def _plot_k(self):
d = self._c_watch.get()
x_data = np.arange(len(d))
collections = mutils._construct_candlestick_collections(
x_data,
d[data.OPEN],
d[data.HIGH],
d[data.LOW],
d[data.CLOSE],
marketcolors=self._color(),
config=dict(_width_config=self._config(self._c_watch.range)))
self.axes.set_ylim(d[data.LOW].min(), d[data.HIGH].max())
for c in collections:
self.axes.add_collection(c)
self._major()
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 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()
