Types and Enums

Declaring types used throughout the project.

class openseries.owntypes.ValueType(*values)[source]

Bases: str, Enum

Enum types of OpenTimeSeries to identify the output.

EWMA = 'EWMA'

PRICE = 'Price(Close)'

RTRN = 'Return(Total)'

RELRTRN = 'Relative return'

ROLLBETA = 'Beta'

ROLLCORR = 'Rolling correlation'

ROLLCVAR = 'Rolling CVaR'

ROLLINFORATIO = 'Information Ratio'

ROLLRTRN = 'Rolling returns'

ROLLVAR = 'Rolling VaR'

ROLLVOL = 'Rolling volatility'

Value Types

class openseries.owntypes.ValueType(*values)[source]

Bases: str, Enum

Enum types of OpenTimeSeries to identify the output.

EWMA = 'EWMA'

PRICE = 'Price(Close)'

RTRN = 'Return(Total)'

RELRTRN = 'Relative return'

ROLLBETA = 'Beta'

ROLLCORR = 'Rolling correlation'

ROLLCVAR = 'Rolling CVaR'

ROLLINFORATIO = 'Information Ratio'

ROLLRTRN = 'Rolling returns'

ROLLVAR = 'Rolling VaR'

ROLLVOL = 'Rolling volatility'

The ValueType enum identifies the type of values in a time series (prices, returns, etc.).

Type Aliases

openseries.owntypes.SeriesOrFloat_co = +SeriesOrFloat_co

Type variable.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, classes, and type aliases:

class Sequence[T]:  # T is a TypeVar
    ...

This syntax can also be used to create bound and constrained type variables:

# S is a TypeVar bound to str
class StrSequence[S: str]:
    ...

# A is a TypeVar constrained to str or bytes
class StrOrBytesSequence[A: (str, bytes)]:
    ...

Type variables can also have defaults:

class IntDefault[T = int]:
…

However, if desired, reusable type variables can also be constructed manually, like so:

T = TypeVar('T')  # Can be anything
S = TypeVar('S', bound=str)  # Can be any subtype of str
A = TypeVar('A', str, bytes)  # Must be exactly str or bytes
D = TypeVar('D', default=int)  # Defaults to int

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax and when infer_variance=True is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing covariant=True or contravariant=True. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.

openseries.owntypes.CountryStringType

Runtime representation of an annotated type.

At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ with extra annotations. The alias behaves like a normal typing alias. Instantiating is the same as instantiating the underlying type; binding it to types is also the same.

The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.

alias of Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$)]

openseries.owntypes.CountrySetType

Runtime representation of an annotated type.

At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ with extra annotations. The alias behaves like a normal typing alias. Instantiating is the same as instantiating the underlying type; binding it to types is also the same.

The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.

alias of Annotated[set[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$)]], MinLen(min_length=1)]

openseries.owntypes.CountriesType: alias of Annotated[set[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$)]], MinLen(min_length=1)] | Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$)]

openseries.owntypes.CurrencyStringType

Runtime representation of an annotated type.

At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ with extra annotations. The alias behaves like a normal typing alias. Instantiating is the same as instantiating the underlying type; binding it to types is also the same.

The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.

alias of Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$)]

openseries.owntypes.DateStringType

Runtime representation of an annotated type.

At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ with extra annotations. The alias behaves like a normal typing alias. Instantiating is the same as instantiating the underlying type; binding it to types is also the same.

The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.

alias of Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]d|3[01])$)]

openseries.owntypes.DateListType

Runtime representation of an annotated type.

At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ with extra annotations. The alias behaves like a normal typing alias. Instantiating is the same as instantiating the underlying type; binding it to types is also the same.

The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.

alias of Annotated[list[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=True, min_length=10, max_length=10, pattern=^d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]d|3[01])$)]], MinLen(min_length=1)]

openseries.owntypes.ValueListType

Runtime representation of an annotated type.

At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ with extra annotations. The alias behaves like a normal typing alias. Instantiating is the same as instantiating the underlying type; binding it to types is also the same.

The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.

alias of Annotated[list[float], MinLen(min_length=1)]

openseries.owntypes.DaysInYearType

Runtime representation of an annotated type.

At its core ‘Annotated[t, dec1, dec2, …]’ is an alias for the type ‘t’ with extra annotations. The alias behaves like a normal typing alias. Instantiating is the same as instantiating the underlying type; binding it to types is also the same.

The metadata itself is stored in a ‘__metadata__’ attribute as a tuple.

alias of Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), Ge(ge=1), Le(le=366)])]

openseries.owntypes.DateType = str | datetime.date | datetime.datetime | numpy.datetime64 | pandas._libs.tslibs.timestamps.Timestamp

Represent a PEP 604 union type

E.g. for int | str

Literal Types

openseries.owntypes.LiteralJsonOutput: alias of Literal[‘values’, ‘tsdf’]

openseries.owntypes.LiteralTrunc: alias of Literal[‘before’, ‘after’, ‘both’]

openseries.owntypes.LiteralLinePlotMode: alias of Literal[‘lines’, ‘markers’, ‘lines+markers’, ‘lines+text’, ‘markers+text’, ‘lines+markers+text’] | None

openseries.owntypes.LiteralHowMerge: alias of Literal[‘outer’, ‘inner’]

openseries.owntypes.LiteralQuantileInterp: alias of Literal[‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’]

openseries.owntypes.LiteralBizDayFreq: alias of Literal[‘B’, ‘BME’, ‘BQE’, ‘BYE’]

openseries.owntypes.LiteralPandasReindexMethod: alias of Literal[‘pad’, ‘ffill’, ‘backfill’, ‘bfill’, ‘nearest’] | None

openseries.owntypes.LiteralNanMethod: alias of Literal[‘fill’, ‘drop’]

openseries.owntypes.LiteralCaptureRatio: alias of Literal[‘up’, ‘down’, ‘both’]

openseries.owntypes.LiteralBarPlotMode: alias of Literal[‘stack’, ‘group’, ‘overlay’, ‘relative’]

openseries.owntypes.LiteralPlotlyOutput: alias of Literal[‘file’, ‘div’]

openseries.owntypes.LiteralPlotlyJSlib: alias of Literal[True, False, ‘cdn’]

openseries.owntypes.LiteralPlotlyHistogramPlotType: alias of Literal[‘bars’, ‘lines’]

openseries.owntypes.LiteralPlotlyHistogramBarMode: alias of Literal[‘stack’, ‘group’, ‘overlay’, ‘relative’]

openseries.owntypes.LiteralPlotlyHistogramCurveType: alias of Literal[‘normal’, ‘kde’]

openseries.owntypes.LiteralPlotlyHistogramHistNorm: alias of Literal[‘percent’, ‘probability’, ‘density’, ‘probability density’]

openseries.owntypes.LiteralPortfolioWeightings: alias of Literal[‘eq_weights’, ‘inv_vol’, ‘max_div’, ‘min_vol_overweight’]

openseries.owntypes.LiteralMinimizeMethods: alias of Literal[‘SLSQP’, ‘Nelder-Mead’, ‘Powell’, ‘CG’, ‘BFGS’, ‘Newton-CG’, ‘L-BFGS-B’, ‘TNC’, ‘COBYLA’, ‘trust-constr’, ‘dogleg’, ‘trust-ncg’, ‘trust-exact’, ‘trust-krylov’]

openseries.owntypes.LiteralSeriesProps: alias of 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_cal_year’, ‘max_drawdown’, ‘max_drawdown_date’, ‘first_idx’, ‘last_idx’, ‘length’, ‘span_of_days’, ‘yearfrac’, ‘periods_in_a_year’]

openseries.owntypes.LiteralFrameProps: alias of 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’]

Validation Classes

class openseries.owntypes.Countries(*, countryinput)[source]

Bases: BaseModel

Declare Countries.

Parameters:: countryinput (Annotated[set[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$)]], MinLen(min_length=1)] | Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=2, max_length=2, pattern=^[A-Z]{2}$)])

countryinput: CountriesType

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openseries.owntypes.Currency(*, ccy)[source]

Bases: BaseModel

Declare Currency.

Parameters:: ccy (Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=True, min_length=3, max_length=3, pattern=^[A-Z]{3}$)])

ccy: CurrencyStringType

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openseries.owntypes.PropertiesList(iterable=(), /)[source]

Bases: list[str]

Base class for allowed property arguments definition.

allowed_strings: ClassVar[set[str]] = {'arithmetic_ret', 'cvar_down', 'downside_deviation', 'geo_ret', 'kappa3_ratio', 'kurtosis', 'max_drawdown', 'max_drawdown_cal_year', 'max_drawdown_date', 'omega_ratio', 'positive_share', 'ret_vol_ratio', 'skew', 'sortino_ratio', 'value_ret', 'var_down', 'vol', 'vol_from_var', 'worst', 'worst_month', 'z_score'}

class openseries.owntypes.OpenTimeSeriesPropertiesList(*args)[source]

Bases: PropertiesList

Allowed property arguments for the OpenTimeSeries class.

Parameters:: args (LiteralSeriesProps)

allowed_strings: ClassVar[set[str]] = {'arithmetic_ret', 'cvar_down', 'downside_deviation', 'first_idx', 'geo_ret', 'kappa3_ratio', 'kurtosis', 'last_idx', 'length', 'max_drawdown', 'max_drawdown_cal_year', 'max_drawdown_date', 'omega_ratio', 'periods_in_a_year', 'positive_share', 'ret_vol_ratio', 'skew', 'sortino_ratio', 'span_of_days', 'value_ret', 'var_down', 'vol', 'vol_from_var', 'worst', 'worst_month', 'yearfrac', 'z_score'}

__init__(*args)[source]

Property arguments for the OpenTimeSeries class.

Parameters:

self (Self)
args (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_cal_year', 'max_drawdown', 'max_drawdown_date', 'first_idx', 'last_idx', 'length', 'span_of_days', 'yearfrac', 'periods_in_a_year'])

Return type:

None

class openseries.owntypes.OpenFramePropertiesList(*args)[source]

Bases: PropertiesList

Allowed property arguments for the OpenFrame class.

Parameters:: args (LiteralFrameProps)

allowed_strings: ClassVar[set[str]] = {'arithmetic_ret', 'cvar_down', 'downside_deviation', 'first_indices', 'geo_ret', 'kappa3_ratio', 'kurtosis', 'last_indices', 'lengths_of_items', 'max_drawdown', 'max_drawdown_cal_year', 'max_drawdown_date', 'omega_ratio', 'positive_share', 'ret_vol_ratio', 'skew', 'sortino_ratio', 'span_of_days_all', 'value_ret', 'var_down', 'vol', 'vol_from_var', 'worst', 'worst_month', 'z_score'}

__init__(*args)[source]

Property arguments for the OpenFrame class.

Parameters:

self (Self)
args (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'])

Return type:

None

Custom Exceptions

exception openseries.owntypes.MixedValuetypesError[source]

Bases: Exception

Raised when provided timeseries valuetypes are not the same.

exception openseries.owntypes.AtLeastOneFrameError[source]

Bases: Exception

Raised when none of the possible frame inputs is provided.

exception openseries.owntypes.DateAlignmentError[source]

Bases: Exception

Raised when date input is not aligned with existing range.

exception openseries.owntypes.NumberOfItemsAndLabelsNotSameError[source]

Bases: Exception

Raised when number of labels is not matching the number of timeseries.

exception openseries.owntypes.InitialValueZeroError[source]

Bases: Exception

Raised when a calculation cannot be performed due to initial value(s) zero.

exception openseries.owntypes.CountriesNotStringNorListStrError[source]

Bases: Exception

Raised when countries argument is not provided in correct format.

exception openseries.owntypes.MarketsNotStringNorListStrError[source]

Bases: Exception

Raised when markets argument is not provided in correct format.

exception openseries.owntypes.TradingDaysNotAboveZeroError[source]

Bases: Exception

Raised when trading days argument is not above zero.

exception openseries.owntypes.BothStartAndEndError[source]

Bases: Exception

Raised when both start and end dates are provided.

exception openseries.owntypes.NoWeightsError[source]

Bases: Exception

Raised when no weights are provided to function where necessary.

exception openseries.owntypes.LabelsNotUniqueError[source]

Bases: Exception

Raised when provided label names are not unique.

exception openseries.owntypes.RatioInputError[source]

Bases: Exception

Raised when ratio keyword not provided correctly.

exception openseries.owntypes.MergingResultedInEmptyError[source]

Bases: Exception

Raised when a merge resulted in an empty DataFrame.

exception openseries.owntypes.IncorrectArgumentComboError[source]

Bases: Exception

Raised when correct combination of arguments is not provided.

exception openseries.owntypes.PropertiesInputValidationError[source]

Bases: Exception

Raised when duplicate strings are provided.

exception openseries.owntypes.ResampleDataLossError[source]

Bases: Exception

Raised when user attempts to run resample_to_business_period_ends on returns.