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HTML reporting capability implemented.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,99 +1,7 @@ | ||
| from typing import NewType, Literal, Tuple, Union | ||
| from IPython.display import HTML | ||
| import pandas as pd | ||
|
|
||
| # Type aliases | ||
| StockSymbol = str | ||
| StockDataSubset = Tuple[Literal['Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits']] | ||
|
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| Days = int | ||
|
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|
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| class nCrResult(list): | ||
| """ | ||
| Class to store the result of a nCrEngine calculation. | ||
| """ | ||
| def __init__(self, *args): | ||
| super().__init__(*args) | ||
|
|
||
| @staticmethod | ||
| def beautify_portfolio(portfolio_dict): | ||
| portfolio = portfolio_dict['portfolio'].split(' - ') | ||
| weights = portfolio_dict['weights'] | ||
| expected_returns = portfolio_dict['expected_returns'] | ||
| cov_matrix = portfolio_dict['cov_matrix'] | ||
| betas = portfolio_dict['betas'] | ||
|
|
||
| # Create a DataFrame for the portfolio and weights | ||
| portfolio_df = pd.DataFrame({ | ||
| "Weight": weights, | ||
| "Expected Return": expected_returns, | ||
| "Beta": betas | ||
| }, index=portfolio) | ||
|
|
||
| # Generate HTML for the table | ||
| portfolio_html = portfolio_df.to_html(float_format="%.4f") | ||
| cov = pd.DataFrame(cov_matrix, index=portfolio, columns=portfolio).to_html(float_format="%.4f") | ||
|
|
||
| # Create additional HTML content for metrics | ||
| summary_html = f""" | ||
| <h2>Portfolio Analysis</h2> | ||
| <h3>Summary Metrics</h3> | ||
| <ul> | ||
| <li><strong>Expected Portfolio Return:</strong> {portfolio_dict['return'] * 100:.4f}%</li> | ||
| <li><strong>Portfolio Variance:</strong> {portfolio_dict['portfolio_variance'] * 100:.4f}%</li> | ||
| </ul> | ||
| <h3>Portfolio Composition</h3> | ||
| {portfolio_html} | ||
| <h3>Covariance Matrix</h3> | ||
| {cov} | ||
| """ | ||
|
|
||
| # Combine all HTML content | ||
| full_html = summary_html | ||
|
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| return HTML(full_html) | ||
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| def _best_portfolio(self) -> HTML | dict: | ||
| return max( | ||
| self, | ||
| key=lambda x: x['return'] / x['portfolio_variance'] | ||
| ) | ||
|
|
||
| def _max_return(self, display: bool = False) -> dict: | ||
| if display: | ||
| return self.beautify_portfolio(self.max_return()) | ||
| return max( | ||
| self, | ||
| key=lambda x: x['return'] | ||
| ) | ||
|
|
||
| def _min_volatility(self) -> dict: | ||
| return min( | ||
| self, | ||
| key=lambda x: x['portfolio_variance'] | ||
| ) | ||
|
|
||
| def max_return(self, display: bool = False) -> dict | HTML: | ||
| """ | ||
| Get the maximum return from the result. | ||
| """ | ||
| if display: | ||
| return self.beautify_portfolio(self._max_return()) | ||
| return self._max_return() | ||
|
|
||
| def min_volatility(self, display: bool = False) -> dict | HTML: | ||
| """ | ||
| Get the minimum volatility from the result. | ||
| """ | ||
| if display: | ||
| return self.beautify_portfolio(self._min_volatility()) | ||
| return self._min_volatility() | ||
|
|
||
| def best_portfolio(self, display: bool = False) -> dict | HTML: | ||
| """ | ||
| Get the best portfolio from the result. | ||
| """ | ||
| if display: | ||
| return self.beautify_portfolio(self._best_portfolio()) | ||
| return self._best_portfolio() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| import numpy as np | ||
| from IPython.display import HTML, display | ||
| import pandas as pd | ||
| import plotly.express as px | ||
|
|
||
| class nCrResult(list): | ||
| """ | ||
| Class to store the result of a nCrEngine calculation. | ||
| """ | ||
|
|
||
| def __init__(self, *args, rf_rate: float): | ||
| super().__init__(*args) | ||
| self.horizon_rf_rate = rf_rate | ||
|
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||
|
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||
| def _beautify_portfolio(self, portfolio_dict): | ||
| portfolio = portfolio_dict['portfolio'].split(' - ') | ||
| weights = portfolio_dict['weights'] | ||
| expected_returns = portfolio_dict['expected_returns'] | ||
| cov_matrix = portfolio_dict['cov_matrix'] | ||
| betas = portfolio_dict['betas'] | ||
|
|
||
| # Create a DataFrame for the portfolio and weights | ||
| portfolio_df = pd.DataFrame({ | ||
| "Weight": weights, | ||
| "Expected Return": expected_returns, | ||
| "Beta": betas | ||
| }, index=portfolio) | ||
|
|
||
| # Generate HTML for the table | ||
| portfolio_html = portfolio_df.to_html(float_format="%.4f") | ||
| cov = pd.DataFrame(cov_matrix, index=portfolio, columns=portfolio).to_html(float_format="%.4f") | ||
|
|
||
| # Create plotly scatter of all portfolios | ||
| all_portfolios = { | ||
| 'Portfolio': [x['portfolio'] for x in self], | ||
| 'Return': [x['return'] for x in self], | ||
| 'Variance': [x['portfolio_variance'] for x in self] | ||
| } | ||
|
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||
| df = pd.DataFrame(all_portfolios) | ||
| df.set_index('Portfolio', inplace=True) | ||
|
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| df['Sharpe Ratio'] = (df['Return'] - self.horizon_rf_rate) / np.sqrt(df['Variance']) | ||
|
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| max_return = df.loc[df['Return'].idxmax(), ['Return', 'Variance', 'Sharpe Ratio']] | ||
| min_volatility = df.loc[df['Variance'].idxmin(), ['Return', 'Variance', 'Sharpe Ratio']] | ||
| best_portfolio = df.loc[(df['Return'] / df['Variance']).idxmax(), ['Return', 'Variance', 'Sharpe Ratio']] | ||
|
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||
| fig = px.scatter(df, | ||
| x='Variance', | ||
| y='Return', | ||
| color='Sharpe Ratio', | ||
| color_continuous_scale='Viridis', | ||
| title='Optimized Portfolios') | ||
|
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||
| # Add traces for each key portfolio with proper colors and symbols | ||
| fig.add_trace( | ||
| px.scatter( | ||
| x=[max_return['Variance']], y=[max_return['Return']], | ||
| size=[10], symbol=[f'Max Return: {max_return.name}'], labels={'x': 'Variance', 'y': 'Return'}, | ||
| ).data[0] | ||
| ) | ||
| fig.data[-1].marker.color = 'red' | ||
| fig.data[-1].marker.size = 10 | ||
|
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||
| fig.add_trace( | ||
| px.scatter( | ||
| x=[min_volatility['Variance']], y=[min_volatility['Return']], | ||
| size=[10], symbol=[f'Min Volatility: {min_volatility.name}'], labels={'x': 'Variance', 'y': 'Return'}, | ||
| ).data[0] | ||
| ) | ||
| fig.data[-1].marker.color = 'green' | ||
| fig.data[-1].marker.size = 10 | ||
|
|
||
| fig.add_trace( | ||
| px.scatter( | ||
| x=[best_portfolio['Variance']], y=[best_portfolio['Return']], | ||
| size=[10], symbol=[f'Best Portfolio: {best_portfolio.name}'], labels={'x': 'Variance', 'y': 'Return'}, | ||
| ).data[0] | ||
| ) | ||
| fig.data[-1].marker.color = 'violet' | ||
| fig.data[-1].marker.size = 10 | ||
|
|
||
| # Update layout to improve legend placement and color scale | ||
| fig.update_layout( | ||
| xaxis_title='Variance', | ||
| yaxis_title='Return', | ||
| title='Optimized Portfolios', | ||
| coloraxis_colorbar=dict( | ||
| title='Sharpe Ratio', | ||
| tickvals=[min(df['Sharpe Ratio']), max(df['Sharpe Ratio'])], | ||
| ticktext=[f'{min(df["Sharpe Ratio"]):.2f}', f'{max(df["Sharpe Ratio"]):.2f}'], | ||
| ), | ||
| legend=dict( | ||
| orientation="h", | ||
| yanchor="bottom", | ||
| y=1.1, | ||
| xanchor="right", | ||
| x=0.5 | ||
| ) | ||
| ) | ||
|
|
||
| # Create additional HTML content for metrics | ||
| summary_html = f""" | ||
| <h2>Portfolio Analysis</h2> | ||
| <h3>Summary Metrics</h3> | ||
| <ul> | ||
| <li><strong>Expected Portfolio Return:</strong> {portfolio_dict['return'] * 100:.4f}%</li> | ||
| <li><strong>Portfolio Variance:</strong> {portfolio_dict['portfolio_variance'] * 100:.4f}%</li> | ||
| </ul> | ||
| <h3>Portfolio Composition</h3> | ||
| {portfolio_html} | ||
| <h3>Covariance Matrix</h3> | ||
| {cov} | ||
| <h3>Optimized Combination Space</h3> | ||
| """ | ||
|
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| display(HTML(summary_html)) | ||
| fig.show() | ||
|
|
||
| def _best_portfolio(self) -> HTML | dict: | ||
| return max( | ||
| self, | ||
| key=lambda x: x['return'] / x['portfolio_variance'] | ||
| ) | ||
|
|
||
| def _max_return(self, display: bool = False) -> dict: | ||
| if display: | ||
| return self.beautify_portfolio(self.max_return()) | ||
| return max( | ||
| self, | ||
| key=lambda x: x['return'] | ||
| ) | ||
|
|
||
| def _min_volatility(self) -> dict: | ||
| return min( | ||
| self, | ||
| key=lambda x: x['portfolio_variance'] | ||
| ) | ||
|
|
||
| def max_return(self, display: bool = False) -> dict | HTML: | ||
| """ | ||
| Get the maximum return from the result. | ||
| """ | ||
| if display: | ||
| return self._beautify_portfolio(self._max_return()) | ||
| return self._max_return() | ||
|
|
||
| def min_volatility(self, display: bool = False) -> dict | HTML: | ||
| """ | ||
| Get the minimum volatility from the result. | ||
| """ | ||
| if display: | ||
| return self._beautify_portfolio(self._min_volatility()) | ||
| return self._min_volatility() | ||
|
|
||
| def best_portfolio(self, display: bool = False) -> dict | HTML: | ||
| """ | ||
| Get the best portfolio from the result. | ||
| """ | ||
| if display: | ||
| return self._beautify_portfolio(self._best_portfolio()) | ||
| return self._best_portfolio() |