Candlestick OHLC graphs with Matplotlib

In this Matplotlib tutorial, we're going to cover how to create open, high, low, close (OHLC) candlestick charts within Matplotlib. These graphs are used to display time-series stock price information in a condensed form. To do this, we first need a few more imports:

import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc

We bring in ticker to allow us to modify the ticker information at the bottom of the graph. Then we bring in the candlestick_ohlc functionality from the matplotlib.finance module.

Now, we need to organize our data to work with what matplotlib wants. If you're just now joining us, we're getting data like so:

    # Unfortunately, Yahoo's API is no longer available
    # feel free to adapt the code to another source, or use this drop-in replacement.
    stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
    source_code = urllib.request.urlopen(stock_price_url).read().decode()
    stock_data = []
    split_source = source_code.split('\n')
    for line in split_source[1:]:
        split_line = line.split(',')
        if len(split_line) == 7:
            if 'values' not in line and 'labels' not in line:
                stock_data.append(line)

    
    date, closep, highp, lowp, openp, adj_closep, volume = np.loadtxt(stock_data,
                                                          delimiter=',',
                                                          unpack=True,
                                                          converters={0: bytespdate2num('%Y-%m-%d')})

Now, we need to build a Python list, where each element is that data. We could modify our loadtxt function to just not unpack, but later on we will also want to reference specific data points. We could work around this, but we might as well just have two separate data sets in the end. To do this, we'll just do:

    x = 0
    y = len(date)
    ohlc = []

    while x < y:
        append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
        ohlc.append(append_me)
        x+=1

With this, we now have a list of OHLC data stored to our variable called ohlc. Now we can plot this with:

    candlestick_ohlc(ax1, ohlc)

Graphing this should give us something like:

Unfortunately, the datetime data on the x axis is not in datestamp form. We can handle for this:

    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))

Also, the red/black coloring isn't the best choice in my opinion. We should use green for rises and red for falls. To do this, we can do:

    candlestick_ohlc(ax1, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')

Finally, we can set the number of x labels we want, like so:

    ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))

Now, our full code should look something like:

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc

import numpy as np
import urllib
import datetime as dt


def bytespdate2num(fmt, encoding='utf-8'):
    strconverter = mdates.strpdate2num(fmt)
    def bytesconverter(b):
        s = b.decode(encoding)
        return strconverter(s)
    return bytesconverter
    

def graph_data(stock):

    fig = plt.figure()
    ax1 = plt.subplot2grid((1,1), (0,0))
    
    # Unfortunately, Yahoo's API is no longer available
    # feel free to adapt the code to another source, or use this drop-in replacement.
    stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
    source_code = urllib.request.urlopen(stock_price_url).read().decode()
    stock_data = []
    split_source = source_code.split('\n')
    for line in split_source[1:]:
        split_line = line.split(',')
        if len(split_line) == 7:
            if 'values' not in line and 'labels' not in line:
                stock_data.append(line)

    
    date, closep, highp, lowp, openp, adj_closep, volume = np.loadtxt(stock_data,
                                                          delimiter=',',
                                                          unpack=True,
                                                          converters={0: bytespdate2num('%Y-%m-%d')})

    x = 0
    y = len(date)
    ohlc = []

    while x < y:
        append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
        ohlc.append(append_me)
        x+=1


    candlestick_ohlc(ax1, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')

    for label in ax1.xaxis.get_ticklabels():
        label.set_rotation(45)

    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
    ax1.grid(True)
    

    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.title(stock)
    plt.legend()
    plt.subplots_adjust(left=0.09, bottom=0.20, right=0.94, top=0.90, wspace=0.2, hspace=0)
    plt.show()


graph_data('EBAY')

The result:

Also note that we've removed most of the ax1 modifications from the previous tutorials.

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