Read e-book online Quantitative Trading with R: Understanding Mathematical and PDF

Read e-book online Quantitative Trading with R: Understanding Mathematical and PDF

By Harry Georgakopoulos

Quantitative Finance with R bargains a profitable approach for devising expertly-crafted and manageable buying and selling versions utilizing the R open resource programming language, delivering readers with a step by step method of realizing complicated quantitative finance difficulties and development useful laptop code.

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Read or Download Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant’s Perspective PDF

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Additional info for Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant’s Perspective

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The camel case variable name firstFunction should be written as first function instead. • For code indentation, use two spaces. Don’t mix tabs and spaces. • Use <- rather than =, for assignment. A pairwise correlation example Now, back to writing our pairwise correlation function. In an attempt to break down the task, we will write a few helper functions. The first helper function will validate whether a vector of stock symbols contains valid names, and will return only the valid ones for further processing.

Numbers will not be allowed as part of the symbol identifier. We will make use of regular expressions11 to accomplish this initial filtering task. Tools of the Trade 37 filter_and_sort_symbols <- function(symbols) { # Name: filter_symbols # Purpose: Convert to upper case if not # and remove any non valid symbols # Input: symbols = vector of stock tickers # Output: filtered_symbols = filtered symbols # Convert symbols to uppercase symbols <- toupper(symbols) # Validate the symbol names valid <- regexpr("^[A-Z]{2,4}$", symbols) # Return only the valid ones return(sort(symbols[valid == 1])) } Regular expressions are a powerful string filtering mechanism.

An entry of 1 is used to denote the valid names, and an entry of −1, the invalid ones. The topper() function is used to convert all the letters into uppercase prior to applying the regular expression. csv file and extract only the relevant data for those symbols. This function can later be augmented to read in price data from multiple sources, including external databases. csv file that contains 1856 trading days of prices for the following nine stocks: AAPL, CVX, IBM, XOM, GS, BA, MON, TEVA and CME.

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