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https://github.com/NVIDIA/dgx-spark-playbooks.git
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113 lines
4.1 KiB
Python
113 lines
4.1 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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from dataclasses import dataclass
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from typing import Optional, Union
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import numpy as np
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@dataclass
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class CvarParameters:
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"""
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User‑tunable parameters and constraint limits for CVaR optimization.
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Most parameters are scalars. Weight bounds ``w_min`` / ``w_max`` can be:
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- numpy arrays (length n_assets) for per-asset bounds
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- dict mapping asset names to bounds
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- float for uniform bounds across all assets
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- None for no bounds
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Optional constraints (T_tar, cvar_limit, cardinality) default to None when
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not specified.
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"""
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# Weight / cash bounds
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w_min: Union[np.ndarray, dict, float] = 1.0 # Lower bound for each risky weight
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w_max: Union[np.ndarray, dict, float] = 0.0 # Upper bound for each risky weight
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c_min: float = 0 # Lower bound for cash allocation
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c_max: float = 1 # Upper bound for cash allocation
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# Risk model Parameters
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risk_aversion: float = 1 # λ – penalty applied to CVaR inside objective
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confidence: float = 0.95 # α in CVaR_α (e.g. 0.95 -> 95 % CVaR)
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# Soft / hard constraint targets
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L_tar: float = 1.6 # Leverage constraint (Σ|wᵢ|)
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T_tar: Optional[float] = None # Turnover constraint
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cvar_limit: Optional[float] = None # Hard CVaR limit (None means "no hard limit")
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cardinality: Optional[int] = None # number of assets to be selected
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group_constraints: Optional[list[dict]] = None
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# Group constraints:
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# [{'group_name': group_name,
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# 'tickers': tickers
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# 'weight_bounds': {'w_min': w_min, 'w_max': w_max}}]
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def update_w_min(self, new_w_min: Union[np.ndarray, dict, float]):
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self.w_min = new_w_min
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def update_w_max(self, new_w_max: Union[np.ndarray, dict, float]):
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if new_w_max <= 1:
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self.w_max = new_w_max
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else:
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raise ValueError("Invalid upper bound for weights!")
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def update_c_min(self, new_c_min: float):
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if new_c_min >= 0:
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self.c_min = new_c_min
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else:
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raise ValueError("Cash should be non-negative!")
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def update_c_max(self, new_c_max: float):
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if new_c_max >= 0 and new_c_max <= 1:
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self.c_max = new_c_max
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else:
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raise ValueError("Invalid upper bound for cash!")
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def update_z_min(self, new_c_min: float):
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self.z_min = new_c_min
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def update_z_max(self, new_z_max: float):
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self.z_max = new_z_max
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def update_T_tar(self, new_T_tar: float):
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self.T_tar = new_T_tar
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def update_L_tar(self, new_L_tar: float):
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self.L_tar = new_L_tar
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def update_cvar_limit(self, new_cvar_limit: float):
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self.cvar_limit = new_cvar_limit
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def update_cardinality(self, new_cardinality: int):
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if new_cardinality is None or (
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isinstance(new_cardinality, int) and new_cardinality > 0
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):
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self.cardinality = new_cardinality
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else:
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raise ValueError("Cardinality must be a positive integer or None")
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def update_risk_aversion(self, new_risk_aversion: float):
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if new_risk_aversion >= 0:
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self.risk_aversion = new_risk_aversion
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else:
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raise ValueError("Invalid risk aversion")
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def update_confidence(self, new_confidence: float):
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if new_confidence > 0 and new_confidence <= 1:
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self.confidence = new_confidence
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else:
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raise ValueError(
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"Invalid confidence level (should be between 0 and 1, "
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"e.g. 95%, 99%, etc.)"
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)
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