openseries.OpenFrame

class openseries.OpenFrame(constituents, weights=None)[source]

Bases: _CommonModel[Series]

OpenFrame objects hold OpenTimeSeries in the list constituents.

The intended use is to allow comparisons across these timeseries.

Parameters:

  • constituents (list[OpenTimeSeries]) – List of objects of Class OpenTimeSeries.

  • weights (list[float] | None) – List of weights in float format. Optional.

__init__(constituents, weights=None)[source]

OpenFrame objects hold OpenTimeSeries in the list constituents.

The intended use is to allow comparisons across these timeseries.

Parameters:

  • constituents (list[OpenTimeSeries]) – List of objects of Class OpenTimeSeries.

  • weights (list[float] | None) – List of weights in float format. Optional.

  • self (Self)

Return type:

None

Methods

__init__(constituents[, weights])

OpenFrame objects hold OpenTimeSeries in the list constituents.

add_timeseries(new_series)

To add an OpenTimeSeries object.

align_index_to_local_cdays([countries, ...])

Align the index of .tsdf with local calendar business days.

all_properties([properties])

Calculate chosen timeseries properties.

arithmetic_ret_func([months_from_last, ...])

Annualized arithmetic mean of returns.

beta(asset, market[, dlta_degr_freedms])

Market Beta.

calc_range([months_offset, from_dt, to_dt])

Create a user-defined date range aligned to index.

capture_ratio_func(ratio[, base_column, ...])

Capture Ratio.

construct([_fields_set])

copy(*[, include, exclude, update, deep])

Returns a copy of the model.

cvar_down_func([level, months_from_last, ...])

Downside Conditional Value At Risk (CVaR).

delete_timeseries(lvl_zero_item)

To delete an OpenTimeSeries object.

dict(*[, include, exclude, by_alias, ...])

ewma_risk([lmbda, day_chunk, ...])

Exponentially Weighted Moving Average Volatilities and Correlation.

from_deepcopy()

Create copy of the OpenFrame object.

from_orm(obj)

geo_ret_func([months_from_last, from_date, ...])

Compounded Annual Growth Rate (CAGR).

info_ratio_func([base_column, ...])

Information Ratio.

jensen_alpha(asset, market[, riskfree_rate, ...])

Jensen's alpha.

json(*[, include, exclude, by_alias, ...])

kurtosis_func([months_from_last, from_date, ...])

Kurtosis of the return distribution.

lower_partial_moment_func([...])

Lower partial moment and downside deviation (order=2).

make_portfolio(name[, weight_strat])

Calculate a basket timeseries based on the supplied weights.

max_drawdown_func([months_from_last, ...])

Maximum drawdown without any limit on date range.

merge_series([how])

Merge index of Pandas Dataframes of the constituent OpenTimeSeries.

model_construct([_fields_set])

Creates a new instance of the Model class with validated data.

model_copy(*[, update, deep])

!!! abstract "Usage Documentation"

model_dump(*[, mode, include, exclude, ...])

!!! abstract "Usage Documentation"

model_dump_json(*[, indent, ensure_ascii, ...])

!!! abstract "Usage Documentation"

model_json_schema([by_alias, ref_template, ...])

Generates a JSON schema for a model class.

model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct.

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, extra, ...])

Validate a pydantic model instance.

model_validate_json(json_data, *[, strict, ...])

!!! abstract "Usage Documentation"

model_validate_strings(obj, *[, strict, ...])

Validate the given object with string data against the Pydantic model.

multi_factor_linear_regression(dependent_column)

Perform a multi-factor linear regression.

omega_ratio_func([min_accepted_return, ...])

Omega Ratio.

ord_least_squares_fit(y_column, x_column, *)

Ordinary Least Squares fit.

outliers([threshold, months_from_last, ...])

Detect outliers using z-score analysis.

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

plot_bars([mode, title, tick_fmt, filename, ...])

Create a Plotly Bar Figure.

plot_histogram([plot_type, histnorm, ...])

Create a Plotly Histogram Figure.

plot_series([mode, title, tick_fmt, ...])

Create a Plotly Scatter Figure.

positive_share_func([months_from_last, ...])

Share of percentage changes greater than zero.

rebalanced_portfolio(name[, items, ...])

Create a rebalanced portfolio from the OpenFrame constituents.

relative([long_column, short_column, base_zero])

Calculate cumulative relative return between two series.

resample([freq])

Resample the timeseries frequency.

resample_to_business_period_ends([freq, method])

Resamples timeseries frequency to the business calendar month end dates.

ret_vol_ratio_func([riskfree_rate, ...])

Ratio between arithmetic mean of returns and annualized volatility.

return_nan_handle([method])

Handle missing values in a return series.

rolling_beta([asset_column, market_column, ...])

Calculate rolling Market Beta.

rolling_corr([first_column, second_column, ...])

Calculate rolling Correlation.

rolling_cvar_down([column, level, observations])

Calculate rolling annualized downside CVaR.

rolling_info_ratio([long_column, ...])

Calculate rolling Information Ratio.

rolling_return([column, observations])

Calculate rolling returns.

rolling_var_down([column, level, ...])

Calculate rolling annualized downside Value At Risk (VaR).

rolling_vol([column, observations, ...])

Calculate rolling annualized volatilities.

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

skew_func([months_from_last, from_date, to_date])

Skew of the return distribution.

sortino_ratio_func([riskfree_rate, ...])

Sortino ratio or Kappa-3 ratio.

target_weight_from_var([target_vol, level, ...])

Target weight from VaR.

to_cumret()

Convert series of returns into cumulative series of values.

to_drawdown_series()

Convert timeseries into a drawdown series.

to_json(what_output, filename[, directory])

Dump timeseries data into a JSON file.

to_xlsx(filename[, sheet_title, directory, ...])

Save .tsdf DataFrame to an Excel spreadsheet file.

tracking_error_func([base_column, ...])

Tracking Error.

trunc_frame([start_cut, end_cut, where])

Truncate DataFrame such that all timeseries have the same time span.

update_forward_refs(**localns)

validate(value)

value_nan_handle([method])

Handle missing values in a value series.

value_ret_calendar_period(year[, month])

Calculate simple return for a specific calendar period.

value_ret_func([months_from_last, ...])

Calculate simple return.

value_to_diff([periods])

Convert series of values to series of their period differences.

value_to_log()

Convert value series to log-weighted series.

value_to_ret()

Convert series of values into series of returns.

var_down_func([level, months_from_last, ...])

Downside Value At Risk (VaR).

vol_from_var_func([level, months_from_last, ...])

Implied annualized volatility from downside VaR.

vol_func([months_from_last, from_date, ...])

Annualized volatility.

worst_func([observations, months_from_last, ...])

Most negative percentage change over a rolling window.

z_score_func([months_from_last, from_date, ...])

Z-score of the last return.

Attributes

arithmetic_ret

Annualized arithmetic mean of returns.

columns_lvl_one

Level 1 values of the MultiIndex columns in the .tsdf DataFrame.

columns_lvl_zero

Level 0 values of the MultiIndex columns in the .tsdf DataFrame.

correl_matrix

Correlation matrix.

cvar_down

Downside 95% Conditional Value At Risk "CVaR".

downside_deviation

Downside Deviation.

first_idx

The first date in the timeseries.

first_indices

The first dates in the timeseries of all constituents.

geo_ret

Compounded Annual Growth Rate (CAGR).

item_count

Number of constituents.

kappa3_ratio

Kappa-3 ratio.

kurtosis

Kurtosis of the return distribution.

last_idx

The last date in the timeseries.

last_indices

The last dates in the timeseries of all constituents.

length

Number of observations.

lengths_of_items

Number of observations of all constituents.

max_drawdown

Maximum drawdown without any limit on date range.

max_drawdown_cal_year

Maximum drawdown in a single calendar year.

max_drawdown_date

Date when the maximum drawdown occurred.

model_computed_fields

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra

Get extra fields set during validation.

model_fields

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

omega_ratio

Omega ratio.

periods_in_a_year

The average number of observations per year.

positive_share

The share of percentage changes that are greater than zero.

ret_vol_ratio

Ratio of annualized arithmetic mean of returns and annualized volatility.

skew

Skew of the return distribution.

sortino_ratio

Sortino ratio.

span_of_days

Number of days from the first date to the last.

span_of_days_all

Number of days from the first date to the last for all items in the frame.

value_ret

Simple return.

var_down

Downside 95% Value At Risk (VaR).

vol

Annualized volatility.

vol_from_var

Implied annualized volatility from Downside 95% Value at Risk.

worst

Most negative percentage change.

worst_month

Most negative month.

yearfrac

Length of series in years assuming 365.25 days per year.

z_score

Z-score.

constituents

tsdf

weights

constituents: list[OpenTimeSeries]
tsdf: DataFrame
weights: list[float] | None
__init__(constituents, weights=None)[source]

OpenFrame objects hold OpenTimeSeries in the list constituents.

The intended use is to allow comparisons across these timeseries.

Parameters:

  • constituents (list[OpenTimeSeries]) – List of objects of Class OpenTimeSeries.

  • weights (list[float] | None) – List of weights in float format. Optional.

  • self (Self)

Return type:

None

from_deepcopy()[source]

Create copy of the OpenFrame object.

Returns:

An OpenFrame object.

Parameters:

self (Self)

Return type:

Self

merge_series(how='outer')[source]

Merge index of Pandas Dataframes of the constituent OpenTimeSeries.

Parameters:

  • how (Literal['outer', 'inner']) – The Pandas merge method. Defaults to “outer”.

  • self (Self)

Returns:

An OpenFrame object.

Return type:

Self

all_properties(properties=None)[source]

Calculate chosen timeseries properties.

Parameters:

  • properties (list[Literal['value_ret', 'geo_ret', 'arithmetic_ret', 'vol', 'downside_deviation', 'ret_vol_ratio', 'sortino_ratio', 'kappa3_ratio', 'z_score', 'skew', 'kurtosis', 'positive_share', 'var_down', 'cvar_down', 'vol_from_var', 'worst', 'worst_month', 'max_drawdown', 'max_drawdown_date', 'max_drawdown_cal_year', 'first_indices', 'last_indices', 'lengths_of_items', 'span_of_days_all']] | None) – The properties to calculate. Defaults to calculating all available. Optional.

  • self (Self)

Returns:

Properties of the constituent OpenTimeSeries.

Return type:

DataFrame

property lengths_of_items: Series[int]

Number of observations of all constituents.

Returns:

Number of observations of all constituents.

property item_count: int

Number of constituents.

Returns:

Number of constituents.

property columns_lvl_zero: list[str]

Level 0 values of the MultiIndex columns in the .tsdf DataFrame.

Returns:

Level 0 values of the MultiIndex columns in the .tsdf DataFrame.

property columns_lvl_one: list[ValueType]

Level 1 values of the MultiIndex columns in the .tsdf DataFrame.

Returns:

Level 1 values of the MultiIndex columns in the .tsdf DataFrame.

property first_indices: Series[dt.date]

The first dates in the timeseries of all constituents.

Returns:

The first dates in the timeseries of all constituents.

property last_indices: Series[dt.date]

The last dates in the timeseries of all constituents.

Returns:

The last dates in the timeseries of all constituents.

property span_of_days_all: Series[int]

Number of days from the first date to the last for all items in the frame.

Returns:

Number of days from the first date to the last for all items in the frame.

value_to_ret()[source]

Convert series of values into series of returns.

Returns:

The returns of the values in the series.

Parameters:

self (Self)

Return type:

Self

value_to_diff(periods=1)[source]

Convert series of values to series of their period differences.

Parameters:

  • periods (int) – The number of periods between observations over which difference is calculated. Defaults to 1.

  • self (Self)

Returns:

An OpenFrame object.

Return type:

Self

to_cumret()[source]

Convert series of returns into cumulative series of values.

Returns:

An OpenFrame object.

Parameters:

self (Self)

Return type:

Self

resample(freq='BME')[source]

Resample the timeseries frequency.

Parameters:

  • freq (Literal['B', 'BME', 'BQE', 'BYE'] | str) – The date offset string that sets the resampled frequency. Defaults to “BME”.

  • self (Self)

Returns:

An OpenFrame object.

Return type:

Self

resample_to_business_period_ends(freq='BME', method='nearest')[source]

Resamples timeseries frequency to the business calendar month end dates.

Stubs left in place. Stubs will be aligned to the shortest stub.

Parameters:

  • freq (Literal['B', 'BME', 'BQE', 'BYE']) – The date offset string that sets the resampled frequency. Defaults to “BME”.

  • method (Literal['pad', 'ffill', 'backfill', 'bfill', 'nearest'] | None) – Controls the method used to align values across columns. Defaults to nearest.

  • self (Self)

Returns:

An OpenFrame object.

Return type:

Self

ewma_risk(lmbda=0.94, day_chunk=11, dlta_degr_freedms=0, first_column=0, second_column=1, corr_scale=2.0, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source]

Exponentially Weighted Moving Average Volatilities and Correlation.

Exponentially Weighted Moving Average (EWMA) for Volatilities and Correlation.

Reference: https://www.investopedia.com/articles/07/ewma.asp.

Parameters:

  • lmbda (float) – Scaling factor to determine weighting. Defaults to 0.94.

  • day_chunk (int) – Sampling the data which is assumed to be daily. Defaults to 11.

  • dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. Defaults to 0.

  • first_column (int) – Column of first timeseries. Defaults to 0.

  • second_column (int) – Column of second timeseries. Defaults to 1.

  • corr_scale (float) – Correlation scale factor. Defaults to 2.0.

  • months_from_last (int | None) – Number of months offset as positive integer. Overrides use of from_date and to_date. Optional.

  • from_date (dt.date | None) – Specific from date. Optional.

  • to_date (dt.date | None) – Specific to date. Optional.

  • periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify test cases and comparisons. Optional.

  • self (Self)

Returns:

Series volatilities and correlation.

Return type:

DataFrame

property correl_matrix: DataFrame

Correlation matrix.

This property returns the correlation matrix of the time series in the frame.

Returns:

Correlation matrix of the time series in the frame.

add_timeseries(new_series)[source]

To add an OpenTimeSeries object.

Parameters:

  • new_series (OpenTimeSeries) – The timeseries to add.

  • self (Self)

Returns:

An OpenFrame object.

Return type:

Self

delete_timeseries(lvl_zero_item)[source]

To delete an OpenTimeSeries object.

Parameters:

  • lvl_zero_item (str) – The .tsdf column level 0 value of the timeseries to delete.

  • self (Self)

Returns:

An OpenFrame object.

Return type:

Self

trunc_frame(start_cut=None, end_cut=None, where='both')[source]

Truncate DataFrame such that all timeseries have the same time span.

Parameters:

  • start_cut (dt.date | None) – New first date. Optional.

  • end_cut (dt.date | None) – New last date. Optional.

  • where (LiteralTrunc) – Determines where dataframe is truncated also when start_cut or end_cut is None. Defaults to both.

  • self (Self)

Returns:

An OpenFrame object.

Return type:

Self

relative(long_column=0, short_column=1, *, base_zero=True)[source]

Calculate cumulative relative return between two series.

Parameters:

  • long_column (int) – Column number of timeseries bought. Defaults to 0.

  • short_column (int) – Column number of timeseries sold. Defaults to 1.

  • base_zero (bool) – If set to False 1.0 is added to allow for a capital base and to allow a volatility calculation. Defaults to True.

  • self (Self)

Return type:

None

tracking_error_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source]

Tracking Error.

Calculates Tracking Error which is the standard deviation of the difference between the fund and its index returns.

Reference: https://www.investopedia.com/terms/t/trackingerror.asp.

Parameters:

  • base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. Defaults to -1.

  • months_from_last (int | None) – Number of months offset as positive integer. Overrides use of from_date and to_date. Optional.

  • from_date (dt.date | None) – Specific from date. Optional.

  • to_date (dt.date | None) – Specific to date. Optional.

  • periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify test cases and comparisons. Optional.

  • self (Self)

Returns:

Tracking Errors.

Return type:

Series[float]

info_ratio_func(base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source]

Information Ratio.

The Information Ratio equals ( fund return less index return ) divided by the Tracking Error. And the Tracking Error is the standard deviation of the difference between the fund and its index returns. The ratio is calculated using the annualized arithmetic mean of returns.

Parameters:

  • base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. Defaults to -1.

  • months_from_last (int | None) – Number of months offset as positive integer. Overrides use of from_date and to_date. Optional.

  • from_date (dt.date | None) – Specific from date. Optional.

  • to_date (dt.date | None) – Specific to date. Optional.

  • periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify test cases and comparisons. Optional.

  • self (Self)

Returns:

Information Ratios.

Return type:

Series[float]

capture_ratio_func(ratio, base_column=-1, months_from_last=None, from_date=None, to_date=None, periods_in_a_year_fixed=None)[source]

Capture Ratio.

The Up (Down) Capture Ratio is calculated by dividing the CAGR of the asset during periods that the benchmark returns are positive (negative) by the CAGR of the benchmark during the same periods. CaptureRatio.BOTH is the Up ratio divided by the Down ratio. Source: ‘Capture Ratios: A Popular Method of Measuring Portfolio Performance in Practice’, Don R. Cox and Delbert C. Goff, Journal of Economics and Finance Education (Vol 2 Winter 2013).

Reference: https://www.economics-finance.org/jefe/volume12-2/11ArticleCox.pdf.

Parameters:

  • ratio (LiteralCaptureRatio) – The ratio to calculate.

  • base_column (tuple[str, ValueType] | int) – Column of timeseries that is the denominator in the ratio. Defaults to -1.

  • months_from_last (int | None) – Number of months offset as positive integer. Overrides use of from_date and to_date. Optional.

  • from_date (dt.date | None) – Specific from date. Optional.

  • to_date (dt.date | None) – Specific to date. Optional.

  • periods_in_a_year_fixed (DaysInYearType | None) – Allows locking the periods-in-a-year to simplify test cases and comparisons. Optional.

  • self (Self)

Returns:

Capture Ratios.

Return type:

Series[float]

beta(asset, market, dlta_degr_freedms=1)[source]

Market Beta.

Calculates Beta as Co-variance of asset & market divided by Variance of the market.

Reference: https://www.investopedia.com/terms/b/beta.asp.

Parameters:

  • asset (tuple[str, ValueType] | int) – The column of the asset.

  • market (tuple[str, ValueType] | int) – The column of the market against which Beta is measured.

  • dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. Defaults to 1.

  • self (Self)

Returns:

Beta as Co-variance of x & y divided by Variance of x.

Return type:

float

ord_least_squares_fit(y_column, x_column, *, fitted_series=True)[source]

Ordinary Least Squares fit.

Performs a linear regression and adds a new column with a fitted line using Ordinary Least Squares fit.

Parameters:

  • y_column (tuple[str, ValueType] | int) – The column level values of the dependent variable y.

  • x_column (tuple[str, ValueType] | int) – The column level values of the exogenous variable x.

  • fitted_series (bool) – If True the fit is added as a new column in the .tsdf Pandas.DataFrame. Defaults to True.

  • self (Self)

Returns:

A dictionary with the coefficient, intercept and rsquared outputs.

Return type:

dict[str, float]

jensen_alpha(asset, market, riskfree_rate=0.0, dlta_degr_freedms=1)[source]

Jensen’s alpha.

The Jensen’s measure, or Jensen’s alpha, is a risk-adjusted performance measure that represents the average return on a portfolio or investment, above or below that predicted by the capital asset pricing model (CAPM), given the portfolio’s or investment’s beta and the average market return. This metric is also commonly referred to as simply alpha.

Reference: https://www.investopedia.com/terms/j/jensensmeasure.asp.

Parameters:

  • asset (tuple[str, ValueType] | int) – The column of the asset.

  • market (tuple[str, ValueType] | int) – The column of the market against which Jensen’s alpha is measured.

  • riskfree_rate (float) – The return of the zero volatility riskfree asset. Defaults to 0.0.

  • dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. Defaults to 1.

  • self (Self)

Returns:

Jensen’s alpha.

Return type:

float

make_portfolio(name, weight_strat=None)[source]

Calculate a basket timeseries based on the supplied weights.

Parameters:

  • name (str) – Name of the basket timeseries.

  • weight_strat (Literal['eq_weights', 'inv_vol', 'max_div', 'min_vol_overweight'] | None) – Weight calculation strategies. Optional.

  • self (Self)

Returns:

A basket timeseries.

Return type:

DataFrame

rolling_info_ratio(long_column=0, short_column=1, observations=21, periods_in_a_year_fixed=None)[source]

Calculate rolling Information Ratio.

The Information Ratio equals ( fund return less index return ) divided by the Tracking Error. And the Tracking Error is the standard deviation of the difference between the fund and its index returns.

Parameters:

  • long_column (int) – Column of timeseries that is the numerator in the ratio. Defaults to 0.

  • short_column (int) – Column of timeseries that is the denominator in the ratio. Defaults to 1.

  • observations (int) – The length of the rolling window to use is set as number of observations. Defaults to 21.

  • periods_in_a_year_fixed (Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])] | None) – Allows locking the periods-in-a-year to simplify test cases and comparisons. Optional.

  • self (Self)

Returns:

Rolling Information Ratios.

Return type:

DataFrame

rolling_beta(asset_column=0, market_column=1, observations=21, dlta_degr_freedms=1)[source]

Calculate rolling Market Beta.

Calculates Beta as Co-variance of asset & market divided by Variance of the market.

Reference: https://www.investopedia.com/terms/b/beta.asp.

Parameters:

  • asset_column (int) – Column of timeseries that is the asset. Defaults to 0.

  • market_column (int) – Column of timeseries that is the market. Defaults to 1.

  • observations (int) – The length of the rolling window to use is set as number of observations. Defaults to 21.

  • dlta_degr_freedms (int) – Variance bias factor taking the value 0 or 1. Defaults to 1.

  • self (Self)

Returns:

Rolling Betas.

Return type:

DataFrame

rolling_corr(first_column=0, second_column=1, observations=21)[source]

Calculate rolling Correlation.

Calculates correlation between two series. The period with at least the given number of observations is the first period calculated.

Parameters:

  • first_column (int) – The position as integer of the first timeseries to compare. Defaults to 0.

  • second_column (int) – The position as integer of the second timeseries to compare. Defaults to 1.

  • observations (int) – The length of the rolling window to use is set as number of observations. Defaults to 21.

  • self (Self)

Returns:

Rolling Correlations.

Return type:

DataFrame

multi_factor_linear_regression(dependent_column)[source]

Perform a multi-factor linear regression.

This function treats one specified column in the DataFrame as the dependent variable (y) and uses all remaining columns as independent variables (X). It utilizes a scikit-learn LinearRegression model and returns a DataFrame with summary output and an OpenTimeSeries of predicted values.

Parameters:

  • dependent_column (tuple[str, ValueType]) – A tuple key to select the column in the OpenFrame.tsdf.columns to use as the dependent variable.

  • self (Self)

Returns:

  • A DataFrame with the R-squared, the intercept and the regression coefficients

  • An OpenTimeSeries of predicted values

Return type:

A tuple containing

Raises:

  • KeyError – If the column tuple is not found in the OpenFrame.tsdf.columns.

  • ValueError – If not all series are returnseries (ValueType.RTRN).

model_config = {'arbitrary_types_allowed': True, 'revalidate_instances': 'always', 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rebalanced_portfolio(name, items=None, bal_weights=None, frequency=1, cash_index=None, *, equal_weights=False, drop_extras=True)[source]

Create a rebalanced portfolio from the OpenFrame constituents.

Parameters:

  • name (str) – Name of the portfolio.

  • items (list[str] | None) – List of items to include in the portfolio. If None, uses all items. Optional.

  • bal_weights (list[float] | None) – List of weights for rebalancing. If None, uses frame weights. Optional.

  • frequency (int) – Rebalancing frequency. Defaults to 1.

  • cash_index (OpenTimeSeries | None) – Cash index series for cash component. Optional.

  • equal_weights (bool) – If True, use equal weights for all items. Defaults to False.

  • drop_extras (bool) – If True, only return TWR series; if False, return all details. Defaults to True.

  • self (Self)

Returns:

OpenFrame containing the rebalanced portfolio.

Return type:

OpenFrame