Ask a heat dataset "which themes is this stock associated with?" and you get a ranked list. The top entry looks like the stock's main story. Often it is not. It may be a label that has been attached t
"Which stocks are carrying the discussion around this theme right now?" is one of the most common questions people ask a heat monitoring dataset. In Lava, the answer comes from the concept member rank
When a stock suddenly gets talked about, the first useful question is not "why" but "in what order did things happen?" Which hour did discussion pick up? Did a media flash come before or after the fir
Two stocks can sit side by side on a heat ranking with exactly the same mention count and still have very different stories behind them. One might be mentioned once each by forty different institution
Put five stocks from the same sector next to each other on a heat chart and the result is almost always the same: the largest, most widely followed names sit on top. That is not wrong, but it is rarel
An AI-related concept ranks second on the one-hour heat board and eleventh on the three-day board. Is that a sudden burst, or a theme cooling off? Usually it is neither. It is two rolling windows meas
摘要 股票MCP是什么意思？MCP（Model Context Protocol，模型上下文协议）是一种让 AI 客户端调用外部工具与数据的开放协议；「股票 MCP」就是以 MCP 形式提供股票数据的服务端。接入之后，你可以在支持 MCP 的 AI 办公软件或自建 Agent 里直接用中文提问，例如「宁德时代最近一周消息面热不热」，模型会自己选择工具、填参数、读取结构化结果，而不是凭训练数据猜。
摘要 情绪因子量化怎么做？业界常见的新闻情绪因子大致由四个部分组成：个股与文本的关联、每条文本的情感方向、相关度加权与时间衰减，以及按交易日的聚合。其中「这只股票被讨论了多少、被谁讨论」是关注度分量，「讨论偏正面还是偏负面」是方向分量。熔岩（Lava）提供的是前者：按个股与概念聚合的消息面提及数 mentions、参与主体数 agencies、按机构/大V 分加权的 weighted_heat，以
摘要 股票舆情监控怎么做？核心是三件事：持续采集与某只股票相关的公开信息，把它们变成可比较的数字，再在数字异常时把对应的原始记录调出来看。自己写爬虫可以做到，但要长期维护采集源、去重、股票识别和时间对齐；更省事的做法是直接使用已经结构化好的消息面数据。熔岩（Lava）把新闻、券商研报与机构/大V 观点结构化为「时间、涉及股票、涉及概念、匿名 agency_id、短摘要」，并按个股和概念聚合成热度，