<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title><![CDATA[自举量化｜研究笔记 (ZIJU QUANT Research)]]></title>
    <link>https://ziju.cn/research</link>
    <description><![CDATA[让假设经受样本外检验。自举量化投研方法论、多重检验假发现防范与实盘执行摩擦研究。]]></description>
    <language>zh-CN</language>
    <lastBuildDate>Mon, 26 Oct 2026 16:00:00 GMT</lastBuildDate>
    <atom:link href="https://ziju.cn/research/feed.xml" rel="self" type="application/rss+xml"/>
    <item>
      <title><![CDATA[为什么一次回测不能当作结论 | Why one backtest is not a conclusion]]></title>
      <link>https://ziju.cn/research/one-backtest</link>
      <guid isPermaLink="true">https://ziju.cn/research/one-backtest</guid>
      <pubDate>Sat, 24 Oct 2026 16:00:00 GMT</pubDate>
      <description><![CDATA[回测是对一个假设的一次检验。要把结果当作结论，还需要样本外数据、交易成本和事先写下的失效条件。

A backtest is one test of one hypothesis. Treating the result as a conclusion needs out-of-sample data, trading costs, and invalidation rules written in advance.]]></description>
    </item>
    <item>
      <title><![CDATA[多重检验与因子挖掘中的假发现陷阱 | Multiple testing and the false discovery trap in factor research]]></title>
      <link>https://ziju.cn/research/multiple-testing</link>
      <guid isPermaLink="true">https://ziju.cn/research/multiple-testing</guid>
      <pubDate>Sun, 25 Oct 2026 16:00:00 GMT</pubDate>
      <description><![CDATA[在成千上万个候选特征中筛选显著因子，传统统计阈值必然失效。控制假发现率与样本外再现，是量化研究区分噪声与真实规律的关键界碑。

When scanning thousands of candidate signals, traditional significance thresholds inevitably break down. Controlling the false discovery rate and verifying out-of-sample replicability separates genuine anomalies from statistical noise.]]></description>
    </item>
    <item>
      <title><![CDATA[从回测曲线到实盘收益：调仓滑点与容量边界 | From backtest curves to live execution: slippage and capacity limits]]></title>
      <link>https://ziju.cn/research/execution-drag</link>
      <guid isPermaLink="true">https://ziju.cn/research/execution-drag</guid>
      <pubDate>Sun, 25 Oct 2026 16:00:00 GMT</pubDate>
      <description><![CDATA[纸面回测的超额收益极易在订单执行的摩擦中消耗殆尽。把市场冲击、换手率约束和流动性天花板内生进研究框架，是策略存活的基石。

Theoretical backtest gains quickly vanish in the frictions of live execution. Embedding market impact, turnover constraints, and liquidity ceilings directly into the research model is what keeps strategies viable.]]></description>
    </item>
    <item>
      <title><![CDATA[时间点数据（Point-in-Time）与前瞻偏差防线 | Point-in-time data integrity and the defense against lookahead bias]]></title>
      <link>https://ziju.cn/research/point-in-time-data</link>
      <guid isPermaLink="true">https://ziju.cn/research/point-in-time-data</guid>
      <pubDate>Mon, 26 Oct 2026 16:00:00 GMT</pubDate>
      <description><![CDATA[在回测中借用未来信息是导致策略实盘迅速夭折的最隐蔽诱因。建立严格的时间点数据与动态可获得性时序，是量化研究不可逾越的底线。

Leaking future information into backtests is the most covert cause of live trading failure. Maintaining point-in-time data and tracking information availability timestamps is a non-negotiable quantitative baseline.]]></description>
    </item>
    <item>
      <title><![CDATA[组合清除交叉验证（CPCV）与时序标签重叠防线 | Combinatorial Purged Cross-Validation (CPCV) and Label-Overlap Leakage Defense]]></title>
      <link>https://ziju.cn/research/combinatorial-purged-cv</link>
      <guid isPermaLink="true">https://ziju.cn/research/combinatorial-purged-cv</guid>
      <pubDate>Mon, 26 Oct 2026 16:00:00 GMT</pubDate>
      <description><![CDATA[金融时间序列具有强自相关与跨期标签重叠特征。标准K折交叉验证会导致严重未来信息泄漏；必须通过清除重叠样本（Purging）与禁运自相关衰减期（Embargoing）构建无偏验证分布。

Financial time series exhibit serial correlation and overlapping event labels. Standard k-fold cross-validation causes severe information leakage; purging overlapping samples and embargoing post-test autocorrelation are required to construct unbiased evaluation distributions.]]></description>
    </item>
  </channel>
</rss>
