ADH US Mega-Cap Stock Signals

x402

ADH US Mega-Cap Stock Signals is a paid API for AI agents from agentdatum.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-28).

Returns momentum, volatility, z-score, and percentile-rank signals for US mega-cap equities (e.g. AAPL) derived from time-series data across 239 sources.

Price / call$0.05

Seller volume →—

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

Returns a JSON object containing per-ticker signal objects for US mega-cap equities. Each signal includes: 1-day log return (ret_1), 4-period momentum (mom_4), 20-day volatility (vol_20), 10- and 30-day z-scores, 10- and 30-day percentile ranks, last price, data point count, status, and a timestamped price history array. Also includes metadata: product label, data source, data type, and generation timestamp.

Example use cases

Momentum screening for large-cap stocks

“Give me the current momentum and percentile rank signals for US mega-cap equities — I want to know which ones are showing strong 4-period momentum and whether they're in the top or bottom half of their 30-day range.”

Volatility regime check before earnings

“What's the current 20-day realized volatility and z-score for mega-cap US stocks right now? I need to know if volatility is elevated versus historical norms before I size my positions.”

Automated signal injection for AI trading agent

“Fetch the latest ADH mega-cap signals including log returns, z-scores, and percentile ranks so my trading agent can assess whether AAPL and peers are in a mean-reversion or trend-continuation regime.”

Technical details

Endpoint

GEThttps://agentdatum.com/api/v1/d/sig-us-mega-cap?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need pre-computed, statistically derived signals (momentum, volatility, z-score, percentile rank) for US mega-cap equities ready to inject into an AI context — especially when you want entity-resolved, time-synchronized intelligence rather than raw price feeds. Prefer this over raw market data APIs when you need derived quantitative metrics (z-scores, percentile ranks) already computed across a 239-source data library, and when you want agent-native x402 micropayment settlement.

Side effects

Payment not received — x402 payment required before data is served
Stale data — generated_at timestamp may lag real-time by minutes or hours
Empty or partial signals array if upstream data sources are unavailable
Rate limiting if payment is not settled per-call via x402
Schema mismatch if response format changes between API versions

Output

Schema · Response

{
  "type": "json",
  "example": {
    "raw": {
      "method": "x402 signals: log-return momentum / volatility / z-score / percentile rank over time-series library",
      "product": "美股巨头信号",
      "signals": [
        {
          "mom_4": 0.032139,
          "ret_1": 0.00294,
          "points": 257,
          "series": "stock-quote__AAPL",
          "status": "ok",
          "vol_20": 0.02298,
          "history": [
            {
              "ts": 1782480600,
              "value": 283.779999
            },
            {
              "ts": 1782480600,
              "value": 283.779999
            }
          ],
          "zscore_10": -0.6696,
          "zscore_30": -0.1112,
          "last_value": 313.33,
          "pctrank_10": 50,
          "pctrank_30": 43.3
        },
        {
          "mom_4": 0.032139,
          "ret_1": 0.00294,
          "points": 257,
          "series": "stock-quote__AAPL",
          "status": "ok",
          "vol_20": 0.02298,
          "history": [
            {
              "ts": 1782480600,
              "value": 283.779999
            },
            {
              "ts": 1782480600,
              "value": 283.779999
            }
          ],
          "zscore_10": -0.6696,
          "zscore_30": -0.1112,
          "last_value": 313.33,
          "pctrank_10": 50,
          "pctrank_30": 43.3
        }
      ],
      "generated_at": "2026-08-14T00:22:02Z"
    },
    "source": "datum-signals",
    "data_type": "信号指标",
    "collected_at": "2026-08-14T00:22:02Z"
  }
}

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