import pandas as pd from mplfinance._arg_validators import _process_kwargs, _validate_vkwargs_dict def _get_widths_df(): ''' Provide a dataframe of width data that appropriate scales widths of various aspects of the plot (candles,ohlc bars,volume bars) based on the amount or density of data. These numbers were arrived at by carefully testing many use-cases of plots with various styles, and observing which numbers gave the "best" appearance. ''' numpoints = [n for n in range(30,241,30)] volume_width = (0.98, 0.96, 0.95, 0.925, 0.9, 0.9, 0.875, 0.825 ) volume_linewidth = tuple([0.65]*8) candle_width = (0.65, 0.575, 0.50, 0.445, 0.435, 0.425, 0.420, 0.415) candle_linewidth = (1.00, 0.875, 0.75, 0.625, 0.500, 0.438, 0.435, 0.435) ohlc_tickwidth = tuple([0.35]*8) ohlc_linewidth = (1.50, 1.175, 0.85, 0.525, 0.525, 0.525, 0.525, 0.525) line_width = (2.25, 1.8, 1.3, 0.813, 0.807, 0.801, 0.796, 0.791) widths = {} widths['vw'] = volume_width widths['vlw'] = volume_linewidth widths['cw'] = candle_width widths['clw'] = candle_linewidth widths['ow'] = ohlc_tickwidth widths['olw'] = ohlc_linewidth widths['lw'] = line_width return pd.DataFrame(widths,index=numpoints) _widths = _get_widths_df() def _valid_scale_width_kwargs(): vkwargs = { 'ohlc' : { 'Default' : None, 'Description' : 'length of horizontal open/close tickmarks on ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume' : { 'Default' : None, 'Description' : 'width of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle' : { 'Default' : None, 'Description' : 'width of candles', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'lines' : { 'Default' : None, 'Description' : 'width of lines (for line plots and moving averages)', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume_linewidth' : { 'Default' : None, 'Description' : 'width of edges of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'ohlc_linewidth' : { 'Default' : None, 'Description' : 'width (thickness) of ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle_linewidth' : { 'Default' : None, 'Description' : 'width of candle edges and wicks', 'Validator' : lambda value: isinstance(value,(float,int)) }, } _validate_vkwargs_dict(vkwargs) return vkwargs def _valid_update_width_kwargs(): vkwargs = { 'ohlc_ticksize' : { 'Default' : None, 'Description' : 'length of horizontal open/close tickmarks on ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'ohlc_linewidth' : { 'Default' : None, 'Description' : 'width (thickness) of ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume_width' : { 'Default' : None, 'Description' : 'width of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume_linewidth' : { 'Default' : None, 'Description' : 'width of edges of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle_width' : { 'Default' : None, 'Description' : 'width of candles', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle_linewidth' : { 'Default' : None, 'Description' : 'width of candle edges and wicks', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'line_width' : { 'Default' : None, 'Description' : 'width of lines (for line plots and moving averages)', 'Validator' : lambda value: isinstance(value,(float,int)) }, } _validate_vkwargs_dict(vkwargs) return vkwargs def _scale_width_config(scale,width_config): if scale['volume'] is not None: width_config['volume_width'] *= scale['volume'] if scale['ohlc'] is not None: width_config['ohlc_ticksize'] *= scale['ohlc'] if scale['candle'] is not None: width_config['candle_width'] *= scale['candle'] if scale['lines'] is not None: width_config['line_width'] *= scale['lines'] if scale['volume_linewidth'] is not None: width_config['volume_linewidth'] *= scale['volume_linewidth'] if scale['ohlc_linewidth'] is not None: width_config['ohlc_linewidth' ] *= scale['ohlc_linewidth'] if scale['candle_linewidth'] is not None: width_config['candle_linewidth'] *= scale['candle_linewidth'] def _determine_width_config( xdates, config ): ''' Given x-axis xdates, and `mpf.plot()` kwargs config, determine the widths and linewidths for candles, volume bars, ohlc bars, etc. ''' datalen = len(xdates) avg_dist_between_points = (xdates[-1] - xdates[0]) / float(datalen) tweak = 1.06 if datalen > 100 else 1.03 adjust = tweak*avg_dist_between_points if config['show_nontrading'] else 1.0 width_config = {} if config['width_adjuster_version'] == 'v0': # Behave like original version of code: width_config['volume_width' ] = 0.5*avg_dist_between_points width_config['volume_linewidth'] = None width_config['ohlc_ticksize' ] = avg_dist_between_points / 2.5 width_config['ohlc_linewidth' ] = None width_config['candle_width' ] = avg_dist_between_points / 2.0 width_config['candle_linewidth'] = None width_config['line_width' ] = None else: # config['width_adjuster_version'] == 'v1' width_config['volume_width' ] = _dfinterpolate(_widths,datalen,'vw' ) * adjust width_config['volume_linewidth'] = _dfinterpolate(_widths,datalen,'vlw') width_config['ohlc_ticksize' ] = _dfinterpolate(_widths,datalen,'ow' ) * adjust width_config['ohlc_linewidth' ] = _dfinterpolate(_widths,datalen,'olw') width_config['candle_width' ] = _dfinterpolate(_widths,datalen,'cw' ) * adjust width_config['candle_linewidth'] = _dfinterpolate(_widths,datalen,'clw') width_config['line_width' ] = _dfinterpolate(_widths,datalen,'lw') if 'scale_width_adjustment' in config['style']: scale = _process_kwargs(config['style']['scale_width_adjustment'],_valid_scale_width_kwargs()) _scale_width_config(scale,width_config) if config['scale_width_adjustment'] is not None: scale = _process_kwargs(config['scale_width_adjustment'],_valid_scale_width_kwargs()) _scale_width_config(scale,width_config) if config['update_width_config'] is not None: update = _process_kwargs(config['update_width_config'],_valid_update_width_kwargs()) uplist = [ (k,v) for k,v in update.items() if v is not None ] width_config.update(uplist) return width_config def _dfinterpolate(df,key,column): ''' Given a DataFrame, with all values and the Index as floats, and given a float key, find the row that matches the key, or find the two rows surrounding that key, and return the interpolated value for the specified column, based on where the key falls between the two rows. If they key is an exact match for a key in the index, the return the exact value from the column. If the key is less than or greater than any key in the index, then return either the first or last value for the column. ''' s = df[column] s1 = s.loc[:key] if len(s1) < 1: return s.iloc[0] j1 = s1.index[-1] v1 = s1.iloc[-1] s2 = s.loc[key:] if len(s2) < 1: return s.iloc[-1] j2 = s2.index[0] v2 = s2.iloc[0] if j1 == j2: return v1 delta = j2 - j1 portion = (key - j1)/delta ans = v1 + (v2-v1)*portion return ans