Analyzing predictive performance of linear models on high-frequency currency exchange rates
Analyzing predictive performance of linear models on high-frequency currency exchange rates
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DOI：
10.1007/s40595-018-0108-x
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发表时间：
2018-05
影响因子：
0.9
通讯作者：
Chanakya Serjam;A. Sakurai
中科院分区：
文献类型：
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作者：
Chanakya Serjam;A. Sakurai
我们通过将线性核SVR应用于从高频交易中获得的三种货币对的历史货币汇率出价数据，生成大量预测模型。投标报价单数据被转换为等间隔（1分钟）数据。之前连续时间框架之间的价格差异被用作预测下一时间框架中价格运动方向的特征。在学习模型时，使用不同的训练样本数量、特征数量和时间帧长度值。这些模型用于在学习模型的下一年进行模拟货币交易。利润（订单执行时最佳买入价格的实际差异之和），命中率和使用这些模型执行的交易数量都被记录下来。实验表明，虽然很难只使用历史数据构建一贯表现良好的模型，但有些模型在某些预定义的条件下表现良好，并且其中许多模型具有有趣的属性。通过对这些模型参数的检验，我们发现它们都具有负的系数和极小的截距，同时具有正的利润和良好的命中率。这是一个简单而有效的交易策略。最后，我们检查历史数据，以找到生成的模型所建议的模式的佐证，并给出结果。
We generate a large number of predictive models by applying linear kernel SVR to historical currency rates’ bid data for three currency pairs obtained from high-frequency trading. The bid tick data are converted into equally spaced (1 min) data. Differences of price between the previous successive timeframes are used as features to predict the direction of movement of the price in the next timeframe. Different values for the number of training samples, number of features, and the length of the timeframes are used when learning the models. These models are used to conduct simulated currency trading in the year following the one in which the model was learned. Profits (sum of realized differences in best bid prices when order is executed), hit ratios, and number of trades executed using these models are recorded. The experiments indicate that while it is difficult to construct models using only historical data that consistently perform well, there are models that show good performance under certain pre-defined conditions, and that many of these models have an interesting property. Upon examining the parameters of these models, we discover that they have all negative coefficients and a negligibly small intercept, while having positive profits and good hit ratio. This suggests a simple yet effective trading strategy. Finally, we examine the historical data to find corroboration for the pattern suggested by the generated models and present the results.