最优交易与阿尔法预测

Filippo Passerini, Samuel E. Vázquez
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引用次数: 10

摘要

我们使用具有线性成本和暂时影响的一般alpha预测因子研究最优交易问题。我们在有限视界随机优化的框架内,利用极限订单和市场订单来解决这个问题。与其他研究一致,我们发现线性成本的存在在使用市价指令时导致了一个无交易区域,在使用限价指令时导致了一个相应的做市区域。我们表明,当结合市场订单和限价订单时,问题被进一步划分为我们更积极地使用市场订单进行交易的区域。虽然我们不能解析地解决完整的优化问题,但我们提出了明确和简单的近似完整解的解析方法,并且易于在实践中实现。我们使用蒙特卡罗模拟测试算法,并展示它们如何改善我们的损益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Trading with Alpha Predictors
We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-trading zone when using market orders, and a corresponding market-making zone when using limit orders. We show that, when combining both market and limit orders, the problem is further divided into zones in which we trade more aggressively using market orders. Even though we do not solve analytically the full optimization problem, we present explicit and simple analytical recipes which approximate the full solution and are easy to implement in practice. We test the algorithms using Monte Carlo simulations and show how they improve our Profit and Losses.
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