chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
@@ -0,0 +1,63 @@
+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from AlgorithmImports import *
+from scipy.optimize import *
+
+### <summary>
+### Provides an implementation of a risk parity portfolio optimizer that calculate the optimal weights 
+### with the weight range from 0 to 1 and equalize the risk carried by each asset
+### </summary>
+class RiskParityPortfolioOptimizer:
+    
+    def __init__(self, 
+                 minimum_weight = 1e-05, 
+                 maximum_weight = sys.float_info.max):
+        '''Initialize the RiskParityPortfolioOptimizer
+        Args:
+            minimum_weight(float): The lower bounds on portfolio weights
+            maximum_weight(float): The upper bounds on portfolio weights'''
+        self.minimum_weight = minimum_weight if minimum_weight >= 1e-05 else 1e-05
+        self.maximum_weight = maximum_weight if maximum_weight >= minimum_weight else minimum_weight
+
+    def optimize(self, historical_returns, budget = None, covariance = None):
+        '''
+        Perform portfolio optimization for a provided matrix of historical returns and an array of expected returns
+        args:
+            historical_returns: Matrix of annualized historical returns where each column represents a security and each row returns for the given date/time (size: K x N).
+            budget: Risk budget vector (size: K x 1).
+            covariance: Multi-dimensional array of double with the portfolio covariance of annualized returns (size: K x K).
+        Returns:
+            Array of double with the portfolio weights (size: K x 1)
+        '''
+        if covariance is None:
+            covariance = np.cov(historical_returns.T)
+
+        size = historical_returns.columns.size   # K x 1
+        
+        # Optimization Problem
+        # minimize_{x >= 0} f(x) = 1/2 * x^T.S.x - b^T.log(x)
+        # b = 1 / num_of_assets (equal budget of risk)
+        # df(x)/dx = S.x - b / x
+        # H(x) = S + Diag(b / x^2)
+        # lw <= x <= up
+        x0 = np.array(size * [1. / size])
+        budget = budget if budget is not None else x0
+        objective = lambda weights: 0.5 * weights.T @ covariance @ weights - budget.T @ np.log(weights)
+        gradient = lambda weights: covariance @ weights - budget / weights
+        hessian = lambda weights: covariance + np.diag((budget / weights**2).flatten())
+        solver = minimize(objective, jac=gradient, hess=hessian, x0=x0, method="Newton-CG")
+
+        if not solver["success"]: return x0
+        # Normalize weights: w = x / x^T.1
+        return np.clip(solver["x"]/np.sum(solver["x"]), self.minimum_weight, self.maximum_weight)