Creating Customized Chart Styles and Color Schemes in mplfinance

Creating visually appealing and easy-to-understand financial charts is essential for data analysts and financial experts. mplfinance is a powerful library based on matplotlib that comes in handy when visualizing stock data. In this article, we will explore how to create customized chart styles and color schemes using mplfinance, which can enhance the visibility and comprehension of your charts.

1. Overview

mplfinance provides out-of-the-box styles that you can use, but sometimes you may want to tweak the charts to match your preferences or branding requirements. We will be covering:

• Setting up and installing mplfinance
• Creating custom chart styles
• Modifying color schemes
• Saving and reusing custom styles

2. Installation

Firstly, ensure you have Python and pip installed on your machine. You can install mplfinance using pip:

pip install mplfinance

3. Basic Usage

Let’s start by importing the necessary libraries and plotting a basic candlestick chart. Here's how you can do it:

import mplfinance as mpf
import pandas as pd

# Suppose we have some stock data loaded
data = pd.read_csv('your_stock_data.csv', index_col=0, parse_dates=True)

mpf.plot(data, type='candle', style='classic')

4. Creating Custom Styles

mplfinance allows customization of plot background, gridlines, and other components through its style dictionary. Here's how you can create a custom style:

custom_style = mpf.make_mpf_style(
    base_mpf_style='nightclouds', 
    rc={
        'axes.edgecolor': 'gray',
        'grid.color': 'gray'
    },
    base_mpl_style="seaborn")

This code customizes a predefined style by changing the grid color and base style to "seaborn". You can apply this style when plotting:

mpf.plot(data, type='candle', style=custom_style)

5. Customizing Color Schemes

Alter the color settings to reflect a unique theme. Customize primary and secondary colors including the body, edges, and shadows of the candles:

custom_colors = mpf.make_marketcolors(
    up='green',
    down='red',
    wick='black',
    edge='black',
    volume='blue')

custom_style = mpf.make_mpf_style(marketcolors=custom_colors)

This redefines the market color scheme, useful for distinguishing bullish and bearish candlesticks more vividly.

6. Saving and Reusing Styles

Once you've developed a style that you like, you may wish to reuse it. Save your settings in a function or a separate configuration module to streamline future chart generation. An example could be:

def get_custom_style():
    market_colors = mpf.make_marketcolors(
        up='blue',
        down='orange',
        wick={'up': 'blue', 'down': 'red'},
        edge='i")

    return mpf.make_mpf_style(marketcolors=market_colors, base_mpf_style='classic')

Using and invoking get_custom_style() will apply these saved settings to any data plot command, promoting effective code reuse.

Conclusion

Customizing chart styles and color schemes in mplfinance provides flexibility for creating personalized financial charts. With the knowledge of style adjustments as outlined, creating a visually appealing chart to stand out in presentations or reports becomes straightforward.