def _valid_plot_kwargs():
'''
Construct and return the "valid kwargs table" for the mplfinance.plot() function.
A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are the
valid key-words for the function. The value for each key is a dict containing
2 specific keys: "Default", and "Validator" with the following values:
"Default" - The default value for the kwarg if none is specified.
"Validator" - A function that takes the caller specified value for the kwarg,
and validates that it is the correct type, and (for kwargs with
a limited set of allowed values) may also validate that the
kwarg value is one of the allowed values.
'''
vkwargs = {
'columns' : { 'Default' : None, # use default names: ('Open', 'High', 'Low', 'Close', 'Volume')
'Validator' : lambda value: isinstance(value, (tuple, list))
and len(value) == 5
and all(isinstance(c, str) for c in value) },
'type' : { 'Default' : 'ohlc',
'Validator' : lambda value: value in _get_valid_plot_types() },
'style' : { 'Default' : None,
'Validator' : _styles._valid_mpf_style },
'volume' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) or isinstance(value,mpl_axes.Axes) },
'mav' : { 'Default' : None,
'Validator' : _mav_validator },
'renko_params' : { 'Default' : dict(),
'Validator' : lambda value: isinstance(value,dict) },
'pnf_params' : { 'Default' : dict(),
'Validator' : lambda value: isinstance(value,dict) },
'study' : { 'Default' : None,
'Validator' : lambda value: _kwarg_not_implemented(value) },
'marketcolors' : { 'Default' : None, # use 'style' for default, instead.
'Validator' : lambda value: isinstance(value,dict) },
'no_xgaps' : { 'Default' : True, # None means follow default logic below:
'Validator' : lambda value: _warn_no_xgaps_deprecated(value) },
'show_nontrading' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'figscale' : { 'Default' : None, # scale base figure size up or down.
'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) },
'figratio' : { 'Default' : None, # aspect ratio; scaled to 8.0 height
'Validator' : lambda value: isinstance(value,(tuple,list))
and len(value) == 2
and isinstance(value[0],(float,int))
and isinstance(value[1],(float,int)) },
'figsize' : { 'Default' : None, # figure size; overrides figratio and figscale
'Validator' : lambda value: isinstance(value,(tuple,list))
and len(value) == 2
and isinstance(value[0],(float,int))
and isinstance(value[1],(float,int)) },
'linecolor' : { 'Default' : None, # line color in line plot
'Validator' : lambda value: mcolors.is_color_like(value) },
'title' : { 'Default' : None, # Figure Title
'Validator' : lambda value: isinstance(value,(str,dict)) },
'axtitle' : { 'Default' : None, # Axes Title (subplot title)
'Validator' : lambda value: isinstance(value,str) },
'ylabel' : { 'Default' : 'Price', # y-axis label
'Validator' : lambda value: isinstance(value,str) },
'ylabel_lower' : { 'Default' : None, # y-axis label default logic below
'Validator' : lambda value: isinstance(value,str) },
'addplot' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) },
'savefig' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) },
'block' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,bool) },
'returnfig' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'return_calculated_values' : {'Default' : None,
'Validator' : lambda value: isinstance(value, dict) and len(value) == 0},
'set_ylim' : {'Default' : None,
'Validator' : lambda value: _warn_set_ylim_deprecated(value) },
'ylim' : {'Default' : None,
'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
and all([isinstance(v,(int,float)) for v in value])},
'xlim' : {'Default' : None,
'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
and all([isinstance(v,(int,float)) for v in value])},
'set_ylim_panelB' : {'Default' : None,
'Validator' : lambda value: _warn_set_ylim_deprecated(value) },
'hlines' : { 'Default' : None,
'Validator' : lambda value: _hlines_validator(value) },
'vlines' : { 'Default' : None,
'Validator' : lambda value: _vlines_validator(value) },
'alines' : { 'Default' : None,
'Validator' : lambda value: _alines_validator(value) },
'tlines' : { 'Default' : None,
'Validator' : lambda value: _tlines_validator(value) },
'panel_ratios' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) <= 10 and
all([isinstance(v,(int,float)) for v in value]) },
'main_panel' : { 'Default' : 0,
'Validator' : lambda value: _valid_panel_id(value) },
'volume_panel' : { 'Default' : 1,
'Validator' : lambda value: _valid_panel_id(value) },
'num_panels' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,int) and value in range(1,10+1) },
'datetime_format' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'xrotation' : { 'Default' : 45,
'Validator' : lambda value: isinstance(value,(int,float)) },
'axisoff' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'closefig' : { 'Default' : 'auto',
'Validator' : lambda value: isinstance(value,bool) },
'fill_between' : { 'Default' : None,
'Validator' : lambda value: _num_or_seq_of_num(value) or
(isinstance(value,dict) and 'y1' in value and
_num_or_seq_of_num(value['y1'])) },
'tight_layout' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'width_adjuster_version' : { 'Default' : 'v1',
'Validator' : lambda value: value in ('v0', 'v1') },
'scale_width_adjustment' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 },
'update_width_config' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 },
'return_width_config' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) and len(value)==0 },
'saxbelow' : { 'Default' : True, # Issue#115 Comment#639446764
'Validator' : lambda value: isinstance(value,bool) },
'scale_padding' : { 'Default' : 1.0, # Issue#193
'Validator' : lambda value: _scale_padding_validator(value) },
'ax' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,mpl_axes.Axes) },
'volume_exponent' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,int) or value == 'legacy'},
'tz_localize' : { 'Default' : True,
'Validator' : lambda value: isinstance(value,bool) },
}
_validate_vkwargs_dict(vkwargs)
return vkwargs
def plot( data, **kwargs ):
"""
Given a Pandas DataFrame containing columns Open,High,Low,Close and optionally Volume
with a DatetimeIndex, plot the data.
Available plots include ohlc bars, candlestick, and line plots.
Also provide visually analysis in the form of common technical studies, such as:
moving averages, renko, etc.
Also provide ability to plot trading signals, and/or addtional user-defined data.
"""
config = _process_kwargs(kwargs, _valid_plot_kwargs())
# translate alias types:
config['type'] = _get_valid_plot_types(config['type'])
dates,opens,highs,lows,closes,volumes = _check_and_prepare_data(data, config)
if config['type'] in VALID_PMOVE_TYPES and config['addplot'] is not None:
err = "`addplot` is not supported for `type='" + config['type'] +"'`"
raise ValueError(err)
external_axes_mode = _check_for_external_axes(config)
if external_axes_mode:
if config['figscale'] is not None:
warnings.warn('\n\n ================================================================= '+
'\n\n WARNING: `figscale` has NO effect in External Axes Mode.'+
'\n\n ================================================================ ',
category=UserWarning)
if config['figratio'] is not None:
warnings.warn('\n\n ================================================================= '+
'\n\n WARNING: `figratio` has NO effect in External Axes Mode.'+
'\n\n ================================================================ ',
category=UserWarning)
if config['figsize'] is not None:
warnings.warn('\n\n ================================================================= '+
'\n\n WARNING: `figsize` has NO effect in External Axes Mode.'+
'\n\n ================================================================ ',
category=UserWarning)
else:
if config['figscale'] is None: config['figscale'] = 1.0
if config['figratio'] is None: config['figratio'] = DEFAULT_FIGRATIO
style = config['style']
if external_axes_mode and hasattr(config['ax'],'mpfstyle') and style is None:
style = config['ax'].mpfstyle
elif style is None:
style = 'default'
if isinstance(style,str):
style = _styles._get_mpfstyle(style)
config['style'] = style
if isinstance(style,dict):
if not external_axes_mode: _styles._apply_mpfstyle(style)
else:
raise TypeError('style should be a `dict`; why is it not?')
# ----------------------------------------------------------------------
# TODO: Add some warnings, or raise an exception, if external_axes_mode
# and user is trying to figscale, figratio, or figsize.
# ----------------------------------------------------------------------
if not external_axes_mode:
fig = plt.figure()
_adjust_figsize(fig,config)
else:
fig = None
if config['volume'] and volumes is None:
raise ValueError('Request for volume, but NO volume data.')
if external_axes_mode:
panels = None
if config['volume']:
volumeAxes = config['volume']
volumeAxes.set_axisbelow(config['saxbelow'])
else:
panels = _build_panels(fig, config)
volumeAxes = panels.at[config['volume_panel'],'axes'][0] if config['volume'] is True else None
fmtstring = _determine_format_string( dates, config['datetime_format'] )
ptype = config['type']
if config['show_nontrading']:
formatter = mdates.DateFormatter(fmtstring)
xdates = dates
else:
formatter = IntegerIndexDateTimeFormatter(dates, fmtstring)
xdates = np.arange(len(dates))
if external_axes_mode:
axA1 = config['ax']
axA1.set_axisbelow(config['saxbelow'])
else:
axA1 = panels.at[config['main_panel'],'axes'][0]
# Will have to handle widths config separately for PMOVE types ??
config['_width_config'] = _determine_width_config(xdates, config)
rwc = config['return_width_config']
if isinstance(rwc,dict) and len(rwc)==0:
config['return_width_config'].update(config['_width_config'])
collections = None
if ptype == 'line':
lw = config['_width_config']['line_width']
axA1.plot(xdates, closes, color=config['linecolor'], linewidth=lw)
else:
collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
if ptype in VALID_PMOVE_TYPES:
collections, new_dates, volumes, brick_values, size = collections
formatter = IntegerIndexDateTimeFormatter(new_dates, fmtstring)
xdates = np.arange(len(new_dates))
if collections is not None:
for collection in collections:
axA1.add_collection(collection)
if ptype in VALID_PMOVE_TYPES:
mavprices = _plot_mav(axA1,config,xdates,brick_values)
else:
mavprices = _plot_mav(axA1,config,xdates,closes)
avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
if not config['tight_layout']:
minx = xdates[0] - avg_dist_between_points
maxx = xdates[-1] + avg_dist_between_points
else:
minx = xdates[0] - (0.45 * avg_dist_between_points)
maxx = xdates[-1] + (0.45 * avg_dist_between_points)
if len(xdates) == 1: # kludge special case
minx = minx - 0.75
maxx = maxx + 0.75
if ptype not in VALID_PMOVE_TYPES:
_lows = lows
_highs = highs
else:
_lows = brick_values
_highs = [brick+size for brick in brick_values]
miny = np.nanmin(_lows)
maxy = np.nanmax(_highs)
if config['ylim'] is not None:
axA1.set_ylim(config['ylim'][0], config['ylim'][1])
elif config['tight_layout']:
ydelta = 0.01 * (maxy-miny)
axA1.set_ylim(miny-ydelta,maxy+ydelta)
if config['xlim'] is not None:
axA1.set_xlim(config['xlim'][0], config['xlim'][1])
elif config['tight_layout']:
axA1.set_xlim(minx,maxx)
if (config['ylim'] is None and
config['xlim'] is None and
not config['tight_layout']):
corners = (minx, miny), (maxx, maxy)
axA1.update_datalim(corners)
if config['return_calculated_values'] is not None:
retdict = config['return_calculated_values']
if ptype in VALID_PMOVE_TYPES:
prekey = ptype
retdict[prekey+'_bricks'] = brick_values
retdict[prekey+'_dates'] = mdates.num2date(new_dates)
retdict[prekey+'_size'] = size
if config['volume']:
retdict[prekey+'_volumes'] = volumes
if config['mav'] is not None:
mav = config['mav']
if len(mav) != len(mavprices):
warnings.warn('len(mav)='+str(len(mav))+' BUT len(mavprices)='+str(len(mavprices)))
else:
for jj in range(0,len(mav)):
retdict['mav' + str(mav[jj])] = mavprices[jj]
retdict['minx'] = minx
retdict['maxx'] = maxx
retdict['miny'] = miny
retdict['maxy'] = maxy
# Note: these are NOT mutually exclusive, so the order of this
# if/elif is important: VALID_PMOVE_TYPES must be first.
if ptype in VALID_PMOVE_TYPES:
dtix = pd.DatetimeIndex([dt for dt in mdates.num2date(new_dates)])
elif not config['show_nontrading']:
dtix = data.index
else:
dtix = None
line_collections = []
line_collections.append(_construct_aline_collections(config['alines'], dtix))
line_collections.append(_construct_hline_collections(config['hlines'], minx, maxx))
line_collections.append(_construct_vline_collections(config['vlines'], dtix, miny, maxy))
tlines = config['tlines']
if isinstance(tlines,(list,tuple)) and all([isinstance(item,dict) for item in tlines]):
pass
else:
tlines = [tlines,]
for tline_item in tlines:
line_collections.append(_construct_tline_collections(tline_item, dtix, dates, opens, highs, lows, closes))
for collection in line_collections:
if collection is not None:
axA1.add_collection(collection)
datalen = len(xdates)
if config['volume']:
vup,vdown = style['marketcolors']['volume'].values()
#-- print('vup,vdown=',vup,vdown)
vcolors = _updown_colors(vup, vdown, opens, closes, use_prev_close=style['marketcolors']['vcdopcod'])
#-- print('len(vcolors),len(opens),len(closes)=',len(vcolors),len(opens),len(closes))
#-- print('vcolors=',vcolors)
w = config['_width_config']['volume_width']
lw = config['_width_config']['volume_linewidth']
adjc = _adjust_color_brightness(vcolors,0.90)
volumeAxes.bar(xdates,volumes,width=w,linewidth=lw,color=vcolors,ec=adjc)
vymin = 0.3 * np.nanmin(volumes)
vymax = 1.1 * np.nanmax(volumes)
volumeAxes.set_ylim(vymin,vymax)
xrotation = config['xrotation']
if not external_axes_mode:
_set_ticks_on_bottom_panel_only(panels,formatter,rotation=xrotation)
else:
axA1.tick_params(axis='x',rotation=xrotation)
axA1.xaxis.set_major_formatter(formatter)
addplot = config['addplot']
if addplot is not None and ptype not in VALID_PMOVE_TYPES:
# NOTE: If in external_axes_mode, then all code relating
# to panels and secondary_y becomes irrrelevant.
# If the user wants something on a secondary_y then user should
# determine that externally, and pass in the appropriate axes.
if not external_axes_mode:
# Calculate the Order of Magnitude Range ('mag')
# If addplot['secondary_y'] == 'auto', then: If the addplot['data']
# is out of the Order of Magnitude Range, then use secondary_y.
lo = math.log(max(math.fabs(np.nanmin(lows)),1e-7),10) - 0.5
hi = math.log(max(math.fabs(np.nanmax(highs)),1e-7),10) + 0.5
panels['mag'] = [None]*len(panels) # create 'mag'nitude column
panels.at[config['main_panel'],'mag'] = {'lo':lo,'hi':hi} # update main panel magnitude range
if config['volume']:
lo = math.log(max(math.fabs(np.nanmin(volumes)),1e-7),10) - 0.5
hi = math.log(max(math.fabs(np.nanmax(volumes)),1e-7),10) + 0.5
panels.at[config['volume_panel'],'mag'] = {'lo':lo,'hi':hi}
if isinstance(addplot,dict):
addplot = [addplot,] # make list of dict to be consistent
elif not _list_of_dict(addplot):
raise TypeError('addplot must be `dict`, or `list of dict`, NOT '+str(type(addplot)))
for apdict in addplot:
panid = apdict['panel']
if not external_axes_mode:
if panid == 'main' : panid = 0 # for backwards compatibility
elif panid == 'lower': panid = 1 # for backwards compatibility
if apdict['y_on_right'] is not None:
panels.at[panid,'y_on_right'] = apdict['y_on_right']
aptype = apdict['type']
if aptype == 'ohlc' or aptype == 'candle':
ax = _addplot_collections(panid,panels,apdict,xdates,config)
_addplot_apply_supplements(ax,apdict)
else:
apdata = apdict['data']
if isinstance(apdata,list) and not isinstance(apdata[0],(float,int)):
raise TypeError('apdata is list but NOT of float or int')
if isinstance(apdata,pd.DataFrame):
havedf = True
else:
havedf = False # must be a single series or array
apdata = [apdata,] # make it iterable
for column in apdata:
ydata = apdata.loc[:,column] if havedf else column
ax = _addplot_columns(panid,panels,ydata,apdict,xdates,config)
_addplot_apply_supplements(ax,apdict)
# fill_between is NOT supported for external_axes_mode
# (caller can easily call ax.fill_between() themselves).
if config['fill_between'] is not None and not external_axes_mode:
fb = config['fill_between']
panid = config['main_panel']
if isinstance(fb,dict):
if 'x' in fb:
raise ValueError('fill_between dict may not contain `x`')
if 'panel' in fb:
panid = fb['panel']
del fb['panel']
else:
fb = dict(y1=fb)
fb['x'] = xdates
ax = panels.at[panid,'axes'][0]
ax.fill_between(**fb)
# put the primary axis on one side,
# and the twinx() on the "other" side:
if not external_axes_mode:
for panid,row in panels.iterrows():
ax = row['axes']
y_on_right = style['y_on_right'] if row['y_on_right'] is None else row['y_on_right']
_set_ylabels_side(ax[0],ax[1],y_on_right)
else:
y_on_right = style['y_on_right']
_set_ylabels_side(axA1,None,y_on_right)
# TODO: ================================================================
# TODO: Investigate:
# TODO: ===========
# TODO: It appears to me that there may be some or significant overlap
# TODO: between what the following functions actually do:
# TODO: At the very least, all four of them appear to communicate
# TODO: to matplotlib that the xaxis should be treated as dates:
# TODO: -> 'ax.autoscale_view()'
# TODO: -> 'ax.xaxis_dates()'
# TODO: -> 'plt.autofmt_xdates()'
# TODO: -> 'fig.autofmt_xdate()'
# TODO: ================================================================
#if config['autofmt_xdate']:
#print('CALLING fig.autofmt_xdate()')
#fig.autofmt_xdate()
axA1.autoscale_view() # Is this really necessary??
# It appears to me, based on experience coding types 'ohlc' and 'candle'
# for `addplot`, that this IS necessary when the only thing done to the
# the axes is .add_collection(). (However, if ax.plot() .scatter() or
# .bar() was called, then possibly this is not necessary; not entirely
# sure, but it definitely was necessary to get 'ohlc' and 'candle'
# working in `addplot`).
axA1.set_ylabel(config['ylabel'])
if config['volume']:
if external_axes_mode:
volumeAxes.tick_params(axis='x',rotation=xrotation)
volumeAxes.xaxis.set_major_formatter(formatter)
vxp = config['volume_exponent']
if vxp == 'legacy':
volumeAxes.figure.canvas.draw() # This is needed to calculate offset
offset = volumeAxes.yaxis.get_major_formatter().get_offset()
if len(offset) > 0:
offset = (' x '+offset)
elif isinstance(vxp,int) and vxp > 0:
volumeAxes.ticklabel_format(useOffset=False,scilimits=(vxp,vxp),axis='y')
offset = ' $10^{'+str(vxp)+'}$'
elif isinstance(vxp,int) and vxp == 0:
volumeAxes.ticklabel_format(useOffset=False,style='plain',axis='y')
offset = ''
else:
offset = ''
scilims = plt.rcParams['axes.formatter.limits']
if scilims[0] < scilims[1]:
for power in (5,4,3,2,1):
xp = scilims[1]*power
if vymax >= 10.**xp:
volumeAxes.ticklabel_format(useOffset=False,scilimits=(xp,xp),axis='y')
offset = ' $10^{'+str(xp)+'}$'
break
elif scilims[0] == scilims[1] and scilims[1] != 0:
volumeAxes.ticklabel_format(useOffset=False,scilimits=scilims,axis='y')
offset = ' $10^'+str(scilims[1])+'$'
volumeAxes.yaxis.offsetText.set_visible(False)
if config['ylabel_lower'] is None:
vol_label = 'Volume'+offset
else:
if len(offset) > 0:
offset = '\n'+offset
vol_label = config['ylabel_lower'] + offset
volumeAxes.set_ylabel(vol_label)
if config['title'] is not None:
if config['tight_layout']:
# IMPORTANT: 0.89 is based on the top of the top panel
# being at 0.18+0.7 = 0.88. See _panels.py
# If the value changes there, then it needs to change here.
title_kwargs = dict(size='x-large',weight='semibold', va='bottom', y=0.89)
else:
title_kwargs = dict(size='x-large',weight='semibold', va='center')
if isinstance(config['title'],dict):
title_dict = config['title']
if 'title' not in title_dict:
raise ValueError('Must have "title" entry in title dict')
else:
title = title_dict['title']
del title_dict['title']
title_kwargs.update(title_dict) # allows override default values set by mplfinance above
else:
title = config['title'] # config['title'] is a string
fig.suptitle(title,**title_kwargs)
if config['axtitle'] is not None:
axA1.set_title(config['axtitle'])
if not external_axes_mode:
for panid,row in panels.iterrows():
if not row['used2nd']:
row['axes'][1].set_visible(False)
if external_axes_mode:
return None
# Should we create a new kwarg to return a flattened axes list
# versus a list of tuples of primary and secondary axes?
# For now, for backwards compatibility, we flatten axes list:
axlist = [ax for axes in panels['axes'] for ax in axes]
if config['axisoff']:
for ax in axlist:
ax.set_axis_off()
if config['savefig'] is not None:
save = config['savefig']
if isinstance(save,dict):
if config['tight_layout'] and 'bbox_inches' not in save:
plt.savefig(**save,bbox_inches='tight')
else:
plt.savefig(**save)
else:
if config['tight_layout']:
plt.savefig(save,bbox_inches='tight')
else:
plt.savefig(save)
if config['closefig']: # True or 'auto'
plt.close(fig)
elif not config['returnfig']:
plt.show(block=config['block']) # https://stackoverflow.com/a/13361748/1639359
if config['closefig'] == True or (config['block'] and config['closefig']):
plt.close(fig)
if config['returnfig']:
if config['closefig'] == True: plt.close(fig)
return (fig, axlist)
