import matplotlib.dates as mdates import matplotlib.pyplot as plt import matplotlib.colors as mcolors import matplotlib.axes as mpl_axes import matplotlib.figure as mpl_fig import pandas as pd import numpy as np import copy import io import os import math import warnings import statistics as stat from itertools import cycle #from pandas.plotting import register_matplotlib_converters #register_matplotlib_converters() from mplfinance._utils import _construct_aline_collections from mplfinance._utils import _construct_hline_collections from mplfinance._utils import _construct_vline_collections from mplfinance._utils import _construct_tline_collections from mplfinance._utils import _construct_mpf_collections from mplfinance._utils import _construct_pnf_scatter from mplfinance._widths import _determine_width_config from mplfinance._utils import _updown_colors from mplfinance._utils import IntegerIndexDateTimeFormatter from mplfinance._utils import _mscatter from mplfinance._utils import _check_and_convert_xlim_configuration from mplfinance import _styles from mplfinance._arg_validators import _check_and_prepare_data, _mav_validator, _label_validator from mplfinance._arg_validators import _get_valid_plot_types, _fill_between_validator from mplfinance._arg_validators import _process_kwargs, _validate_vkwargs_dict from mplfinance._arg_validators import _kwarg_not_implemented, _bypass_kwarg_validation from mplfinance._arg_validators import _hlines_validator, _vlines_validator from mplfinance._arg_validators import _alines_validator, _tlines_validator from mplfinance._arg_validators import _scale_padding_validator, _yscale_validator from mplfinance._arg_validators import _valid_panel_id, _check_for_external_axes from mplfinance._arg_validators import _xlim_validator, _mco_validator, _is_marketcolor_object from mplfinance._panels import _build_panels from mplfinance._panels import _set_ticks_on_bottom_panel_only from mplfinance._helpers import _determine_format_string from mplfinance._helpers import _list_of_dict from mplfinance._helpers import _num_or_seq_of_num from mplfinance._helpers import _adjust_color_brightness VALID_PMOVE_TYPES = ['renko', 'pnf'] DEFAULT_FIGRATIO = (8.00,5.75) def with_rc_context(func): ''' This decoractor creates an rcParams context around a function, so that any changes the function makes to rcParams will be reversed when the decorated function returns (therefore those changes have no effect outside of the decorated function). ''' def decorator(*args, **kwargs): with plt.rc_context(): return func(*args, **kwargs) return decorator def _warn_no_xgaps_deprecated(value): warnings.warn('\n\n ================================================================= '+ '\n\n WARNING: `no_xgaps` is /deprecated/:'+ '\n Default value is now `no_xgaps=True`'+ '\n However, to set `no_xgaps=False` and silence this warning,'+ '\n use instead: `show_nontrading=True`.'+ '\n\n ================================================================ ', category=DeprecationWarning) return isinstance(value,bool) def _warn_set_ylim_deprecated(value): warnings.warn('\n\n ================================================================= '+ '\n\n WARNING: `set_ylim=(ymin,ymax)` kwarg '+ '\n has been replaced with: '+ '\n `ylim=(ymin,ymax)`.'+ '\n\n ================================================================ ', category=DeprecationWarning) return isinstance(value,bool) def _valid_plot_kwargs(): ''' Construct and return the "valid kwargs table" for the mplfinance.plot() function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are the valid key-words for the function. The value for each key is a dict containing 2 specific keys: "Default", and "Validator" with the following values: "Default" - The default value for the kwarg if none is specified. "Validator" - A function that takes the caller specified value for the kwarg, and validates that it is the correct type, and (for kwargs with a limited set of allowed values) may also validate that the kwarg value is one of the allowed values. ''' vkwargs = { 'columns' : { 'Default' : None, # use default names: ('Open', 'High', 'Low', 'Close', 'Volume') 'Description' : ('Column names to be used when plotting the data.'+ ' Default: ("Open", "High", "Low", "Close", "Volume")'), 'Validator' : lambda value: isinstance(value, (tuple, list)) and len(value) == 5 and all(isinstance(c, str) for c in value) }, 'type' : { 'Default' : 'ohlc', 'Description' : 'Plot type: '+str(_get_valid_plot_types()), 'Validator' : lambda value: value in _get_valid_plot_types() }, 'style' : { 'Default' : None, 'Description' : 'plot style; see `mpf.available_styles()`', 'Validator' : _styles._valid_mpf_style }, 'volume' : { 'Default' : False, 'Description' : 'Plot volume: True, False, or set to Axes object on which to plot.', 'Validator' : lambda value: isinstance(value,bool) or isinstance(value,mpl_axes.Axes) }, 'mav' : { 'Default' : None, 'Description' : 'Moving Average window size(s); (int or tuple of ints)', 'Validator' : _mav_validator }, 'ema' : { 'Default' : None, 'Description' : 'Exponential Moving Average window size(s); (int or tuple of ints)', 'Validator' : _mav_validator }, 'mavcolors' : { 'Default' : None, 'Description' : 'color cycle for moving averages (list or tuple of colors)'+ '(overrides mpf style mavcolors).', 'Validator' : lambda value: isinstance(value,(list,tuple)) and all([mcolors.is_color_like(v) for v in value]) }, 'renko_params' : { 'Default' : dict(), 'Description' : 'dict of renko parameters; call `mpf.kwarg_help("renko_params")`', 'Validator' : lambda value: isinstance(value,dict) }, 'pnf_params' : { 'Default' : dict(), 'Description' : 'dict of point-and-figure parameters; call `mpf.kwarg_help("pnf_params")`', 'Validator' : lambda value: isinstance(value,dict) }, 'study' : { 'Default' : None, 'Description' : 'kwarg not implemented', 'Validator' : lambda value: _kwarg_not_implemented(value) }, 'marketcolor_overrides' : { 'Default' : None, 'Description' : 'sequence of color objects to override market colors.'+ 'sequence must be same length as ohlc(v) DataFrame. Each'+ 'color object may be a color, marketcolor object, or None.', 'Validator' : _mco_validator }, 'mco_faceonly' : { 'Default' : False, # If True: Override only the face of the candle 'Description' : 'True/False marketcolor_overrides only apply to face of candle.', 'Validator' : lambda value: isinstance(value,bool) }, 'no_xgaps' : { 'Default' : True, # None means follow default logic below: 'Description' : 'deprecated', 'Validator' : lambda value: _warn_no_xgaps_deprecated(value) }, 'show_nontrading' : { 'Default' : False, 'Description' : 'True/False show spaces for non-trading days/periods', 'Validator' : lambda value: isinstance(value,bool) }, 'figscale' : { 'Default' : None, # scale base figure size up or down. 'Description' : 'Scale figure size up (if > 1) or down (if < 1)', 'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) }, 'figratio' : { 'Default' : None, # aspect ratio; scaled to 8.0 height 'Description' : 'Aspect ratio of the figure. Default: (8.00,5.75)', 'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) == 2 and isinstance(value[0],(float,int)) and isinstance(value[1],(float,int)) }, 'figsize' : { 'Default' : None, # figure size; overrides figratio and figscale 'Description' : ('Figure size: overrides both figscale and figratio,'+ ' else defaults to figratio*figscale'), 'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) == 2 and isinstance(value[0],(float,int)) and isinstance(value[1],(float,int)) }, 'fontscale' : { 'Default' : None, # scale all fonts up or down 'Description' : 'Scale font sizes up (if > 1) or down (if < 1)', 'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) }, 'linecolor' : { 'Default' : None, # line color in line plot 'Description' : 'Line color for `type=line`', 'Validator' : lambda value: mcolors.is_color_like(value) }, 'title' : { 'Default' : None, # Figure Title 'Description' : 'Figure Title (see also `axtitle`)', 'Validator' : lambda value: isinstance(value,(str,dict)) }, 'axtitle' : { 'Default' : None, # Axes Title (subplot title) 'Description' : 'Axes Title (subplot title)', 'Validator' : lambda value: isinstance(value,(str,dict)) }, 'xlabel' : { 'Default' : None, # x-axis label 'Description' : 'label for x-axis of plot', 'Validator' : lambda value: isinstance(value,str) }, 'ylabel' : { 'Default' : 'Price', # y-axis label 'Description' : 'label for y-axis of main plot', 'Validator' : lambda value: isinstance(value,str) }, 'ylabel_lower' : { 'Default' : None, # y-axis label default logic below 'Description' : 'label for y-axis of volume', 'Validator' : lambda value: isinstance(value,str) }, 'addplot' : { 'Default' : None, 'Description' : 'addplot object or sequence of addplot objects (from `mpf.make_addplot()`)', 'Validator' : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) }, 'savefig' : { 'Default' : None, 'Description' : 'file name, or BytesIO, or dict with key `fname` plus other keys allowed as '+ ' kwargs to matplotlib `Figure.savefig()`', 'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) or isinstance(value, os.PathLike) }, 'block' : { 'Default' : None, 'Description' : 'True/False wait for figure to be closed before returning', 'Validator' : lambda value: isinstance(value,bool) }, 'returnfig' : { 'Default' : False, 'Description' : 'return Figure and list of Axes objects created by mplfinance;'+ ' user must display plot when ready, usually by calling `mpf.show()`', 'Validator' : lambda value: isinstance(value,bool) }, 'return_calculated_values' : { 'Default' : None, 'Description' : 'set to a variable containing an empty dict; `mpf.plot()` will fill'+ ' the dict with various mplfinance calculated values', 'Validator' : lambda value: isinstance(value, dict) and len(value) == 0}, 'set_ylim' : { 'Default' : None, 'Description' : 'deprecated', 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, 'ylim' : { 'Default' : None, 'Description' : 'Limits for y-axis as tuple (min,max), i.e. (bottom,top)', 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 and all([isinstance(v,(int,float)) for v in value])}, 'xlim' : { 'Default' : None, 'Description' : 'Limits for x-axis as tuple (min,max), i.e. (left,right)', 'Validator' : lambda value: _xlim_validator(value) }, 'set_ylim_panelB' : { 'Default' : None, 'Description' : 'deprecated', 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, 'hlines' : { 'Default' : None, 'Description' : 'Draw one or more HORIZONTAL LINES across entire plot, by'+ ' specifying a price, or sequence of prices. May also be a dict'+ ' with key `hlines` specifying a price or sequence of prices, plus'+ ' one or more of the following keys: `colors`, `linestyle`,'+ ' `linewidths`, `alpha`.', 'Validator' : lambda value: _hlines_validator(value) }, 'vlines' : { 'Default' : None, 'Description' : 'Draw one or more VERTICAL LINES across entire plot, by'+ ' specifying a date[time], or sequence of date[time]. May also'+ ' be a dict with key `vlines` specifying a date[time] or sequence'+ ' of date[time], plus one or more of the following keys:'+ ' `colors`, `linestyle`, `linewidths`, `alpha`.', 'Validator' : lambda value: _vlines_validator(value) }, 'alines' : { 'Default' : None, 'Description' : 'Draw one or more ARBITRARY LINES anywhere on the plot, by'+ ' specifying a sequence of two or more date/price pairs, or by'+ ' specifying a sequence of sequences of two or more date/price pairs.'+ ' May also be a dict with key `alines` (as date/price pairs described above),'+ ' plus one or more of the following keys:'+ ' `colors`, `linestyle`, `linewidths`, `alpha`.', 'Validator' : lambda value: _alines_validator(value) }, 'tlines' : { 'Default' : None, 'Description' : 'Draw one or more TREND LINES by specifying one or more pairs of date[times]'+ ' between which each trend line should be drawn. May also be a dict with key'+ ' `tlines` as just described, plus one or more of the following keys:'+ ' `colors`, `linestyle`, `linewidths`, `alpha`, `tline_use`,`tline_method`.', 'Validator' : lambda value: _tlines_validator(value) }, 'panel_ratios' : { 'Default' : None, 'Description' : 'sequence of numbers indicating relative sizes of panels; sequence len'+ ' must be same as number of panels, or len 2 where first entry is for'+ ' main panel, and second entry is for all other panels', 'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) <= 32 and all([isinstance(v,(int,float)) for v in value]) }, 'main_panel' : { 'Default' : 0, 'Description' : 'integer - which panel is the main panel for `.plot()`', 'Validator' : lambda value: _valid_panel_id(value) }, 'volume_panel' : { 'Default' : 1, 'Description' : 'integer - which panel is the volume panel', 'Validator' : lambda value: _valid_panel_id(value) }, 'num_panels' : { 'Default' : None, 'Description' : 'total number of panels', 'Validator' : lambda value: isinstance(value,int) and value in range(1,32+1) }, 'datetime_format' : { 'Default' : None, 'Description' : 'x-axis tick format as valid `strftime()` format string', 'Validator' : lambda value: isinstance(value,str) }, 'xrotation' : { 'Default' : 45, 'Description' : 'Angle (degrees) for x-axis tick labels; 90=vertical', 'Validator' : lambda value: isinstance(value,(int,float)) }, 'axisoff' : { 'Default' : False, 'Description' : '`axisoff=True` means do NOT display any axis.', 'Validator' : lambda value: isinstance(value,bool) }, 'closefig' : { 'Default' : 'auto', 'Description' : 'True|False close the Figure before returning', 'Validator' : lambda value: isinstance(value,bool) }, 'fill_between' : { 'Default' : None, 'Description' : 'fill between specification as y-value, or sequence of'+ ' y-values, or dict containing key "y1" plus any additional'+ ' kwargs for `fill_between()`', 'Validator' : _fill_between_validator }, 'tight_layout' : { 'Default' : False, 'Description' : 'True|False implement tight layout (minimal padding around Figure)'+ ' (see also `scale_padding` kwarg)', 'Validator' : lambda value: isinstance(value,bool) }, 'scale_padding' : { 'Default' : 1.0, # Issue#193 'Description' : 'Increase, > 1.0, or decrease, < 1.0, padding around figure.'+ ' May also be a dict containing one or more of the following keys:'+ ' "top", "bottom", "left", "right", to individual scale padding'+ ' on each side of Figure.', 'Validator' : lambda value: _scale_padding_validator(value) }, 'width_adjuster_version' : { 'Default' : 'v1', 'Description' : 'specify version of object width adjustment algorithm: "v0" or "v1"'+ ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: value in ('v0', 'v1') }, 'scale_width_adjustment' : { 'Default' : None, 'Description' : 'scale width of plot objects wider, > 1.0, or narrower, < 1.0'+ ' may also be a dict to scale individual widths.'+ ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, 'update_width_config' : { 'Default' : None, 'Description' : 'dict - update individual items in width configuration.'+ ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, 'return_width_config' : { 'Default' : None, 'Description' : 'empty dict variable to be filled with width configuration settings.', 'Validator' : lambda value: isinstance(value,dict) and len(value)==0 }, 'saxbelow' : { 'Default' : True, # Issue#115 Comment#639446764 'Description' : 'set the volume Axes below (behind) all other Axes objects', 'Validator' : lambda value: isinstance(value,bool) }, 'ax' : { 'Default' : None, 'Description' : 'Matplotlib Axes object on which to plot', 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, 'volume_exponent' : { 'Default' : None, 'Description' : 'integer exponent on the volume axis'+ ' (or set to "legacy" for old mplfinance style)', 'Validator' : lambda value: isinstance(value,int) or value == 'legacy'}, 'tz_localize' : { 'Default' : True, 'Description' : 'True|False localize the times in the DatetimeIndex', 'Validator' : lambda value: isinstance(value,bool) }, 'yscale' : { 'Default' : None, 'Description' : 'y-axis scale: "linear", "log", "symlog", or "logit"', 'Validator' : lambda value: _yscale_validator(value) }, 'volume_yscale' : { 'Default' : None, 'Description' : 'Volume y-axis scale: "linear", "log", "symlog", or "logit"', 'Validator' : lambda value: _yscale_validator(value) }, 'volume_ylim' : { 'Default' : None, 'Description' : 'Volume y-axis limits as tuple (min,max), i.e. (bottom,top)', 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 and all([isinstance(v,(int,float)) for v in value])}, 'volume_alpha' : { 'Default' : None, # alpha of Volume bars 'Description' : 'opacity for Volume bar: 0.0 (transparent) to 1.0 (opaque)', 'Validator' : lambda value: isinstance(value,(int,float)) or all([isinstance(v,(int,float)) for v in value]) }, 'warn_too_much_data' : { 'Default' : 599, 'Description' : 'Tolerance for data amount in plot. Default=599 rows.'+ ' Values greater than \'warn_too_much_data\' will trigger a warning.', 'Validator' : lambda value: isinstance(value,int) }, } _validate_vkwargs_dict(vkwargs) return vkwargs ###@with_rc_context 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) config['xlim'] = _check_and_convert_xlim_configuration(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) if config['marketcolor_overrides'] is not None: if len(config['marketcolor_overrides']) != len(dates): raise ValueError('`marketcolor_overrides` must be same length as dataframe.') 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?') if config['mavcolors'] is not None: config['_ma_color_cycle'] = cycle(config['mavcolors']) elif style['mavcolors'] is not None: config['_ma_color_cycle'] = cycle(style['mavcolors']) else: config['_ma_color_cycle'] = None if not external_axes_mode: fig = plt.figure() _adjust_figsize(fig,config) else: fig = None _adjust_fontsize(config) if config['volume'] and volumes is None: raise ValueError('Request for volume, but NO volume data.') if external_axes_mode: panels = None axA1 = config['ax'] axA1.set_axisbelow(config['saxbelow']) if config['volume']: volumeAxes = config['volume'] volumeAxes.set_axisbelow(config['saxbelow']) else: panels = _build_panels(fig, config) axA1 = panels.at[config['main_panel'],'axes'][0] if config['volume']: if config['volume_panel'] == config['main_panel']: # ohlc and volume on same panel: move volume to secondary axes: volumeAxes = panels.at[config['volume_panel'],'axes'][1] volumeAxes.set_zorder(axA1.get_zorder()-0.1) # Make sure ohlc is above volume axA1.patch.set_visible(False) # Let volume show through panels.at[config['main_panel'],'used2nd'] = True else: volumeAxes = panels.at[config['volume_panel'],'axes'][0] else: volumeAxes = 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)) # 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) elif ptype == 'pnf': pnf_results = _construct_pnf_scatter(axA1,ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style) else: collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style) if ptype == 'pnf': volumes = pnf_results['pnf_volumes'] pnf_values = pnf_results['pnf_values'] pnf_mdates = mdates.date2num(list(pnf_values.keys())) formatter = IntegerIndexDateTimeFormatter(pnf_mdates,fmtstring) xdates = pnf_results['pnf_xdates'] elif ptype == 'renko': collections, renko_results = collections volumes = renko_results['volumes'] renko_dates = renko_results['dates'] renko_values = renko_results['values'] formatter = IntegerIndexDateTimeFormatter(renko_dates, fmtstring) renko_avgvals = renko_values renko_size = renko_results['size'] xdates = np.arange(len(renko_dates)) if collections is not None: for collection in collections: axA1.add_collection(collection) #formatter = IntegerIndexDateTimeFormatter(xdates, fmtstring) if (ptype == 'pnf' and (config['mav'] is not None or config['ema'] is not None)): warnings.warn('\n\n ================================================================ '+ '\n\n MOVING Averages IGNORED for POINT and FIGURE PLOTS!'+ '\n\n ================================================================ ', category=UserWarning) elif ptype == 'renko': mavprices = _plot_mav(axA1,config,xdates,renko_avgvals) emaprices = _plot_ema(axA1,config,xdates,renko_avgvals) else: mavprices = _plot_mav(axA1,config,xdates,closes) emaprices = _plot_ema(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 == 'renko': _lows = renko_avgvals _highs = [value+renko_size for value in renko_avgvals] else: _lows = lows _highs = highs if ptype == 'pnf': miny, maxy = pnf_results['pnf_ylimits'] else: 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) if miny > 0.0: # don't let it go negative: setminy = max(0.9*miny,miny-ydelta) else: setminy = miny-ydelta axA1.set_ylim(setminy,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 == 'renko': retdict['renko_bricks' ] = renko_values retdict['renko_dates' ] = mdates.num2date(renko_dates) retdict['renko_size' ] = renko_size retdict['renko_volumes'] = volumes if config['volume'] else None elif ptype == 'pnf': retdict['pnf_dates' ] = mdates.num2date(pnf_mdates) retdict['pnf_values' ] = pnf_values retdict['pnf_size' ] = pnf_results['pnf_boxsize'] retdict['pnf_volumes' ] = volumes[:len(pnf_values)] if config['volume'] else None if config['mav'] is not None and ptype != 'pnf': 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] if config['ema'] is not None and ptype != 'pnf': ema = config['ema'] if len(ema) != len(emaprices): warnings.warn('len(ema)='+str(len(ema))+' BUT len(emaprices)='+str(len(emaprices))) else: for jj in range(0, len(ema)): retdict['ema' + str(ema[jj])] = emaprices[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: pmove_dates = pnf_mdates if ptype == 'pnf' else renko_dates dtix = pd.DatetimeIndex([dt for dt in mdates.num2date(pmove_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']: mc = style['marketcolors'] vup,vdown = mc['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'] veup, vedown = mc['vcedge'].values() if mc['volume'] == mc['vcedge']: edgecolors = _adjust_color_brightness(vcolors,0.90) elif veup != vedown: edgecolors = _updown_colors(veup, vedown, opens, closes, use_prev_close=style['marketcolors']['vcdopcod']) else: edgecolors = veup if config['volume_alpha']: valp = config['volume_alpha'] elif 'volume_alpha' in mc: valp = mc['volume_alpha'] else: valp = 1.0 volumeAxes.bar(xdates,volumes,width=w,linewidth=lw,color=vcolors,ec=edgecolors,alpha=valp) if config['volume_ylim'] is not None: vymin = config['volume_ylim'][0] vymax = config['volume_ylim'][1] else: 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, xlabel=config['xlabel']) else: axA1.tick_params(axis='x',rotation=xrotation) axA1.xaxis.set_major_formatter(formatter) axA1.set_xlabel(config['xlabel']) if config['type'] == 'pnf': pnf_xs = list(pnf_results['pnf_df'].XBox.values) pnf_os = list(pnf_results['pnf_df'].OBox.values) tick_vals = sorted( set(pnf_xs + pnf_os) ) axA1.set_yticks(tick_vals) skip = int( round(len(xdates)/10.0, 0) ) skip = max(1,skip) # must be at least 1 tick_vals = [t for t in range(0-skip,len(xdates)+1,skip)] #print('len(xdates)=',len(xdates),'len(pnf_mdates)=',len(pnf_mdates)) #print('skip=',skip,'\nxdates=',xdates,'\npnf_dates=',[str(d.date()) for d in mdates.num2date(pnf_mdates)]) axA1.set_xticks(tick_vals) ysd = config['yscale'] if isinstance(ysd,dict): yscale = ysd['yscale'] del ysd['yscale'] axA1.set_yscale(yscale,**ysd) elif isinstance(ysd,str): axA1.set_yscale(ysd) 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))) contains_legend_label=[] # a list of axes that contains legend labels 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,xdates) 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 if havedf and apdict['label']: if not isinstance(apdict['label'],(list,tuple,np.ndarray)): nlabels = 1 else: nlabels = len(apdict['label']) ncolumns = len(apdata.columns) #print('nlabels=',nlabels,'ncolumns=',ncolumns) if nlabels < ncolumns: warnings.warn('\n =======================================\n'+ ' addplot MISMATCH between data and labels:\n'+ ' have '+str(ncolumns)+' columns to plot \n'+ ' BUT '+str(nlabels)+' labels for them.\n') colcount = 0 for column in apdata: ydata = apdata.loc[:,column] if havedf else column ax = _addplot_columns(panid,panels,ydata,apdict,xdates,config,colcount) _addplot_apply_supplements(ax,apdict,xdates) colcount += 1 if apdict['label']: # not supported for aptype == 'ohlc' or 'candle' contains_legend_label.append(ax) for ax in set(contains_legend_label): # there might be duplicates ax.legend() # 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: fblist = copy.deepcopy(config['fill_between']) if _num_or_seq_of_num(fblist): fblist = [dict(y1=fblist),] elif isinstance(fblist,dict): fblist = [fblist,] if not _list_of_dict(fblist): raise TypeError('Bad type for `fill_between` specifier.') for fb in fblist: if 'x' in fb: raise ValueError('fill_between dict may not contain `x`') panid = config['main_panel'] if 'panel' in fb: panid = fb['panel'] del fb['panel'] fb['x'] = xdates # add 'x' to caller's fb dict 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) vscale = 'linear' ysd = config['volume_yscale'] if isinstance(ysd,dict): yscale = ysd['yscale'] del ysd['yscale'] volumeAxes.set_yscale(yscale,**ysd) vscale = yscale elif isinstance(ysd,str): volumeAxes.set_yscale(ysd) vscale = ysd offset = '' if vscale == 'linear': 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: `y=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(va='bottom', y=0.89) else: title_kwargs = dict(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: fig.savefig(**save,bbox_inches='tight') else: fig.savefig(**save) else: if config['tight_layout']: fig.savefig(save,bbox_inches='tight') else: fig.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) # rcp = copy.deepcopy(plt.rcParams) # rcpdf = rcParams_to_df(rcp) # print('type(rcpdf)=',type(rcpdf)) # print('rcpdfhead(3)=',rcpdf.head(3)) # return # rcpdf def _adjust_figsize(fig,config): if fig is None: return 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.set_size_inches(fsize) def _adjust_fontsize(config): if config['fontscale'] is None: return if not isinstance(plt.rcParams['font.size'],(float,int)): warnings.warn('\n\n ================================================================= '+ '\n\n WARNING: Unable to scale fonts: plt.rcParams["font.size"] is NOT a float!'+ '\n\n ================================================================ ', category=UserWarning) return plt.rcParams['font.size'] *= config['fontscale'] # -------------------------------------------- # From: matplotlib.font_manager.font_scalings: # font_scalings = { # 'xx-small': 0.579, # 'x-small': 0.694, # 'small': 0.833, # 'medium': 1.0, # 'large': 1.200, # 'x-large': 1.440, # 'xx-large': 1.728, # 'larger': 1.2, # 'smaller': 0.833, # None: 1.0, # } # -------------------------------------------- fontstuff = ['axes.labelsize','axes.titlesize', 'figure.titlesize','legend.fontsize', 'legend.title_fontsize','xtick.labelsize','ytick.labelsize'] for item in fontstuff: if isinstance(plt.rcParams[item],(float,int)): plt.rcParams[item] *= config['fontscale'] def _addplot_collections(panid,panels,apdict,xdates,config): apdata = apdict['data'] aptype = apdict['type'] external_axes_mode = apdict['ax'] is not None #--------------------------------------------------------------# # Note: _auto_secondary_y() sets the 'magnitude' column in the # `panels` dataframe, which is needed for automatically # determining if secondary_y is needed. Therefore we call # _auto_secondary_y() for *all* addplots, even those that # are set to True or False (not 'auto') for secondary_y # because their magnitudes may be needed if *any* apdicts # contain secondary_y='auto'. # In theory we could first loop through all apdicts to see # if any have secondary_y='auto', but since that is the # default value, we will just assume we have at least one. valid_apc_types = ['ohlc','candle'] if aptype not in valid_apc_types: raise TypeError('Invalid aptype='+str(aptype)+'. Must be one of '+str(valid_apc_types)) if not isinstance(apdata,pd.DataFrame): raise TypeError('addplot type "'+aptype+'" MUST be accompanied by addplot data of type `pd.DataFrame`') d,o,h,l,c,v = _check_and_prepare_data(apdata,config) mc = apdict['marketcolors'] if _is_marketcolor_object(mc): apstyle = config['style'].copy() apstyle['marketcolors'] = mc else: apstyle = config['style'] collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,apstyle) if not external_axes_mode: lo = math.log(max(math.fabs(np.nanmin(l)),1e-7),10) - 0.5 hi = math.log(max(math.fabs(np.nanmax(h)),1e-7),10) + 0.5 secondary_y = _auto_secondary_y( panels, panid, lo, hi ) if 'auto' != apdict['secondary_y']: secondary_y = apdict['secondary_y'] if secondary_y: ax = panels.at[panid,'axes'][1] panels.at[panid,'used2nd'] = True else: ax = panels.at[panid,'axes'][0] else: ax = apdict['ax'] for coll in collections: ax.add_collection(coll) if apdict['mav'] is not None: apmavprices = _plot_mav(ax,config,xdates,c,apdict['mav']) ax.autoscale_view() return ax def _addplot_columns(panid,panels,ydata,apdict,xdates,config,colcount): external_axes_mode = apdict['ax'] is not None if not external_axes_mode: secondary_y = False if apdict['secondary_y'] == 'auto': 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 = _auto_secondary_y( panels, panid, ymlo, ymhi ) 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] else: ax = apdict['ax'] aptype = apdict['type'] if isinstance(apdict['label'],(list,tuple,np.ndarray)): label = apdict['label'][colcount] else: # isinstance(...,str) label = apdict['label'] if aptype == 'scatter': size = apdict['markersize'] mark = apdict['marker'] color = apdict['color'] alpha = apdict['alpha'] edgecolors = apdict['edgecolors'] linewidths = apdict['linewidths'] if isinstance(mark,(list,tuple,np.ndarray)): _mscatter(xdates, ydata, ax=ax, m=mark, s=size, color=color, alpha=alpha, edgecolors=edgecolors, linewidths=linewidths) else: ax.scatter(xdates, ydata, s=size, marker=mark, color=color, alpha=alpha, edgecolors=edgecolors, linewidths=linewidths,label=label) elif aptype == 'bar': width = 0.8 if apdict['width'] is None else apdict['width'] bottom = apdict['bottom'] color = apdict['color'] alpha = apdict['alpha'] ax.bar(xdates,ydata,width=width,bottom=bottom,color=color,alpha=alpha,label=label) elif aptype == 'line': ls = apdict['linestyle'] color = apdict['color'] width = apdict['width'] if apdict['width'] is not None else 1.6*config['_width_config']['line_width'] alpha = apdict['alpha'] ax.plot(xdates,ydata,linestyle=ls,color=color,linewidth=width,alpha=alpha,label=label) elif aptype == 'step': stepwhere = apdict['stepwhere'] ls = apdict['linestyle'] color = apdict['color'] width = apdict['width'] if apdict['width'] is not None else 1.6*config['_width_config']['line_width'] alpha = apdict['alpha'] ax.step(xdates,ydata,where = stepwhere,linestyle=ls,color=color,linewidth=width,alpha=alpha,label=label) else: raise ValueError('addplot type "'+str(aptype)+'" NOT yet supported.') if apdict['mav'] is not None: apmavprices = _plot_mav(ax,config,xdates,ydata,apdict['mav']) return ax def _addplot_apply_supplements(ax,apdict,xdates): if (apdict['ylabel'] is not None): ax.set_ylabel(apdict['ylabel']) # Note that xlabel is NOT supported for addplot. This is because # in Panels Mode, there is only one xlabel (on the bottom axes) # which is handled by the `xlabel` kwarg of `mpf.plot()`, # whereas in External Axes Mode, users can call `Axes.set_xlabel()` on # the axes object of their choice. if apdict['ylim'] is not None: ax.set_ylim(apdict['ylim'][0],apdict['ylim'][1]) if apdict['title'] is not None: ax.set_title(apdict['title']) ysd = apdict['yscale'] if isinstance(ysd,dict): yscale = ysd['yscale'] del ysd['yscale'] ax.set_yscale(yscale,**ysd) elif isinstance(ysd,str): ax.set_yscale(ysd) # added by Wen if "fill_between" in apdict and apdict['fill_between'] is not None: # deep copy because mplfinance code sometimes modifies the fill_between dict fblist = copy.deepcopy(apdict['fill_between']) if isinstance(fblist,dict): fblist = [fblist,] if _list_of_dict(fblist): for fb in fblist: if 'x' in fb: raise ValueError('fill_between dict may not contain `x`') fb['x'] = xdates # add 'x' to caller's fb dict ax.fill_between(**fb) else: raise ValueError('Invalid addplot fill_between: must be `dict` or `list of dict`') def _set_ylabels_side(ax_pri,ax_sec,primary_on_right): # put the primary axis on one side, # and the twinx() on the "other" side: if primary_on_right == True: ax_pri.yaxis.set_label_position('right') ax_pri.yaxis.tick_right() if ax_sec is not None: ax_sec.yaxis.set_label_position('left') ax_sec.yaxis.tick_left() else: # treat non-True as False, whether False, None, or anything else. ax_pri.yaxis.set_label_position('left') ax_pri.yaxis.tick_left() if ax_sec is not None: ax_sec.yaxis.set_label_position('right') ax_sec.yaxis.tick_right() def _plot_mav(ax,config,xdates,prices,apmav=None,apwidth=None): style = config['style'] if apmav is not None: mavgs = apmav else: mavgs = config['mav'] mavp_list = [] if mavgs is not None: shift = None if isinstance(mavgs,dict): shift = mavgs['shift'] mavgs = mavgs['period'] if isinstance(mavgs,int): mavgs = mavgs, # convert to tuple if len(mavgs) > 7: mavgs = mavgs[0:7] # take at most 7 mavc = config['_ma_color_cycle'] for idx,mav in enumerate(mavgs): mean = pd.Series(prices).rolling(mav).mean() if shift is not None: mean = mean.shift(periods=shift[idx]) mavprices = mean.values lw = config['_width_config']['line_width'] if mavc: ax.plot(xdates, mavprices, linewidth=lw, color=next(mavc)) else: ax.plot(xdates, mavprices, linewidth=lw) mavp_list.append(mavprices) return mavp_list def _plot_ema(ax,config,xdates,prices,apmav=None,apwidth=None): '''ema: exponential moving average''' style = config['style'] if apmav is not None: mavgs = apmav else: mavgs = config['ema'] mavp_list = [] if mavgs is not None: shift = None if isinstance(mavgs,dict): shift = mavgs['shift'] mavgs = mavgs['period'] if isinstance(mavgs,int): mavgs = mavgs, # convert to tuple if len(mavgs) > 7: mavgs = mavgs[0:7] # take at most 7 mavc = config['_ma_color_cycle'] for idx,mav in enumerate(mavgs): # mean = pd.Series(prices).rolling(mav).mean() mean = pd.Series(prices).ewm(span=mav,adjust=False).mean() if shift is not None: mean = mean.shift(periods=shift[idx]) emaprices = mean.values lw = config['_width_config']['line_width'] if mavc: ax.plot(xdates, emaprices, linewidth=lw, color=next(mavc)) else: ax.plot(xdates, emaprices, linewidth=lw) mavp_list.append(emaprices) return mavp_list def _auto_secondary_y( panels, panid, ylo, yhi ): # 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. secondary_y = False p = panid,'mag' if panels.at[p] is None: panels.at[p] = {'lo':ylo,'hi':yhi} elif ylo < panels.at[p]['lo'] or yhi > 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) return secondary_y def _valid_addplot_kwargs(): valid_linestyles = ('-','solid','--','dashed','-.','dashdot',':','dotted',None,' ','') valid_types = ('line','scatter','bar', 'ohlc', 'candle','step') valid_stepwheres = ('pre','post','mid') valid_edgecolors = ('face', 'none', None) vkwargs = { 'scatter' : { 'Default' : False, 'Description' : "Deprecated. (Use kwarg `type='scatter' instead.", 'Validator' : lambda value: isinstance(value,bool) }, 'type' : { 'Default' : 'line', 'Description' : 'addplot type: "line","scatter","bar", "ohlc", "candle","step"', 'Validator' : lambda value: value in valid_types }, 'mav' : { 'Default' : None, 'Description' : 'Moving Average window size(s); (int or tuple of ints)', 'Validator' : _mav_validator }, 'panel' : { 'Default' : 0, 'Description' : 'Panel (int 0-31) to use for this addplot', 'Validator' : lambda value: _valid_panel_id(value) }, 'marker' : { 'Default' : 'o', 'Description' : "marker for `type='scatter'` plot", 'Validator' : lambda value: _bypass_kwarg_validation(value) }, 'markersize' : { 'Default' : 18, 'Description' : 'size of marker for `type="scatter"`; default=18', 'Validator' : lambda value: isinstance(value,(int,float, pd.Series, np.ndarray)) }, 'color' : { 'Default' : None, 'Description' : 'color (or sequence of colors) of line(s), scatter marker(s), or bar(s).', 'Validator' : lambda value: mcolors.is_color_like(value) or (isinstance(value,(list,tuple,np.ndarray)) and all([mcolors.is_color_like(v) for v in value])) }, 'linestyle' : { 'Default' : None, 'Description' : 'line style for `type=line` ('+str(valid_linestyles)+')', 'Validator' : lambda value: value in valid_linestyles }, 'linewidths' : { 'Default': None, 'Description' : 'edge widths of scatter markers', 'Validator' : lambda value: isinstance(value,(int,float)) }, 'edgecolors' : { 'Default': None, 'Description' : 'edgecolors of scatter markers', 'Validator': lambda value: mcolors.is_color_like(value) or value in valid_edgecolors}, 'width' : { 'Default' : None, # width of `bar` or `line` 'Description' : 'width of bar or line for `type="bar"` or `type="line"', 'Validator' : lambda value: isinstance(value,(int,float)) or all([isinstance(v,(int,float)) for v in value]) }, 'bottom' : { 'Default' : 0, # bottom for `type=bar` plots 'Description' : 'bottom value for `type=bar` bars. Default=0', 'Validator' : lambda value: isinstance(value,(int,float)) or all([isinstance(v,(int,float)) for v in value]) }, 'alpha' : { 'Default' : 1, # alpha of `bar`, `line`, or `scatter` 'Description' : 'opacity for 0.0 (transparent) to 1.0 (opaque)', 'Validator' : lambda value: isinstance(value,(int,float)) or all([isinstance(v,(int,float)) for v in value]) }, 'secondary_y' : { 'Default' : 'auto', 'Description' : "True|False|'auto' place the additional plot data on a"+ " secondary y-axis. 'auto' compares the magnitude or the"+ " addplot data, to data already on the axis, and if it appears"+ " they are of different magnitudes, then it uses a secondary y-axis."+ " True or False always override 'auto'.", 'Validator' : lambda value: isinstance(value,bool) or value == 'auto' }, 'y_on_right' : { 'Default' : None, 'Description' : 'True|False put y-axis tick labels on the right, for this addplot'+ ' regardless of what the mplfinance style says to to.', 'Validator' : lambda value: isinstance(value,bool) }, 'ylabel' : { 'Default' : None, 'Description' : 'label for y-axis (for this addplot)', 'Validator' : lambda value: isinstance(value,str) }, 'ylim' : {'Default' : None, 'Description' : 'Limits for addplot y-axis as tuple (min,max), i.e. (bottom,top)', 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 and all([isinstance(v,(int,float)) for v in value])}, 'title' : { 'Default' : None, 'Description' : 'Axes Title (subplot title) for this addplot.', 'Validator' : lambda value: isinstance(value,str) }, 'ax' : { 'Default' : None, 'Description' : 'Matplotlib Axes object on which to plot this addplot', 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, 'yscale' : { 'Default' : None, 'Description' : 'addplot y-axis scale: "linear", "log", "symlog", or "logit"', 'Validator' : lambda value: _yscale_validator(value) }, 'stepwhere' : { 'Default' : 'pre', 'Description' : "'pre','post', or 'mid': where to place step relative"+ " to data for `type='step'`", 'Validator' : lambda value : value in valid_stepwheres }, 'marketcolors': { 'Default' : None, # use 'style' for default, instead. 'Description' : "marketcolors for this addplot (instead of the mplfinance"+ " style\'s marketcolors). For addplot `type='ohlc'`"+ " and type='candle'", 'Validator' : lambda value: _is_marketcolor_object(value) }, 'fill_between': { 'Default' : None, # added by Wen 'Description' : " fill region", 'Validator' : _fill_between_validator }, 'label' : { 'Default' : None, 'Description' : 'Label for the added plot. One per added plot.', 'Validator' : _label_validator }, } _validate_vkwargs_dict(vkwargs) return vkwargs def make_addplot(data, **kwargs): ''' Take data (pd.Series, pd.DataFrame, np.ndarray of floats, list of floats), and kwargs (see valid_addplot_kwargs_table) and construct a correctly structured dict to be passed into plot() using kwarg `addplot`. NOTE WELL: len(data) here must match the len(data) passed into plot() ''' if not isinstance(data, (pd.Series, pd.DataFrame, np.ndarray, list)): raise TypeError('Wrong type for data, in make_addplot()') config = _process_kwargs(kwargs, _valid_addplot_kwargs()) # kwarg `type` replaces kwarg `scatter` if config['scatter'] == True and config['type'] == 'line': config['type'] = 'scatter' return dict( data=data, **config)