A complementary package for specialized operations on dates and times is lubridate , which includes consideration of time zones, leap days, daylight savings times. Finally, the popular data. table package allows efficient operations on data structures with short code, particularly subsetting, grouping, updating and univariate variable transformation.

Hence, it is particularly suitable for extracting analytical summaries from large databases. The objective of the package is to reduce programming and computing time. Even non-programmers eventually build their own functions to perform special operations in different contexts.

Functions reduce quantity and errors of code. They also can make the intention of code much clearer. As a rule of thumb, a snippet of code should be transformed into a function if it is being copied and pasted more than two times. It is often best practice to start the creation by [1] solving a specific simple example problem with a snippet, [2] testing and cleaning up the snippet, and then [3] applying a clearly written working snippet to a function template.

Importantly, R is a functional programming FP language. This means that it provides many tools for the creation and manipulation of functions. In particular, R has first-class functions. This means that one can do anything with functions that one can do with data structures , including [i] assigning them to variables, [ii] storing them in lists, [iii] passing them as arguments to other functions, [iv] creating them inside functions, and [v] returning them as the result of a function.

Functional programming simply uses functions as arguments in other functions. It is typically an alternative to for loops and preferable when for loops obscure the purpose of code by displaying repetitive standard procedures. For example, if a macro trading strategy requires a special way of transforming market or macroeconomic data and if that transformation has been captured in a function, this transformation can be applied efficiently and with little code to all relevant data collections.

In particular, a functional is a function that takes another function as an argument. Functionals make code more succinct. Functionals reduce bugs in by better communicating intent. In R, the fundamental unit of shareable code is the package.

A package bundles together code, data, documentation, and tests, and is easy to share with others. The ability to find a suitable package for the job at hand can save much programming resources.

Moreover, portfolio managers can create their own packages, maybe with the help of a more experienced R programmer. In some sense, the creation of a custom package is just the natural progression of creating custom functions. An in-house package typically improves the documentation of such functions and makes them easier to use and to share. Visualization is the key link between scientific data analysis and decisions in macro portfolio management.

Confidence in data-based decisions requires a good intuitive understanding of the data and trust in statistical findings. Graphics support intuition and trust better than words. Many are executed through the generic plot function. Basic graphics are usually used for quick exploratory graphs, with some examples shown below. For more flexible and advanced visualization the ggplot2 package provides a system for creating graphics. There is hardly a relevant visualization that ggplot2 cannot do except maybe the manual drawing of trend elements in time series charts that is such a popular feature on Bloomberg and Reuters Eikon.

A collection of the top 50 ggplot2 visualizations with related code can be found on r-statistics. co by Selva Prabhakaran , many of which have relevance for macro trading. A shortlist of simple visualization for macro trading based on ggplot2 and some other specialized packages that can be accomplished with little code includes the following:. Ranges : It often is important to view the range and rough distribution of position returns or trading signals across different markets. This calls for a classical discrete-x-continuous-y geometric representation.

In particular, one often needs a quick view of averages, standard deviations, and outliers across sections. In ggplot2 one can visualize cross-section ranges and distributions either in form of a box-whisker plot or in form of a jitter-points plot.

The box-whisker plot is easier to read and puts more focus on outliers. The jitter-points plot provides more visual information on distribution. Distributions : The main purpose of viewing distributions is to detect abnormalities. The most common objective of viewing distribution graphs is to detect skewness asymmetry in the distribution with tails longer on one side or kurtosis high or low weight of the tails relative to a normal distribution.

Time-series graphs : These standard graphs can be plotted in various facets and grids to compare time series across sections and categories. Categories : It is often important to visualize the relative frequency of categories of data. For example, one may need to know how often a series turns negative for example to see if deflation has been a major issue , how often a series produces zero values for example to see if there is a problem in updating or how often various combinations of positive and negative values to two or more series occur for example to see if equity and FX returns in EM are mostly pointing in the same direction.

Heatmaps : The purpose of heatmaps is to visualize a large number of numerical values across many categories in order to spot specific patterns or value regions.

Scatterplots : Various forms of scatter plots with added fittings can visualize the relation between variables across times and across markets. Cross-correlations : A matrix of cross-correlations gives serves to give a quick graphical overview. There two convenient tools for this, both taking a dataframe and visualizing the correlations of all series.

First, the ggpairs function of the GGally package is suitable for a smaller set of series and gives scatters, correlation coefficients and series densities. The corrplot function of the corrplot package is a visualization function for a correlation matrix that can be calculated by cor function of the base packages. It has various visualization options, each with less information than the ggpairs function, but with a greater capacity to visualize correlations of a large number of series.

You should also experiment with other Neural Net parameters such as the number of iterations maxit , the learning decay decay , etc. The confusion matrix shows us the necessary information for calculating TP, FP, TN,FN rates for each class ie for each signal type.

Similarly we can train and test a Random Forest :. We can issue the following command :. train SVM. We then issue the command tuned to see which combination of parameters gives us the lowest classification error.

Knowing these parameters we can then use these parameters to train an SVM classifier and see how this model performs as was shown previously. Be aware of the following key points : Three sets of data should be used : Training, Test and Validation. After downloading the file from step 2 , place the file to a directory of your choice. Now copy and paste the following commands in R :. library e library nnet library randomforest.

library quantmod. library tseries. get data OHLC from csv file. frame stripday,raw. transform to an xts object. xts read. Now we define some technical indicators, the model to work on and a function that generates our trading signals :. setup Technical Indicators.

R is an object-oriented programming language and work environment for statistical analysis. It is not just for programmers, but for everyone conducting data analysis, including portfolio managers and traders.

Even with limited coding skills R outclasses Excel spreadsheets and boosts information efficiency. First, like Excel, the R environment is built around data structures, albeit far more flexible ones. Operations on data are simple and efficient, particularly for import, wrangling, and complex transformations. Second, R is a functional programming language. This means that functions can use other functions as arguments, making code succinct and readable. Third, R users have access to a repository of almost 15, packages of function for all sorts of operations and analyses.

Finally, R supports vast arrays of visualizations, which are essential in financial research for building intuition and trust in statistical findings. The post ties in with the SRSV summary on information efficiency.

A second part of the post focusing on statistical inference and learning will follow. The R project for statistical computing provides the leading open-source programming language and environment for statistical analysis. It has been developed by academics and statisticians for over 25 years.

Put simply, R can do whatever a spreadsheet can do but much faster and far more efficiently and extends to vastly more applications. The primary benefits of R are data wrangling making untidy data usable , data transformation building customized data sets , data analysis applying statistical models , all forms of visualization, and machine learning.

The default workspace or integrated development environment IDE for R is R Studio. Among the many on-line resources for learning R the data science courses of DataCamp stand out. Most macro traders or portfolio managers rely on quantitative statistical analysis, typically in form of charts often inside Bloomberg, Reuters Eikon and so forth , calculators and spreadsheets.

As responsible trading is full-time demanding work, senior traders often lack time and experience for coding up their analytical tools to professional standards. Even cooperation with desk quants that offer programming support can be difficult, as many traders do not wish to reveal their personal methods and struggle to translate their needs into suitable instructions for programmers.

R makes statistical programming and data science accessible. In particular, R is not just for programmers but for all finance professionals with some interest in statistical analysis. That is because R can initially be run interactively with a limited set of basic commands.

In some sense, R can be used by non-programmers much like a sophisticated calculator. Even short snippets of code can go a long way in performing operations that would be very tedious in Excel. This means that R can be deployed with minimal programming skills and typically enhances the information efficiency of the investment process quickly. Interest in and advances of programming skills then follow almost naturally. A personal analytical framework in R is highly extensible and goes far beyond the capacity of Excel spreadsheets are.

This is because it allows far greater creativity and far more data to be used view post here. The import data structures into the R mostly relies on two types of tools. The first is web APIs. application programming interfaces that link the local R environment with external databases, such as Bloomberg, DataStream. Macrobond and the data services of investment banks. The second is special R functions that read and import data files into the environment, including from Excel spreadsheets, csv files, and SQL databases.

A particularly useful set of functions is provided by the readr package , which supports the customized import of all sorts of rectangular data. R offers a whole host of techniques to deal with the immensely important job of data wrangling , i. the transformation of raw irregular data into a clean tidy data set. The tidyr package provides functions through which one can reshape imported data into a standardized format that is conducive to standard operations, estimation and analysis, particular for other packages of the tidyverse , a collection standard R packages for data science.

The R language is geared towards the manipulation of tidy data structures, much more so than Python. Selecting and subsetting data structures simply requires position indices, names or logical conditions. Going beyond basic operations, the dplyr package supports a wide range of manipulations of tidy data tables, particularly. Most data used for macro trading are time series. The xts eXtensible Time Series package has been developed for just this purpose. The package supports a special object class and functions for uniform handling of many R time series classes.

An xts object is effectively an extension or special class of zoo object class of indexed totally ordered observations. Its practical benefits include easy conversion and reconversion from and to other classes and bespoke functionality for time series data. Key advantages of xts dataframes include reliable implementation of time lags, easy and intuitive subsetting with date names, easy extraction of periodicity and time stamps and consideration of different time zones.

A complementary package for specialized operations on dates and times is lubridate , which includes consideration of time zones, leap days, daylight savings times. Finally, the popular data. table package allows efficient operations on data structures with short code, particularly subsetting, grouping, updating and univariate variable transformation.

Hence, it is particularly suitable for extracting analytical summaries from large databases. The objective of the package is to reduce programming and computing time.

Even non-programmers eventually build their own functions to perform special operations in different contexts. Functions reduce quantity and errors of code. They also can make the intention of code much clearer. As a rule of thumb, a snippet of code should be transformed into a function if it is being copied and pasted more than two times.

It is often best practice to start the creation by [1] solving a specific simple example problem with a snippet, [2] testing and cleaning up the snippet, and then [3] applying a clearly written working snippet to a function template.

Importantly, R is a functional programming FP language. This means that it provides many tools for the creation and manipulation of functions. In particular, R has first-class functions. This means that one can do anything with functions that one can do with data structures , including [i] assigning them to variables, [ii] storing them in lists, [iii] passing them as arguments to other functions, [iv] creating them inside functions, and [v] returning them as the result of a function.

Functional programming simply uses functions as arguments in other functions. It is typically an alternative to for loops and preferable when for loops obscure the purpose of code by displaying repetitive standard procedures. For example, if a macro trading strategy requires a special way of transforming market or macroeconomic data and if that transformation has been captured in a function, this transformation can be applied efficiently and with little code to all relevant data collections.

In particular, a functional is a function that takes another function as an argument. Functionals make code more succinct. Functionals reduce bugs in by better communicating intent. In R, the fundamental unit of shareable code is the package. A package bundles together code, data, documentation, and tests, and is easy to share with others. The ability to find a suitable package for the job at hand can save much programming resources. Moreover, portfolio managers can create their own packages, maybe with the help of a more experienced R programmer.

In some sense, the creation of a custom package is just the natural progression of creating custom functions.

An in-house package typically improves the documentation of such functions and makes them easier to use and to share. Visualization is the key link between scientific data analysis and decisions in macro portfolio management. Confidence in data-based decisions requires a good intuitive understanding of the data and trust in statistical findings. Graphics support intuition and trust better than words. Many are executed through the generic plot function. Basic graphics are usually used for quick exploratory graphs, with some examples shown below.

For more flexible and advanced visualization the ggplot2 package provides a system for creating graphics. There is hardly a relevant visualization that ggplot2 cannot do except maybe the manual drawing of trend elements in time series charts that is such a popular feature on Bloomberg and Reuters Eikon. A collection of the top 50 ggplot2 visualizations with related code can be found on r-statistics. co by Selva Prabhakaran , many of which have relevance for macro trading.

A shortlist of simple visualization for macro trading based on ggplot2 and some other specialized packages that can be accomplished with little code includes the following:. Ranges : It often is important to view the range and rough distribution of position returns or trading signals across different markets. This calls for a classical discrete-x-continuous-y geometric representation. In particular, one often needs a quick view of averages, standard deviations, and outliers across sections.

In ggplot2 one can visualize cross-section ranges and distributions either in form of a box-whisker plot or in form of a jitter-points plot. The box-whisker plot is easier to read and puts more focus on outliers. The jitter-points plot provides more visual information on distribution. Distributions : The main purpose of viewing distributions is to detect abnormalities. The most common objective of viewing distribution graphs is to detect skewness asymmetry in the distribution with tails longer on one side or kurtosis high or low weight of the tails relative to a normal distribution.

Time-series graphs : These standard graphs can be plotted in various facets and grids to compare time series across sections and categories. Categories : It is often important to visualize the relative frequency of categories of data. For example, one may need to know how often a series turns negative for example to see if deflation has been a major issue , how often a series produces zero values for example to see if there is a problem in updating or how often various combinations of positive and negative values to two or more series occur for example to see if equity and FX returns in EM are mostly pointing in the same direction.

Heatmaps : The purpose of heatmaps is to visualize a large number of numerical values across many categories in order to spot specific patterns or value regions. Scatterplots : Various forms of scatter plots with added fittings can visualize the relation between variables across times and across markets.

Cross-correlations : A matrix of cross-correlations gives serves to give a quick graphical overview. There two convenient tools for this, both taking a dataframe and visualizing the correlations of all series. First, the ggpairs function of the GGally package is suitable for a smaller set of series and gives scatters, correlation coefficients and series densities.

For example, if a macro trading strategy requires a special way of transforming market or macroeconomic data and if that transformation has been captured in a function, this transformation can be applied efficiently and with little code to all relevant data collections. Best forex conference Forex Trading Us. MORE STORIES. It has various visualization options, each with less information than the ggpairs function, but with a greater capacity to visualize correlations of a large number of series. The import data structures into the R mostly relies on two types of tools.

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