Manual Financial numerical recipes in C++

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  1. Financial Numerical Recipes (in C++)
  2. Account Options
  3. List of numerical libraries
  4. Table of Contents
  5. Ask HN: Good C++ Numerical Libraries | Hacker News

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Financial Numerical Recipes (in C++)

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List of numerical libraries

Moreover, the stock market is so vast that it provides opportunities for everyone willing to participate: from small investors to large hedge funds, you will find an investment style for each kind of participant. This is an especially important consideration for financial software development, where we want to create fast and expressive applications. The STL offers a set of generic, commonly used containers that may be applied to almost any situation.

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In this chapter, you will learn programming recipes that clarify some of the most common uses of the STL for financial programming, including containers and algorithms. At the heart of any high-performance financial application there is a set of well-designed numerical classes. These classes are responsible for the implementation of concepts that are an integral part of tasks such as financial modeling, forecasting, and analysis of investment decisions.

Without the support of mathematical models, it would be very difficult to propose and evaluate effective investment methodologies. Although it is not necessary to become a math expert to use these programming techniques, it helps to possess a basic understanding of the most important numerical issues that need to be dealt with in your financial programming assignments.

Table of Contents

A very common activity in financial programming is the generation of price-related data that needs to be visualized by traders or other business stakeholders. Most of the time, the data is expected to be plotted in the form of a chart for easy visualization.

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Visualization strategies for financial data range from simple line charts for daily prices to complex graphical output using candles, superposed studies, and other less conventional notation. Linear algebra is a fundamental set of mathematical tools that has applications in many areas of science and engineering. Consequently, linear algebra LA techniques also play an important role in the practice of financial programming, and they are frequently used throughout the area of financial engineering.

LA techniques are frequently used in the development of trading strategies. Interpolation is a commonly used technique that approximates a mathematical function, based on a set of points given as input. Fast interpolation is the secret for high-performance algorithms in several areas of financial engineering. You will explore the main procedures used in applications and see examples of how they work in practice. Solving equations is one of the building blocks of many engineering and scientific algorithms. As financial algorithms become more sophisticated, there is a great need to calculate the results of equations in general.

These results are frequently used in the analysis of investments and in new trading strategies. It is important not only to be able to solve equations but also to calculate the roots of such equations in an efficient way. Integrating a function is a common step in many financial algorithms. However, in several of these algorithms you can find equations that have no known analytical solution, and need to be integrated numerically. Even if an equation can be integrated analytically, it may be more efficient to perform this task using numerical algorithms.

For this purpose, this chapter explores some of the common ways of performing numerical integration. After reading this chapter you will have a better understanding of how these numerical integration algorithms work in practice and how to use them in your own projects. The solution of ODEs ordinary differential equations and PDEs partial differential equations is at the heart of many techniques used in the analysis of financial markets.

Ask HN: Good C++ Numerical Libraries | Hacker News

Important analytical tools for derivative valuation such as the Black-Scholes model for stock options and other derivatives can be directly represented as differential equations. Such equations need to be regularly solved in order to determine the price of financial instruments traded in the global markets. This creates the need for high-performance code, capable of finding efficient solutions to these mathematical models. Optimization is a topic that covers a set of techniques used to find the minimum or maximum of a function over a predetermined group of conditions.