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Financial Data Manipulation in dplyr for Quant Traders

Financial Data Manipulation in dplyr for Quant Traders

Posted January 28, 2021 at 11:14 am
Robot James
Robot Wealth

In this post, we’re going to show how a quant trader can manipulate stock price data using the dplyr R package.

dplyr

Getting set up and loading data

Load the dplyr package via the tidyverse package.

if (!require(‘tidyverse’)) install.packages(‘tidyverse’)
library(tidyverse)

First, load some price data.

energystockprices.RDS contains a data frame of daily price observations for 3 energy stocks.

prices <- readRDS(‘energystockprices.RDS’)
prices

Financial Data

We’ve organised our data so that

  • Every column is variable.
  • Every row is an observation.

In this data set:

  • We have 13,314 rows in our data frame.
  • Each row represents a daily price observation for a given stock.
  • For each observation measure the open, high, low and close prices, and the volume traded.

This is a very helpful way to structure your price data. We’ll see how we can use the dplyr package to manipulate price data for quant analysis.

The main dplyr verbs

There are 6 main functions to master in dplyr.

filter() picks out observations (rows) by some filter criteria
arrange() reorders the observations (rows)
select() picks out the variables (columns)
mutate() creates new variables (columns) by applying transformations to existing variables
summarise() allows you to group and summarise data – reducing the data into a grouped summary with fewer rows.

Finally, the group_by() causes the verbs above to act on a group at a time, rather than the whole dataset.

In the next installment, the author will go through 6 main functions to master in dplyr.

Visit Robot Wealth website to read the full article and download code: https://robotwealth.com/financial-data-manipulation-in-dplyr-for-quant-traders/

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Information posted on IBKR Campus that is provided by third-parties does NOT constitute a recommendation that you should contract for the services of that third party. Third-party participants who contribute to IBKR Campus 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 Robot Wealth and is being posted with its permission. The views expressed in this material are solely those of the author and/or Robot Wealth and Interactive Brokers 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 buy or sell any security. It should not be construed as research or investment advice or a recommendation to buy, sell or hold any security or commodity. 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.

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