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Candlestick

General

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s generate a dataset using the following data.

N = 20 # Number of records

data = vo.VastFrame({
    "date": [1990 + i for i in range(N)] * 5,
    "population": [100 + i for i in range(N)] + [300 + i * 2 for i in range(N)] + [200 + i ** 2 - 3 * i for i in range(N)] + [50 + i ** 2 - 6 * i for i in range(N)] + [700 + i ** 2 - 10 * i for i in range(N)],
})

In the context of data visualization, we have the flexibility to harness multiple plotting libraries to craft a wide range of graphical representations. vastorbit, as a versatile tool, provides support for several graphic libraries, such as Matplotlib and Plotly. Each of these libraries offers unique features and capabilities, allowing us to choose the most suitable one for our specific data visualization needs.

_images/plotting_libs.png

Note

To select the desired plotting library, we simply need to use the set_option() function. vastorbit offers the flexibility to smoothly transition between different plotting libraries. In instances where a particular graphic is not supported by the chosen library or is not supported within the vastorbit framework, the tool will automatically generate a warning and then switch to an alternative library where the graphic can be created.

Please click on the tabs to view the various graphics generated by the different plotting libraries.

We can switch to using the plotly module.

vo.set_option("plotting_lib", "plotly")

vastorbit’s candlestick visualization feature empowers data analysts and financial professionals to gain valuable insights from time-series data. By representing open, close, high, and low price values for a specific period, candlestick charts offer a clear and intuitive view of price movements, helping users identify trends, reversals, and patterns in financial data. This tool enhances decision-making in areas such as stock trading, investment analysis, and financial forecasting, making it an indispensable asset for those working with time-series financial data.

data["population"].candlestick(ts = "date")

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k candlestick
SELECT
    date,
    MIN(population) AS min,
    APPROX_PERCENTILE(population
                          , 0.25) AS q1,
    APPROX_PERCENTILE(population
                          , 0.50) AS q2,
    APPROX_PERCENTILE(population
                          , 0.75) AS q3,
    MAX(population) AS max,
    SUM(population) AS sum
FROM
:data
GROUP BY 1
ORDER BY 1

We can switch to using the matplotlib module.

vo.set_option("plotting_lib", "matplotlib")

vastorbit’s candlestick visualization feature empowers data analysts and financial professionals to gain valuable insights from time-series data. By representing open, close, high, and low price values for a specific period, candlestick charts offer a clear and intuitive view of price movements, helping users identify trends, reversals, and patterns in financial data. This tool enhances decision-making in areas such as stock trading, investment analysis, and financial forecasting, making it an indispensable asset for those working with time-series financial data.

data["population"].candlestick(ts = "date")

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k candlestick
SELECT
    date,
    MIN(population) AS min,
    APPROX_PERCENTILE(population
                          , 0.25) AS q1,
    APPROX_PERCENTILE(population
                          , 0.50) AS q2,
    APPROX_PERCENTILE(population
                          , 0.75) AS q3,
    MAX(population) AS max,
    SUM(population) AS sum
FROM
:data
GROUP BY 1
ORDER BY 1;
_images/plotting_matplotlib_candlestick.png

Custom Parameters

Hint

In vastorbit, when working with candlestick visualizations, you have the flexibility to select the quantiles and the primary aggregation method that best suits your data analysis needs. This capability allows you to tailor your candlestick charts to precisely represent the data distribution and trends, providing a customized and insightful view of your financial data.

Note

In SQL, aggregations can be computed directly within the input SQL statement, but in Python, the process is a bit different.

Quantiles

data["population"].candlestick(ts = "date", q = (0.1, 0.9))

Note

By selecting the tuple (0.1, 0.9), we are effectively utilizing the values corresponding to the first and ninth deciles of the data distribution.

Quantiles

data["population"].candlestick(ts = "date", q = (0.1, 0.9))

Note

By selecting the tuple (0.1, 0.9), we are effectively utilizing the values corresponding to the first and ninth deciles of the data distribution.

Chart Customization

vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots. This customization extends to essential elements such as color schemes, text labels, and plot sizes, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit’s customization options enable you to craft graphics that suit your exact needs.

Hint

For SQL users who use Jupyter Magic cells, chart customization must be done in Python. They can then export the graphic using the last magic cell result.

chart = _

Now, the chart variable includes the graphic. Depending on the library you are using, you will obtain a different object.

Important

Different customization parameters are available for Plotly and Matplotlib. For a comprehensive list of customization features, please consult the documentation of the respective libraries: plotly, matplotlib.

Colors

Custom colors

fig = data["population"].candlestick(ts = "date", colors = ["blue","pink"])

Note

The first color in the list is for increasing values, while the second color is for decreasing values.

Custom colors

data["population"].candlestick(ts = "date", colors = ["blue","pink"])

Note

The first color in the list is for increasing values, while the second color is for decreasing values.

Size

Custom Width and Height

data["population"].candlestick(ts = "date", width = 300, height = 300)

Custom Width and Height

data["population"].candlestick(ts = "date", width = 6, height = 3)

Text

Custom Title

data["population"].candlestick(ts = "date").update_layout(title_text = "Custom Title")

Custom Axis Titles

data["population"].candlestick(ts = "date", yaxis_title = "Custom Y-Axis Title")

Custom Title Text

data["population"].candlestick(ts = "date").set_title("Custom Title")

Custom Axis Titles

data["population"].candlestick(ts = "date").set_ylabel("Custom Y Axis")