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.constraints(Optional[List[Tuple[float, float]]]): Constraints for optimization.
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': '1d', '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 │
│ -0.972214 ┆ -0.490732 ┆ -1.24518 ┆ -1.600599 ┆ 5.145166 │
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.895487 │
│ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.008915 │
│ … ┆ … ┆ … ┆ … ┆ … │
│ 0.051787 ┆ -0.966242 ┆ -0.157471 ┆ 0.280033 ┆ 2.169436 │
│ 0.22264 ┆ -0.113131 ┆ 0.323461 ┆ 0.21249 ┆ -1.876028 │
│ -0.542425 ┆ -0.247741 ┆ 0.202517 ┆ 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: '' [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, 0.0, 0.0, 0.644976443153294, 0.355023556846706], 'optimal_portfolio_returns': shape: (1_461,)
Series: '' [f64]
[
0.0
-1.058655
0.794306
0.317919
0.003165
…
0.950816
-0.528983
-0.440119
0.04849
0.091192
], 'Daily Return': 0.18411850183442402, 'Daily Volatility': 2.516041287507096, 'Cumulative Return': 824.2277142199297, 'Annualized Return': 58.971064110412286, 'Annualized Volatility': 39.94091721068724, 'Alpha': -0.027832253294844934, 'Beta': 0.33236318140694787, 'Sharpe Ratio': 1.4263834706121417, 'Sortino Ratio': 1.8999668142605184, 'Active Return': 46.17296286172934, 'Active Risk': 29.982240391057896, 'Information Ratio': 1.540010428156672, 'Calmar Ratio': 0.6448516277607749, 'Maximum Drawdown': 91.44904280568738, 'Value at Risk': -3.6839837246076517, 'Expected Shortfall': -5.673427689724985}
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”.
Example:
portfolio.report("performance")