# Quant Skills

A career in quantitative finance is a fusion of mathematics, programming, and finance. It demands not only a foundational understanding of these disciplines but also familiarity with cutting-edge technologies and methodologies. As technology evolves rapidly, so do the skills required in the quant field. Mastering this combination of competencies is crucial for success in a quant career.

In this page, I will explore essential quantitative skills, ranging from basic to advanced levels, and cover key areas relevant to this profession. I am currently pursuing a Master of Quantitative Finance at Rutgers Business School. The topics discussed here draw from both my studies and my professional experience. I hope you find this information valuable.

## Math

### Basic

- Calculus
- Linear Algebra
- Statistics and probability
- Differential equations
- Probability

### Advanced

Math in advanced section will be more rigorous and detailed, emphasizing the theoretical foundations of mathematical methods compared to the basic math sections. Even same topics will be examined through the lens of more advanced theories. For example, probability theory will be examined through the lens of measure theory, which provides a rigorous foundation for probability theory.

- Advanced probability theory
- Econometrics
- Optimization

## Programming

### Basic

- Programming basics
- Python
- C++

### Advanced

- Design patterns
- Architecture patterns
- Machine learning with PyTorch

## Finance

### Basic

- Time value of money and discounted cash flow
- Stock valuation and financial statement analysis
- Modern portfolio theory
- Efficient market hypothesis and CAPM

### Advanced

- Derivatives
- Fixed income
- Risk management
- Common trading strategies