Visualizations for Algorithmic Trading in R

Visualizations for algorithmic trading is rising in demand by the economic sector. In R there are a lot of great packages for getting data, visualizations and model strategies for algorithmic trading. In this article, you learn how to perform visualizations and modeling for algorithmic trading in R.

Introduction to Algorithmic Trading

Algorithmic trading is a very popular machine learning method within the economic and financial sector. Typically it involves a lot of programming in advanced visualizations and modelling. The programming is necessary in order to get the financial data for the Algorithmic Trading analysis. This article involves the first part of algorithmic trading: Advanced Visualizations.

Read packages into R library

First things first! We need to read these great packages into the R library:

# Load R package
library(PortfolioEffectHFT)
library(rvest)
library(pbapply)
library(TTR)
library(dygraphs)
library(lubridate)
library(tidyquant)
library(timetk)
library(pacman)
library(quantmod)
library(parallelMap)
library(BiocParallel)
library(parallel)
library(plotly)
pacman::p_load(dygraphs,DT)

In the above packages you need to install BiocParallel with this code:

## Install BiocParallel
source("https://bioconductor.org/biocLite.R")
biocLite("BiocParallel")

To get the R scripts, and see the full article, visit DataScience+ Blog

About the Author:
Kristian Larsen is a passionate economic data scientist with an expertise in R, Excel, VBA, SQL, STATA, SAS and Python. He creates Automated dashboards, business intelligence, machine learning, data analysis, AI, deep learning, data management, statistical analysis and programming.