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 https://datascienceplus.com/visualizations-for-algorithmic-trading-in-r/
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.
Disclosure: Interactive Brokers
Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.
This material is from DataScience+ and is being posted with permission from DataScience+. The views expressed in this material are solely those of the author and/or DataScience+ and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.