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ADH US Macro Signals — agentdatum.com
x402
ADH US Macro Signals — agentdatum.com is a paid API for AI agents from agentdatum.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-17).
Returns computed US macroeconomic signals (log-return momentum, volatility, z-score, percentile rank) derived from FRED and other macro data sources, normalized and timestamped for AI agent consumption.
Price / call$0.05
Success—
StatusUnknown
How this service works
ADH 金融情报层：把 239 个原始数据源洗成带实体解析、时间同步、溯源与派生结论的情报（宏观regime / 加密尽调 / 事件窗口），免费注入你的 AI 上下文。Agent 走 x402 结算，人类订阅（PayPal）即将上线。
Output
A JSON object containing an array of macro signal records, each with: series ID (e.g. econ-fred__GDP), status, number of data points, most recent value, log-return over 1 period, 4-period momentum, 20-period volatility, 10- and 30-period z-scores, 10- and 30-period percentile ranks, and a history array of {timestamp, value} pairs. Also includes source, data_type, and collection/generation timestamps.
Example use cases
Macro regime detection for portfolio
“Get me the current US macro regime signals — z-scores, momentum, and percentile ranks across economic series — so I can decide whether to rotate into defensive assets.”
Quantitative model feature engineering
“I'm building a quant model and need normalized US macro features: log-return momentum over 4 periods, 20-day volatility, and 30-period z-scores for the GDP series from ADH.”
AI agent economic context injection
“Before my agent makes any market calls today, fetch the latest ADH US macro signals and inject the current economic conditions — momentum, vol, and z-scores — into its context.”
Technical details
Endpoint
GEThttps://agentdatum.com/api/v1/d/sig-macro-us?utm_source=zero.xyzWhen to prefer
Choose this endpoint when your agent needs pre-computed, normalized US macroeconomic signals (momentum, volatility, z-score, percentile rank) rather than raw economic data — it aggregates 239 sources, resolves entities, and time-syncs series so your agent gets analysis-ready intelligence without doing its own signal computation. Prefer it over raw FRED API calls when you need regime context, cross-series comparability, or derived conclusions baked in.
Side effects
- Payment not settled via x402 — returns 402 Payment Required
- Upstream FRED or data source temporarily unavailable — may return stale or partial data
- Series with insufficient history returns zeros for computed metrics (zscore_10=0, pctrank_10=0)
- Invalid or missing x402 payment header — request rejected before processing
Output
Schema · Response
{
"type": "json",
"example": {
"raw": {
"method": "x402 signals: log-return momentum / volatility / z-score / percentile rank over time-series library",
"product": "美国宏观信号",
"signals": [
{
"mom_4": 0,
"ret_1": 0,
"points": 38,
"series": "econ-fred__GDP",
"status": "ok",
"vol_20": 0,
"history": [
{
"ts": 1751328000,
"value": 31098.027
},
{
"ts": 1751328000,
"value": 31098.027
}
],
"zscore_10": 0,
"zscore_30": 0.3604,
"last_value": 32475.21,
"pctrank_10": 0,
"pctrank_30": 13.3
},
{
"mom_4": 0,
"ret_1": 0,
"points": 38,
"series": "econ-fred__GDP",
"status": "ok",
"vol_20": 0,
"history": [
{
"ts": 1751328000,
"value": 31098.027
},
{
"ts": 1751328000,
"value": 31098.027
}
],
"zscore_10": 0,
"zscore_30": 0.3604,
"last_value": 32475.21,
"pctrank_10": 0,
"pctrank_30": 13.3
}
],
"generated_at": "2026-08-14T00:22:02Z"
},
"source": "datum-signals",
"data_type": "信号指标",
"collected_at": "2026-08-14T00:22:02Z"
}
}Tags
x402
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