Matplotlib – Finance volume overlay

The volume_overlay3 did not work for me. So I tried your idea to add a bar plot to the candlestick plot.

After creating a twin axis for the volume re-position this axis (make it short) and modify the range of the candlestick y-data to avoid collisions.

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# from import candlestick
# from import volume_overlay3
# finance module is no longer part of matplotlib
# see:
from mpl_finance import candlestick_ochl as candlestick
from mpl_finance import volume_overlay3
from matplotlib.dates import num2date
from matplotlib.dates import date2num
import matplotlib.mlab as mlab
import datetime

r = mlab.csv2rec(datafile, delimiter=";")

# the dates in my example file-set are very sparse (and annoying) change the dates to be sequential
for i in range(len(r)-1):
    r['date'][i+1] = r['date'][i] + datetime.timedelta(days=1)

candlesticks = zip(date2num(r['date']),r['open'],r['close'],r['max'],r['min'],r['volume'])

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.set_ylabel('Quote ($)', size=20)
candlestick(ax, candlesticks,width=1,colorup='g', colordown='r')

# shift y-limits of the candlestick plot so that there is space at the bottom for the volume bar chart
pad = 0.25
yl = ax.get_ylim()

# create the second axis for the volume bar-plot
ax2 = ax.twinx()

# set the position of ax2 so that it is short (y2=0.32) but otherwise the same size as ax

# get data from candlesticks for a bar plot
dates = [x[0] for x in candlesticks]
dates = np.asarray(dates)
volume = [x[5] for x in candlesticks]
volume = np.asarray(volume)

# make bar plots and color differently depending on up/down for the day
pos = r['open']-r['close']<0
neg = r['open']-r['close']>0[pos],volume[pos],color="green",width=1,align='center')[neg],volume[neg],color="red",width=1,align='center')

#scale the x-axis tight
# the y-ticks for the bar were too dense, keep only every third one
yticks = ax2.get_yticks()

ax2.set_ylabel('Volume', size=20)

# format the x-ticks with a human-readable date. 
xt = ax.get_xticks()
new_xticks = [ for d in xt]
ax.set_xticklabels(new_xticks,rotation=45, horizontalalignment="right")



data.csv is up here:

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