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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,13 +1,15 @@ | ||
| from .nse_product import NSEProduct | ||
| from .nse_index import NSEIndex | ||
| from .nse_tri import NSETRI | ||
| from .visual import Visual | ||
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| __all__ = [ | ||
| 'NSEProduct', | ||
| 'NSEIndex', | ||
| 'NSETRI' | ||
| 'NSETRI', | ||
| 'Visual' | ||
| ] | ||
|
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||
|
|
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| __version__ = '0.1.4' | ||
| __version__ = '0.1.5' |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,172 @@ | ||
| import typing | ||
| import pandas | ||
| import matplotlib | ||
| import matplotlib.pyplot | ||
| import matplotlib.figure | ||
| from .core import Core | ||
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|
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| class Visual: | ||
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| ''' | ||
| Provides functionality for plotting data. | ||
| ''' | ||
|
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| def plot_category_sort_index_cagr_from_launch( | ||
| self, | ||
| excel_file: str, | ||
| figure_file: str, | ||
| threshold_cagr: typing.Optional[float] = None, | ||
| threshold_close: typing.Optional[float] = None | ||
| ) -> matplotlib.figure.Figure: | ||
|
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| ''' | ||
| Returns a bar plot of indices' clsong value since launch. | ||
| Parameters | ||
| ---------- | ||
| excel_file : str | ||
| Path to the input Excel file. | ||
| figure_file : str | ||
| Path to a file to save the output figue. | ||
| threshold_close : float, optional | ||
| Only plot indices with a closing value higher than the specified threshold. | ||
| threshold_cagr : float, optional | ||
| Only plot indices with a CAGR (%) higher than the specified threshold. | ||
| Returns | ||
| ------- | ||
| Figure | ||
| A bar plot displaying indices' closing values along with | ||
| CAGR (%), Multiplier (X), and Age (Y) since launch. | ||
| ''' | ||
|
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| # input DataFrame | ||
| df = pandas.read_excel(excel_file, index_col=[0, 1]) | ||
| df = df[df['Close Value'] >= threshold_close] if threshold_close is not None else df | ||
| df = df[df['CAGR(%)'] >= threshold_cagr] if threshold_cagr is not None else df | ||
|
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| # check filtered dataframe | ||
| if len(df) == 0: | ||
| raise Exception('Threshold values return an empty DataFrame.') | ||
| else: | ||
| pass | ||
|
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| categories = df.index.get_level_values('Category').unique() | ||
| close_date = df['Close Date'].iloc[0].strftime('%d-%b-%Y') | ||
|
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| # color for NSE indices category | ||
| colormap = matplotlib.colormaps.get_cmap('Set2') | ||
| category_color = { | ||
| categories[count]: colormap(count / len(categories)) for count in range(len(categories)) | ||
| } | ||
|
|
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| # figure | ||
| fig_height = int(len(df) / 3.5) + 1 if len(df) > 7 else 3 | ||
| xtick_gap = 10000 | ||
| xaxis_max = int(((df['Close Value'].max() + 20000) / xtick_gap) + 1) * xtick_gap | ||
| fig_width = int((xaxis_max / xtick_gap) * 1.2) + 1 | ||
| figure = matplotlib.pyplot.figure( | ||
| figsize=(fig_width, fig_height) | ||
| ) | ||
| subplot = figure.subplots(1, 1) | ||
|
|
||
| # check validity of input figure file path | ||
| check_file = Core().is_valid_figure_extension(figure_file) | ||
| if check_file is True: | ||
| pass | ||
| else: | ||
| # close the figure to prevent a blank plot from appearing | ||
| matplotlib.pyplot.close(figure) | ||
| raise Exception('Input figure file extension is not supported.') | ||
|
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| # plotting indices closing values | ||
| categories_legend = set() | ||
| for count, (index, row) in enumerate(df.iterrows()): | ||
| category = index[0] | ||
| color = category_color[category] | ||
| if category not in categories_legend: | ||
| subplot.barh( | ||
| row['Index Name'], row['Close Value'], | ||
| color=color, | ||
| label=category | ||
| ) | ||
| categories_legend.add(category) | ||
| else: | ||
| subplot.barh( | ||
| row['Index Name'], row['Close Value'], | ||
| color=color | ||
| ) | ||
| age = row['Years'] + (row['Days'] / 365) | ||
| bar_label = f"({row['CAGR(%)']:.1f}%,{round(row['Close/Base'])}X,{age:.1f}Y)" | ||
| subplot.text( | ||
| row['Close Value'] + 100, count, bar_label, | ||
| va='center', | ||
| fontsize=10 | ||
| ) | ||
|
|
||
| # x-axis customization | ||
| subplot.set_xlim(0, xaxis_max) | ||
| subplot.set_xticks( | ||
| list(range(0, xaxis_max + 1, xtick_gap)) | ||
| ) | ||
| xtick_labels = [ | ||
| f'{int(val / 1000)}k' for val in list(range(0, xaxis_max + 1, xtick_gap)) | ||
| ] | ||
| subplot.set_xticklabels(xtick_labels, fontsize=12) | ||
| subplot.tick_params( | ||
| axis='x', which='both', | ||
| direction='in', length=6, width=1, | ||
| top=True, bottom=True, | ||
| labeltop=True, labelbottom=True | ||
| ) | ||
| subplot.grid( | ||
| visible=True, | ||
| which='major', axis='x', | ||
| color='gray', | ||
| linestyle='--', linewidth=0.3 | ||
| ) | ||
| subplot.set_xlabel( | ||
| f'Close Value (Date: {close_date})', | ||
| fontsize=15, | ||
| labelpad=15 | ||
| ) | ||
|
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||
| # reverse y-axis | ||
| subplot.invert_yaxis() | ||
|
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| # y-axis customization | ||
| subplot.set_ylabel( | ||
| 'Index Name', | ||
| fontsize=20, | ||
| labelpad=15 | ||
| ) | ||
| subplot.set_ylim(len(df), -1) | ||
|
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| # legend | ||
| subplot.legend( | ||
| title="Index Category", | ||
| loc='lower right', | ||
| fontsize=12, | ||
| title_fontsize=12 | ||
| ) | ||
|
|
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| # figure customization | ||
| figure.suptitle( | ||
| 'NSE Equity Indices Since Launch: Closing Value Bars with CAGR (%), Multiplier (X), and Age (Y)', | ||
| fontsize=15, | ||
| y=1 | ||
| ) | ||
| figure.tight_layout() | ||
| figure.savefig( | ||
| figure_file, | ||
| bbox_inches='tight' | ||
| ) | ||
|
|
||
| # close the figure to prevent duplicate plots from displaying | ||
| matplotlib.pyplot.close(figure) | ||
|
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| return figure |
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