BarChart()

Documentation for the BarPlotter class

To use BarPlotter, use the following import statement:

from marketquant.tools import BarPlotter

The BarPlotter class is a utility designed to generate customizable bar charts from a pandas DataFrame. It is highly configurable, allowing you to set colors, grid options, and more. This class is intended to be modular and reusable across different applications and strategies.

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Constructor Arguments:

Argument	Type	Description	Required
dataframe	pd.DataFrame	The pandas DataFrame containing the data to be plotted. It must contain columns corresponding to the x-axis and y-axis values.	Yes
x_col	str	The column name in the DataFrame to be used for the x-axis (e.g., 'strikePrice').	Yes
y_col	str	The column name in the DataFrame to be used for the y-axis (e.g., 'gammaExposure').	Yes

Example Usage:

import pandas as pd
from marketquant.tools.utils.bar_plotter import BarPlotter

# Example DataFrame
data = pd.DataFrame({
    'strikePrice': [200, 210, 220, 230, 240],
    'gammaExposure': [100, -50, 200, -150, 300]
})

# Initialize the BarPlotterwith the DataFrame and column names
plotter = BarPlotter(dataframe=data, x_col='strikePrice', y_col='gammaExposure')

# Plot the bar chart
plotter.plot_barchart()

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Methods in BarPlotter

validate_data()

Validates that the provided DataFrame contains the required columns (x_col and y_col).

• Returns:

  • True: If the required columns are found.

  • False: If one or more required columns are missing.

Example:

if plotter.validate_data():
    print("Data is valid.")
else:
    print("Data validation failed.")

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plot_barchart()

Generates and displays a bar chart with customizable options for positive and negative y-values, grid settings, and axis properties. Also allows for the inclusion of an optional line plot over the bars.

Argument	Type	Default Value	Description

dataframe	pd.DataFrame	Required	A pandas DataFrame containing the data to be plotted.

x_col	str	Required	The column name for the x-axis (e.g., 'strikePrice').

y_col	str	Required	The column name for the y-axis (e.g., 'gammaExposure').

figure_size	tuple	(12, 8)	Size of the figure in inches (width, height).

dpi	int	100	Dots per inch (DPI) for the figure resolution.

font_size	int	12	Font size for x and y labels.

bar_width	float	0.8	Width of the bars.

grid	bool	True	Enable or disable the grid.

grid_color	str	'grey'	Color of the grid lines.

grid_line_style	str	'--'	Style of the grid lines (e.g., --, -).

grid_line_width	float	0.5	Thickness of the grid lines.

axis_color	str	'black'	Color of the axis lines.

tick_color	str	'black'	Color of the tick marks.

axis_line_width	float	1.0	Width of the axis lines.

x_rotation	int	45	Rotation angle of the x-axis labels.

legend	bool	True	Enable or disable the legend.

title_font_size	int	16	Font size of the chart title.

title_font_color	str	'navy'	Color of the chart title.

xlabel_font_size	int	14	Font size for the x-axis label.

ylabel_font_size	int	14	Font size for the y-axis label.

label_color	str	'purple'	Color of both x and y-axis labels.

positive_color	str	'blue'	Color of the bars for positive y-values (e.g., positive gamma exposure).

negative_color	str	'red'	Color of the bars for negative y-values (e.g., negative gamma exposure).

line_data	dict	None	Optional dictionary with keys as x-values and values as line y-values (for line overlay on the bar chart).

line_color	str	'green'	Color of the line plot data (if provided).

background_color	str	'white'	Background color of the figure.

Returns:

• Displays the bar chart using matplotlib.

Example:

plotter.plot_barchart(
    positive_color='green',
    negative_color='orange',
    title="Custom Gamma Exposure Chart",
    background_color='lightgray',
    axis_color='navy',
    grid_color='darkgrey',
    line_data={210: 50, 220: 100, 230: -50},
    line_color='purple',
    grid=True,
    dpi=150,
    figure_size=(10, 6)
)

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confirm_valid_data()

Confirms that the DataFrame is valid for plotting by calling validate_data().

• Returns:

  • True if the data is valid and contains the required columns.

  • False if the data is missing the required columns.

Example:

if plotter.confirm_valid_data():
    print("The data is ready for plotting.")

---

Full Example

import pandas as pd
from marketquant.tools.utils.bar_plotter import BarPlotter

# Sample DataFrame for plotting
data = pd.DataFrame({
    'strikePrice': [210, 215, 220, 225, 230],
    'gammaExposure': [5000, -1000, 8000, -2000, 7000]
})

# Initialize the BarPlotter with all custom options
plotter = BarPlotter(
    dataframe=data,
    x_col="strikePrice",
    y_col="gammaExposure",
    figure_size=(14, 10),      # Set figure size
    dpi=120,                   # Set DPI for high resolution
    font_size=14,              # Font size for axis labels
    bar_width=0.6,             # Bar width
    grid=True,                 # Enable grid
    grid_color='lightgrey',    # Set grid color
    grid_line_style="--",      # Grid line style
    grid_line_width=0.8,       # Grid line thickness
    axis_color='darkblue',     # Color of the axis
    tick_color='darkgreen',    # Color of the tick marks
    axis_line_width=1.5,       # Width of the axis lines
    x_rotation=30,             # Rotate x-axis labels by 30 degrees
    legend=True,               # Enable legend
    title_font_size=20,        # Title font size
    title_font_color="darkred",# Title font color
    xlabel_font_size=16,       # X-axis label font size
    ylabel_font_size=16,       # Y-axis label font size
    label_color="black"        # Color of the axis labels
)

# Plot the bar chart with custom options
plotter.plot_barchart(
    positive_color='green',        # Set color for positive bars
    negative_color='orange',       # Set color for negative bars
    title="Customized Gamma Exposure Chart",  # Custom title
    background_color='white',      # Background color of the chart
    line_data={215: 1000, 225: -500},  # Add optional line plot data
    line_color='blue'              # Set line color for line plot
)