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R Stocks: Interactive Stock Analysis with R Shiny

R Stocks: Interactive Stock Analysis with R Shiny

November 27, 2022

An interactive R Shiny App that revolutionizes the way public financial information is displayed.

Built on top of well-known R packages for financial analysis: yfR, QuantMod, PriceR and Quandl

R Stocks

After some time of using R and loving it, I was wondering if there was any open source project combining some of my favourite packages to easily get financial data and have it displayed interactively in Shiny Dashboard.

Since I found nothing on Github, I decided to start it myself - This is how the R Stocks project started.

ℹ️
R/Stocks Source Code. Deploy with GHCR image or build a container DIY version

With such a project, you not only get the chance to create something cool but also to learn and discover great tools.

And of course I am also using Plotly and Shiny for the visualization and interactive dashboard building.

The App: R Stocks

  • More about R_Stocks:
    • R Stocks Project Details:
    • Disclaimer: ☝️
      • This is just a Shiny Dashboard sample that displays freely available financial information.
      • It’s important understand that the dashboard is NOT created to provide financial advice ❗.

  • 📊 Interactive Data Visualization:

    • I chose Shiny Dashboard for its ability to create interactive and dynamic visualizations of data, enhancing user the insights.

  • 🚀 Deployment:

    • Shiny Dashboard provides an easy deployment process, whether hosting on a server or sharing as a standalone application, ensuring accessibility for users.
And few other Reasons that made me user R Shiny👇

  • 🔄 Real-time Updates:

    • Shiny Dashboard enables real-time updates of the dashboard as new data becomes available, crucial for monitoring live data feeds.

  • 🎨 Customization:

    • Shiny Dashboard offers extensive customization options, allowing me to tailor the interface to meet specific user needs with custom styling and interactive elements.
    • Yes, I was playing a little bit with CSS 🙈

  • 🔗 Integration with R:

    • Shiny Dashboard seamlessly integrates with R Language, leveraging the language’s power for data manipulation, analysis, and visualization within the dashboard environment.

The R Stock App has right now six tabs:

Interactive Stocks Analysis with R

In the first tab, you will have available general information of the selected tickers:

The first panel is just using the yfR library to get the data on historical value trends displayed:

The panels below, are using QuanDL, so remember to have your API_key available:

Interactive Dividend Analysis with R

In the second tab of the dashboards, you can find information related to the historical dividends per selected ticker.

This panels have been possible thanks to QuantMod, where we can get access to historical dividend data and then create the following:

Also, thanks to QuanDL, we can have access to some interesting ratios concerning dividends:

Interactive Indexes Analysis with R

Once again, thanks to yfR we can query general market indexes, like SP500, DJ, Nikkei and check on how were the markets behaving globally at a given moment:

Interactive Commodities Analysis with R

Another interesting source of information can be created when comparing the price of commodities with the one of any of the indexes.

Thanks to yfR and this view, we can see the evolution in the relation of any pair index/commodity.

Conclusions for RStocks

This is been an interesting ride: EDA, Flexdashboards, Shiny, Containers…

But with a lot of learnings and food for thought.

Ive also added a FlexDashboard to the Repo! 👇📌

This code snippet is an R Markdown document designed to create an interactive dashboard using the flexdashboard package. Let’s break down what you need to use this app:

1. R and RStudio:

  • R: This is the programming language that will execute the code. You’ll need to have R installed on your computer. You can download it from the official CRAN website (Comprehensive R Archive Network).
  • RStudio: This is an integrated development environment (IDE) for R. It makes writing and running R code much easier. While not strictly required, it’s highly recommended. You can download RStudio Desktop from the RStudio website.

2. R Packages:

The code uses several R packages. You’ll need to install these packages if you don’t already have them. You can install them using the install.packages() function in the R console:

install.packages(c("flexdashboard", "ggplot2", "readr", "dplyr", "tidyr", "rmarkdown"))
install.packages("plotly")
  • flexdashboard: This package is used to create interactive dashboards.
  • ggplot2: This package is used for creating data visualizations (graphs).
  • readr: This package is used for reading data files (specifically CSV files in this case).
  • dplyr: This package is used for data manipulation (filtering, transforming, etc.).
  • tidyr: This package is used for data cleaning and tidying.
  • rmarkdown: This package is used for creating dynamic documents (like this dashboard).

3. Data File (data_sp500.csv):

  • The code reads data from a CSV file named data_sp500.csv. This file should contain historical data for the S&P 500 index, with at least two columns:
    • Date: The date of the data point.
    • SP500: The value of the S&P 500 index on that date.
  • This file must be located in a subdirectory named data within the same directory as your R Markdown file. So the file path will be ./data/data_sp500.csv
  • The date column must be formatted as YYYY-MM-DD.

4. How to Use the App:

  1. Save the Code: Save the code as an .Rmd file (e.g., R_Flexdashboards_Cycles.Rmd).
  2. Place Data File: Create a folder named “data” in the same directory as the .Rmd file, and place the data_sp500.csv inside the data folder.
  3. Open in RStudio: Open the .Rmd file in RStudio.
  4. Run the Code:
    • You can run individual code chunks by clicking the “Run” button within each chunk.
    • To create the entire dashboard, click the “Knit” button at the top of the RStudio window. This will generate an HTML file containing the interactive dashboard.
  5. View the Dashboard: Open the generated HTML file in your web browser.

Key Points:

  • The code performs data analysis on the S&P 500 data, classifying periods as “Boom” or “Burst” based on consecutive decreases in the index.
  • It then generates visualizations (line graph, histograms) to show the trends and characteristics of these economic cycles.
  • The setwd() command is commented out. If you have issues with the file path, you can uncomment that line, and change the directory within the parenthesis to the absolute path of the directory that contains your .Rmd file, and your data folder.
  • The render commands are also commented out. These are commands that can be run from the R console to create the html output. The knit button in R studio is the easiest way to create the html output.

See the RStocks FlexDashboard in action: https://jalcocert.github.io/R_Stocks/

FAQ

How Can I try the R Stocks Shiny App?

Ready to Try?: If you’ve read this far, you’re probably eager to give the app a try.

Try the R Stocks Shiny App (baremetal):

  1. Install R Language
  2. Install the required Packages:
RUN R -e 'install.packages(c("shiny", "plotly", "viridis","dplyr", "tidyr","lubridate","shinythemes","shinyWidgets","DT","bslib","priceR","quantmod"))'

RUN R -e 'install.packages("yfR", dependencies = TRUE)' #this one will work when you install its deps

Docker Image: The Docker image I’m using is available on DockerHub: fossengineer/r_stocks

I’ve recommend to do it using the latest Docker image.

What if the site can’t be reached?

  1. Temporary Unavailability: If the site can’t be reached, it might be because my Raspberry Pi isn’t powered on at the moment or I’ve decided to free up resources for other projects.
  2. But don’t worry, you can also self-host R_Stocks on your own system.

How can I self-host R-Stocks?

ℹ️
See the Z_Deploy_me folder of the RStocks repository.
Option 2 - With Containers from DockerHub👇
version: '3.8'
services:
  r_stocks_shiny:
    image: fossengineer/r_stocks
    container_name: r_stocks
    ports:
      - 3838:3838
    restart: unless-stopped
  • Self-hosting Effort: Host your own free and open-source version of this R Shiny App at home using Docker and Cloudflare tunnels.
    • Option 1:
      • Pros: Full control over the deployment process & can customize it to fit your needs perfectly.
      • Cons: Requires some technical expertise to set up and maintain the environment.
    • Option 2: You can use the Docker image I’ve already built:
      • Pros: Quick and easy to get started, no need to build the image yourself ✅
      • Cons: Less flexibility compared to self-hosting from source, may not be tailored to your specific requirements.

How to deploy R Stocks with Docker and a GUI?

You can install Portainer with Docker and use the configuration above to self-host your own instance of RStocks with a GUI to manage the container.

Similar Apps to RStocks

Open source competition for RStocks

  1. https://www.privacytools.io/guides/open-source-portfolio-manager-privacy
  2. https://github.com/rotki/rotki
  3. https://github.com/ghostfolio/ghostfolio

Roadmap for RStocks

OwnerActionDue DateComentarios
JesusShinyapp working & publishedDONE
JLDividend Lib & linear and log plots separados13 FebStack Overflow
c=c(‘uno’, ‘dos’, ’tres’, ‘cuatro’, ‘cinco’)
JesusCheck if dividends are growing or not per company (last X years & one joker) - JL27 FebDONE
JLCheck historical yield27 FebDONE
JesusPriceR working & shares correction13 FebDONE
JesusHistorical combined value of the portfolio - stacked area chart27 Feb
JLDividendos Plot + Regresion/alternativa27 Feb
Alquiler vs stock analysisApr
Dividend retirement analysis (current dividend, growth avg per company)Mar
DCA - dollar cost average (avg buying price -> given the list of transactions per ticket)
Total yield on costDONE
Total yield actualDONE
Animated pyramid (% win / loose) per year animatedFlowing Data
Given S&P 500 tickers, classify them in king aristocrats etc based on a simple UI (years missed etc)
Check % decrease stock during crisis compared to the sector/S&P 500 index
Check ‘’linearity’’ R2 cerca de 1 para menos volatilidad
JesusHistorical % yieldtu no zave como hera hante jejeje
JesusHistorical % dividend payout
JesusHistorical PE
JesusVolatilidad Portfolio DGI vs Indice Seleccionadoezo he perde her dinero, meho en er banco jeje
Evolución índice vs oroDONE
Evolución índice/portfolio vs monedas FIATDONE

Other Resources

Aquí tienes una recopilación de librerías de R, APIs, sitios web y otros recursos útiles para el análisis financiero y estrategias de dividendos:

Librerías de R:

Repositorios y Recursos de Código:

Visualización de Datos:

  • data-to-viz:
  • Dash Yield Curve:
    • https://dash.gallery/dash-yield-curve/
    • Ejemplo de visualización interactiva de la curva de rendimiento con Dash.
    • The yield curve shows how much it costs the federal government to borrow money for a given amount of time, revealing the relationship between long- and short-term interest rates.

Información sobre ETFs y Componentes:

APIs Públicas:

Mapas de Dividendos:

Estrategias de Dividendos:

Gráfico de Precios (Sparkline):

=SPARKLINE(GoogleFinance(N15; "price"; HOY()-60; HOY()))
  • Muestra un gráfico de línea del precio de la acción especificada en la celda N15, durante los últimos 60 días.

Suma Condicional (QUERY):

=INDICE(QUERY(A$20:L;"SELECT sum(G) where (F='"&N15&"' and A<>'Yes')");2;0)
  • Suma los valores de la columna G donde la columna F coincide con el valor de la celda N15 y la columna A no es “Yes”.

Extracción de Datos de Yahoo Finance (IMPORTHTML, SPLIT):

=SPLIT(INDICE(IMPORTHTML(CONCATENAR("[https://finance.yahoo.com/quote/](https://finance.yahoo.com/quote/)";"PG");"table";2);6;2);" ")
  • Extrae datos de la tabla 2 de la página de Yahoo Finance para la acción “PG”, específicamente la sexta fila y segunda columna, y luego divide el resultado en palabras.

Reemplazo de Punto por Coma (SUSTITUIR):

=SUSTITUIR((INDICE(SPLIT(INDICE(IMPORTHTML(CONCATENAR("[https://finance.yahoo.com/quote/](https://finance.yahoo.com/quote/)";"PG");"table";2);6;2);" ");0;1));".";",")
  • Toma el primer elemento del resultado de la fórmula anterior y reemplaza los puntos por comas.

Extracción de Texto Después de Dos Puntos (REGEXEXTRACT):

=REGEXEXTRACT(N33;"\:(.*)")
  • Extrae todo el texto que sigue a los dos puntos en la celda N33.

Búsqueda de Datos en Google Sheets Externo (IMPORTRANGE, QUERY, IFERROR):

=IFERROR(INDEX(QUERY(IMPORTRANGE("[https://docs.google.com/spreadsheets/d/1yClKDQLFcZjjoWdv9oLZyTSqxGwRqjYbMgjmVaUK1HY/edit#gid=0](https://docs.google.com/spreadsheets/d/1yClKDQLFcZjjoWdv9oLZyTSqxGwRqjYbMgjmVaUK1HY/edit#gid=0)";"USA_DGI!C1:AT228");"SELECT * where (Col1='"&C7&"')");2;32);0)
  • Busca en una hoja de cálculo externa (especificada por la URL) la fila donde la primera columna coincide con el valor de la celda C7, y devuelve el valor de la columna 32. Si hay un error, devuelve 0.

Suma de Celdas en Múltiples Hojas (MAP, SEQUENCE, INDIRECT, IFERROR):

SUM(IFERROR(MAP(SEQUENCE(1000;1;21);LAMBDA(x;INDIRECT("20"&(x)&"!K8")));0))
  • Suma el valor de la celda K8 en las hojas “2021”, “2022”, …, “201020”. Si hay un error, lo trata como 0.

Suma Condicional y Multiplicación en Múltiples Hojas (MAP, SEQUENCE, INDIRECT, QUERY, SUMPRODUCT, IFERROR):

=SUM(IFERROR(MAP(SEQUENCE(1000;1;21);LAMBDA(x;SUMPRODUCT(INDEX(QUERY(INDIRECT("20"&(x)&"!B2"):INDIRECT("20"&(x)&"!G1000");"Select* where (B='Buy' and C='"&C13&"')";0);0;4);INDEX(QUERY(INDIRECT("20"&(x)&"!B2"):INDIRECT("20"&(x)&"!G1000");"Select* where (B='Buy' and C='"&C13&"')";0);0;6))));0))/SUM(IFERROR(MAP(SEQUENCE(1000;1;21);LAMBDA(x;SUM(INDEX(QUERY(INDIRECT("20"&(x)&"!B2"):INDIRECT("20"&(x)&"!G1000");"Select* where (B='Buy' and C='"&C13&"')";0);0;4))));0))
  • Calcula un promedio ponderado de un valor (calculado a partir de la multiplicación de dos columnas) en múltiples hojas, donde se cumplen ciertas condiciones.

Sitio Web para Datos de Mercado:

  • markets.ft.com
R Shiny Apps in a Docker ContainerNegotiation Skills for DAs. Ensure that requirements are met.