pip install https://github.com/matplotlib/mpl_finance/archive/master.zip

#!/usr/bin/python3
# Ref: https://matplotlib.org/api/finance_api.html
 
import pandas as pd
from mpl_finance import candlestick_ohlc
 
import matplotlib
matplotlib.use('Agg') # Bypass the need to install Tkinter GUI framework
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
 
# Avoid FutureWarning: Pandas will require you to explicitly register matplotlib converters.
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
 
 
# Load data from CSV file.
##########################
my_headers = ['date', 'open', 'high', 'low', 'close', 'volume']
my_dtypes = {'date': 'str', 'open': 'float', 'high': 'float', 'low': 'float', 
                                                'close': 'float', 'volume': 'int'}
my_parse_dates = ['date']
loaded_data = pd.read_csv('apple.csv', sep='\t', header=1, names=my_headers, 
                            dtype=my_dtypes, parse_dates=my_parse_dates)
 
 
# Convert 'Timestamp' to 'float'.
#   candlestick_ohlc needs time to be in float days format - see date2num().
loaded_data['date'] = [mdates.date2num(d) for d in loaded_data['date']]
 
# Re-arrange data so that each row contains values of a day: 'date','open','high','low','close'.
quotes = [tuple(x) for x in loaded_data[['date','open','high','low','close']].values]
 
# Plot candlestick.
##########################
fig, ax = plt.subplots()
candlestick_ohlc(ax, quotes, width=0.5, colorup='g', colordown='r');
 
 
# Customize graph.
##########################
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Apple')
 
# Format time.
ax.xaxis_date()
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))
 
plt.gcf().autofmt_xdate()   # Beautify the x-labels
plt.autoscale(tight=True)
 
# Save graph to file.
plt.savefig('mpl_finance-apple.png')

date open    high    low close   volume
2019-03-01  174.279999  175.149994  172.889999  174.970001  25873500
2019-03-04  175.690002  177.750000  173.970001  175.850006  27436200
2019-03-05  175.940002  176.000000  174.539993  175.529999  19737400
2019-03-06  174.669998  175.490005  173.940002  174.520004  20810400
2019-03-07  173.869995  174.440002  172.020004  172.500000  24796400
2019-03-08  170.320007  173.070007  169.500000  172.910004  23989900
2019-03-11  175.490005  179.119995  175.350006  178.899994  31964000
2019-03-12  180.000000  182.669998  179.369995  180.910004  32467600
2019-03-13  182.250000  183.300003  180.919998  181.710007  30970100
2019-03-14  183.899994  184.100006  182.559998  183.729996  23579500
2019-03-15  184.850006  187.330002  183.740005  186.119995  39025100
2019-03-18  185.800003  188.389999  185.789993  188.020004  26196000
2019-03-19  188.350006  188.990005  185.919998  186.529999  31529400
2019-03-20  186.229996  189.490005  184.729996  188.160004  31018400
2019-03-21  190.020004  196.330002  189.809998  195.089996  50980100
2019-03-22  195.339996  197.690002  190.779999  191.050003  42359300
2019-03-25  191.509995  191.979996  186.600006  188.740005  43765700
2019-03-26  191.660004  192.880005  184.580002  186.789993  49719500
2019-03-27  188.750000  189.759995  186.550003  188.470001  29598500
2019-03-28  188.949997  189.559998  187.529999  188.720001  20639200