论文标题
通过对市场国家进行分类来构建交易策略集合
Constructing trading strategy ensembles by classifying market states
论文作者
论文摘要
我们没有直接预测未来的价格或回报，而是遵循资产管理的最新趋势，并根据标签对市场状况进行分类。我们使用许多标准标签，甚至构建自己的标签。标签依靠未来的数据进行计算，可以使用适当的市场功能，例如移动平均值。这些功能的构建依赖于它们的标签分离能力。只有一组合理的不同特征才能近似标签。对于每个标签，我们使用特定的神经网络使用我们功能空间中的市场功能对状态进行分类。每个分类器都有可能购买或出售和合并所有建议（在这里仅以线性方式完成）导致我们称为交易策略的可能性。有许多这样的策略，其中一些策略有些可疑和误导。我们基于过去的回报来构建自己的指标，但会对交易数量少或资本参与少而受到惩罚。只有最高得分绩效的交易策略最终才能成为最终合奏。使用比特币市场，我们表明该策略在样本外时期的回报和风险调整后的收益都优于回报。更重要的是，我们证明了过去取得的成功（如果在我们的自定义指标中）与未来之间的成功之间存在明显的相关性。
Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. We use numerous standard labels and even construct our own ones. The labels rely on future data to be calculated, and can be used a target for training a market state classifier using an appropriate set of market features, e.g. moving averages. The construction of those features relies on their label separation power. Only a set of reasonable distinct features can approximate the labels. For each label we use a specific neural network to classify the state using the market features from our feature space. Each classifier gives a probability to buy or to sell and combining all their recommendations (here only done in a linear way) results in what we call a trading strategy. There are many such strategies and some of them are somewhat dubious and misleading. We construct our own metric based on past returns but penalising for a low number of transactions or small capital involvement. Only top score-performance-wise trading strategies end up in final ensembles. Using the Bitcoin market we show that the strategy ensembles outperform both in returns and risk-adjusted returns in the out-of-sample period. Even more so we demonstrate that there is a clear correlation between the success achieved in the past (if measured in our custom metric) and the future.
