support sustainable economic transformation in emerging and developing economies. My long-term goal is to bridge quantitative financial modeling and economic policy research, applying data-driven approaches to both financial markets and real-world economic development challenges.

My quantitative research spans the full lifecycle of systematic trading and financial modeling:

• Market data acquisition and feature engineering
• Strategy research and hypothesis testing
• Risk-adjusted performance evaluation
• Portfolio construction and drawdown control
• Paper trading and execution monitoring
• Machine learning applications for trading and risk management

I primarily work with futures, equities, and options, focusing on robustness, interpretability, and scalable quantitative systems.

My economic research focuses on development challenges and policy design in the Global South, including:

• Economic governance and institutional reforms
• Industrial policy and employment generation
• Financial market development in emerging economies
• Energy economics (hydropower, renewables, infrastructure)
• Trade, capital flows, and macroeconomic stability
• Evidence-based policy design using econometric methods

I aim to contribute research that informs practical policy decisions for sustainable economic development.

• Master of Science in Financial Economics
  University of Wisconsin–Madison
  Focus: Fixed Income, Econometrics, Machine Learning

• Master of Arts in Economics
  Focus: Econometrics and Development Economics

• Master of Science in Financial Engineering
  Focus: Quantitative Trading, Derivatives, Algorithmic Systems

• Chartered Accountant (CA)
  Institute of Chartered Accountants of India

• Capital Markets & Securities Analyst (CMSA)
  Corporate Finance Institute, Canada

Developed Python-based pipelines to process raw market data and compute returns, volatility, PnL attribution, and drawdowns. Emphasis on clean data handling, performance diagnostics, and exploratory market behavior analysis.

Designed and backtested systematic strategies using historical data. Evaluated performance using risk-adjusted metrics, stability across regimes, and sensitivity to parameter choices.

Built trend-following strategies on futures contracts incorporating volatility targeting, leverage control, contract rollovers, and drawdown-based risk limits.

Modeled option payoffs and Greeks. Analyzed implied versus realized volatility and evaluated option strategies under different volatility and market regime assumptions.

Applied machine learning models to financial time-series for signal evaluation. Focused on feature engineering, walk-forward validation, and avoiding overfitting in predictive models.

Implemented automated paper-trading systems integrating strategy signals, execution logic, PnL tracking, and real-time risk monitoring to simulate live trading environments.

Developed risk-focused models incorporating volatility forecasting, Value-at-Risk, Conditional VaR, stress testing, and machine-learning-based risk estimation frameworks.

Python | C++ | R | SQL | Advanced Excel | Linux

Time Series Analysis
Econometrics
Optimization
Machine Learning
Volatility Modeling
Portfolio Risk Management

NumPy
Pandas
SciPy
Statsmodels
Scikit-learn
PyTorch
TensorFlow
Backtrader
Zipline

Apache Airflow
Anaconda
MySQL
Azure
AWS
Google Cloud

Matplotlib
Plotly
Power BI
Tableau

Bloomberg Terminal
FactSet
Reuters Eikon
S&P Capital IQ

I am particularly interested in roles involving:

• Quantitative research and systematic trading
• Derivatives and volatility modeling
• Risk analytics and portfolio construction
• Algorithmic trading infrastructure
• Applied machine learning in financial markets
• Economic policy research in emerging economies

Nathan S. Brand Award 2025
Excellence in Finance & Investment Banking Presentation

McKinsey & Co. Leadership Program
United Nations Delegate

Portfolio Website
https://canirajneupane.notion.site/quant