from finalytics import Portfolio
portfolio = Portfolio(ticker_symbols=["AAPL", "GOOG", "MSFT", "NVDA", "BTC-USD"],
benchmark_symbol="^GSPC",
start_date="2020-01-01",
end_date="2024-01-01",
interval="1d",
confidence_level=0.95,
risk_free_rate=0.02,
objective_function="max_sharpe",
constraints=[(0, 1), (0, 1), (0, 1), (0, 1), (0,1)])Portfolio Module Documentation
Portfolio
A class representing a Portfolio object.
__new__
Create a new Portfolio object.
Parameters:
ticker_symbols(List[str]): List of ticker symbols in the portfolio.benchmark_symbol(str): The ticker symbol of the benchmark.start_date(str): The start date for historical data.end_date(str): The end date for historical data.interval(str): The interval for historical data.confidence_level(float): The confidence level for risk calculations.risk_free_rate(float): The risk-free rate for calculations.objective_function(Optional[str]): The objective function for optimization:max_sharpe: Maximize return per unit of risk.min_vol: Minimize overall volatility.max_return: Maximize expected return.min_var: Minimize Value-at-Risk (VaR).min_cvar: Minimize Conditional Value-at-Risk (CVaR).min_drawdown: Minimize maximum portfolio drawdown.
constraints(Optional[List[Tuple[float, float]]]): Constraints for optimization.weights(Optional[List[float]]): Weights for the assets in the portfolio. If provided, it will override the optimization process.
Returns:
Portfolio: A Portfolio object.
Example:
optimization_results
Get the optimization results for the portfolio.
Returns:
dict: Dictionary containing the portfolio optimization results.
Example:
optimization_results = portfolio.optimization_results()
print(optimization_results){'ticker_symbols': ['AAPL', 'GOOG', 'MSFT', 'NVDA', 'BTC-USD'], 'benchmark_symbol': '^GSPC', 'start_date': '2020-01-01', 'end_date': '2024-01-01', 'interval': 1.0, 'confidence_level': 0.95, 'risk_free_rate': 0.02, 'portfolio_returns': shape: (1_461, 5)
┌───────────┬───────────┬───────────┬───────────┬───────────┐
│ AAPL ┆ GOOG ┆ MSFT ┆ NVDA ┆ BTC-USD │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ -2.981929 │
│ -0.972236 ┆ -0.490736 ┆ -1.245142 ┆ -1.600602 ┆ 5.145166 │
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.895487 │
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.008915 │
│ … ┆ … ┆ … ┆ … ┆ … │
│ 0.051787 ┆ -0.966238 ┆ -0.157492 ┆ 0.28004 ┆ 2.169436 │
│ 0.222642 ┆ -0.113135 ┆ 0.323492 ┆ 0.212486 ┆ -1.876028 │
│ -0.542419 ┆ -0.247734 ┆ 0.202506 ┆ 0.0 ┆ -1.23969 │
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.136582 │
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.256862 │
└───────────┴───────────┴───────────┴───────────┴───────────┘, 'benchmark_returns': shape: (1_461,)
Series: 'roc-1' [f64]
[
0.0
0.0
-0.705987
0.0
0.0
…
0.143046
0.037017
-0.282648
0.0
0.0
], 'objective_function': 'Maximize Sharpe Ratio', 'optimization_method': 'Simple Gradient Descent', 'constraints': [(0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0)], 'optimal_weights': [0.0, 9.629649721936179e-33, 5.483024002279236e-18, 0.654564010144285, 0.345435989855715], 'optimal_portfolio_returns': shape: (1_461,)
Series: 'Portfolio Returns' [f64]
[
0.0
-1.030066
0.729629
0.309333
0.003079
…
0.932705
-0.508962
-0.428233
0.04718
0.088729
], 'Daily Return': 0.1841332762535209, 'Daily Volatility': 2.5161473903720664, 'Cumulative Return': 824.5696946448293, 'Annualized Return': 95.71091273736363, 'Annualized Volatility': 48.070928396254196, 'Alpha': -0.028097205442391623, 'Beta': 0.33377542844834635, 'Sharpe Ratio': 1.9494300581194062, 'Sortino Ratio': 2.598689485382585, 'Active Return': 73.30812095365008, 'Active Risk': 35.99398490693686, 'Information Ratio': 2.036676993202882, 'Calmar Ratio': 1.0466953990041188, 'Maximum Drawdown': 91.44103702799117, 'Value at Risk': -3.6996011430145908, 'Expected Shortfall': -5.676228942012344}
optimization_chart
Display the efficient frontier and allocation chart for the portfolio.
Parameters:
height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the portfolio optimization chart.
Example:
optimization_chart = portfolio.optimization_chart()
optimization_chart.show()
performance_chart
Display the performance chart for the portfolio.
Parameters:
height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the performance chart.
Example:
performance_chart = portfolio.performance_chart()
performance_chart.show()
asset_returns_chart
Display the individual asset returns chart for the portfolio.
Parameters:
height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the asset returns chart.
Example:
asset_returns_chart = portfolio.asset_returns_chart()
asset_returns_chart.show()
returns_matrix
Display the returns correlation matrix for the portfolio.
Parameters:
height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the returns correlation matrix.
Example:
returns_matrix = portfolio.returns_matrix()
returns_matrix.show()
report
Generate a report for the portfolio.
Parameters:
report_type(Optional[str]): The type of report to generate. Options are “performance”.- display (Optional[str]): The display mode for the report. set to “notebook” for Jupyter Notebook display, defaults to “browser”.
Example:
portfolio.report("performance")