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Real-Time Market Viewer

Live stock quotes, interactive charts, zero API keys required.

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Real-time stock monitoring workflow with freshness and source checks

Make every moving stock price explainable

A stock app becomes useful to an AI research workflow when every number carries enough context to be interpreted correctly. Symbol identity, venue, currency, market session, source time, delay, and adjustment policy matter as much as the displayed price.

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Resolve the instrument

Normalize ticker, exchange, asset type, share class, and currency before requesting a quote. The same symbol can refer to different securities or venues, and an unresolved identifier can contaminate every later comparison.

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Show freshness and session

Keep source time separate from ingestion time. Label pre-market, regular, after-hours, delayed, or closed-session values so an agent does not compare a live quote with yesterday’s close as if they were equivalent.

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Define the comparison baseline

State whether percentage change uses prior close, session open, or a rolling interval. Corporate actions and adjusted history can change the meaning of a move, especially around splits and dividends.

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Design alerts with context

Use thresholds, persistence windows, liquidity checks, cooldowns, and confirmation signals. A one-tick spike should not trigger the same workflow as a sustained move confirmed by volume, news, or a filing.

Minimum quote contract for an AI agent

  • Canonical symbol, company or instrument name, exchange, currency, asset type, and market-session label.
  • Price, change basis, source timestamp, ingestion timestamp, stated delay, data provider, and entitlement status.
  • Bid, ask, spread, volume, and quality flags when the workflow uses the quote for monitoring or execution-adjacent decisions.
  • Explicit behavior for stale data, missing symbols, feed interruption, quota exhaustion, and disagreement between providers.

Good alert output: “NVDA is up 2.3% from the prior close as of 09:45:12 ET on the regular session feed; volume is above its recent intraday baseline.” The evidence belongs in the alert, not only inside the dashboard.

让每一个跳动的股价都能被解释

股票应用只有在每个数字都带有足够上下文时,才能真正服务于 AI 研究工作流。股票身份、交易场所、币种、交易时段、来源时间、延迟和复权口径,与页面上的价格同样重要。

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先确认标的

请求报价前先统一股票代码、交易所、资产类型、股份类别和币种。同一个代码可能对应不同证券或交易场所,身份错误会污染后续所有比较。

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标明新鲜度与交易时段

将来源时间与系统接收时间分开,并清楚标注盘前、常规交易、盘后、延迟或休市数据,避免 Agent 把实时价格与昨日收盘价当成同一口径。

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定义涨跌基准

明确涨跌幅是相对前收盘、开盘价还是滚动时间窗口。拆股、分红和复权历史会改变波动含义,企业行动附近尤其需要谨慎。

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让预警带有上下文

组合使用阈值、持续时间、流动性检查、冷却期和确认信号。单个异常 Tick 不应触发与“成交量、新闻或公告共同确认的持续波动”相同的流程。

AI Agent 所需的最小报价契约

  • 标准化代码、公司或标的名称、交易所、币种、资产类型和交易时段标签。
  • 价格、涨跌基准、来源时间、接收时间、声明延迟、数据服务商和授权状态。
  • 当报价用于监控或接近执行的决策时,还应提供买价、卖价、价差、成交量和质量标记。
  • 明确规定数据过期、代码不存在、行情中断、配额耗尽及多来源冲突时的处理方式。

合格的预警示例:“NVDA 在常规交易时段截至 09:45:12 ET 较前收盘上涨 2.3%,且成交量高于近期盘中基线。”证据应直接进入预警内容,而不是只留在仪表盘里。