Simulated portfolio returns using dynamic risk allocation

This code simulates data for analyzing the performance of a dynamic risk allocation strategy over multiple time periods. Here's a breakdown of the data generation and analysis process:

Simulated Data:
Random returns are generated for a specified number of assets (3 in this case) over a certain number of time periods (100 in this case). These returns follow a normal distribution with a mean of 0.05 and a standard deviation of 0.1.
Simulated transaction costs, model complexity, data requirements, and scalability factors are generated to represent different aspects of the dynamic risk allocation strategy.
Dynamic Risk Allocation Strategy:
A function dynamic_risk_allocation is defined to implement the dynamic risk allocation strategy.
Within this function, portfolio weights are calculated based on the inverse of the standard deviation of returns and adjusted for model complexity.
Transaction costs are subtracted from the weights, and negative weights are set to zero to ensure non-negativity.
Finally, portfolio returns are calculated as the weighted sum of asset returns.
Portfolio Returns Visualization:
The calculated portfolio returns using the dynamic risk allocation strategy are plotted over time.
The plot provides insights into the performance of the portfolio strategy, showing how returns vary across different time periods.

本代码模拟了多时间段内动态风险分配策略的性能分析数据。以下是数据生成和分析过程的详细说明：

模拟数据：
针对指定的资产数量（本例中为3）和一定数量的时间段（本例中为100），生成随机回报。这些回报遵循均值为0.05、标准差为0.1的正态分布。
模拟了交易成本、模型复杂度、数据需求和可扩展性因素，以表征动态风险分配策略的不同方面。
动态风险分配策略：
定义了一个名为 dynamic_risk_allocation 的函数，以实现动态风险分配策略。
在该函数内部，根据回报的标准差倒数计算投资组合权重，并针对模型复杂度进行调整。
从权重中扣除交易成本，并将负权重设置为零，以确保非负性。
最终，计算投资组合回报，作为资产回报的加权总和。
投资组合回报可视化：
使用动态风险分配策略计算的投资组合回报随时间绘制图表。
该图表提供了关于投资组合策略性能的洞察，展示了回报在不同时间段内的变化情况。