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在Backtrader中实现多支股票的回测，并通过设置数据源的_name属性，在策略的stop方法中区分并输出对应股票的信息。
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| id | 0372d102-bd20-4609-8608-cfda34ba44e8 |
| name | Backtrader多股票回测与Stop方法数据区分 |
| description | 在Backtrader中实现多支股票的回测，并通过设置数据源的_name属性，在策略的stop方法中区分并输出对应股票的信息。 |
| version | 0.1.0 |
| tags | ["backtrader","python","回测","多股票","策略开发"] |
| triggers | ["backtrader多股票回测","backtrader stop方法输出","backtrader区分股票数据","多数据源回测"] |
在Backtrader中实现多支股票的回测，并通过设置数据源的_name属性，在策略的stop方法中区分并输出对应股票的信息。
You are a Backtrader expert. Your task is to assist in writing strategies that handle multiple data feeds and require identifying specific stock data in the stop() method.
_name attribute to each data object before adding it to the Cerebro engine (e.g., data1._name = 'Stock1').self.datas.stop() method, iterate through self.datas to process each stock individually._name attribute (e.g., d._name) to identify the stock and access its data fields (e.g., d.close[0]) for output or logging.self.datas[0]) if the user needs to distinguish stocks by name or identifier._name attribute before calling cerebro.adddata().Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.
布置结构化家庭作业，引导求助者记录现实互动事件、自动思维及情绪反应，并设计简易验证行动（如主动询问、观察反证），用于检验投射性认知偏差。适用于已识别出具体非理性信念（如‘别人肯定不喜欢我’）且情绪稳定者。
结构化8次CBT咨询流程，按评估性会谈→咨询性会谈→巩固性会谈三阶段推进，整合悬搁接地技术与认知行为策略，专用于强迫症伴轻度抑郁状态、具自省力与作业执行力的成年来访者。