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Fear & Greed Index

Real-Time Financial Intelligence for AI Agents

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INDEX EXPLANATION
Fear and greed index workflow from components to evidence

Use sentiment as regime context, not a trade command

A fear and greed score compresses several market observations into one number. That makes it easy to scan, but it also hides methodology, timing, missing inputs, and disagreement between components. An AI agent should retain that evidence before using the score in a brief, alert, or risk explanation.

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Inspect the components

Record which momentum, volatility, breadth, safe-haven, volume, options, or sentiment inputs contribute to the index. Note the lookback window, normalization method, direction, weight, and data source for each component.

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Anchor the as-of time

A score can remain visible after one or more inputs have stopped updating. Keep component timestamps, publication time, timezone, update schedule, and missing-data policy with the composite result.

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Interpret the regime

Use extreme fear, fear, neutral, greed, and extreme greed as descriptions of the environment. Compare the current score with its recent range and the components driving the move instead of treating a threshold crossing as an automatic buy or sell signal.

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Add decision guardrails

Require confirmation from price action, volatility, liquidity, news, macro events, or portfolio exposure. Use cooldown periods and human review when sentiment changes would alter risk limits or trigger external actions.

Evidence to attach to every score

  • Composite score, regime label, as-of time, change from the prior observation, and recent percentile or range.
  • Component values and weights, transformations or inversions, lookback windows, and methodology version.
  • Missing or delayed inputs, fallback values, confidence impact, and the next scheduled update.
  • The role of the score in the workflow: context for a brief, alert-priority input, risk explanation, or research trigger.

Responsible interpretation: “Greed” describes the current risk-seeking regime; it does not identify what to buy, when to enter, or how much capital to commit. Preserve the evidence and combine it with the decision’s actual risk factors.

把情绪当作市场环境,而不是交易指令

恐惧与贪婪指数把多个市场观察压缩成一个数字,方便浏览,也会同时隐藏方法、时间、缺失输入和各分项之间的分歧。AI Agent 在将其用于简报、预警或风险解释前,应保留这些证据。

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检查指数构成

记录动量、波动率、市场宽度、避险需求、成交量、期权或舆情等哪些输入参与计算,并说明每个分项的回看窗口、标准化方式、方向、权重和来源。

📅

绑定截至时间

页面仍可能显示一个分数,但其中部分输入已经停止更新。应将各分项时间、发布时间、时区、更新频率和缺失数据处理规则与综合结果放在一起。

🧭

解释市场状态

“极度恐惧、恐惧、中性、贪婪、极度贪婪”应被当作环境描述。应比较当前值与近期区间及主要驱动分项,而不是把越过某个阈值直接变成买卖指令。

🛡️

加入决策护栏

要求价格走势、波动率、流动性、新闻、宏观事件或组合敞口提供确认。当情绪变化会调整风险上限或触发外部动作时,应设置冷却期并保留人工复核。

每个分数都应附带的证据

  • 综合分数、状态标签、截至时间、较上一期变化,以及近期分位或区间位置。
  • 分项数值与权重、转换或反向处理、回看窗口和计算方法版本。
  • 缺失或延迟的输入、替代值、对可信度的影响,以及下一次计划更新时间。
  • 该分数在流程中的真实作用:简报背景、预警优先级输入、风险解释还是进一步研究的触发器。

负责任的解释:“贪婪”只说明当前环境偏向风险追逐,并不回答买什么、何时买或投入多少资金。应保留计算证据,并将它与实际决策所涉及的风险因素结合。