I've made a python script to convert a csv file in a candlestick like this using mpl_finance, this is the script:
import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc
import pandas as pd
import matplotlib.dates as mpl_dates
plt.style.use('ggplot')
# Extracting Data for plotting
data = pd.read_csv('CSV.csv')
ohlc = data.loc[:, ['Date', 'Open', 'High', 'Low', 'Close']]
ohlc['Date'] = pd.to_datetime(ohlc['Date'])
ohlc['Date'] = ohlc['Date'].apply(mpl_dates.date2num)
ohlc = ohlc.astype(float)
# Creating Subplots
fig, ax = plt.subplots()
plt.axis('off')
fig.patch.set_facecolor('black')
candlestick_ohlc(ax, ohlc.values, width=0.6, colorup='green', colordown='red', alpha=0.8)
plt.show()
Now I need to do the same thing but using mplfinance instead of mpl_finance and I've tried like this:
import mplfinance as mpf
# Load data file.
df = pd.read_csv('CSV.csv', index_col=0, parse_dates=True)
# Plot candlestick.
# Add volume.
# Add moving averages: 3,6,9.
# Save graph to *.png.
mpf.plot(df, type='candle', style='charles',
title='',
ylabel='',
ylabel_lower='',
volume=True,
mav=(3,6,9),
savefig='test-mplfiance.png')
And I have this result:
So, now I need to change background color from white to black, remove grid and remove axes but I have no idea how to do it.
Thanks to all will spend time for reply me.
[EDIT]: this is an old question I've ade when mpl_finance was at It's first stage, now a lot of things are changed and this question is obsolete.
The best way to do this is to define your own style using mpf.make_mpf_style()
rather than using the default mpf styles.
If using external axes method in mplfinance, you can plot multiple charts as below:
# add your own style by passing in kwargs
s = mpf.make_mpf_style(base_mpf_style='charles', rc={'font.size': 6})
fig = mpf.figure(figsize=(10, 7), style=s) # pass in the self defined style to the whole canvas
ax = fig.add_subplot(2,1,1) # main candle stick chart subplot, you can also pass in the self defined style here only for this subplot
av = fig.add_subplot(2,1,2, sharex=ax) # volume chart subplot
mpf.plot(price_data, type='candle', ax=ax, volume=av)
The default mpf styles are as below. I believe 'mike' and 'nighclouds' have dark background, not 100% sure about others, you can choose to work on top of these two.
In [5]:
mpf.available_styles()
Out[5]:
['binance',
'blueskies',
'brasil',
'charles',
'checkers',
'classic',
'default',
'mike',
'nightclouds',
'sas',
'starsandstripes',
'yahoo']
Link to visualize the default mplfinance styles
The arguments that can be passed in mpf.make_mpf_style()
are as below, you can use base_mpf_style, facecolor, gridcolor, gridstyle, gridaxis, rc
to customise your own style, and give it a name by using style_name
. You can play around with these arguments to see how they turn out.
def _valid_make_mpf_style_kwargs():
vkwargs = {
'base_mpf_style': { 'Default' : None,
'Validator' : lambda value: value in _styles.keys() },
'base_mpl_style': { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) }, # and is in plt.style.available
'marketcolors' : { 'Default' : None, #
'Validator' : lambda value: isinstance(value,dict) },
'mavcolors' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,list) }, # TODO: all([mcolors.is_color_like(v) for v in value.values()])
'facecolor' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'edgecolor' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'figcolor' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'gridcolor' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'gridstyle' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'gridaxis' : { 'Default' : None,
'Validator' : lambda value: value in [ 'vertical'[0:len(value)], 'horizontal'[0:len(value)], 'both'[0:len(value)] ] },
'y_on_right' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,bool) },
'rc' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) },
'style_name' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
}
_validate_vkwargs_dict(vkwargs)
return vkwargs
First you should upgrade your package to the latest version, then in your code, you can write: mpf.plot(......, axisoff= True) in my experience sometimes when using "axisoff= True", it is usefull to use figscale to obtain better results.
To hide axes add this:
axisoff=True
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