以下文章来源于量化投资 ，作者十拳剑 量化投资 . 百万滚到千万的实盘分享与思考，不定期分享资产配置研究： 1.广度上通过爬虫定期对全市场所有公募 基金进行自动数据获取、存储； 2.深度上计算时间序列特征、协方差矩阵；3.分别锚定2%和10%波动 率，求解最大回报率下的配置方案。 风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者： 十拳剑灵 来源：雪球 本文将展示一个魔法： 基本数据 首先，历史数据分析表明，黄金和微盘股策略之间几乎是0相关性的，为大类资产配置提供了很好 的素材。 这两种资产也一直是 $十拳剑灵活配置$ 这个组合的重要组成部分。 再看两类典型基金的波动率数据， 国泰黄金ETF联接A 的近三年波动率为12.21。 1+2<1 将黄金与高波动的微盘基金组合，波动率更低、收益率几乎不变。 也就是说，可以利用相关性实现收益不变的同时降低波动率。 回测基于雪球的三分法工具，对比了100%纯黄金组合与黄金+微盘： 微盘股基金 中信保诚多策略混合(LOF)A 的近三年波动率24.85%，约为黄金的2倍。 最优配置比例 理论上可以证明，将两个基金按照4:1的比例配置， ...

Group 1 - The core idea of the article emphasizes the importance of fixed overall volatility in asset allocation to maximize returns and Sharpe ratios, derived from the pursuit of portfolio efficiency [3][41]. - The article outlines a three-step process to reconstruct investment paradigms: setting risk budgets, decomposing volatility allocation, and maximizing efficiency [6][10][11]. - The article presents backtesting results using the "three-part method" tool, comparing various strategies and highlighting the performance of long bond and equity combinations [4][12]. Group 2 - The first step involves anchoring the overall annualized volatility target, which should align with the investor's risk tolerance, with examples indicating a target of around 2% volatility corresponding to a maximum drawdown of 2% [6][7]. - The second step focuses on decomposing total volatility across asset classes using dynamic optimization models, aiming for negative correlation between asset classes to enhance returns [10][16]. - The third step aims to maximize the overall portfolio efficiency by adjusting the volatility exposure allocated to different asset classes [11][21]. Group 3 - Backtesting results show that an 88.5% long bond and 11.5% equity combination achieved a cumulative return of 22.20% with an annualized volatility of only 2.19%, significantly lower than the 17.72% volatility of the CSI 300 index [16][42]. - The long bond portion contributed an annualized volatility of 1.85%, while the equity portion contributed only 0.68% due to their negative correlation of -0.33, resulting in a compressed overall portfolio volatility [16][22]. - The article highlights that the long bond and equity strategy outperformed pure long bond strategies, achieving a higher Sharpe ratio of 2.47 compared to 2.02 for pure long bonds [21][41]. Group 4 - The article discusses the advantages of the long bond and equity strategy, noting that long bond funds have a higher unit risk-return ratio compared to equity funds in recent market conditions [44][45]. - It emphasizes that a stronger negative correlation between long bonds and equities allows for higher volatility exposure while maintaining lower overall portfolio volatility [46][47]. - The conclusion suggests that in the current market environment, anchoring around 2% volatility, the optimal solution is a combination of long bonds and equities, despite a potentially higher maximum drawdown compared to short bonds and equities [48].