CVaR=-tail_losses.mean() #打印结果 print(f"在95%的置信水平下,VaR(价值在险)为:{VaR:.4f}") print(f"在95%的置信水平下,CVaR(条件价值在险)或ES(期望损失)为:{CVaR:.4f}") 在95%的置信水平下,VaR(价值在险)为:0.0291 在95%的置信水平下,CVaR(条件价值在险)或ES(期望损失)为:0.0464 VaR告...
This time, the goal of the article is to show how to create trading strategies using Markowitz’s portfolio optimization and the Modern Portfolio Theory. In this article, I first give a brief introduction/reminder on the mean-variance optimization and then show how to implement it into...
我们可以使用 PCA 来直观地了解哪组更适合进行预测。 group_1 = ['mean symmetry','symmetry error','worst symmetry', 'mean smoothness','smoothness error','worst smoothness'] group_2 = ['mean perimeter','perimeter error','worst perimeter', 'mean...
()), axis = 1).mean()*252 print("Weights Mean Opt: " + str(r_weights)) print("Performance Mean Opt: " + str(opt_mu)) # Variance Optimization ___ # Optimization problem solving res = minimize(volatility_criterion, x0, method="SLSQP", args=(f.dropna()),bounds=bound,constraints=c...
group_1=['mean symmetry','symmetry error','worst symmetry','mean smoothness','smoothness error','worst smoothness']group_2=['mean perimeter','perimeter error','worst perimeter','mean concavity','concavity error','worst concavity'] 我们首先创建两组特征。然后分别对每组进行 PCA。这将为我们提供...
repeat { for i= to m # 计算每个样例属于的类 c(i) := index (from 1 to K) of cluster centroid closest to x(i) for k = 1 to K # 聚类中心的移动,重新计算该类的质心 u(k) := average (mean) of points assigned to cluster K } 西瓜书中的伪代码 优化目标Optimization Objective K-均...
Negative solution x-values mean that you’d be paying the buyers! Try out the code below to solve this problem. First, import the modules you need and then set variables to determine the number of buyers in the market and the number of shares you want to sell: Python 1import numpy ...
prior distribution里面需要确定mean function以及covariance function的结构,在具体选择kernel的时候会有一些超参数需要调整,如RBF中的length scale、signal variance或noise variance等超参数: 这部分需要利用观测到的样本矩阵进行训练拟合 GPR就是Posterior Distribution的建模,然后得到其它测试点的函数分布 ...
What does that mean for list[bool] compared to list[int]? These are technical questions. The good news is that with the new syntax, you don’t need to be explicit about variance. Instead, the type checkers will be able to deduce the correct categorization when needed....
port_variance = [] for p in range(4000): weights = np.random.random(noa) weights /=np.sum(weights) port_returns.append(np.sum(returns.mean()*252*weights)) port_variance.append(np.sqrt(np.dot(weights.T, np.dot(returns.cov()*252, weights))) port...