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tsl-devkit/docs/tsl/finance/series_and_indicator_model.md
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2.5 KiB

Series And Indicator Model

文档类型:业务骨架 是否可直接用于生成代码:否 是否含已验证可执行示例:否 是否含已验证反例:否 遇到不确定时跳转到:market_data_context.mdselection_and_signal_patterns.md../syntax/index.md

这一篇处理金融序列与指标计算模式。

这一篇解决什么问题

回答“指标、序列、逐 bar 计算和相关金融表达方式如何组织,以及 AI 应该先建立什么样的业务心智模型”。

必须记住的规则

  • 序列与指标属于业务模型,不属于通用语法。
  • 当你需要解释循环、表达式、数组和字符串时,应回到 syntax 层。
  • 当你需要解释“指标如何依赖历史序列”时,才留在这里。

输入上下文

  • 先确认标的、周期、起止区间和复权口径。
  • 先确认你在处理“单值输入”还是“逐 bar 序列输入”。
  • 先确认指标依赖多少历史窗口,以及窗口未满时如何处理。

逐 bar 心智模型

  • 每个 bar 只应使用当前 bar 及其之前已经可见的信息。
  • 先准备输入序列,再计算当前 bar 的指标值,最后再决定当前 bar 的输出。
  • 不要把后面的 bar 结果回填到前面的 bar。
  • 不要把“能写成一个表达式”误解成“就不需要业务上下文”。

窗口依赖

  • 任何均线、滚动统计、历史比较,先写清窗口长度。
  • 窗口未满前,先决定是跳过、返回空值,还是走 warm-up 逻辑。
  • 多条序列一起参与计算时,先确认时间轴是否对齐。
  • 当结果依赖前值时,先确认你是在做“当前 bar 计算”还是“状态延续”。

常见误判

  • 把指标写法误当成“TSL 基础表达式”的定义来源。
  • 在没有澄清数据频率、窗口和上下文前,就直接抽象成通用语法规则。
  • 把未来数据混进当前 bar 的计算里。
  • 先写公式,再补上下文,导致窗口长度和序列来源都不明确。

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