Summary

This context provides a comprehensive guide on using the mplfinance library in Python to create an advanced financial stock chart with features such as candlestick patterns, moving averages, volume, MACD, and stochastic oscillators as subplots.

Abstract

The mplfinance library, a new matplotlib finance API, simplifies the process of plotting sophisticated financial charts in Python. The guide begins with downloading stock price data using the yfinance library and progresses to plotting a basic candlestick chart. It then demonstrates how to enhance the chart with moving averages and volume data. The tutorial further explains the addition of MACD (Moving Average Convergence Divergence) and the stochastic oscillator as subplots, providing technical analysis tools for traders. The article also covers customization options in mplfinance, such as changing styles, adjusting figure size, and modifying subplot size ratios. Finally, the author compiles all the demonstrated features into a complete example, creating a rich, informative financial chart, and provides a link to the full code and a demo notebook for further exploration.

Opinions

The author suggests that using mplfinance is easier than matplotlib for financial data visualization, implying that it's a more specialized and convenient tool for traders and analysts.
The article conveys that the default style of mplfinance may be somewhat dull, and it encourages users to explore and apply the library's preset styles for more visually appealing charts.
The author appears to value the ease of customization in mplfinance, emphasizing the convenience of features like figscale for figure size adjustment and panel_ratios for subplot customization.
By providing a full code example and a demo notebook, the author shows a commitment to practical, hands-on learning, indicating that they believe in learning by doing.
The author's invitation to support their work through Medium subscription or a one-time coffee purchase suggests a belief in the value of their content and a desire to foster a community of support and engagement around their tutorials.

Plot Stock Chart Using mplfinance in Python

An introductory guide to plot a candlestick chart along with volume, MACD & stochastic using mplfinance.

If you are a Python user, there’s a high chance you have worked with matplotlib before. To make it easier to work with financial data, a new matplotlib finance API called mplfinance was created (it was first released as mpl-finance but was later renamed).

In this tutorial, we are going to learn how to use mplfinance to plot the following financial chart.

A candlestick chart with moving averages, volume, MACD, and stochastic oscillator plotted using *mplfinance*.

If you want to know how to plot the above chart using plotly, check out this article too:

A Simple Guide to Plotly for Plotting Financial Chart

Plot stock price chart along with volume, MACD & stochastic.

python.plainenglish.io

Let us begin!

1. Download stock price & plot candlestick chart

First thing first, let’s get ourselves some stock prices to plot. To do that, we will be using the yfinance library.

Then, we are ready to plot our first candlestick chart of the day.

That’s all we need. We now have a simple-looking candlestick chart.

2. Add moving averages and volume

Let’s spice things up a little by adding some moving averages and volume to the chart. With mplfinance, it is as easy as this:

Note that the mav argument in the plot method is only for simple moving average. To plot other types of moving average like exponential moving average, we will need to calculate and add it to the chart separately.

3. Add MACD & Stochastics as subplots

Here comes the good part. Let’s begin with MACD.

3.1 MACD

Line 2–11: A function that calculates and returns the MACD.
Line 13: Call the MACD function with periods of 12, 26, and 9.
Line 14–19: We are utilizing the make_addplot function to construct a list of plots we want to add to the main chart: 1. MACD line 2. Signal line 3. Histogram (green bar) 4. Histogram (red bar)
Line 21: Add MACD plots to the main chart

Running the code above gives us the following chart:

3.2 Stochastic Oscillator

Let’s proceed with the stochastic oscillator.

Line 2–10: A function that calculates and returns the stochastic values.
Line 12: Call the Stochastic function with window=14 and smoothing_window=3.
Line 13–15: Again, we are utilizing the make_addplot function to construct the plot we want to add to the main chart
Line 17: Add the Stochastic plot to the main chart

And we get:

4. Customization

Before we put everything together, let’s explore some of the customizations we can perform in mplfinance.

4.1 Styles

As you have realized, so far, we have been using the default mplfinance style and it looks kinda dull. mplfinance does provide some preset styles and here are some examples:

To get all the available styles:

from mplfinance import _styles
_styles.available_styles()

# ['binance',  'blueskies',  'brasil',  'charles',  'checkers',  'classic',  'default',  'ibd',  'kenan',  'mike',  'nightclouds',  'sas',  'starsandstripes',  'yahoo']

4.2 Figure size

There is a very convenient way to change the figure size in mplfinance using figscale:

mpf.plot(df, type='candle', figscale=1.5)

4.3 Subplot size ratio

When dealing with multiple subplots, oftentimes we want to adjust the ratio between different subplots. To do that in mplfinance, we simply use the panel_ratios option:

mpf.plot(df, type='candle', volume=True, addplot=macd_plot, panel_ratios=(4,1,3))

To see more examples of customization, you can check out the official examples here.

5. Putting everything together

Putting everything together, we can use the following codes to create the plot we saw earlier:

The full code and demo notebook of this tutorial is available here.

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