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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SE</journal-id><journal-title-group><journal-title>Society and Economy</journal-title></journal-title-group><issn>2995-4959</issn><eissn>2995-4975</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/SE.2026070019</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于第一性原理的散户量化交易规则自举研究</title><url>https://artdesignp.com/journal/SE/4/7/10.61369/SE.2026070019</url><author>冯棋森,王姣</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>7</issue><history><date date-type="pub"><published-time>2026-07-20</published-time></date></history><abstract>散户量化实践普遍将历史数据中经参数优化获得的统计巧合误认为盈利逻辑，这一归纳主义范式在非平稳市场中必然失效。本研究引入第一性原理，从流动性供需物理事实、概率论公理及大数定律出发重构规则生成机制，提出规则自举框架。该框架强调参数由微观结构理论推导，而非由样本内优化确定。利用2015年7月至2026年6月A 股沪深300成分股数据，将自举策略与传统对照策略进行样本外回测。结果显示，自举策略样本外年化收益率为17.69%，夏普比率0.876，最大回撤仅-16.99%。经网格搜索优化的均线对照策略样本外夏普比率转负（-0.161），最大回撤达-52.13%。Jobson-Korkie 检验表明差异在1% 水平上显著。策略在2023年注册制改革前后均保持正收益。本研究证明，脱离底层物理逻辑的策略优化仅为样本内的统计巧合，基于市场本质生成的规则方能适应非平稳时序变化。</abstract><keywords>第一性原理,量化交易,规则自举,市场微观结构,非平稳序列,过拟合</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] O'Hara M. Market Microstructure Theory. Blackwell Publishers, 1995.[2] Fama E F. Efficient Capital Markets: A Review of Theory and Empirical Work.Journal of Finance, 1970, 25(2): 383-417.[3] Lo A W. The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 2004, 30(5): 15-29.[4] Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk.Econometrica, 1979, 47(2): 263-291.[5] Glosten L R, Milgrom P R. Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders. Journal of Financial Economics, 1985,14(1): 71-100.[6] Jegadeesh N, Titman S. Returns to Buying Winners and Selling Losers:Implications for Stock Market Efficiency. Journal of Finance, 1993, 48(1): 65-91.[7] De Bondt W F M, Thaler R H. Does the Stock Market Overreact. Journal of Finance, 1985, 40(3): 793-808.[8] Barber B M, Odean T. Trading Is Hazardous to Your Wealth: The Common StockInvestment Performance of Individual Investors. Journal of Finance, 2000, 55(2):773-806.[9] L&amp;oacute;pez de Prado M. Advances in Financial Machine Learning. Wiley, 2018.[10] Bailey D H, Borwein J M, L&amp;oacute;pez de Prado M, Zhu Q J. The Probability of Backtest Overfitting. Journal of Computational Finance, 2014, 18(3): 1-26.[11] Taleb N N. The Black Swan: The Impact of the Highly Improbable. Random House, 2007.[12] Hasbrouck J. Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading. Oxford University Press, 2007.[13] Wilder J W. New Concepts in Technical Trading Systems. Trend Research, 1978.[14] Jobson J D, Korkie B M. Performance Hypothesis Testing with the Sharpe and Treynor Measures. Journal of Finance, 1981, 36(4): 889-908.[15] Campbell J Y, Lo A W, MacKinlay A C. The Econometrics of Financial Markets.Princeton University Press, 1997.
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