Quantitative Finance & Financial Engineering with Python

This course contains the use of artificial intelligence

Disclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor.

Welcome to Quantitative Finance & Financial Engineering with Python course. This is a comprehensive project based course where you will learn how to analyze financial data, apply financial mathematics concepts, build quantitative models, manage risk, optimize portfolios, simulate financial scenarios, and solve real world finance problems. This course is a perfect combination between Python and quantitative finance, making it an ideal opportunity for you to practice your programming skills while improving your technical knowledge in statistical modelling. In the introduction session, you will learn the basic fundamentals of quantitative finance and financial engineering, such as getting to know financial engineering use cases and quant models that will be used. Then, in the next section, we will learn about basic Python fundamentals for computational finance, for example we will learn how to access financial data using Yahoo Finance, clean and prepare financial datasets by handling missing values, then, we will analyze and visualize financial data using Pandas and Matplotlib. Afterward, in the next section, we will learn about financial mathematics, descriptive statistics, probability, covariance, correlation, and regression analysis. This section will provide the mathematical and statistical foundation needed to analyze financial data, understand relationships between financial variables, and prepare you for more advanced projects. Then, in the next section, we will learn about time series analysis and volatility modeling using GARCH. We will learn how to analyze financial time series data, model changing market volatility, and interpret volatility patterns for quantitative risk analysis. After that, we will learn about stochastic calculus and Geometric Brownian Motion, where we will simulate asset price movements under uncertainty using stochastic processes. Next, we will conduct Monte Carlo Simulation to model uncertainty and simulate thousands of possible financial outcomes. Then, following that, we will learn about risk management, stress testing, and scenario analysis to assess portfolio risk and evaluate how financial models perform under different market conditions and extreme events. Additionally, we are also going to learn about Value at Risk by implementing Historical VaR, Parametric VaR, and Expected Shortfall to estimate potential portfolio losses and measure downside risk. Then, in the next section, we will learn about portfolio optimization and Capital Asset Pricing Model to construct efficient portfolios, evaluate risk return tradeoffs, and estimate expected asset returns. Following that, we are also going to implement financial derivatives pricing models, including forward pricing, the Black–Scholes option pricing model, and the Binomial Tree Model to understand how different derivative contracts are evaluated. Then, after that, we will build real world financial engineering projects. In the first project, we are going to construct an implied volatility surface using options data to analyze how volatility changes across different prices and maturities. Additionally, we will also create a 3D visualization to explore volatility patterns. In the second project, we are going to build a fixed income model focusing on fixed coupon bond pricing to understand how bond values are determined based on cash flows, interest rates, and time to maturity. Lastly, at the end of the course, we are going to build a cointegration and pair analysis model to identify relationships between financial assets and simulate mean reversion behavior using statistical techniques.

First of all, before getting into the course, we need to ask this question to ourselves, why should we learn about quantitative finance and financial engineering. Well, here is my answer, these fields are crucial for the finance industry because they enable professionals to make data driven decisions, quantify uncertainty, and develop advanced solutions for complex financial systems. In addition, understanding these concepts will provide you with valuable analytical and problem solving skills.

Below are things that you can expect to learn from this course;

• Learn the basic fundamentals of quantitative finance and financial engineering

• Learn how to access market data from Yahoo Finance, clean the data, analyse and visualise the data using Pandas and Matplotlib

• Learn about financial mathematics and computational finance

• Learn about descriptive statistics, probability, correlation, covariance, and regression analysis

• Learn about time series analysis and volatility modelling using GARCH

• Learn about stochastic calculus and Geometric Brownian Motion

• Learn about risk management, stress testing, and scenario analysis

• Learn how to calculate historical value at risk, parametric value at risk, and conditional value at risk

• Learn how to conduct Monte Carlo risk simulation

• Learn how to optimize portfolio using Capital Asset Pricing Model and mean variance optimization

• Learn how to conduct forward pricing and financial derivative valuation

• Learn how to conduct option pricing using Black Scholes model

• Learn how to conduct derivatives pricing using Binomial Tree Model

• Learn how to conduct fixed income modelling and fixed coupon bond pricing

• Learn how to build implied volatility surface and create 3D visualization

• Learn how to create cointegrated pair simulation and apply mean reversion concept