portfolio_toolkit.math.get_matrix_returns module

portfolio_toolkit.math.get_matrix_returns.get_matrix_returns(market_assets: List[MarketAsset]) → DataFrame[source]

Creates a DataFrame of logarithmic returns from a list of MarketAsset objects.

Parameters:

market_assets (List[MarketAsset]) – List of MarketAsset objects, each containing a ‘price’ attribute as pd.Series with price data

Returns:

DataFrame where:

  • Rows are dates (datetime index from price series)

  • Columns are ticker symbols from MarketAsset.ticker

  • Values are logarithmic returns calculated using get_log_returns()

Return type:

pd.DataFrame

Example

>>> market_assets = [
...     MarketAsset(ticker="AAPL", price=aapl_prices),
...     MarketAsset(ticker="MSFT", price=msft_prices),
...     MarketAsset(ticker="GOOGL", price=googl_prices)
... ]
>>> returns_df = get_matrix_returns(market_assets)
>>> print(returns_df.head())

AAPL MSFT GOOGL

2023-01-01 NaN NaN NaN 2023-01-02 0.0123 0.0089 0.0156 2023-01-03 -0.0067 0.0034 -0.0023

Notes

  • First row will contain NaN values (no previous price for return calculation)

  • Uses get_log_returns() function for consistent logarithmic return calculation

  • Automatically aligns dates across all assets

  • Missing data points will appear as NaN in the resulting DataFrame

portfolio_toolkit.math.get_matrix_returns.get_matrix_returns_aligned(market_assets: List[MarketAsset], start_date: str = None, end_date: str = None) → DataFrame[source]

Creates an aligned DataFrame of returns with optional date filtering.

Parameters:

  • market_assets – List of MarketAsset objects

  • start_date – Start date in ‘YYYY-MM-DD’ format (optional)

  • end_date – End date in ‘YYYY-MM-DD’ format (optional)

Returns:

Aligned returns DataFrame with only overlapping dates

Return type:

pd.DataFrame

Example

>>> # Get returns for specific period
>>> returns_df = get_matrix_returns_aligned(
...     market_assets,
...     start_date="2023-01-01",
...     end_date="2023-12-31"
... )