OpenBB/website/content/sdk/reference/portfolio/po/dividend.md

title, description, keywords

title	description	keywords
dividend	The 'dividend' feature of the OpenBB library allows optimized weight allocation in portfolio based on dividend yield. The document offers detailed examples and parameters of the implementation.	dividend portfolio optimization po model portfolio engine weights allocation AAPL MSFT AMZN

import HeadTitle from '@site/src/components/General/HeadTitle.tsx';

Optimize weighted according to dividend yield

Source Code: [link]

openbb.portfolio.po.dividend(symbols: List[str] = None, portfolio_engine: portfolio_optimization.po_engine.PoEngine = None, kwargs: Any)

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Parameters

Name	Type	Description	Default	Optional
portfolio_engine	PoEngine	Portfolio optimization engine, by default None Use portfolio.po.load to load a portfolio engine	None	True
symbols	List[str]	List of symbols, by default None	None	True
interval	str	Interval to get data, by default '3y'	None	True
start_date	str	If not using interval, start date string (YYYY-MM-DD), by default ""	None	True
end_date	str	If not using interval, end date string (YYYY-MM-DD). If empty use last weekday, by default ""	None	True
log_returns	bool	If True use log returns, else arithmetic returns, by default False	None	True
freq	str	Frequency of returns, by default 'D'. Options: 'D' for daily, 'W' for weekly, 'M' for monthly	None	True
maxnan	float	Maximum percentage of NaNs allowed in the data, by default 0.05	None	True
threshold	float	Value used to replace outliers that are higher than threshold, by default 0.0	None	True
method	str	Method used to fill nan values, by default 'time' For more information see interpolate <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.interpolate.html>__.	None	True
value	float	Amount to allocate to portfolio in long positions, by default 1.0	None	True

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Returns

Type	Description
Tuple[pd.DataFrame, Dict]	Tuple with weights and performance dictionary

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Examples

from openbb_terminal.sdk import openbb
openbb.portfolio.po.dividend(symbols=["AAPL", "MSFT", "AMZN"])

(         value
 AAPL  0.350575
 MSFT  0.649425
 AMZN  0.000000,
 {'Return': 0.26879215033541076,
  'Volatility': 0.3348681656035649,
  'Sharpe ratio': 0.8026805111526232})

from openbb_terminal.sdk import openbb
p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
weights, performance = openbb.portfolio.po.dividend(portfolio_engine=p)

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