英文标题：
《Trade Selection with Supervised Learning and OCA》
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作者：
David Saltiel and Eric Benhamou
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最新提交年份：
2018
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英文摘要：
In recent years, state-of-the-art methods for supervised learning have exploited increasingly gradient boosting techniques, with mainstream efficient implementations such as xgboost or lightgbm. One of the key points in generating proficient methods is Feature Selection (FS). It consists in selecting the right valuable effective features. When facing hundreds of these features, it becomes critical to select best features. While filter and wrappers methods have come to some maturity, embedded methods are truly necessary to find the best features set as they are hybrid methods combining features filtering and wrapping. In this work, we tackle the problem of finding through machine learning best a priori trades from an algorithmic strategy. We derive this new method using coordinate ascent optimization and using block variables. We compare our method to Recursive Feature Elimination (RFE) and Binary Coordinate Ascent (BCA). We show on a real life example the capacity of this method to select good trades a priori. Not only this method outperforms the initial trading strategy as it avoids taking loosing trades, it also surpasses other method, having the smallest feature set and the highest score at the same time. The interest of this method goes beyond this simple trade classification problem as it is a very general method to determine the optimal feature set using some information about features relationship as well as using coordinate ascent optimization.
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中文摘要：
近年来，最先进的监督学习方法利用了越来越多的梯度增强技术，主流的高效实现如xgboost或lightgbm。生成熟练方法的关键点之一是特征选择（FS）。它包括选择正确的有价值的有效特性。当面对数百个这样的功能时，选择最佳功能就变得至关重要。虽然filter和wrappers方法已经达到了一定的成熟度，但嵌入式方法确实是找到最佳特征集所必需的，因为它们是结合特征过滤和包装的混合方法。在这项工作中，我们解决了通过机器学习从算法策略中找到最佳先验交易的问题。我们使用坐标上升优化和块变量推导了这种新方法。我们将我们的方法与递归特征消除（RFE）和二进制坐标上升（BCA）进行了比较。我们在一个真实的例子中展示了这种方法先验地选择好交易的能力。该方法不仅优于初始交易策略，因为它避免了损失交易，而且优于其他方法，具有最小的特征集，同时得分最高。这种方法的兴趣超出了这个简单的贸易分类问题，因为它是一种非常普遍的方法，可以使用有关特征关系的一些信息以及使用坐标上升优化来确定最佳特征集。
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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