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 │
│ -0.972198 ┆ -0.490727 ┆ -1.245177 ┆ -1.60056 ┆ 5.145166 │
│ -0.972198 ┆ -0.490727 ┆ -1.245177 ┆ -1.60056 ┆ 0.895487 │
│ … ┆ … ┆ … ┆ … ┆ … │
│ 0.222629 ┆ -0.113124 ┆ 0.323462 ┆ 0.212481 ┆ -1.876028 │
│ -0.542411 ┆ -0.24774 ┆ 0.202516 ┆ 0.0 ┆ -1.23969 │
│ -0.542411 ┆ -0.24774 ┆ 0.202516 ┆ 0.0 ┆ 0.136582 │
│ -0.542411 ┆ -0.24774 ┆ 0.202516 ┆ 0.0 ┆ 0.256862 │
└───────────┴───────────┴───────────┴──────────┴───────────┘, 'benchmark_returns': shape: (1_461,)
Series: '' [f64]
[
0.0
0.0
-0.705987
-0.705987
-0.705987
0.353337
-0.280324
0.490245
0.665526
-0.285518
-0.285518
-0.285518
…
0.586641
-1.468427
1.030147
0.166006
0.166006
0.166006
0.166006
0.423169
0.143046
0.037017
-0.282648
-0.282648
-0.282648
], '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': [0.0011137223445658465, 0.0, 0.16918753774951317, 0.4484824059002946, 0.38121633400562643], 'optimal_portfolio_returns': shape: (1_461,)
Series: '' [f64]
[
0.0
-1.13676
1.031847
-0.5882
-0.926176
2.073632
2.323751
-0.036059
-0.241061
1.552802
-0.44039
0.896813
…
-0.711046
-0.224612
1.142026
0.011898
-0.323837
-0.730492
0.429109
-0.539468
0.92603
-0.564905
-0.438931
0.085726
0.131579
], 'Daily Return': 0.17728848355982682, 'Daily Volatility': 2.485146427319241, 'Cumulative Return': 745.3095266981472, 'Annualized Return': 56.263174024248656, 'Annualized Volatility': 39.450476509604194, 'Alpha': -0.009825574574644953, 'Beta': 0.40254485469585044, 'Sharpe Ratio': 1.3754757565738989, 'Sortino Ratio': 1.9491594586635441, 'Active Return': 33.845371001864756, 'Active Risk': 28.4372172655156, 'Information Ratio': 1.1901787255009426, 'Calmar Ratio': 2.2058559175318075, 'Maximum Drawdown': 25.506277892892957, 'Value at Risk': -3.5070950883940397, 'Expected Shortfall': -5.434861411110765}
performance_stats_table
Display the performance stats table plot 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 stats table.
Example:
performance_stats = portfolio.performance_stats_table()
performance_stats.show()
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()