Learning Modern C++ for Finance - Helion
ISBN: 9781098100759
stron: 430, Format: ebook
Data wydania: 2024-11-04
Księgarnia: Helion
Cena książki: 211,65 zł (poprzednio: 246,10 zł)
Oszczędzasz: 14% (-34,45 zł)
This practical book demonstrates why C++ is still one of the dominant production-quality languages for financial applications and systems. Many programmers believe that C++ is too difficult to learn. Author Daniel Hanson demonstrates that this is no longer the case, thanks to modern features added to the C++ Standard beginning in 2011.
Financial programmers will discover how to leverage C++ abstractions that enable safe implementation of financial models. You’ll also explore how popular open source libraries provide additional weapons for attacking mathematical problems. C++ programmers unfamiliar with financial applications also benefit from this handy guide.
- Learn C++ basics from a modern perspective: syntax, inheritance, polymorphism, composition, STL containers, and algorithms
- Dive into newer features and abstractions including functional programming using lambdas, task-based concurrency, and smart pointers
- Implement basic numerical routines in modern C++
- Understand best practices for writing clean and efficient code
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Spis treści
Learning Modern C++ for Finance eBook -- spis treści
- Preface
- Navigating This Book
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
- 1. An Overview of C++
- C++ and Quantitative Finance
- C++11: The Modern Era Is Born
- Open Source Mathematical Libraries
- Some Myths about C++
- Compiled Versus Interpreted Code
- The Components of C++
- C++ Language Features
- The C++ Standard Library
- Some New Language Features Since C++11
- The auto Keyword
- Range-Based for Loops
- The using Keyword
- Uniform Initialization
- Formatting Output
- Class Template Argument Deduction
- Enumerated Constants and Scoped Enumerations
- Enumerated constants
- Potential conflicts with enums
- Scoped enumerations with enum classes
- Lambda Expressions
- Mathematical Operators, Functions, and Constants in C++
- Standard Arithmetic Operators
- Mathematical Functions in the Standard Library
- Mathematical Special Functions
- Standard Library Mathematical Constants
- Naming Conventions
- Summary
- Further Resources
- C++ and Quantitative Finance
- 2. Writing User-Defined Classes with Modern C++ Features
- A Black-Scholes Class
- Representing the Payoff
- Writing the Class Declaration
- Writing the Class Implementation
- Using a Functor for Root Finding: Implied Volatility
- Move Semantics and Special Member Functions
- Data Members and Performance Considerations
- An Introduction to Move Semantics
- Initialization of Constructor Arguments with std::move(.)
- Anonymous Temporary Objects and Move Semantics
- Return Value Optimization
- Default Constructor
- Three-Way Comparison Operator (Spaceship Operator)
- Lambda Expressions and User-Defined Class Members
- Summary
- Additional References
- A Black-Scholes Class
- 3. Inheritance, Polymorphism, and Smart Pointers
- Polymorphism
- Resource Ownership with Raw Pointers
- Using Clone Methods
- Creating an Instance of OptionInfo
- Preventing Shallow Copy
- Implementing the OptionInfo Destructor
- Pricing an Option
- Implementing Copy Operations
- The (Old) Rule of Three
- Introducing Smart Pointers
- Unique Pointers
- Shared Pointers
- Managing Resources with Unique Pointers
- Just Move It
- Using the Result in a Pricing Model
- If Copy Operations Are Required
- Clone methods, revisited
- Implementation of the updated copy operations
- The rule of zero/the rule of five
- Summary
- Further Resources
- 4. The Standard Template Library Part I: Containers and Iterators
- Templates
- Using Function Templates
- Using Class Templates
- Compiling Template Code
- STL Containers
- Sequential Containers
- The vector sequential container
- Storage of objects in a vector
- Polymorphic objects
- Allocation and contiguous memory
- The clear() method
- The front(), back(), and pop_back() methods
- Random access in a vector: at(.) versus [.]
- A potential pitfall with the meaning of () and {} with a vector
- C-arrays and the data() member function
- The std::deque sequential container
- The std::list sequential container
- Fixed-length std::array
- When in doubt, use a vector
- The vector sequential container
- Associative Containers
- std::map as a model data container
- Sequential Containers
- STL Iterators
- Using auto to Reduce Verbosity
- Using Constant Iterators
- Iterators or Indices?
- Iterators on Associative Containers
- Summary
- Further Resources
- Templates
- 5. The Standard Template Library Part II: Algorithms and Ranges
- STL Algorithms
- A First STL Algorithm Example
- A First Example with Ranges
- Some Commonly Used Algorithms
- The for_each algorithm
- The transform algorithm
- Modify and replace elements in the same container
- Modify elements and place in a separate container
- Function Objects as Auxiliary Functions
- Class Member Functions as Auxiliary Functions
- Locating, Sorting, Searching, Copying, and Moving Elements
- Maximum and minimum elements
- Sorting values
- Searching for elements in containers
- Copying and moving elements
- Numeric Algorithms
- Generating incremented values with std::iota
- Using std::accumulate for sums and generalized accumulations
- Computing dot products and generalizations with std::inner_product
- Pairwise differences and generalizations with std::adjacent_difference
- Computing partial sums of finite series with std::partial_sum
- Considering financial applications of numeric STL algorithms
- Range Views, Range Adaptors, and Functional Programming
- Range Views
- Chaining for Functional Composition
- Views, Containers, and Range-Based for Loops
- Summary
- Additional References
- STL Algorithms
- 6. Random Number Generation and Concurrency
- Distributional Random Number Generation
- Introducing Engines and Distributions
- Generating Random Normal Draws
- Using Other Distributions
- Shuffling
- Monte Carlo Option Pricing
- A Review of Monte Carlo Option Pricing
- Generating Random Equity Price Scenarios
- Calculating the Option Price
- Pricing Path-Dependent Options
- Concurrency and Parallelism
- Parallel Algorithms from the Standard Library
- Descriptive statistics
- Inner product
- Performance and guidance
- Execution policies: in general
- Task-Based Concurrency
- Creating and managing a task
- Revisiting Monte Carlo option pricing
- Considering caveats and performance
- Concluding Remarks on async and future
- Parallel Algorithms from the Standard Library
- Summary
- Further Resources
- Distributional Random Number Generation
- 7. Dates and Fixed Income Securities
- Representation of a Date
- Serial Representation
- Accessor Functions for Year, Month, and Day
- Checking the Validity of a Date
- Checking Leap Years and Last Day of the Month
- Identifying Weekdays and Weekends
- Adding Years, Months, and Days
- Adding years
- Adding months and handling end-of-month cases
- Adding days
- A Date Class Wrapper
- Class Declaration
- Constructors
- Public member functions and operators
- Private members and member functions
- The ChronoDate class declaration, in full
- Class Implementation
- Constructors
- Member functions and operators
- Accessors
- Date properties
- Operators
- Addition of years, months, and days
- Business-day roll rule
- Stream operator
- Class Declaration
- Day Count Basis
- Yield Curves
- Deriving a Yield Curve from Market Data
- Discount Factors
- Calculating Forward Discount Factors
- Implementing a Yield-Curve Class
- Implementing a Linearly Interpolated Yield Curve Class
- A Bond Class
- Bond Payments and Valuation
- Determining the payment schedule
- Valuing a bond
- Designing a Bond Class
- Implementing the Bond Class
- Bond Payments and Valuation
- A Bond Valuation Example
- Summary
- Additional Reference
- 8. Linear Algebra
- Lazy Evaluation and Expression Templates
- Lazy Evaluation
- Expression Templates
- The Eigen Linear Algebra Library
- Eigen Matrices and Vectors
- Matrix and Vector Math Operations
- STL Compatibility
- STL and VectorXd
- Applying STL algorithms to a matrix
- STL-like variants and mathematical functions in Eigen
- Matrix Decompositions and Applications
- Fund Tracking with Multiple Regression
- Correlated Random Equity Paths and the Cholesky Decomposition
- Yield-Curve Dynamics and Principal Component Analysis
- Future Directions: Linear Algebra in the Standard Library
- mdspan
- BLAS Interface
- Summary
- Further Resources
- Lazy Evaluation and Expression Templates
- 9. The Boost Libraries
- Mathematical Constants
- Statistical Distributions
- Probability Functions
- Drawdown Example, Revisited
- Random Number Generation with Boost Distributions
- MultiArray
- A Simple Two-Dimensional MultiArray
- Binomial Lattice Option Pricing
- American options
- Implementation with Boost MultiArray
- Convergence
- Extensions
- Accumulators
- Max and Min Example
- Mean and Variance
- Rolling Mean and Variance
- Trading Indicator Examples
- Summary
- Further Reading
- 10. Modules and Concepts
- Modules
- Standard Library Header Units
- Templates in Modules
- import Versus #include
- Declarations in Module Interfaces
- Separating Declarations from Implementation
- Namespaces
- Partitions
- Concepts
- Defining Concepts
- Defining Concepts with Multiple Conditions
- Standard Library Concepts
- Summary
- Modules
- A. Virtual Default Destructor
- B. Object Slicing
- C. Implementation of Move Special Member Functions
- D. Resolving Conflicts in the Initialization of a vector
- E. valarray and Matrix Operations
- Arithmetic Operators and Math Functions
- valarray as a Matrix Proxy
- Index