Abstract: The robustness of a trading model means that the model's profitability curve is less volatile and does not fluctuate significantly. To solve the problem of robustness of an algorithmic trading model based on Support Vector Regression (SVR), several strategies to derive a unified trading model and a portfolio diversification method were proposed. Firstly, the algorithm trade model based on SVR was introduced. Then, based on the commonly used indicators, a number of derived indicators were constructed for short term forecasting of stock prices. The typical patterns of recent price movements, overbought/oversold market conditions, and divergence of market conditions were characterized by these indicators. These indicators were normalized and used to train the trading model so that the model can be generalized to different stocks. Finally, a portfolio diversification method was designed. In the portfolio, the correlation between various stocks, sometimes leads to great investment losses; because the price of the stock with strong correlation changes in the same direction. If the trading model doesn't predict the price trend correctly, then stop loss will be triggered, and these stocks will cause loss in a mutual accelerated manner. Stocks were clustered into different categories according to the similarity, and a diversified portfolio was formed by selecting a number of stocks from different clustered categories. The similarity of stocks, was defined as the similarity of the recent profit curves on different stocks by trading models.Experiments were carried out on the data of 900 stocks for 10 years. The experimental results show that the transaction model can obtain excess profit rate over time deposit, and the annualized profit rate is 8.06%. The maximum drawdown of the trading model was reduced from 13.23% to 5.32%, and the Sharp ratio increased from 81.23% to 88.79%. The volatility of the profit margin curve of the trading model decreased, which means that the robustness of the trading model was improved.

Key words: algorithm trading, Support Vector Regression (SVR), robustness, portfolio diversification, big data

摘要： 交易模型的稳健性，指的是该模型的利润率曲线的波动性较小，没有大起大落。针对一个基于支持向量回归（SVR）技术的算法交易模型的稳健性问题，提出了使用若干导出指标训练统一的交易模型的策略，以及投资组合多样化的方法。首先，介绍基于支持向量回归技术的算法交易模型；然后，基于常用指标，构造了若干导出指标，用于股票价格的短期预测。这些指标，刻画了近期价格运动的典型模式、超买/超卖市场状态，以及背离市场状态。对这些指标进行了规范化，用于训练交易模型，使得模型可以泛化到不同的股票；最后，设计了投资组合多样化方法。在投资组合里，各个股票之间的相关性，有时会导致较大的投资损失；因为具有较强相关关系的股票，其价格朝相同方向变化。如果交易模型预测的价格走势不正确，引起止损操作，那么这些具有较强相关关系的股票，将引发雪崩式的止损，于是导致损失加剧。把股票根据相似性聚类到不同类别，通过从不同聚类类别中选择若干股票来构成多样化的投资组合，其中，股票的相似性，通过交易模型在不同股票上近期的利润曲线的相似度进行计算。在900只股票10年的价格大数据上进行了实验，实验结果显示，交易模型能够获得超过定期存款的超额利润率，年化利润率为8.06%。交易模型的最大回撤由13.23%降为5.32%，夏普指数由81.23%提高到88.79%，交易模型的利润率曲线波动性降低，说明交易模型的稳健性获得了提高。

关键词: 算法交易, 支持向量回归, 稳健性, 投资组合多样化, 大数据