Compact metric feed shaped for agent prompts — z-scores, percentiles, t-scores by factor class for chosen assets. Same coverage as /api/asset-factors but optimized for LLM context efficiency. Use as raw context for quant-aware agents.

Compact signal feed shaped for agent prompts — normalized field names, comma-separated asset filter, JSON-only. Same 143-signal coverage as /api/signals/active but optimized for LLM context efficiency. Use when injecting signal data directly into an agent's prompt.

Asset × asset rolling correlation matrix over a configurable window. Use for portfolio construction, diversification checks, and identifying regime shifts in cross-asset relationships.

Regime label history per day for a single asset. Use to study how an asset behaves in each regime — entry timing, sizing, and stop calibration vary materially by regime.

Class × horizon factor t-scores per asset across CRYPTYX's 8 canonical factor classes (CORR, EFF, FLOW, FUT, OB, OPT, TR, VOL). Computed from 440+ underlying metrics with policy-aware winsorization and z-capping. Use for cross-sectional ranking, sector rotation, and factor exposure analysis.

Order-book depth per asset at 50, 100, and 200 basis points — bid USD notional, ask USD notional, imbalance ratio. Aggregated across Binance spot books, refreshed every 15 minutes. Use for slippage estimation, exchange routing, and pre-trade liquidity checks.

Macro regime classification per asset — expansion / consolidation / contraction — with confidence (0-1). Regimes are factor-derived (not price-derived), so they shift before price action confirms. Use to gate strategies by market state — mean-reversion logic fails in trending regimes.

All currently-triggered signals for a single asset with confidence, grade, and trigger reason. Filtered to one name. Use as the per-asset live signal feed.

Return asymmetry stats per asset — skew, tail ratio, expected shortfall (5%). Identifies fat-tailed names. Use for tail-aware position sizing and risk-parity overlays.

Peer cluster for a single asset — cluster label and similarity-ranked peers with correlation. Computed from rolling 30-day factor correlations. Use for pairs trading, basket construction, and hedge selection.

Current macro regime label and confidence for a single asset. Single-call regime check. Use before sizing — strategies that work in expansion regimes often fail in consolidation.

Time series of signal firings on a single asset over the last N days — day, signal_id, triggered, confidence. Use for signal-level backtesting on a specific name and "when did X last fire" lookups.

Complete per-asset thesis — composite score, rank, dominant factor class with t-score, top firing signals with grades, regime, key metrics. The canonical "should I trade this asset" call. Replaces 10+ separate API calls.

Daily history of an asset's thesis — composite score, regime, signal count, dominant factor class by day. Use to track conviction evolution over time and study how an asset behaved before a major move.

Top metrics for a single asset ranked by IC and hit rate. Asset-specific — the strongest predictors for SOL aren't the same as for BTC. Use to find the most reliable inputs for an asset before designing a strategy.

Top-grade signals firing on a single asset right now with hit rate and mean forward return per signal. Use as the entry point for asset-level deep dives — "why should I look at SOL today?" answered in one call.

Composite scores and ranks across ~200 digital assets — comp_score, comp_rank, 1d / 7d returns, factor breadth, conviction count. Snapshot or time-series mode. Use as the master ranking feed for portfolio construction, watchlist generation, and rotation strategies.

Perpetual futures funding rate per asset — current rate, annualized, z-score vs trailing 30-day distribution. Sourced from Deribit and CoinGlass aggregated feeds. Use for carry trades, crowding detection, and timing perp entries when funding stretches to extremes.

Natural-language intelligence query over the full CRYPTYX state — returns answer plus structured sources cited (factor scores, signal triggers, regime context). Use when the question doesn't fit a fixed schema, or when piping an LLM agent into CRYPTYX without per-endpoint plumbing.

Deribit implied-volatility surface for an asset — expiry × strike grid with IV, delta, and gamma. Use for options structuring, skew reads, and risk-reversal pricing.

Daily OHLCV for an asset (up to 365 days lookback) — open, high, low, close, volume. UTC−1 canonical anchor day. Sourced from CoinMarketCap. Use as the spot price feed underlying all CRYPTYX metrics — same data our factor scores compute on.

Market-wide signal pulse — total triggers across 143 atomic signals, positive/negative counts per factor class, net breadth, asof day. Single-call market state snapshot for the universe of ~200 assets. Use to time risk-on / risk-off shifts and detect early breadth divergence.

Highest- and lowest-conviction assets by composite score, with the count of atomic signals contributing per name. Pre-ranked, top and bottom of the universe. Use for filtered watchlists, short-list generation, and daily "where to look" briefings.

Cross-factor-class divergence flags per asset (e.g. TR bullish while VOL bearish) with spread magnitude. Configurable horizons. Use to find non-consensus setups before they become crowded — divergences mark inflection points in factor agreement.

Asset × factor-class t-score matrix with rank per class — full universe of ~200 assets. Use for factor-tilt construction, sector rotation, and "show me the strongest momentum assets right now" queries.

Regime distribution across the universe — count of assets in each macro regime (expansion/consolidation/contraction), per-asset regime + confidence, summary totals. Use to read the breadth of risk-on/off positioning across the universe at a glance.

Single-metric z-score backtest — hit rate, mean forward return, per-asset breakdown across a configurable z-threshold. Backed by 5+ years of fact history (440+ metrics available). Use to test a single metric's standalone edge before incorporating it into a composite.

Multi-factor z-score intersection backtest — 2-4 metric conditions chained with AND logic. Hit rate, mean forward return, sample count. Use to test confluence strategies ("when TR is bullish AND VOL is compressing AND OB depth is rising") before live deployment.

Scan a single metric across all ~200 assets for current z-score extremes — ranked, with percentile context. Use to find live anomalies and outliers — "who is at 3+ sigma on TR_TREND_STRENGTH_14D right now?"

Generate a branded narrative report (daily digest / asset deep dive / signal summary) backed by live CRYPTYX data and AI-synthesized analysis. Outputs structured sections plus markdown narrative. Use for client-facing deliverables, weekly digests, and PM commentary.

All currently-firing atomic and composite signals across ~200 digital assets — signal_id, asset, triggered, confidence (0-1), geometry_type (5 types: single_metric / state_transition / cross_timeframe / multi_factor / regime_conditional). 143 atomic signals + 9 IC-weighted composites, walk-forward validated against 5+ years of price data. Use as a live "what is lighting up right now" feed for agent reasoning.

Backtest a single atomic or composite signal over a custom date range — per-day events plus aggregate trigger rate, total events, average confidence. Backed by 5+ years of indexed factor history (~200 assets). Optional dryrun mode (no writes). Use to validate any signal before deploying it in a live strategy.

Full signal registry — 143 atomic signals and 9 IC-weighted composites (6 thematic, 3 regime) with active parameters, 30-day trigger statistics, geometry type, factor class (CORR/EFF/FLOW/FUT/OB/OPT/TR/VOL), and category. Logic SQL deliberately excluded (protected IP). Use as the canonical capability-discovery call before backtests or live signal subscriptions.

Per-factor-class contribution and weight to an asset's composite score at a given horizon — decomposes the composite into its drivers. Use to explain WHY an asset is ranked where it is — essential for client-facing narratives and model audits.

Backtest a custom-weighted composite across the 8 factor classes — per-day composite return series with Sharpe, Sortino, max drawdown. Backed by 5+ years of factor history. Use to design and validate multi-factor strategies before promoting them to live composites.

Bullish / bearish breadth per factor class at a chosen horizon — total counts, per-class splits, net positioning. Use to read market participation: is this rally broad, or concentrated in a few names?

Re-rank the entire ~200-asset universe by a custom class-weight composite — pass your own weights across the 8 factor classes. Use to encode an institutional house view as a ranking system or A/B test alternative composite recipes.

Composite intensity heatmap — asset × factor-class intensity grid (0-1 normalized) over the last N days. Use to spot factor concentration visually — when one class is hot across many assets, regime is forming.

Composite score momentum over a configurable window — score_1d, score_7d, delta per asset, ranked by acceleration. Detects regime shifts before they show in price. Use as an early-warning system for trend changes.

Evaluate one or all signals for a given day — triggered count, total assets considered, per-asset breakdown. Optional dryrun (no signal_log writes). Use as a smoke test for the eval pipeline or to recompute a missed eval day.

Per-day explainability for a single (signal × asset × day) — full trigger reason, factor scores by class, composite score, parameters in effect, regime context. Audit-grade output. Use when an agent (or human) needs to justify a position or audit a model's reasoning.

Fork a signal with new parameters into an inactive parameter version — does not disrupt production firing. Versioned through signals.signal_params with full audit trail. Use as the experimentation primitive for agent-driven signal optimization.

Ranked signal leaderboard with IC (Spearman rank correlation), hit rate, IR (information ratio), grade, and asset coverage at a chosen forward horizon. Evaluated against ~200 assets × 5+ years of returns. Use to surface the strongest predictors across the universe and benchmark new signals.

Recent signal firings stream (last N events) — signal_id, asset, day, triggered, confidence — sorted newest-first. Use for change-detection feeds, agent digests, and "what changed since I last checked" summaries.

Estimate a proposed threshold change's effect on a signal — projected trigger rate, sample triggers, per-asset distribution. Runs against 5+ years of fact history without writing. Use to size parameter changes before committing them to the registry.

Top-grade signals ranked by IC-weighted performance at a forward horizon (7d / 14d / 30d) — signal_id, grade (A/B/C/U), hit rate, mean forward return, sample size. Updated daily from rolling Spearman-rank evaluations across ~200 assets × 5+ years. Use to select which signals to wire into a live trading strategy.

Aggregated taker buy/sell volume per asset with buy-sell ratio. Reads aggressor pressure independently of price — flow can lead price by hours. Use for high-frequency entry timing and tape-reading at the asset level.

Full state snapshot for LLM grounding — factor breadth (bullish/bearish), top/bottom composite rankings, signal trigger summary, pipeline run status, asof day. Single parallel-read call returning everything an agent needs before reasoning. Use BEFORE any market analysis call.

Daily AI-readable market brief — top-ranked assets by composite score, total signal triggers across the universe, anchor day, structured narrative summary. Built for LLM context windows. Use as a single-call grounding before any market reasoning task.

Top-ranked trade ideas across the universe — each idea bundles composite score, regime context, top backtested signal setups with hit rate, and derivatives overlay (IV, funding) in a single call. Pre-ranked by conviction. The canonical "where should my agent look right now?" call for autonomous trading systems.