# How to make a stock returns data set using r?

, in category: Technical Analysis , 6 months ago

How to make a stock returns data set using r?

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by skyla , 6 months ago

﻿@elda.osinski ﻿

To create a stock returns dataset using R, you can follow these steps:

1. Install and load the necessary packages:
 ```1 2 ``` ```install.packages("quantmod") library(quantmod) ```

1. Set the start and end dates for your desired time period of stock data:
 ```1 2 ``` ```start_date <- "2022-01-01" end_date <- "2022-12-31" ```

1. Define the ticker symbol of the stock you want to gather data for:
 ```1 ``` ```stock_symbol <- "AAPL" ```

1. Use the getSymbols function from the quantmod package to download the stock data:
 ```1 ``` ```getSymbols(stock_symbol, from = start_date, to = end_date) ```

1. Extract the closing prices from the stock data:
 ```1 ``` ```stock_data <- Cl(get(stock_symbol)) ```

1. Calculate the daily returns by taking the percentage change in closing prices:
 ```1 ``` ```stock_returns <- dailyReturn(stock_data) ```

1. Remove any NA values from the dataset:
 ```1 ``` ```stock_returns <- na.omit(stock_returns) ```

1. Optionally, save the stock returns dataset to a CSV file:
 ```1 ``` ```write.csv(stock_returns, file = "stock_returns.csv") ```

Now you have created a stock returns dataset using R, which you can use for further analysis or modeling.

Member

by conor , 3 months ago

﻿@elda.osinski ﻿

You may also consider using the tidyverse package in R to streamline the process of data manipulation. Here is an updated version of the steps using the tidyverse:

1. Install and load the necessary packages:
 ```1 2 3 ``` ```install.packages(c("quantmod", "tidyverse")) library(quantmod) library(tidyverse) ```

1. Set the start and end dates for your desired time period of stock data:
 ```1 2 ``` ```start_date <- "2022-01-01" end_date <- "2022-12-31" ```

1. Define the ticker symbol of the stock you want to gather data for:
 ```1 ``` ```stock_symbol <- "AAPL" ```

1. Use the getSymbols function from the quantmod package to download the stock data:
 ```1 ``` ```getSymbols(stock_symbol, from = start_date, to = end_date) ```

1. Extract the closing prices from the stock data and create a data frame:
 ```1 ``` ```stock_data <- data.frame(date = index(get(stock_symbol)), price = Cl(get(stock_symbol))) ```

1. Calculate the daily returns by taking the percentage change in closing prices and add it to the data frame:
 ```1 2 ``` ```stock_data <- stock_data %>% mutate(daily_return = Delt(price, k = 1, type = "arithmetic")) ```

1. Remove any NA values from the dataset:
 ```1 2 ``` ```stock_data <- stock_data %>% drop_na() ```

1. Optionally, save the stock returns dataset to a CSV file:
 ```1 ``` ```write_csv(stock_data, file = "stock_returns.csv") ```

This code snippet utilizes the tidyverse package for data manipulation and offers a more modern and streamlined approach. You can further customize and analyze the stock returns data as per your requirements using the tidyverse functions.