About this Book

Unleash the power of .NET Blazor to build cutting-edge financial applications with “Quantitative Finance with C# .NET Blazor”. This comprehensive guide walks you through the entire process, from setting up your development environment to implementing complex trading strategies.

Learn to:

• Master .NET Blazor Grasp the fundamentals of building dynamic web applications using .NET Blazor and Visual Studio.
• Harness Data Efficiently manage market data, from fetching real-time quotes to storing historical data using SQLite and Entity Framework Core.
• Visualize Insights Create compelling data visualizations with QuickGrid and Plotly.Blazor to uncover hidden patterns and trends.
• Apply Quantitative Techniques: Dive into linear analysis, technical indicators, and machine learning to make informed financial decisions.
• Price Financial Instruments Understand the intricacies of option pricing using the Black-Scholes model and the QuantLib library.
• Model Fixed-Income Explore the world of bonds, interest rates, and credit default swaps with practical examples.
• Backtest trading Strategies Develop and refine trading strategies using a robust backtesting framework.

Whether you're a seasoned quant developer or a curious developer eager to explore quantitative finance, this book provides the knowledge and tools to excel in the world of quantitative finance.

Start building your financial future today!

Table of Contents

Contents	v
Introduction	1
Overview	1
What this Book Includes	2
Is this Book for You?	3
What Do You Need to Use this Book?	4
How this Book Is Organized	4
Use Code Examples	5
Customer Support	6
1	Introduction to .NET Blazor	7
1.1	Install Visual Studio and .NET SDK	7
1.2	Blazor Render Modes	8
1.3	Create the Blazor Project	8
1.3.1	Create a New Application	8
1.3.2	Structure of the Project	10
1.3.3	Run the Project	12
1.4	Set up Navigation	13
1.4.1	Change Layout	13
1.4.2	Update Home Page	14
1.4.3	Update Navigation Menu	15
1.5	Use QuickGrid	17
1.5.1	Customer Model	18
1.5.2	QuickGrid Implementation	19
1.5.3	Test QuickGrid	20
1.5.4	Pagination	22
2	Database and Entity Framework Core	25
2.1	Install SQLite and Tools	25
2.2	Data Model	26
2.2.1	Connection String	26
2.2.2	Define Model Data Types	26
2.2.3	Define Data Context	27
2.2.4	Check Database	29
2.3	Add Seed Data	30
2.3.1	Helpers Class	30
2.3.2	SeedData Class	32
2.3.3	Add Stock Tickers to Database	33
2.3.4	Add Stock Prices to Database	34
2.3.5	Add Index Data to Database	34
2.3.6	Check Database	35
2.4	Data Service	35
2.4.1	Service Class	35
2.4.2	Display Stock Data	39
2.4.3	Display Stock Price Data	41
2.4.4	Display Index Data	44
2.5	CRUD Operations	46
2.5.1	Create Operation	46
2.5.2	Update and Delete Operations	49
3	Market Data	55
3.1	Market Data from Yahoo Finance	55
3.1.1	Stock Data	56
3.1.2	Download Market Data	57
3.1.3	Save Market Data to Database	60
3.1.4	Stock Quote	65
3.2	Market Data from Fynance	70
3.2.1	Stock Data	70
3.2.2	Download Market Data	71
3.2.3	Intraday Stock Data	73
3.3	Market Data from Alpha Vantage	76
3.3.1	Create API Key Service	76
3.3.2	EOD Stock Data	77
3.3.3	Intraday Stock Data	81
3.3.4	Stock Quotes	85
3.4	ISDA Rates from IHS Markit	87
3.4.1	ISDA Interest Rate API	88
3.4.2	Email Service	91
3.4.3	Test ISDA Interest Rate API	92
4	Data Visualization	97
4.1	Line Charts	98
4.1.1	Simple Line Charts	98
4.2	Multiple-Line Charts	101
4.3	Line Charts with Two Y-Axes	103
4.4	Specialized 2D Charts	107
4.4.1	Area Charts	107
4.4.2	Multiple-Area Charts	109
4.4.3	Bar Charts	113
4.4.4	Pie Charts	120
4.4.5	Polar Charts	123
4.5	3D Charts	126
4.5.1	3D Line Charts	126
4.5.2	3D Surface Charts	129
4.6	Stock Charts	141
4.6.1	Candlestick Charts	141
4.6.2	Candlestick and Volume Charts	144
4.6.3	Candlestick with Category	149
5	Linear Analysis	151
5.1	Simple Linear Regression	151
5.1.1	Implementation	152
5.1.2	R-Squared	153
5.1.3	What are Alpha and Beta?	154
5.1.4	Data Service	154
5.1.5	SLR Results for Index Data	156
5.1.6	SLR Results for Stocks	161
5.2	Simple 2D PCA	167
5.2.1	Implementation	169
5.2.2	Data Service	170
5.2.3	2D PCA Results for Index Data	171
5.2.4	2D PCA Results for Stocks	176
5.2.5	Compare SLR and 2D PCA	181
5.3	Multiple Linear Regressions	183
5.3.1	Implementation	183
5.3.2	Data Service	186
5.3.3	MLR Results for Index Data	187
5.3.4	MLR Results for Stocks	191
5.4	Multiple PCA	196
5.4.1	Implementation	196
5.4.2	Data Service	197
5.4.3	MPCA Results for Index Data	198
5.4.4	MPCA Results for Stocks	202
6	Technical Indicators	209
6.1	Implementation	209
6.1.1	Helper Class	210
6.1.2	Data Service	220
6.2	Chart Templates	226
6.2.1	Template for Overlay Indicators	226
6.2.2	Template for Oscillator-Based Indicators	231
6.3	Overlap Indicators	237
6.3.1	Moving Average Based Indicators	237
6.3.2	Bollinger Band Indicator	241
6.3.3	SAR Indicator	243
6.4	Momentum Indicators	246
6.4.1	ADX Indicator	247
6.4.2	APO Indicator	249
6.4.3	Aroon Indicator	251
6.4.4	BOP Indicator	254
6.4.5	CCI Indicator	256
6.4.6	MACD Indicator	258
6.4.7	RSI Indicator	261
6.4.8	Stochastic Indicator	263
6.4.9	Williams %R Indicator	266
6.5	Volume Indicators	268
6.5.1	AD Indicator	268
6.5.2	OBV Indicator	270
6.6	Volatility Indicators	273
6.6.1	TR Indicator	273
6.6.2	ATR Indictor	275
6.6.3	NATR Indicator	277
7	Machine Learning	281
7.1	Data Preparation	281
7.1.1	Implementation	283
7.1.2	Data Service	288
7.1.3	Helper Functions	289
7.1.4	Original ML Data	292
7.1.5	Processed ML Data	294
7.2	KNN Classification	298
7.2.1	Implementation	299
7.2.2	Template for Classification	300
7.2.3	KNN Results	304
7.2.4	Confusion Matrix	306
7.3	Support Vector Machine	308
7.3.1	SVM For Classification	308
7.3.2	SVM for Regression	312
7.4	Artificial Neural Networks	321
7.4.1	Introduction to Neural Networks	321
7.4.2	Helper Class	323
7.4.3	Neural Networks for Classification	325
7.4.4	Neural Networks for Regression	334
8	Option Pricing	345
8.1	Introduction to Options	345
8.1.1	Option Payoffs	346
8.1.2	Option Value	346
8.2	Pricing European Options	347
8.2.1	Black-Scholes Model	347
8.2.2	Generalized Black-Scholes Model	348
8.2.3	Implementation	348
8.2.4	Black-Scholes Greeks	351
8.2.5	Display Results	357
8.3	Pricing American Options	374
8.3.1	BAW Approximation	374
8.3.2	Implementation	375
8.3.3	BAW Results	379
8.4	Pricing Barrier Options	383
8.4.1	Standard Barrier Options Formulas	383
8.4.2	Implementation	385
8.4.3	Barrier Option Results	392
9	Option Pricing Using QuantLib	397
9.1	Introduction to QuantLib	397
9.1.1	Dates, Calendars, and Day Counters	397
9.1.2	Term Structures	399
9.2	European Options	400
9.2.1	Helper Class	400
9.2.2	Price and Greeks Results	404
9.2.3	Helper Functions for Implied Volatility	409
9.2.4	Implied Volatility Results	410
9.3	American Options	412
9.3.1	Helper Functions	413
9.3.2	American Option Results	416
9.4	Barrier Options	421
9.4.1	Helper Functions	421
9.4.2	Barrier Option Results	423
9.5	Bermudan Options	427
9.5.1	Helper Functions	427
9.5.2	Bermudan Option Results	430
9.6	American Options in Real-World	434
9.6.1	Term Structure for Interest Rates	434
9.6.2	Dividend Yield Curve	436
9.6.3	Volatility Smile	437
9.6.4	Helper Functions	438
9.6.5	Real-World Option Results	440
10	Pricing Fixed-Income Instruments	445
10.1	Pricing Simple Bonds	445
10.1.1	Discounting Factors	446
10.1.2	Pricing Bonds with Flat Rates	447
10.1.3	Simple Bond Results	450
10.1.4	Compounding Frequency Conventions	452
10.1.5	Pricing Bonds with a Rate Curve	453
10.1.6	Yield To Maturity	457
10.2	Zero-Coupon Yield Curve	457
10.2.1	Treasury Zero-Coupon Yield Curve	458
10.2.2	Interbank Zero-Coupon Yield Curve	467
10.2.3	Credit Spread Term Structures	472
10.3	Bonds with Embedded Options	482
10.3.1	Callable Bonds	482
10.3.2	Convertible Bonds	487
10.4	CDS Pricing	492
10.4.1	Hazard Rate and Default Probability	493
10.4.2	Risky Annuities and Risky Durations	501
10.4.3	CDS Present Values	501
10.4.4	CDS Prices	509
11	Trading Strategies and Backtesting	515
11.1	Trading Strategy Identification	515
11.2	Crossover Trading Signals	517
11.2.1	Trading Strategy Objects	518
11.2.2	Two-MA Crossover	519
11.2.3	Two-MA Crossover with NATR Filter	530
11.3	Z-Score Trading Signals	541
11.3.1	MA-Based Z-Scores	542
11.3.2	RSI-Based Z-Scores	547
11.3.3	PPO-Based Z-Score	549
11.4	Backtesting System	553
11.4.1	P&L Calculation	553
11.4.2	Risk Measure	556
11.4.3	Backtesting for MA Crossover Strategies	559
11.4.4	Backtesting for Z-Score Strategies	568
Index	573

Introduction

        Overview
        What This Book Includes
        Is This Book for You
        What Do You Need to Use This Book
        How This Book is Organized
        Using Code Examples

---

Overview

Welcome to “Quantitative Finance with C# .NET Blazor”. This book provides all the tools you need to develop modern financial web applications using .NET Blazor. I hope that this book will be useful for quant developers, quant analysts, individual traders, .NET programmers, web developers, and students of all skill levels.

In recent years, quantitative finance has become an attractive field due to the intellectual challenge and high remuneration. Many scientists, engineers, and students wish to change their careers to become quant developers or analysts in investment banks or hedge fund firms. Most of them have a solid background in mathematics, statistical analysis, physics modeling, and programming, but lack knowledge and experience in quantitative finance. A question that they constantly ask is “what do I need to prepare myself to become a quant developer/analyst?” This book will provide an answer to this question and prepare you with solid technical skills in quantitative analysis and development.

On the other hand, especially after the COVID-19 pandemic, more and more individuals want to become independent (“retail”) quantitative traders who are looking to start their own quantitative or algorithmic trading business. This path offers the flexibility of working from home and flexible working hours. The most common issue they face is “what kind of background do I need to be successful in quantitative trading?” Most of these individuals have advanced degrees in physics, math, engineering, or computer science. This kind of training in hard sciences gives them an edge in quantitative analysis and pricing complex derivative instruments. However, the ability to convert trading ideas into trading strategies and the programming skill needed to implement an automated trading system are equally important. This book will prepare you with all the necessary analysis and programming techniques to become a well-equipped individual quant trader.

So, what kinds of applications – desktop or web – are more suitable in quantitative finance? Financial applications development began with desktop applications, which are used on specific machines where the applications have been installed. Generally, writing code for desktop application is relatively easier, and you have much greater control over how your application works. Another benefit is that desktop applications are always offline because they are executable programs that do not need to be accessed over the internet. In my previously published book, “Practical C# and WPF for Financial Markets”, I provided a detailed explanation of how to build desktop applications in finance using C#, WPF, MVVM, and the .NET framework.

In contrast to desktop applications, a web-based application is software that can be accessed through the internet. The software and database reside on a central server rather than on a local desktop system. Web-based applications are the better way to take advantage of today’s technology to enhance the productivity and efficiency. Web or distributed applications are mostly used on client-server networks where the user’s computer accesses information from the server or a cloud computing server. There are some obvious advantages of web applications over desktop applications. Web applications need to be installed only once, which avoids the burden in deploying in each client machine and makes software updates and maintenance much easier. They are platform-independent, adaptable for mobile application, and can be accessed from anywhere in the world.

Visual Studio offers several templates to build web applications, including Angular and React SPA templates. However, Blazor applications are still a better choice in some scenarios. First, Blazor provides a unified development experience since it allows developers to use C# across the entire stack, both on the client-side and server-side. This reduces code duplication and simplifies development compared to Angular/React templates, where separate codebases for frontend and backend exist. It also eliminates the need to learn a front-end language like JavaScript for teams already familiar with C#, allowing developers to build full-stack applications using a single language, C#.

Additionally, Blazor enables developers to create highly interactive and visually appealing user interfaces, significantly enhancing user experience. It facilitates the development of applications that can run across different platforms, increasing the reach of your web apps. Blazor can seamlessly integrate into existing .NET projects, making the development process more efficient for those already working within this ecosystem. Blazor also provides server-side rendering, which offers a quicker initial page load experience for users and improves search engine visibility as search engines can easily index rendered content.

I wrote this book with the intention of providing a complete and comprehensive explanation of modern web application development in quantitative finance using the latest technology stack: .NET 8 Blazor. This book pays special attention to creating various business applications and reusable .NET libraries that can be directly used in real-world finance applications. Much of this book contains original work based on my programming experience developing business applications for quantitative analysis in the financial field.

“Quantitative Finance with C# .NET Blazor” provides everything you need to create your own advanced web applications in quantitative finance and reusable packages using .NET Blazor. It demonstrates how to use Blazor, the latest .NET technology, to build a variety of financial applications ranging from simple databases, market data APIs, and data visualization to quantitative analysis, pricing equity options, complex fixed-income instruments, machine learning, and trading strategy development. I will do my best to introduce you to modern web application development in quantitative finance in a straightforward manner – simple enough to be easily followed by a quant or .NET/web developer with basic prior experience in developing business applications using .NET technology.

What This Book Includes

This book and its sample code listings, which are available for download at my website at https://drxudotnet.com, provide you with

• A complete, in-depth instruction to modern web application development in quantitative finance with .NET Blazor. After reading this book and running the example programs, you will be able to create various sophisticated business applications in quantitative finance using the .NET technology.
• Ready-to-run example programs that allow you to explore the quantitative finance programming techniques and understand how the algorithms work. You can modify the code examples or add new features to them to form the basis of your own projects. Some of the example code listings provided in this book are already sophisticated programming packages in quantitative finance that you can use directly in your own real-world business applications.
• Many classes and components in the sample code listings that you will find useful in your quant development. These classes and components include charting libraries, various quantitative analysis models, pricing engines for options and fixed income instruments, machine learning for trading strategy development and back-testing, and the other useful utility classes. You can extract these classes and components and plug them into your own business applications.

Is This Book for You

You do not need to be an experienced quant developer/analyst or .NET/web developer to use this book. I designed it to be useful to people of all levels of .NET/web programming experience and financial backgrounds. In fact, if you have some prior experience with quantitative analysis/development and programming languages such as C++, Java, R, Python, VBA, C#, or JavaScript, you will be able to sit down at your computer, start Microsoft Visual Studio Community 2022, follow the examples provided in this book, and quickly become proficient in quantitative application development.

For those of you who are already experienced quant analysts/developers or .NET programmers, I believe this book has much to offer as well. A great deal of the information in this book about .NET/web programming in quantitative finance is not available in other tutorials and reference books. Additionally, you can use most of the example programs in this book directly in your own real-world application development. This book will provide you with a level of detail, explanation, instruction, and sample code that will enable you to do just about anything related to quantitative finance application development using .NET Blazor.

Perhaps you are a scientist, engineer, mathematician, or student, rather than a professional quant developer/analyst, .NET programmer, or web developer; nevertheless, this book is still a valuable resource for you. My own background is in theoretical physics, a field that involves extensive physical modeling, numerical calculations, and graphical representations of data. I devoted many years to this field, from undergraduate studies through to my PhD.

My first computer experience was with FORTRAN, and later, I gained programming experience with Basic, C, C++, R, Python, JavaScript, and MATLAB. I always sought an ideal development tool that would allow me not only to generate data easily (computation capability) but also to represent data graphically (graphics and charting power). The .NET framework and Microsoft Visual Studio development environment made it possible to develop such integrated applications. Since the release of Microsoft .NET 1.0, I have been passionate about the C# language and have used it successfully to create powerful business applications for quantitative analysis.

Quant analysts/developers, individual quant traders, and .NET/web developers can routinely use the majority of the example programs in this book. Throughout, I emphasize the usefulness of web application development for real-world quantitative finance problems. If you follow the instructions in this book closely, you will be able to develop various practical business applications in quantitative finance, from linear analysis and machine learning to pricing engines and trading strategy development.

At the same time, I will not spend too much time discussing programming style, execution speed, and code optimization, as many books already cover these topics extensively. Most of the example programs in this book omit error handling to make the code easier to understand by focusing solely on key concepts and practical applications.

What Do You Need to Use This Book?

You do not need any special equipment to make the most of this book and understand the algorithms presented. This book leverages open-source frameworks and libraries. To run and modify the sample programs, you will need a computer capable of running either Windows 10 or 11. The server-side software installed on your computer should include Visual Studio 2022 (the Community version is fine) and the .NET 8 SDK or higher.

If you have the .NET 7 SDK or an older version, you may still run most of the sample code with a few modifications. However, please remember that this book is intended for .NET 8 Blazor applications, and all of the example programs were created and tested on this platform. Therefore, it is best to run the sample code on the same platform.

How This Book Is Organized

This book is structured into eleven chapters, each exploring a distinct topic related to quantitative finance applications using .NET Blazor. Below is a summary of each chapter to provide an overview of the book’s contents:

Chapter 1, Introduction to .NET Blazor
Introduction to .NET Blazor This chapter covers the basics of .NET Blazor, including how to set up the tools, packages, and development environment required for building a Blazor web application using .NET Blazor with the Visual Studio IDE.

Chapter 2, Database and Entity Framework Core
This chapter introduces SQLite and Entity Framework Core, which is a built-in feature included with Visual Studio. It demonstrates how to create simple database and how to interact with the data in Blazor applications through data services.

Chapter 3, Market Data
This chapter explains how to interact with market data providers’ API and retrieve free market data from online sources. This includes end of the day (EOD) stock data, intraday data, real-time stock price quotes, and interest rate data.

Chapter 4, Data Visualization
Data visualization plays a critical role in quantitative finance and trading. Quant analysts and traders need to visually monitor real-time changes in the market and trading signals on their screens. This chapter illustrates how to use the Plotly.Blazor library to create various charts and display the market data in a Blazor application.

Chapter 5, Linear Analysis
This chapter presents fundamental analysis approaches in quantitative finance based on linear analysis. It covers how to develop various business applications using linear regression, principal component analysis (PCA), and correlation.

Chapter 6, Technical Indicators
This chapter discusses various technical indicators commonly used in quantitative analysis. A technical indicator is a mathematical calculation based on historic market data, used to predict market direction. I will show you how to convert various indicators provided in TA-Lib library into data services and how to apply them to your Blazor applications.

Chapter 7, Machine Learning
This chapter covers the advanced quantitative analysis techniques, specifically machine learning. Machine-learning technique has become one of the most promising fields in quantitative finance, widely used in predicting future stock prices. This chapter will focus on supervised learning and present several commonly used machine-learning algorithms in finance, including the K-nearest neighbors, support vector machines, and neural networks.

Chapter 8, Option Pricing
This chapter discusses the Black-Scholes formula used for options pricing. It presents several different implementations for calculating the price and Greeks of European and American options, as well as barrier options. You will gain a better understanding of how the algorithm works in pricing options through coding exercises.

Chapter 9, Option Pricing Using QuantLib
This chapter demonstrates how to use the open-source library QuantLib to calculate the price and Greeks of European and American options. It also discusses how to use this library to price other options, including barrier options, Bermudan options, and real-world options.

Chapter 10, Pricing Fixed-Income Instruments
This chapter demonstrates how to price fixed-income instruments, including interest rates, bonds, and credit default swaps. It also covers related topics such as cash flows, term structures, yield curves, discount factors, and zero-coupon bonds. Detailed procedures are provided on how to use the QuantLib library to price these complex financial instruments.

Chapter 11, Trading Strategies and Backtesting
This chapter presents several trading strategies using simple quantitative analysis techniques, including crossovers and z-score based on commonly used technical indicators. It also covers a long-short based backtesting framework, allowing you to examine the historical performance of your strategies in stock trading.

Using Code Examples

You may use the code in this book in your own applications and documentation without needing to contact the author for permission, unless you are reproducing a significant portion of the code. For instance, writing a program that utilizes several code segments from this book does not necessitate permission. However, selling or distributing the example code listings requires permission. If you plan to integrate a substantial amount of example code from this book into your applications and documentation, you will also need permission. Integrating the example code from this book into commercial products is prohibited without written permission from the author.