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)
def plot(data, **kwargs):
"""
Given open,high,low,close,volume data for a financial instrument (such as a stock, index,
currency, future, option, etc.) 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, macd, trading envelope, etc.
Also provide ability to plot trading signals, and/or addtional user-defined data.
"""
dates, opens, highs, lows, closes, volumes = _check_and_prepare_data(data)
config = _process_kwargs(kwargs, _valid_plot_kwargs())
style = config['style']
if isinstance(style, str):
style = _styles._get_mpfstyle(style)
if isinstance(style, dict):
_styles._apply_mpfstyle(style)
w, h = config['figratio']
r = float(w) / float(h)
if r < 0.25 or r > 4.0:
raise ValueError(
'"figratio" (aspect ratio) must be between 0.25 and 4.0 (but is '
+ str(r) + ')')
base = (w, h)
figscale = config['figscale']
fsize = [d * figscale for d in base]
fig = plt.figure()
fig.set_size_inches(fsize)
if config['volume'] and volumes is None:
raise ValueError('Request for volume, but NO volume data.')
# -------------------------------------------------------------
# For now (06-Feb-2020) to keep the code somewhat simpler for
# implementing `secondary_y` we are going to ALWAYS create
# secondary (twinx) axes, whether we need them or not, and
# then they will be available to use later when we are plotting:
# -------------------------------------------------------------
need_lower_panel = False
addplot = config['addplot']
if addplot is not None:
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:
if apdict['panel'] == 'lower':
need_lower_panel = True
break
# fig.add_axes( [left, bottom, width, height] ) ... numbers are fraction of fig
if need_lower_panel or config['volume']:
ax1 = fig.add_axes([0.15, 0.38, 0.70, 0.50])
ax2 = fig.add_axes([0.15, 0.18, 0.70, 0.20], sharex=ax1)
plt.xticks(rotation=45) # must do this after creation of axis, and
# after `sharex`, but must be BEFORE any 'twinx()'
ax2.set_axisbelow(True) # so grid does not show through volume bars.
ax4 = ax2.twinx()
ax4.grid(False)
else:
ax1 = fig.add_axes([0.15, 0.18, 0.70, 0.70])
plt.xticks(
rotation=45
) # must do this after creation of axis, but before any 'twinx()'
ax2 = None
ax4 = None
ax3 = ax1.twinx()
ax3.grid(False)
avg_days_between_points = (dates[-1] - dates[0]) / float(len(dates))
# avgerage of 3 or more data points per day we will call intraday data:
if avg_days_between_points < 0.33: # intraday
if mdates.num2date(dates[-1]).date() != mdates.num2date(
dates[0]).date():
# intraday data for more than one day:
fmtstring = '%b %d, %H:%M'
else: # intraday data for a single day
fmtstring = '%H:%M'
else: # 'daily' data (or could be weekly, etc.)
if mdates.num2date(dates[-1]).date().year != mdates.num2date(
dates[0]).date().year:
fmtstring = '%Y-%b-%d'
else:
fmtstring = '%b %d'
if config['show_nontrading']:
formatter = mdates.DateFormatter(fmtstring)
xdates = dates
else:
formatter = IntegerIndexDateTimeFormatter(dates, fmtstring)
xdates = np.arange(len(dates))
ax1.xaxis.set_major_formatter(formatter)
ptype = config['type']
collections = None
if ptype == 'candle' or ptype == 'candlestick':
collections = _construct_candlestick_collections(
xdates,
opens,
highs,
lows,
closes,
marketcolors=style['marketcolors'])
elif ptype == 'ohlc' or ptype == 'bars' or ptype == 'ohlc_bars':
collections = _construct_ohlc_collections(
xdates,
opens,
highs,
lows,
closes,
marketcolors=style['marketcolors'])
elif ptype == 'line':
ax1.plot(xdates, closes, color=config['linecolor'])
else:
raise ValueError('Unrecognized plot type = "' + ptype + '"')
if collections is not None:
for collection in collections:
ax1.add_collection(collection)
mavgs = config['mav']
if mavgs is not None:
if isinstance(mavgs, int):
mavgs = mavgs, # convert to tuple
if len(mavgs) > 7:
mavgs = mavgs[0:7] # take at most 7
if style['mavcolors'] is not None:
mavc = cycle(style['mavcolors'])
else:
mavc = None
for mav in mavgs:
mavprices = data['Close'].rolling(mav).mean().values
if mavc:
ax1.plot(xdates, mavprices, color=next(mavc))
else:
ax1.plot(xdates, mavprices)
avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
minx = xdates[0] - avg_dist_between_points
maxx = xdates[-1] + avg_dist_between_points
miny = min([low for low in lows if low != -1])
maxy = max([high for high in highs if high != -1])
corners = (minx, miny), (maxx, maxy)
ax1.update_datalim(corners)
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)
width = 0.5 * avg_dist_between_points
ax2.bar(xdates, volumes, width=width, color=vcolors)
miny = 0.3 * min(volumes)
maxy = 1.1 * max(volumes)
ax2.set_ylim(miny, maxy)
ax2.xaxis.set_major_formatter(formatter)
used_ax3 = False
used_ax4 = False
addplot = config['addplot']
if addplot is not None:
# Calculate the Order of Magnitude Range
# If addplot['secondary_y'] == 'auto', then: If the addplot['data']
# is out of the Order of Magnitude Range, then use secondary_y.
# Calculate omrange for Main panel, and for Lower (volume) panel:
lo = math.log(max(math.fabs(min(lows)), 1e-7), 10) - 0.5
hi = math.log(max(math.fabs(max(highs)), 1e-7), 10) + 0.5
omrange = {'main': {'lo': lo, 'hi': hi}, 'lower': None}
if config['volume']:
lo = math.log(max(math.fabs(min(volumes)), 1e-7), 10) - 0.5
hi = math.log(max(math.fabs(max(volumes)), 1e-7), 10) + 0.5
omrange.update(lower={'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:
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:
if havedf:
ydata = apdata.loc[:, column]
else:
ydata = column
yd = [y for y in ydata if not math.isnan(y)]
ymhi = math.log(max(math.fabs(max(yd)), 1e-7), 10)
ymlo = math.log(max(math.fabs(min(yd)), 1e-7), 10)
secondary_y = False
if apdict['secondary_y'] == 'auto':
if apdict['panel'] == 'lower':
# If omrange['lower'] is not yet set by volume,
# then set it here as this is the first ydata
# to be plotted on the lower panel, so consider
# it to be the 'primary' lower panel axis.
if omrange['lower'] is None:
omrange.update(lower={'lo': ymlo, 'hi': ymhi})
elif ymlo < omrange['lower']['lo'] or ymhi > omrange[
'lower']['hi']:
secondary_y = True
elif ymlo < omrange['main']['lo'] or ymhi > omrange[
'main']['hi']:
secondary_y = True
# if secondary_y:
# print('auto says USE secondary_y')
# else:
# print('auto says do NOT use secondary_y')
else:
secondary_y = apdict['secondary_y']
# print("apdict['secondary_y'] says secondary_y is",secondary_y)
if apdict['panel'] == 'lower':
ax = ax4 if secondary_y else ax2
else:
ax = ax3 if secondary_y else ax1
if ax == ax3:
used_ax3 = True
if ax == ax4:
used_ax4 = True
if apdict['scatter']:
size = apdict['markersize']
mark = apdict['marker']
color = apdict['color']
ax.scatter(xdates, ydata, s=size, marker=mark, color=color)
else:
ls = apdict['linestyle']
color = apdict['color']
ax.plot(xdates, ydata, linestyle=ls, color=color)
# put the twinx() on the "other" side:
if style['y_on_right']:
ax1.yaxis.set_label_position('right')
ax1.yaxis.tick_right()
ax3.yaxis.set_label_position('left')
ax3.yaxis.tick_left()
if ax2 and ax4:
ax2.yaxis.set_label_position('right')
ax2.yaxis.tick_right()
if ax4 != ax2:
ax4.yaxis.set_label_position('left')
ax4.yaxis.tick_left()
else:
ax1.yaxis.set_label_position('left')
ax1.yaxis.tick_left()
ax3.yaxis.set_label_position('right')
ax3.yaxis.tick_right()
if ax2 and ax4:
ax2.yaxis.set_label_position('left')
ax2.yaxis.tick_left()
if ax4 != ax2:
ax4.yaxis.set_label_position('right')
ax4.yaxis.tick_right()
if need_lower_panel or config['volume']:
ax1.spines['bottom'].set_linewidth(0.25)
ax2.spines['top'].set_linewidth(0.25)
plt.setp(ax1.get_xticklabels(), visible=False)
# 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()
ax1.autoscale_view() # Is this really necessary??
ax1.set_ylabel(config['ylabel'])
if config['volume']:
ax2.figure.canvas.draw() # This is needed to calculate offset
offset = ax2.yaxis.get_major_formatter().get_offset()
ax2.yaxis.offsetText.set_visible(False)
if len(offset) > 0:
offset = (' x ' + offset)
if config['ylabel_lower'] is None:
vol_label = 'Volume' + offset
else:
if len(offset) > 0:
offset = '\n' + offset
vol_label = config['ylabel_lower'] + offset
ax2.set_ylabel(vol_label)
if config['title'] is not None:
fig.suptitle(config['title'], size='x-large', weight='semibold')
if not used_ax3 and ax3 is not None:
ax3.get_yaxis().set_visible(False)
if not used_ax4 and ax4 is not None:
ax4.get_yaxis().set_visible(False)
if config['savefig'] is not None:
save = config['savefig']
if isinstance(save, dict):
plt.savefig(**save)
else:
plt.savefig(save)
else:
# https://stackoverflow.com/a/13361748/1639359 suggests plt.show(block=False)
plt.show(block=config['block'])
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())
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)
style = config['style']
if isinstance(style, str):
style = _styles._get_mpfstyle(style)
if isinstance(style, dict):
_styles._apply_mpfstyle(style)
if config['figsize'] is None:
w, h = config['figratio']
r = float(w) / float(h)
if r < 0.20 or r > 5.0:
raise ValueError(
'"figratio" (aspect ratio) must be between 0.20 and 5.0 (but is '
+ str(r) + ')')
default_scale = DEFAULT_FIGRATIO[1] / h
h *= default_scale
w *= default_scale
base = (w, h)
figscale = config['figscale']
fsize = [d * figscale for d in base]
else:
fsize = config['figsize']
fig = plt.figure()
fig.set_size_inches(fsize)
if config['volume'] and volumes is None:
raise ValueError('Request for volume, but NO volume data.')
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))
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)
mavgs = config['mav']
if mavgs is not None:
if isinstance(mavgs, int):
mavgs = mavgs, # convert to tuple
if len(mavgs) > 7:
mavgs = mavgs[0:7] # take at most 7
if style['mavcolors'] is not None:
mavc = cycle(style['mavcolors'])
else:
mavc = None
# Get rcParams['lines.linewidth'] and scale it
# according to the deinsity of data??
for mav in mavgs:
if ptype in VALID_PMOVE_TYPES:
mavprices = pd.Series(brick_values).rolling(mav).mean().values
else:
mavprices = pd.Series(closes).rolling(mav).mean().values
lw = config['_width_config']['line_width']
if mavc:
axA1.plot(xdates, mavprices, linewidth=lw, color=next(mavc))
else:
axA1.plot(xdates, mavprices, linewidth=lw)
avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
if not config['tight_layout']:
#print('plot: xdates[-1]=',xdates[-1])
#print('plot: xdates[ 0]=',xdates[ 0])
#print('plot: len(xdates)=',len(xdates))
#print('plot: avg_dist_between_points =',avg_dist_between_points)
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 len(xdates) > 1:
# stdy = (stat.stdev(_lows) + stat.stdev(_highs)) / 2.0
#else: # kludge special case
# stdy = 0.02 * math.fabs(maxy - miny)
# print('minx,miny,maxx,maxy,stdy=',minx,miny,maxx,maxy,stdy)
if config['set_ylim'] is not None:
axA1.set_ylim(config['set_ylim'][0], config['set_ylim'][1])
elif config['tight_layout']:
axA1.set_xlim(minx, maxx)
ydelta = 0.01 * (maxy - miny)
axA1.set_ylim(miny - ydelta, maxy + ydelta)
else:
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 mavgs is not None:
for i in range(0, len(mavgs)):
retdict['mav' + str(mavgs[i])] = mavprices
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)
miny = 0.3 * np.nanmin(volumes)
maxy = 1.1 * np.nanmax(volumes)
volumeAxes.set_ylim(miny, maxy)
xrotation = config['xrotation']
_set_ticks_on_bottom_panel_only(panels, formatter, rotation=xrotation)
addplot = config['addplot']
if addplot is not None and ptype not in VALID_PMOVE_TYPES:
# 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.
# Calculate omrange for Main panel, and for Lower (volume) panel:
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' 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:
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:
if havedf:
ydata = apdata.loc[:, column]
else:
ydata = column
yd = [y for y in ydata if not math.isnan(y)]
ymhi = math.log(max(math.fabs(np.nanmax(yd)), 1e-7), 10)
ymlo = math.log(max(math.fabs(np.nanmin(yd)), 1e-7), 10)
secondary_y = False
panid = apdict['panel']
if panid == 'main': panid = 0 # for backwards compatibility
elif panid == 'lower': panid = 1 # for backwards compatibility
if apdict['secondary_y'] == 'auto':
# If mag(nitude) for this panel is not yet set, then set it
# here, as this is the first ydata to be plotted on this panel:
# i.e. consider this to be the 'primary' axis for this panel.
p = panid, 'mag'
if panels.at[p] is None:
panels.at[p] = {'lo': ymlo, 'hi': ymhi}
elif ymlo < panels.at[p]['lo'] or ymhi > panels.at[p]['hi']:
secondary_y = True
#if secondary_y:
# print('auto says USE secondary_y ... for panel',panid)
#else:
# print('auto says do NOT use secondary_y ... for panel',panid)
else:
secondary_y = apdict['secondary_y']
#print("apdict['secondary_y'] says secondary_y is",secondary_y)
if secondary_y:
ax = panels.at[panid, 'axes'][1]
panels.at[panid, 'used2nd'] = True
else:
ax = panels.at[panid, 'axes'][0]
if (apdict["ylabel"] is not None):
ax.set_ylabel(apdict["ylabel"])
aptype = apdict['type']
if aptype == 'scatter':
size = apdict['markersize']
mark = apdict['marker']
color = apdict['color']
if isinstance(mark, (list, tuple, np.ndarray)):
_mscatter(xdates,
ydata,
ax=ax,
m=mark,
s=size,
color=color)
else:
ax.scatter(xdates,
ydata,
s=size,
marker=mark,
color=color)
elif aptype == 'bar':
width = apdict['width']
bottom = apdict['bottom']
color = apdict['color']
alpha = apdict['alpha']
ax.bar(xdates,
ydata,
width=width,
bottom=bottom,
color=color,
alpha=alpha)
elif aptype == 'line':
ls = apdict['linestyle']
color = apdict['color']
ax.plot(xdates, ydata, linestyle=ls, color=color)
#elif aptype == 'ohlc' or aptype == 'candle':
# This won't work as is, because here we are looping through one column at a time
# and mpf_collections needs ohlc columns:
# collections =_construct_mpf_collections(aptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
# if len(collections) == 1: collections = [collections]
# for collection in collections:
# ax.add_collection(collection)
else:
raise ValueError('addplot type "' + str(aptype) +
'" NOT yet supported.')
if config['fill_between'] is not None:
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)
if config['set_ylim_panelB'] is not None:
miny = config['set_ylim_panelB'][0]
maxy = config['set_ylim_panelB'][1]
panels.at[1, 'axes'][0].set_ylim(miny, maxy)
# put the twinx() on the "other" side:
if style['y_on_right']:
for ax in panels['axes'].values:
ax[0].yaxis.set_label_position('right')
ax[0].yaxis.tick_right()
ax[1].yaxis.set_label_position('left')
ax[1].yaxis.tick_left()
else:
for ax in panels['axes'].values:
ax[0].yaxis.set_label_position('left')
ax[0].yaxis.tick_left()
ax[1].yaxis.set_label_position('right')
ax[1].yaxis.tick_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??
axA1.set_ylabel(config['ylabel'])
if config['volume']:
volumeAxes.figure.canvas.draw() # This is needed to calculate offset
offset = volumeAxes.yaxis.get_major_formatter().get_offset()
volumeAxes.yaxis.offsetText.set_visible(False)
if len(offset) > 0:
offset = (' x ' + offset)
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.
fig.suptitle(config['title'],
size='x-large',
weight='semibold',
va='bottom',
y=0.89)
else:
fig.suptitle(config['title'],
size='x-large',
weight='semibold',
va='center')
for panid, row in panels.iterrows():
if not row['used2nd']:
row['axes'][1].set_visible(False)
# 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_xlim(xdates[0], xdates[-1])
ax.set_axis_off()
if config['savefig'] is not None:
save = config['savefig']
if isinstance(save, dict):
# Expand to fill chart if axisoff
if config['axisoff'] and 'bbox_inches' not in save:
plt.savefig(**save, bbox_inches='tight')
else:
plt.savefig(**save)
else:
if config['axisoff']:
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)
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())
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)
style = config['style']
if isinstance(style,str):
style = config['style'] = _styles._get_mpfstyle(style)
if isinstance(style,dict):
_styles._apply_mpfstyle(style)
else:
raise TypeError('style should be a `dict`; why is it not?')
if config['figsize'] is None:
w,h = config['figratio']
r = float(w)/float(h)
if r < 0.20 or r > 5.0:
raise ValueError('"figratio" (aspect ratio) must be between 0.20 and 5.0 (but is '+str(r)+')')
default_scale = DEFAULT_FIGRATIO[1]/h
h *= default_scale
w *= default_scale
base = (w,h)
figscale = config['figscale']
fsize = [d*figscale for d in base]
else:
fsize = config['figsize']
fig = plt.figure()
fig.set_size_inches(fsize)
if config['volume'] and volumes is None:
raise ValueError('Request for volume, but NO volume data.')
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))
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)
mav = config['mav']
mavwidth = config['_width_config']['line_width']
mavcolors = style['mavcolors']
if ptype in VALID_PMOVE_TYPES:
mavprices = _plot_mav(axA1, mav, xdates, brick_values, mavwidth, mavcolors)
else:
mavprices = _plot_mav(axA1, mav, xdates, closes, mavwidth, mavcolors)
def plot(data, **kwargs):
"""
Given open,high,low,close,volume data for a financial instrument (such as a stock, index,
currency, future, option, etc.) 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, macd, trading envelope, etc.
Also provide ability to plot trading signals, and/or addtional user-defined data.
"""
dates, opens, highs, lows, closes, volumes = _check_and_prepare_data(data)
config = _process_kwargs(kwargs, _valid_kwargs_table())
base = [11.0, 8.5]
figscale = config['figscale']
fsize = [d * figscale for d in base]
fig = plt.figure()
fig.set_size_inches(fsize)
# check if we need a lower panel for an additional plot.
# if volume=True we will share the lower panel.
need_lower_panel = False
addplot = config['addplot']
if addplot is not None:
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:
if apdict['panel'] == 'lower':
need_lower_panel = True
break
# fig.add_axes( [left, bottom, width, height] ) ... numbers are fraction of fig
if need_lower_panel or config['volume']:
if config['volume'] and volumes is None:
raise ValueError('Request for volume, but NO volume data.')
ax1 = fig.add_axes([0.15, 0.38, 0.70, 0.50])
ax2 = fig.add_axes([0.15, 0.18, 0.70, 0.20], sharex=ax1)
else:
ax1 = fig.add_axes([0.15, 0.18, 0.70, 0.70])
ax2 = None
avg_days_between_points = (dates[-1] - dates[0]) / float(len(dates))
# Default logic for 'no_xgaps': True for intraday data spanning 2 or more days, else False
# Caller provided 'no_xgaps' kwarg OVERRIDES default logic.
no_xgaps = False
# avgerage of 3 or more data points per day we will call intraday data:
if avg_days_between_points < 0.33: # intraday
if mdates.num2date(dates[-1]).date() != mdates.num2date(
dates[0]).date():
# intraday data for more than one day:
no_xgaps = True
fmtstring = '%b %d, %H:%M'
else: # intraday data for a single day
fmtstring = '%H:%M'
else: # 'daily' data (or could be weekly, etc.)
if mdates.num2date(dates[-1]).date().year != mdates.num2date(
dates[0]).date().year:
fmtstring = '%Y-%b-%d'
else:
fmtstring = '%b %d'
if config[
'no_xgaps'] is not None: # override whatever was determined above.
no_xgaps = config['no_xgaps']
if no_xgaps:
formatter = IntegerIndexDateTimeFormatter(dates, fmtstring)
xdates = np.arange(len(dates))
else:
formatter = mdates.DateFormatter(fmtstring)
xdates = dates
ax1.xaxis.set_major_formatter(formatter)
plt.xticks(rotation=45)
ptype = config['type']
collections = None
if ptype == 'candle' or ptype == 'candlestick':
collections = _construct_candlestick_collections(
xdates, opens, highs, lows, closes)
elif ptype == 'ohlc' or ptype == 'bars' or ptype == 'ohlc_bars':
collections = _construct_ohlc_collections(xdates, opens, highs, lows,
closes)
elif ptype == 'line':
ax1.plot(xdates, closes, color='k')
else:
raise ValueError('Unrecognized plot type = "' + ptype + '"')
if collections is not None:
for collection in collections:
ax1.add_collection(collection)
mavgs = config['mav']
if mavgs is not None:
if isinstance(mavgs, int):
mavgs = mavgs, # convert to tuple
if len(mavgs) > 3:
mavgs = mavgs[0:3] # take at most 3
mavcolors = ['turquoise', 'magenta', 'gold']
jj = 0
for mav in mavgs:
mavprices = data['Close'].rolling(mav).mean().values
ax1.plot(xdates, mavprices, color=mavcolors[jj])
jj += 1
avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
minx = xdates[0] - avg_dist_between_points
maxx = xdates[-1] + avg_dist_between_points
miny = min([low for low in lows if low != -1])
maxy = max([high for high in highs if high != -1])
corners = (minx, miny), (maxx, maxy)
ax1.update_datalim(corners)
addplot = config['addplot']
if addplot is not None:
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:
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
if apdict['panel'] == 'lower':
ax = ax2
if config['volume']:
ax = ax2.twinx()
else:
ax = ax1
for column in apdata:
if havedf:
ydata = apdata.loc[:, column]
else:
ydata = column
if apdict['scatter']:
size = apdict['markersize']
mark = apdict['marker']
ax.scatter(xdates, ydata, s=size, marker=mark)
else:
ax.plot(xdates, ydata)
if config['volume']:
ax2.bar(xdates, volumes, width=0.7)
miny = 0.3 * min(volumes)
maxy = 1.1 * max(volumes)
ax2.set_ylim(miny, maxy)
#ax2.yaxis.set_label_position('right')
#ax2.yaxis.tick_right()
ax2.xaxis.set_major_formatter(formatter)
if need_lower_panel or config['volume']:
ax1.spines['bottom'].set_linewidth(0.25)
ax2.spines['top'].set_linewidth(0.25)
plt.setp(ax1.get_xticklabels(), visible=False)
# 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()
ax1.autoscale_view() # Is this really necessary??
# really use rcParams: call plt.rc('axes', grid=True)
# plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)
ax1.set_ylabel('Price', size='x-large', weight='semibold')
if config['volume']:
ax2.figure.canvas.draw() # This is needed to calculate offset
offset = ax2.yaxis.get_major_formatter().get_offset()
ax2.yaxis.offsetText.set_visible(False)
vol_label = 'Volume x ' + str(offset)
ax2.set_ylabel(vol_label, size='x-large', weight='semibold')
plt.show()
def plot(data, is_not_set_visible=False, **kwargs):
"""
Given open,high,low,close,volume data for a financial instrument (such as a stock, index,
currency, future, option, etc.) 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, macd, trading envelope, etc.
Also provide ability to plot trading signals, and/or addtional user-defined data.
"""
config = _process_kwargs(kwargs, _valid_plot_kwargs())
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)
style = config["style"]
if isinstance(style, str):
style = _styles._get_mpfstyle(style)
if isinstance(style, dict):
_styles._apply_mpfstyle(style)
w, h = config["figratio"]
r = float(w) / float(h)
if r < 0.25 or r > 4.0:
raise ValueError(
'"figratio" (aspect ratio) must be between 0.25 and 4.0 (but is '
+ str(r) + ")")
base = (w, h)
figscale = config["figscale"]
fsize = [d * figscale for d in base]
fig = plt.figure()
fig.set_size_inches(fsize)
if config["volume"] and volumes is None:
raise ValueError("Request for volume, but NO volume data.")
# -------------------------------------------------------------
# For now (06-Feb-2020) to keep the code somewhat simpler for
# implementing `secondary_y` we are going to ALWAYS create
# secondary (twinx) axes, whether we need them or not, and
# then they will be available to use later when we are plotting:
# -------------------------------------------------------------
need_lower_panel = False
addplot = config["addplot"]
if addplot is not None:
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:
if apdict["panel"] == "lower":
need_lower_panel = True
break
# fig.add_axes( [left, bottom, width, height] ) ... numbers are fraction of fig
if need_lower_panel or config["volume"]:
ax1 = fig.add_axes([0.15, 0.38, 0.70, 0.50])
ax2 = fig.add_axes([0.15, 0.18, 0.70, 0.20], sharex=ax1)
plt.xticks(rotation=45) # must do this after creation of axis, and
# after `sharex`, but must be BEFORE any 'twinx()'
ax2.set_axisbelow(True) # so grid does not show through volume bars.
ax4 = ax2.twinx()
ax4.grid(False)
else:
ax1 = fig.add_axes([0.15, 0.18, 0.70, 0.70])
plt.xticks(
rotation=45
) # must do this after creation of axis, but before any 'twinx()'
ax2 = None
ax4 = None
ax3 = ax1.twinx()
ax3.grid(False)
avg_days_between_points = (dates[-1] - dates[0]) / float(len(dates))
# avgerage of 3 or more data points per day we will call intraday data:
if avg_days_between_points < 0.33: # intraday
if mdates.num2date(dates[-1]).date() != mdates.num2date(
dates[0]).date():
# intraday data for more than one day:
fmtstring = "%b %d, %H:%M"
else: # intraday data for a single day
fmtstring = "%H:%M"
else: # 'daily' data (or could be weekly, etc.)
if (mdates.num2date(dates[-1]).date().year != mdates.num2date(
dates[0]).date().year):
fmtstring = "%Y-%b-%d"
else:
fmtstring = "%b %d"
ptype = config["type"]
if ptype not in VALID_PMOVE_TYPES:
if config["show_nontrading"]:
formatter = mdates.DateFormatter(fmtstring)
xdates = dates
else:
formatter = IntegerIndexDateTimeFormatter(dates, fmtstring)
xdates = np.arange(len(dates))
ax1.xaxis.set_major_formatter(formatter)
collections = None
if ptype == "candle" or ptype == "candlestick":
collections = _construct_candlestick_collections(
xdates,
opens,
highs,
lows,
closes,
marketcolors=style["marketcolors"])
elif ptype == "ohlc" or ptype == "bars" or ptype == "ohlc_bars":
collections = _construct_ohlc_collections(
xdates,
opens,
highs,
lows,
closes,
marketcolors=style["marketcolors"])
elif ptype == "renko":
(
collections,
new_dates,
volumes,
brick_values,
size,
) = _construct_renko_collections(
dates,
highs,
lows,
volumes,
config["renko_params"],
closes,
marketcolors=style["marketcolors"],
)
elif ptype == "pnf" or ptype == "p&f" or ptype == "pointnfigure":
(
collections,
new_dates,
volumes,
brick_values,
size,
) = _construct_pointnfig_collections(
dates,
highs,
lows,
volumes,
config["pointnfig_params"],
closes,
marketcolors=style["marketcolors"],
)
elif ptype == "line":
ax1.plot(xdates, closes, color=config["linecolor"])
else:
raise ValueError('Unrecognized plot type = "' + ptype + '"')
if ptype in VALID_PMOVE_TYPES:
formatter = IntegerIndexDateTimeFormatter(new_dates, fmtstring)
xdates = np.arange(len(new_dates))
ax1.xaxis.set_major_formatter(formatter)
if collections is not None:
for collection in collections:
ax1.add_collection(collection)
mavgs = config["mav"]
if mavgs is not None:
if isinstance(mavgs, int):
mavgs = (mavgs, ) # convert to tuple
if len(mavgs) > 7:
mavgs = mavgs[0:7] # take at most 7
if style["mavcolors"] is not None:
mavc = cycle(style["mavcolors"])
else:
mavc = None
for mav in mavgs:
if ptype in VALID_PMOVE_TYPES:
mavprices = pd.Series(brick_values).rolling(mav).mean().values
else:
mavprices = data["Close"].rolling(mav).mean().values
if mavc:
ax1.plot(xdates, mavprices, color=next(mavc))
else:
ax1.plot(xdates, mavprices)
if config["return_calculated_values"] is not None:
retdict = config["return_calculated_values"]
if ptype == "renko":
retdict["renko_bricks"] = brick_values
retdict["renko_dates"] = mdates.num2date(new_dates)
if config["volume"]:
retdict["renko_volumes"] = volumes
if mavgs is not None:
for i in range(0, len(mavgs)):
retdict["mav" + str(mavgs[i])] = mavprices
avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
minx = xdates[0] - avg_dist_between_points
maxx = xdates[-1] + avg_dist_between_points
if ptype not in VALID_PMOVE_TYPES:
miny = min([low for low in lows if low != -1])
maxy = max([high for high in highs if high != -1])
else:
miny = min([brick for brick in brick_values])
maxy = max([brick + size for brick in brick_values])
corners = (minx, miny), (maxx, maxy)
ax1.update_datalim(corners)
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)
width = 0.5 * avg_dist_between_points
ax2.bar(xdates, volumes, width=width, color=vcolors)
miny = 0.3 * min(volumes)
maxy = 1.1 * max(volumes)
ax2.set_ylim(miny, maxy)
ax2.xaxis.set_major_formatter(formatter)
used_ax3 = False
used_ax4 = False
addplot = config["addplot"]
if addplot is not None and ptype not in VALID_PMOVE_TYPES:
# Calculate the Order of Magnitude Range
# If addplot['secondary_y'] == 'auto', then: If the addplot['data']
# is out of the Order of Magnitude Range, then use secondary_y.
# Calculate omrange for Main panel, and for Lower (volume) panel:
lo = math.log(max(math.fabs(min(lows)), 1e-7), 10) - 0.5
hi = math.log(max(math.fabs(max(highs)), 1e-7), 10) + 0.5
omrange = {"main": {"lo": lo, "hi": hi}, "lower": None}
if config["volume"]:
lo = math.log(max(math.fabs(min(volumes)), 1e-7), 10) - 0.5
hi = math.log(max(math.fabs(max(volumes)), 1e-7), 10) + 0.5
omrange.update(lower={"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:
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:
if havedf:
ydata = apdata.loc[:, column]
else:
ydata = column
yd = [y for y in ydata if not math.isnan(y)]
ymhi = math.log(max(math.fabs(max(yd)), 1e-7), 10)
ymlo = math.log(max(math.fabs(min(yd)), 1e-7), 10)
secondary_y = False
if apdict["secondary_y"] == "auto":
if apdict["panel"] == "lower":
# If omrange['lower'] is not yet set by volume,
# then set it here as this is the first ydata
# to be plotted on the lower panel, so consider
# it to be the 'primary' lower panel axis.
if omrange["lower"] is None:
omrange.update(lower={"lo": ymlo, "hi": ymhi})
elif (ymlo < omrange["lower"]["lo"]
or ymhi > omrange["lower"]["hi"]):
secondary_y = True
elif ymlo < omrange["main"]["lo"] or ymhi > omrange[
"main"]["hi"]:
secondary_y = True
# if secondary_y:
# print('auto says USE secondary_y')
# else:
# print('auto says do NOT use secondary_y')
else:
secondary_y = apdict["secondary_y"]
# print("apdict['secondary_y'] says secondary_y is",secondary_y)
if apdict["panel"] == "lower":
ax = ax4 if secondary_y else ax2
else:
ax = ax3 if secondary_y else ax1
if ax == ax3:
used_ax3 = True
if ax == ax4:
used_ax4 = True
if apdict["scatter"]:
size = apdict["markersize"]
mark = apdict["marker"]
color = apdict["color"]
ax.scatter(xdates, ydata, s=size, marker=mark, color=color)
else:
ls = apdict["linestyle"]
color = apdict["color"]
ax.plot(xdates, ydata, linestyle=ls, color=color)
# put the twinx() on the "other" side:
if style["y_on_right"]:
ax1.yaxis.set_label_position("right")
ax1.yaxis.tick_right()
ax3.yaxis.set_label_position("left")
ax3.yaxis.tick_left()
if ax2 and ax4:
ax2.yaxis.set_label_position("right")
ax2.yaxis.tick_right()
if ax4 != ax2:
ax4.yaxis.set_label_position("left")
ax4.yaxis.tick_left()
else:
ax1.yaxis.set_label_position("left")
ax1.yaxis.tick_left()
ax3.yaxis.set_label_position("right")
ax3.yaxis.tick_right()
if ax2 and ax4:
ax2.yaxis.set_label_position("left")
ax2.yaxis.tick_left()
if ax4 != ax2:
ax4.yaxis.set_label_position("right")
ax4.yaxis.tick_right()
if need_lower_panel or config["volume"]:
ax1.spines["bottom"].set_linewidth(0.25)
ax2.spines["top"].set_linewidth(0.25)
plt.setp(ax1.get_xticklabels(), visible=False)
# 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()
ax1.autoscale_view() # Is this really necessary??
ax1.set_ylabel(config["ylabel"])
if config["volume"]:
ax2.figure.canvas.draw() # This is needed to calculate offset
offset = ax2.yaxis.get_major_formatter().get_offset()
ax2.yaxis.offsetText.set_visible(False)
if len(offset) > 0:
offset = " x " + offset
if config["ylabel_lower"] is None:
vol_label = "Volume" + offset
else:
if len(offset) > 0:
offset = "\n" + offset
vol_label = config["ylabel_lower"] + offset
ax2.set_ylabel(vol_label)
if config["title"] is not None:
fig.suptitle(config["title"], size="x-large", weight="semibold")
if not used_ax3 and ax3 is not None:
ax3.get_yaxis().set_visible(False)
if not used_ax4 and ax4 is not None:
ax4.get_yaxis().set_visible(False)
if config["returnfig"]:
axlist = [ax1, ax3]
if ax2:
axlist.append(ax2)
if ax4:
axlist.append(ax4)
# 自分で追加した
if is_not_set_visible:
# 枠線を消す + x,y軸の数値（目盛り）消す
try:
ax1.spines["right"].set_color("none") # 右消し
ax1.spines["left"].set_color("none") # 左消し
ax1.spines["top"].set_color("none") # 上消し
ax1.spines["bottom"].set_color("none") # 下消し
ax1.axes.xaxis.set_visible(False)
ax1.axes.yaxis.set_visible(False)
except:
pass
try:
ax2.spines["right"].set_color("none") # 右消し
ax2.spines["left"].set_color("none") # 左消し
ax2.spines["top"].set_color("none") # 上消し
ax2.spines["bottom"].set_color("none") # 下消し
ax2.axes.xaxis.set_visible(False)
ax2.axes.yaxis.set_visible(False)
except:
pass
try:
ax3.spines["right"].set_color("none") # 右消し
ax3.spines["left"].set_color("none") # 左消し
ax3.spines["top"].set_color("none") # 上消し
ax3.spines["bottom"].set_color("none") # 下消し
ax3.axes.xaxis.set_visible(False)
ax3.axes.yaxis.set_visible(False)
except:
pass
try:
ax4.spines["right"].set_color("none") # 右消し
ax4.spines["left"].set_color("none") # 左消し
ax4.spines["top"].set_color("none") # 上消し
ax4.spines["bottom"].set_color("none") # 下消し
ax4.axes.xaxis.set_visible(False)
ax4.axes.yaxis.set_visible(False)
except:
pass
if config["savefig"] is not None:
save = config["savefig"]
if isinstance(save, dict):
plt.savefig(**save)
else:
plt.savefig(save, bbox_inches="tight",
pad_inches=0) # 余白削除オプション付けた
# plt.savefig(save)
elif not config["returnfig"]:
# https://stackoverflow.com/a/13361748/1639359 suggests plt.show(block=False)
plt.show(block=config["block"])
if config["returnfig"]:
return (fig, axlist)
# メモリ解放 forで回してるとどんどんメモリ食われるので
# https://qiita.com/Masahiro_T/items/bdd0482a8efd84cdd270
plt.clf()
plt.close()
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())
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)
style = config['style']
if isinstance(style, str):
style = _styles._get_mpfstyle(style)
if isinstance(style, dict):
_styles._apply_mpfstyle(style)
w, h = config['figratio']
r = float(w) / float(h)
if r < 0.25 or r > 4.0:
raise ValueError(
'"figratio" (aspect ratio) must be between 0.25 and 4.0 (but is '
+ str(r) + ')')
base = (w, h)
figscale = config['figscale']
fsize = [d * figscale for d in base]
fig = plt.figure()
fig.set_size_inches(fsize)
if config['volume'] and volumes is None:
raise ValueError('Request for volume, but NO volume data.')
if config['volume']:
if config['volume'] not in ['B', 'C']: config['volume'] = 'B'
ha, hb, hc = _determine_relative_panel_heights(config['addplot'],
config['volume'],
config['panel_ratio'])
axA1, axA2, axB1, axB2, axC1, axC2, actual_order = _create_panel_axes(
fig, ha, hb, hc, config['panel_order'])
internalAxes = dict(A=(axA1, axA2), B=(axB1, axB2), C=(axC1, axC2))
volumeAxes = internalAxes[
config['volume']][0] if config['volume'] 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))
axA1.xaxis.set_major_formatter(formatter)
collections = None
if ptype == 'line':
axA1.plot(xdates, closes, color=config['linecolor'])
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))
axA1.xaxis.set_major_formatter(formatter)
if collections is not None:
for collection in collections:
axA1.add_collection(collection)
mavgs = config['mav']
if mavgs is not None:
if isinstance(mavgs, int):
mavgs = mavgs, # convert to tuple
if len(mavgs) > 7:
mavgs = mavgs[0:7] # take at most 7
if style['mavcolors'] is not None:
mavc = cycle(style['mavcolors'])
else:
mavc = None
for mav in mavgs:
if ptype in VALID_PMOVE_TYPES:
mavprices = pd.Series(brick_values).rolling(mav).mean().values
else:
mavprices = pd.Series(closes).rolling(mav).mean().values
if mavc:
axA1.plot(xdates, mavprices, color=next(mavc))
else:
axA1.plot(xdates, mavprices)
avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
minx = xdates[0] - avg_dist_between_points
maxx = xdates[-1] + 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 len(xdates) > 1:
# stdy = (stat.stdev(_lows) + stat.stdev(_highs)) / 2.0
#else: # kludge special case
# stdy = 0.02 * math.fabs(maxy - miny)
# print('minx,miny,maxx,maxy,stdy=',minx,miny,maxx,maxy,stdy)
if config['set_ylim'] is not None:
axA1.set_ylim(config['set_ylim'][0], config['set_ylim'][1])
else:
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 mavgs is not None:
for i in range(0, len(mavgs)):
retdict['mav' + str(mavgs[i])] = mavprices
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)
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)
width = 0.5 * avg_dist_between_points
volumeAxes.bar(xdates, volumes, width=width, color=vcolors)
miny = 0.3 * np.nanmin(volumes)
maxy = 1.1 * np.nanmax(volumes)
volumeAxes.set_ylim(miny, maxy)
xrotation = config['xrotation']
_adjust_ticklabels_per_bottom_panel(axA1, axB1, axC1, actual_order, hb, hc,
formatter, xrotation)
used_axA2 = False
used_axB2 = False
used_axC2 = False
addplot = config['addplot']
if addplot is not None and ptype not in VALID_PMOVE_TYPES:
# Calculate the Order of Magnitude Range
# If addplot['secondary_y'] == 'auto', then: If the addplot['data']
# is out of the Order of Magnitude Range, then use secondary_y.
# Calculate omrange for Main panel, and for Lower (volume) panel:
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
# May 2020: Main panel is now called 'A', and Lower is called 'B'
omrange = {'A': {'lo': lo, 'hi': hi}, 'B': None, 'C': None}
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
omrange.update(B={'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:
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:
if havedf:
ydata = apdata.loc[:, column]
else:
ydata = column
yd = [y for y in ydata if not math.isnan(y)]
ymhi = math.log(max(math.fabs(np.nanmax(yd)), 1e-7), 10)
ymlo = math.log(max(math.fabs(np.nanmin(yd)), 1e-7), 10)
secondary_y = False
if apdict['secondary_y'] == 'auto':
if apdict['panel'] == 'lower' or apdict['panel'] == 'B':
# If omrange['lower'] is not yet set by volume,
# then set it here as this is the first ydata
# to be plotted on the lower panel, so consider
# it to be the 'primary' lower panel axis.
if omrange['B'] is None:
omrange.update(B={'lo': ymlo, 'hi': ymhi})
elif ymlo < omrange['B']['lo'] or ymhi > omrange['B'][
'hi']:
secondary_y = True
elif apdict['panel'] == 'C':
if omrange['C'] is None:
omrange.update(B={'lo': ymlo, 'hi': ymhi})
elif ymlo < omrange['C']['lo'] or ymhi > omrange['C'][
'hi']:
secondary_y = True
elif ymlo < omrange['A']['lo'] or ymhi > omrange['A']['hi']:
secondary_y = True
# if secondary_y:
# print('auto says USE secondary_y')
# else:
# print('auto says do NOT use secondary_y')
else:
secondary_y = apdict['secondary_y']
# print("apdict['secondary_y'] says secondary_y is",secondary_y)
if apdict['panel'] == 'lower' or apdict['panel'] == 'B':
ax = axB2 if secondary_y else axB1
elif apdict['panel'] == 'C':
ax = axC2 if secondary_y else axC1
else:
ax = axA2 if secondary_y else axA1
if ax == axA2:
used_axA2 = True
if ax == axB2:
used_axB2 = True
if ax == axC2:
used_axC2 = True
aptype = apdict['type']
if aptype == 'scatter':
size = apdict['markersize']
mark = apdict['marker']
color = apdict['color']
# -------------------------------------------------------- #
# This fixes Issue#77, but breaks other stuff:
# ax.set_ylim(ymin=(miny - 0.4*stdy),ymax=(maxy + 0.4*stdy))
# -------------------------------------------------------- #
ax.scatter(xdates, ydata, s=size, marker=mark, color=color)
elif aptype == 'bar':
width = apdict['width']
bottom = apdict['bottom']
color = apdict['color']
alpha = apdict['alpha']
ax.bar(xdates,
ydata,
width=width,
bottom=bottom,
color=color,
alpha=alpha)
elif aptype == 'line':
ls = apdict['linestyle']
color = apdict['color']
ax.plot(xdates, ydata, linestyle=ls, color=color)
#elif aptype == 'ohlc' or aptype == 'candle':
# This won't work as is, because here we are looping through one column at a time
# and mpf_collections needs ohlc columns:
# collections =_construct_mpf_collections(aptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
# if len(collections) == 1: collections = [collections]
# for collection in collections:
# ax.add_collection(collection)
else:
raise ValueError('addplot type "' + str(aptype) +
'" NOT yet supported.')
if config['set_ylim_panelB'] is not None:
miny = config['set_ylim_panelB'][0]
maxy = config['set_ylim_panelB'][1]
axB1.set_ylim(miny, maxy)
if config['set_ylim_panelC'] is not None:
miny = config['set_ylim_panelC'][0]
maxy = config['set_ylim_panelC'][1]
axC1.set_ylim(miny, maxy)
if config['yscale'] is not None:
yscale = config['yscale']
panel = 'A'
kwargs = None
if isinstance(yscale, dict):
if 'panel' in yscale: panel = yscale['panel']
if 'kwargs' in yscale: kwargs = yscale['kwargs']
yscale = yscale['yscale']
ax = internalAxes[panel][0]
if kwargs is not None:
ax.set_yscale(yscale, **kwargs)
else:
ax.set_yscale(yscale)
# put the twinx() on the "other" side:
if style['y_on_right']:
axA1.yaxis.set_label_position('right')
axA1.yaxis.tick_right()
axA2.yaxis.set_label_position('left')
axA2.yaxis.tick_left()
if axB1 and axB2:
axB1.yaxis.set_label_position('right')
axB1.yaxis.tick_right()
if axB2 != axB1:
axB2.yaxis.set_label_position('left')
axB2.yaxis.tick_left()
else:
axA1.yaxis.set_label_position('left')
axA1.yaxis.tick_left()
axA2.yaxis.set_label_position('right')
axA2.yaxis.tick_right()
if axB1 and axB2:
axB1.yaxis.set_label_position('left')
axB1.yaxis.tick_left()
if axB2 != axB1:
axB2.yaxis.set_label_position('right')
axB2.yaxis.tick_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??
axA1.set_ylabel(config['ylabel'])
if config['volume']:
volumeAxes.figure.canvas.draw() # This is needed to calculate offset
offset = volumeAxes.yaxis.get_major_formatter().get_offset()
volumeAxes.yaxis.offsetText.set_visible(False)
if len(offset) > 0:
offset = (' x ' + offset)
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:
fig.suptitle(config['title'], size='x-large', weight='semibold')
if not used_axA2 and axA2 is not None:
axA2.get_yaxis().set_visible(False)
if not used_axB2 and axB2 is not None:
axB2.get_yaxis().set_visible(False)
if not used_axC2 and axC2 is not None:
axC2.get_yaxis().set_visible(False)
axlist = [axA1, axA2]
if axB1: axlist.append(axB1)
if axB2: axlist.append(axB2)
if axC1: axlist.append(axC1)
if axC2: axlist.append(axC2)
if config['axesoffdark']: fig.patch.set_facecolor('black')
if config['axesoff']: fig.patch.set_visible(False)
if config['axesoffdark'] or config['axesoff']:
for ax in axlist:
ax.set_xlim(xdates[0], xdates[-1])
ax.set_axis_off()
if config['savefig'] is not None:
save = config['savefig']
if isinstance(save, dict):
plt.savefig(**save)
else:
plt.savefig(save)
if config['closefig']:
plt.close(fig)
elif not config['returnfig']:
plt.show(block=config['block']
) # https://stackoverflow.com/a/13361748/1639359
if config['block']:
plt.close(fig)
if config['returnfig']:
return (fig, axlist)