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https://github.com/robertmartin8/PyPortfolioOpt.git
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62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
# TODO module docstring
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import numpy as np
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class BaseOptimizer:
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def __init__(self, n_assets, weight_bounds=(0, 1)):
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"""
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:param weight_bounds: minimum and maximum weight of an asset, defaults to (0, 1).
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Must be changed to (-1, 1) for portfolios with shorting.
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:type weight_bounds: tuple, optional
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"""
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self.n_assets = n_assets
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self.bounds = self._make_valid_bounds(weight_bounds)
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# Optimisation parameters
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self.initial_guess = np.array([1 / self.n_assets] * self.n_assets)
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self.constraints = [{"type": "eq", "fun": lambda x: np.sum(x) - 1}]
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# Outputs
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self.weights = None
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def _make_valid_bounds(self, test_bounds):
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"""
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Private method: process input bounds into a form acceptable by scipy.optimize,
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and check the validity of said bounds.
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:param test_bounds: minimum and maximum weight of an asset
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:type test_bounds: tuple
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:raises ValueError: if ``test_bounds`` is not a tuple of length two.
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:raises ValueError: if the lower bound is too high
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:return: a tuple of bounds, e.g ((0, 1), (0, 1), (0, 1) ...)
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:rtype: tuple of tuples
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"""
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if len(test_bounds) != 2 or not isinstance(test_bounds, tuple):
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raise ValueError(
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"test_bounds must be a tuple of (lower bound, upper bound)"
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)
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if test_bounds[0] is not None:
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if test_bounds[0] * self.n_assets > 1:
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raise ValueError("Lower bound is too high")
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return (test_bounds,) * self.n_assets
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def clean_weights(self, cutoff=1e-4, rounding=5):
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"""
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Helper method to clean the raw weights, setting any weights whose absolute
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values are below the cutoff to zero, and rounding the rest.
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:param cutoff: the lower bound, defaults to 1e-4
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:type cutoff: float, optional
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:param rounding: number of decimal places to round the weights, defaults to 5.
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Set to None if rounding is not desired.
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:type rounding: int, optional
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:return: asset weights
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:rtype: dict
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"""
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if not isinstance(rounding, int) or rounding < 1:
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raise ValueError("rounding must be a positive integer")
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clean_weights = self.weights.copy()
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clean_weights[np.abs(clean_weights) < cutoff] = 0
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if rounding is not None:
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clean_weights = np.round(clean_weights, rounding)
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return dict(zip(self.tickers, clean_weights))
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