portfolio_toolkit.asset package

Submodules

portfolio_toolkit.asset.create module

portfolio_toolkit.asset.create.create_market_asset(data_provider: DataProvider, ticker: str, currency: str | None = None) → PortfolioAsset[source]

Creates a PortfolioAsset object with market data using a data provider.

Parameters:

data_provider – The data provider instance to fetch ticker information and prices.
ticker (str) – The ticker for the asset.
currency (Optional[str]) – The currency for price data. If None, uses the asset’s default currency.

Returns:

The PortfolioAsset object with market data including:

ticker
sector
prices (historical price data)
info (ticker information from data provider)
currency
transactions (empty list)

Return type:

PortfolioAsset

portfolio_toolkit.asset.market_asset module

class portfolio_toolkit.asset.market_asset.MarketAsset(ticker: str, prices: pandas.core.series.Series, info: Dict, currency: str | None = None)[source]

Bases: object

ticker: str

prices: Series

info: Dict

currency: str | None = None

sector: str

country: str

classmethod to_dataframe(assets: List[MarketAsset]) → DataFrame[source]: Convert a list of MarketAsset objects to a pandas DataFrame.

__init__(ticker: str, prices: Series, info: Dict, currency: str | None = None) → None

portfolio_toolkit.asset.optimization_asset module

class portfolio_toolkit.asset.optimization_asset.OptimizationAsset(ticker: str, prices: pandas.core.series.Series, info: Dict, currency: str | None = None, quantity: float = 0.0)[source]

Bases: MarketAsset

quantity: float = 0.0

returns: Series

log_returns: Series

mean_return: float

volatility: float

classmethod to_dataframe(assets: List[OptimizationAsset]) → DataFrame[source]: Convert a list of OptimizationAsset objects to a pandas DataFrame.

__init__(ticker: str, prices: Series, info: Dict, currency: str | None = None, quantity: float = 0.0) → None

portfolio_toolkit.asset.portfolio_asset module

class portfolio_toolkit.asset.portfolio_asset.PortfolioAsset(ticker: str, prices: pandas.core.series.Series, info: Dict, currency: Optional[str] = None, transactions: List[portfolio_toolkit.asset.portfolio_asset_transaction.PortfolioAssetTransaction] = <factory>)[source]

Bases: MarketAsset

transactions: List[PortfolioAssetTransaction]

classmethod to_dataframe(assets: List[PortfolioAsset]) → DataFrame[source]: Convert a list of PortfolioAsset objects to a pandas DataFrame.

add_transaction(transaction: PortfolioAssetTransaction)[source]: Adds a transaction to the portfolio asset.

add_transaction_from_dict(transaction_dict: dict)[source]: Adds a transaction to the account from a dictionary.

add_split(split_dict: dict) → float[source]

Adds a stock split to the portfolio asset by simulating sell all + buy equivalent. Creates a sell transaction for all held shares and a buy transaction for split-adjusted quantity.

Parameters:

split_dict – Dictionary containing split information with keys: - date: Split date (str) - split_factor: Split ratio as float (e.g., 2.0 for 2:1 split, 0.1 for 1:10 reverse split)

Returns:

Cash amount to be added to account due to fractional shares sold: (only applies to reverse splits where shares are lost)

Return type:

float

__init__(ticker: str, prices: ~pandas.core.series.Series, info: ~typing.Dict, currency: str | None = None, transactions: ~typing.List[~portfolio_toolkit.asset.portfolio_asset_transaction.PortfolioAssetTransaction] = <factory>) → None

ticker: str

prices: pd.Series

info: Dict

sector: str

country: str

portfolio_toolkit.asset.portfolio_asset_transaction module

class portfolio_toolkit.asset.portfolio_asset_transaction.PortfolioAssetTransaction(date: str, transaction_type: str, quantity: float, price: float, currency: str, total: float, exchange_rate: float, subtotal_base: float, fees_base: float, total_base: float)[source]

Bases: object

date: str

transaction_type: str

quantity: float

price: float

currency: str

total: float

exchange_rate: float

subtotal_base: float

fees_base: float

total_base: float

classmethod to_dataframe(transactions: List[PortfolioAssetTransaction], ticker: str) → DataFrame[source]: Convert a list of PortfolioAssetTransaction objects to a pandas DataFrame.

__init__(date: str, transaction_type: str, quantity: float, price: float, currency: str, total: float, exchange_rate: float, subtotal_base: float, fees_base: float, total_base: float) → None

Module contents

class portfolio_toolkit.asset.MarketAsset(ticker: str, prices: pandas.core.series.Series, info: Dict, currency: str | None = None)[source]

Bases: object

__init__(ticker: str, prices: Series, info: Dict, currency: str | None = None) → None

currency: str | None = None

classmethod to_dataframe(assets: List[MarketAsset]) → DataFrame[source]: Convert a list of MarketAsset objects to a pandas DataFrame.

ticker: str

prices: Series

info: Dict

sector: str

country: str

class portfolio_toolkit.asset.PortfolioAssetTransaction(date: str, transaction_type: str, quantity: float, price: float, currency: str, total: float, exchange_rate: float, subtotal_base: float, fees_base: float, total_base: float)[source]

Bases: object

__init__(date: str, transaction_type: str, quantity: float, price: float, currency: str, total: float, exchange_rate: float, subtotal_base: float, fees_base: float, total_base: float) → None

classmethod to_dataframe(transactions: List[PortfolioAssetTransaction], ticker: str) → DataFrame[source]: Convert a list of PortfolioAssetTransaction objects to a pandas DataFrame.

date: str

transaction_type: str

quantity: float

price: float

currency: str

total: float

exchange_rate: float

subtotal_base: float

fees_base: float

total_base: float

class portfolio_toolkit.asset.PortfolioAsset(ticker: str, prices: pandas.core.series.Series, info: Dict, currency: Optional[str] = None, transactions: List[portfolio_toolkit.asset.portfolio_asset_transaction.PortfolioAssetTransaction] = <factory>)[source]

Bases: MarketAsset

__init__(ticker: str, prices: ~pandas.core.series.Series, info: ~typing.Dict, currency: str | None = None, transactions: ~typing.List[~portfolio_toolkit.asset.portfolio_asset_transaction.PortfolioAssetTransaction] = <factory>) → None

add_split(split_dict: dict) → float[source]

Adds a stock split to the portfolio asset by simulating sell all + buy equivalent. Creates a sell transaction for all held shares and a buy transaction for split-adjusted quantity.

Parameters:

split_dict – Dictionary containing split information with keys: - date: Split date (str) - split_factor: Split ratio as float (e.g., 2.0 for 2:1 split, 0.1 for 1:10 reverse split)

Returns:

Cash amount to be added to account due to fractional shares sold: (only applies to reverse splits where shares are lost)

Return type:

float

add_transaction(transaction: PortfolioAssetTransaction)[source]: Adds a transaction to the portfolio asset.

add_transaction_from_dict(transaction_dict: dict)[source]: Adds a transaction to the account from a dictionary.

classmethod to_dataframe(assets: List[PortfolioAsset]) → DataFrame[source]: Convert a list of PortfolioAsset objects to a pandas DataFrame.

transactions: List[PortfolioAssetTransaction]

ticker: str

prices: pd.Series

info: Dict

sector: str

country: str

class portfolio_toolkit.asset.OptimizationAsset(ticker: str, prices: pandas.core.series.Series, info: Dict, currency: str | None = None, quantity: float = 0.0)[source]

Bases: MarketAsset

__init__(ticker: str, prices: Series, info: Dict, currency: str | None = None, quantity: float = 0.0) → None

quantity: float = 0.0

classmethod to_dataframe(assets: List[OptimizationAsset]) → DataFrame[source]: Convert a list of OptimizationAsset objects to a pandas DataFrame.

returns: Series

log_returns: Series

mean_return: float

volatility: float

ticker: str

prices: pd.Series

info: Dict

sector: str

country: str

portfolio_toolkit.asset.create_market_asset(data_provider: DataProvider, ticker: str, currency: str | None = None) → PortfolioAsset[source]

Creates a PortfolioAsset object with market data using a data provider.

Parameters:

data_provider – The data provider instance to fetch ticker information and prices.
ticker (str) – The ticker for the asset.
currency (Optional[str]) – The currency for price data. If None, uses the asset’s default currency.

Returns:

The PortfolioAsset object with market data including:

ticker
sector
prices (historical price data)
info (ticker information from data provider)
currency
transactions (empty list)

Return type:

PortfolioAsset