第1章 引言与概览

0 个策略

基于《151 Trading Strategies》· SHFE/LME 真实数据观察

作者简介

祖拉·卡库什阿泽（Zura Kakushadze）：康奈尔大学理论物理博士，曾任哈佛学会博士后、石溪大学杨振宁理论物理研究所助理教授。后转入量化金融领域，历任RBC Capital Markets董事、WorldQuant总经理、Revere Data（现FactSet旗下）执行副总裁，现任Quantigic Solutions CEO、格鲁吉亚自由大学全职教授。17年量化交易实战经验，发表130余篇论文，h指数>30。

胡安·安德烈斯·塞鲁尔（Juan Andrés Serur）：阿根廷CEMA大学金融硕士，7年以上股票市场交易经验，现任阿根廷某资产管理公司量化分析师、策略师，兼任CEMA大学金融硕士项目学术秘书。2016年获阿根廷资本市场模拟竞赛冠军。

媒体评论

"如果你想在华尔街做交易员或量化分析师，这本独特的著作全面介绍了150余种经过市场验证的交易策略。强烈推荐第152个策略：买这本书！" ——彼得·卡尔，NYU金融与风险管理系主任

"这本书是量化投资策略的百科全书式导览，从最简单的趋势跟踪到复杂的奇异衍生品策略均有覆盖。" ——让-菲利普·布肖，Capital Fund Management总裁，法国科学院院士

"Zura和Juan完成了一部量化交易策略的权威百科全书，既严谨又易懂。" ——侯赛因·卡泽米，马萨诸塞大学金融学讲席教授

引用信息

本书引用：全书共收录1966篇参考文献，覆盖量化金融、衍生品定价、投资组合管理、时间序列分析等领域的经典与前沿文献。

引用本书：

Kakushadze, Z., & Serur, J. A. (2018). 151 Trading Strategies. arXiv预印本, arXiv:1912.04492.

Kakushadze, Z., & Serur, J. A. (2019). 151 Estrategias de Trading. ISBN 978-1071261873.

版权与使用声明

本内容为西语版《151 Estrategias de Trading》（ISBN 978-1071261873）的个人学习用途全本翻译，仅作学术研究、量化学习参考，严禁用于任何盈利场景。

原书版权归Zura Kakushadze & Juan Andrés Serur所有，英文原版预印本：arXiv:1912.04492

合规提示：文中涉及的典当、高利贷、虚拟货币等内容，在中国境内属于非法金融业务，仅供学术研究。

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交易策略可定义为一组指令，目标是在特定时间点\(t_1,t_2,\dots\)实现特定的资产配置。在某些时点，配置可以为空（但并非必然）。多数策略的核心目标是直接获利，即通过投资获得正收益。但也有例外：例如对冲策略通常是整体风险管理计划的一部分，而非独立盈利工具——航空公司通过大宗商品期货对冲燃油价格上涨风险，这类交易本身是企业风险管理的一环，企业核心盈利来自客运服务。

对于以获取独立收益为目标的策略，"低买高卖"是常见的通俗总结，但这一定义过于狭隘：它仅适用于单一资产的买卖策略，无法覆盖大量其他类型的策略。例如对冲策略通常需要支付保费，本质是将风险转移给其他交易方；统计套利作为对冲基金的经典策略，组合包含数千只股票，收益来自统计意义上的"部分盈利覆盖部分亏损"，而非单只股票的低买高卖。

本书的目标是覆盖金融领域几乎所有资产类别的交易策略，这里的"资产类别"采用广义定义，不仅包括传统分类（股票、债券、现金、外汇、房地产、大宗商品、基础设施），还涵盖衍生品（期权、期货）、ETF、指数、波动率、结构化产品（CDO、MBS）、可转债、困境资产、加密货币、另类资产（气候、能源、通胀相关衍生品），以及税务套利、全球宏观等跨资产策略。

需要特别注意的是：自然科学定律（如物理规律）是客观恒定不变的，但金融市场是人类活动的产物，几乎时刻都在变化，有时甚至出现剧烈变革。典型案例是2006年底纽约证券交易所从人工专家制度转向电子交易系统后，大量运行多年的统计套利策略一夜之间失效，随后高频交易（HFT）涌入市场，进一步压缩了传统策略的盈利空间。

技术进步也催生了新的策略类型，尤其是基于数据挖掘和机器学习的策略：这类策略通过海量数据分析识别微弱的收益信号，单个信号强度极低无法独立覆盖交易成本，因此需要将数十万甚至数百万个信号加权组合，放大有效信号后实现盈利。本书收录了部分基于机器学习（如人工神经网络、贝叶斯模型、K近邻算法）的策略。

重要免责：本书内容仅为信息参考，不构成投资建议、法律/税务咨询，也不构成对任何交易策略、资产、产品的推荐。
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