Using R and Python for Extracting Financial Data and Creating Eye-Catching Candlestick Charts

Using R and Python for Extracting Financial Data and Creating Eye-Catching Candlestick Charts

Introduction:

In this article, we will explore how to download financial data from investing.com using the investpy package in Python and how to combine the capabilities of Python and R using the reticulate package. We will also learn how to construct a nicely formatted candlestick chart with ggplot2, ggthemes, and two simple custom functions. Additionally, we will discover how to export the resulting chart in different image formats, including high-resolution Scalable Vector Graphics (SVG). This comprehensive guide will take you through the step-by-step process, providing code examples and visuals to illustrate each step. Whether you’re a data analyst, investor, or simply interested in visualizing financial data, this article has something for everyone.

Full Article: Using R and Python for Extracting Financial Data and Creating Eye-Catching Candlestick Charts

Title: Exploring Financial Data Visualization with Python and R

Introduction:
In the world of data analytics, being able to efficiently combine the capabilities of different programming languages can lead to the best results. In this news report, we will explore the use of Python and R in creating a visually appealing candlestick chart. This case study will demonstrate how to download financial data, combine Python and R using the reticulate package, construct a nicely formatted chart with ggplot2 and ggthemes, and export the result in different image formats.

The Python Part:
To start, we need to install the investpy package using pip. This package serves as a powerful wrapper around the public API of investing.com, allowing us to retrieve end-of-day price data for a wide range of financial instruments. A simple function is provided to facilitate the retrieval of currency cross historical data. The function takes parameters such as the currency cross, start date, and end date, and returns the data in a data frame format.

The R Part:
In the R programming language, we rely on the reticulate package to interface with the previously defined Python function and plot the data. To do this, we need to install four packages: reticulate, ggplot2, ggthemes, and scales. These packages provide the necessary functionalities for data manipulation and plotting.

Defining a Pretty Theme:
The ggthemes package offers default themes for ggplot2 graphics, including ones that replicate the look of famous designs such as those found in The Economist. We can also create our own unique default layout by customizing these themes. The article provides a function that demonstrates the customization process and defines a new theme called “theme_aq_black_default_font.”

The Candlestick Function:
Candlestick charts are commonly used to visualize price data and perform technical analysis. To create a candlestick chart, we need daily opening and closing prices, as well as intraday highs and lows. Using the previously retrieved financial data, a custom function called “ggplot_candlestick” is defined. This function takes parameters such as the data frame, width of the candles, chart title, and chart subtitle. It uses ggplot2 to generate the candlestick chart, incorporating color coding for upward and downward price movements.

Plot the Data and Export the Graphic:
The final step involves combining all the modules and executing them step by step. The Python function is loaded using the reticulate package, and the financial data is retrieved. The R functions are then used to create the candlestick plot, with the chart title and subtitle provided. The resulting plot can be easily exported as an SVG or PNG file using the ggsave function.

Conclusion:
By leveraging the capabilities of both Python and R languages, we have successfully created a visually appealing candlestick chart. This case study demonstrates the seamless integration between these two languages, highlighting the importance of using the right tool for the right task. The examples and functions provided can serve as a starting point for data analysts and enthusiasts looking to explore financial data and create stunning visualizations.

Summary: Using R and Python for Extracting Financial Data and Creating Eye-Catching Candlestick Charts

Are you more experienced with R than with Python? Do you prefer coding in R, especially for visualizations? If so, this summary is for you. The author shares their experience creating a candlestick chart using both Python and R. They explain how to download financial data from investing.com using the investpy package in Python and how to combine the capabilities of Python and R using the reticulate package. They also provide instructions on how to construct a nicely formatted candlestick chart with ggplot2, ggthemes, and two custom functions, and how to export the result in different image formats. Check out the article for detailed code examples and illustrations.

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