S&P 500 (SPY)
Top drivers
⌁ mcp.call("adw-103") vADW-103-live-1.0 Identify cyclical volatility regime shifts to time entries and exits with higher predictive accuracy than standard volatility metrics.
S&P 500 (SPY)
Top drivers
⌁ mcp.call("adw-103") vADW-103-live-1.0 A volatility-regime routing agent checks ADW-103 each day; when vol_slope_10d turns positive and the percentile-rank score rises above 60 (current reading is 65.8, 62nd percentile of a 2,485-day history), it shifts the portfolio's options book from short-gamma to long-gamma posture by auto-rolling near-dated short straddles into calendar spreads, attaching the IOM's trend field and methodology_version to each trade ticket so the risk-ops team can reconstruct the regime context months later. A falling score below 35 reverses the posture and signals that the OLS slope of 20-day realized vol has turned negative, indicating a calm-regime entry.
A derivatives strategist at a prime brokerage uses ADW-103's realized_vol_20d_ann alongside the 10-day OLS slope to advise institutional clients on entry timing for variance swaps — specifically, entering long variance when Phase-Slope is low (vol slope negative, cheapening realized vol) and exiting when the score approaches the top quartile. Compared to simply watching the VIX term structure, Phase-Slope provides a ticker-grain, percentile-ranked signal that normalizes the slope magnitude against the full history, so the strategist can compare regimes across years rather than eyeballing a raw number.
20-day rolling realized vol series → 10-day OLS slope → percentile-rank over 252-day slope history → 0-100 score
Version ADW-103-live-1.0 · validated to beat a naive baseline · benchmark: none
One call returns the answer with its reasoning attached — the live Intelligence Object for ADW-103.
{
"product_id": "ADW-103",
"entity": "S&P 500 (SPY)",
"score": 65.8,
"trend": "rising",
"confidence": 0.78,
"top_drivers": [
{
"factor": "vol_slope_10d",
"contribution": 0.001618
},
{
"factor": "realized_vol_20d_ann",
"contribution": 0.1622
},
{
"factor": "slope_percentile_rank",
"contribution": 0.6577
}
],
"recommended_use": "Identify cyclical vol turning points. Score > 60 = vol accelerating — consider defensive positioning or long-vol strategies.",
"methodology_version": "ADW-103-live-1.0",
"freshness": "2026-06-26T20:00:16.189Z",
"coverage": "S&P 500 ETF daily returns — 252 days",
"source_lineage": [
"Stooq daily OHLCV (SPY)"
],
"allowed_use": "informational",
"realized_vol_20d_ann": 0.1622,
"vol_slope_10d": 0.001618,
"vol_window_days": 20,
"slope_window_days": 10,
"validation_status": "descriptive"
} Every product conforms to the Intelligence Object Model — typed, versioned, and discoverable.
Dashboard
Read the score + drivers in the console.
REST API
/v1/intelligence/adw-103
MCP tool
adw.adw_103
Marketplace
Discoverable by any MCP agent via the MCP registry.
White-label
Embed under your own brand (Platinum).
Enables traders to anticipate imminent volatility regime shifts to optimize position sizing and hedge timing before market instability occurs.
Method: Log-returns → 60-day rolling z-score → CUSUM(k=0.5) → Shannon entropy weight → sigmoid-normalize over 252-day history → 0-100 score
Enables traders to detect early-stage increases in fat-tail risk to prevent catastrophic drawdowns during market stress events.
Method: 252-day baseline σ → threshold T=1.5σ → P_base + P_recent (20-day) tail exceedance rates → Shift = P_recent−P_base → sigmoid → 0-100
Helps traders identify assets with expanding volatility ranges to anticipate breakout opportunities and optimize entry timing.
Method: True Range per bar → ATR-14 / ATR-252 ratio → percentile-rank vs rolling history → 0-100 score; >1 = range expanding vs baseline