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123 lines
4.5 KiB
Python
123 lines
4.5 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # noqa
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Base optimization classes and utilities for portfolio optimization.
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Provides abstract base classes and common functionality shared across
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different optimization algorithms, including weight constraint handling
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and portfolio state management.
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"""
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import numpy as np
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class BaseOptimizer:
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"""
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Base class for portfolio optimization algorithms.
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Provides common functionality for different optimization methods including
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weight constraint handling and portfolio state management.
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Attributes
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----------
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returns_dict : dict
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Dictionary containing return data and asset information
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tickers : list
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Asset ticker symbols
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n_assets : int
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Number of assets in the portfolio
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risk_measure : str
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Risk measure type (e.g., "CVaR", "variance")
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weights_previous : np.ndarray
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Previous portfolio weights for turnover calculations
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"""
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def __init__(self, returns_dict, weights_previous, risk_measure):
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"""
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Initialize base optimizer with return data and portfolio state.
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Parameters
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----------
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returns_dict : dict
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Dictionary containing asset returns data and tickers
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weights_previous : array-like or None
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Previous portfolio weights. If None or empty, creates uniform weights
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risk_measure : str
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Risk measure identifier (e.g., "CVaR", "variance")
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"""
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self.returns_dict = returns_dict
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self.tickers = returns_dict["tickers"]
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self.n_assets = len(self.tickers)
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self.risk_measure = risk_measure
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if not weights_previous: # (n_assets,) array of existing portfolio weights;
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# create uniform distributed weights if weights_previous not exist
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self.weights_previous = np.ones(self.n_assets) / self.n_assets
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else:
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self.weights_previous = weights_previous
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def _update_weight_constraints(self, weight_constraints):
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"""
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Convert weight constraints to numpy array format.
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Handles multiple input formats for weight constraints:
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- numpy array: used directly
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- dict: maps ticker names to constraint values
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- float: uniform constraint for all assets
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Parameters
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----------
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weight_constraints : np.ndarray, dict, or float
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Weight constraint specification in various formats
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Returns
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-------
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np.ndarray
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Weight constraints as numpy array (length n_assets)
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Raises
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------
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ValueError
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If constraint format is invalid or missing ticker specifications
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"""
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# if numpy array, then use the array
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if isinstance(weight_constraints, np.ndarray):
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updated_weight_constraints = weight_constraints
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# if dict, then convert to numpy array based on the tickers
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elif isinstance(weight_constraints, dict):
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updated_weight_constraints = np.zeros(self.n_assets)
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for ticker_idx, ticker in enumerate(self.tickers):
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if ticker in weight_constraints.keys():
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updated_weight_constraints[ticker_idx] = weight_constraints[ticker]
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elif "others" in weight_constraints.keys():
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updated_weight_constraints[ticker_idx] = weight_constraints[
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"others"
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]
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else:
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raise ValueError(
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"Must specify a weight constraint for each ticker or 'others'"
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)
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# if float, then create a numpy array with the same bound for all assets
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elif isinstance(weight_constraints, float):
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updated_weight_constraints = np.full(self.n_assets, weight_constraints)
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else:
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raise ValueError("Invalid weight constraints")
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return updated_weight_constraints
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