英文标题：
《Determining Optimal Trading Rules without Backtesting》
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作者：
Peter P. Carr, Marcos Lopez de Prado
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最新提交年份：
2014
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英文摘要：
  Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. In this paper we propose a procedure for determining the optimal trading rule (OTR) without running alternative model configurations through a backtest engine. We present empirical evidence of the existence of such optimal solutions for the case of prices following a discrete Ornstein-Uhlenbeck process, and show how they can be computed numerically. Although we do not derive a closed-form solution for the calculation of OTRs, we conjecture its existence on the basis of the empirical evidence presented.
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中文摘要：
使用历史模拟（也称回测）校准交易规则会导致回测过度拟合，进而导致表现不佳。在本文中，我们提出了一个确定最优交易规则（OTR）的过程，无需通过回溯测试引擎运行替代模型配置。对于价格服从离散Ornstein-Uhlenbeck过程的情况，我们给出了存在此类最优解的经验证据，并展示了如何用数值计算它们。虽然我们没有推导出计算OTR的闭合形式解，但我们根据提供的经验证据推测其存在。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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