mypo.optimizer package

Submodules

mypo.optimizer.base_optimizer module

Optimizer for weights of portfolio.

class mypo.optimizer.base_optimizer.BaseOptimizer(weights: Optional[Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray]], do_re_optimize: bool = False)

Bases: object

Base Optimizer.

do_re_optimize() → bool

Do re optimize?

Returns

whether re optimize.

get_weights() → numpy.ndarray

Get weights.

Returns

Weights.

optimize(market: mypo.market.Market, at: datetime.datetime) → numpy.float64

Optimize weights.

Parameters

  • market – Market data.

  • at – Current date.

mypo.optimizer.minimum_variance_optimizer module

Optimizer for weights of portfolio.

class mypo.optimizer.minimum_variance_optimizer.MinimumVarianceOptimizer(span: int = 260, with_semi_covariance: bool = False, minimum_return: Optional[float] = None, do_re_optimize: bool = False)

Bases: mypo.optimizer.base_optimizer.BaseOptimizer

Minimum variance optimizer.

optimize(market: mypo.market.Market, at: datetime.datetime) → numpy.float64

Optimize weights.

Parameters

  • market – Past market stock prices.

  • at – Current date.

Returns

Optimized weights

mypo.optimizer.no_optimizer module

Optimizer for weights of portfolio.

class mypo.optimizer.no_optimizer.NoOptimizer(weights: Optional[Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray]] = None, do_re_optimize: bool = False)

Bases: mypo.optimizer.base_optimizer.BaseOptimizer

Base Optimizer.

optimize(market: mypo.market.Market, at: datetime.datetime) → numpy.float64

Optimize weights.

Parameters

  • market – Market data.

  • at – Current date.

mypo.optimizer.objective module

mypo.optimizer.sharp_ratio_optimizer module

Module contents