feat: advanced microstructure modules — HLP, Hawkes, Whipsaw, Term Structure, Liq Waterfall, Spoof Detector

6 new modules with 46 new tests (230 total):

#21 HLP Vault Monitor (live/monitors/hlp_vault.py):
  Tracks Hyperliquid's native protocol market maker at address
  0xfefefe... Queries clearinghouseState + metaAndAssetCtxs.
  - Delta exposure per asset (notional + PnL)
  - Overextension detection (notional exceeds  M threshold)
  - Rebalancing signals: fade_short when HLP too short,
    fade_long when HLP too long (front-run forced rebalancing)
  - Toxicity score: HLP losing money = absorbing informed flow
  - Historical delta tracking

#29 Hawkes Processes (microstructure/hawkes.py):
  Multivariate Hawkes calibrator for limit order book dynamics.
  - MLE calibration via SGD gradient descent on log-likelihood
  - Branching ratio enforcement (alpha/beta < 0.99 for stationarity)
  - Intensity computation λ_i(t) with cross-excitation
  - Activity forecasting (expected event count in horizon)
  - Synthetic event generator (Ogata thinning)
  - Pure functions: hawkes_intensity, hawkes_log_likelihood,
    generate_hawkes_events

#23 Funding Whipsaw Trader (live/strategies/funding_whipsaw.py):
  Premium index decay trading in final 60s of funding epoch.
  - Detects deterministic convergence of premium→0 at settlement
  - Time-scaled position sizing (larger closer to settlement)
  - Auto-close after funding epoch completes
  - Confidence scoring based on premium magnitude

#32 Term Structure Monitor (live/monitors/term_structure.py):
  Perp/quarterly/bi-quarterly futures basis curve trading.
  - Quarterly-perp basis with z-score anomaly detection
  - BiQ-quarterly curve steepness monitoring
  - Fair quarterly price via interest rate parity + funding carry
  - Calendar spread signals: buy_basis, sell_basis,
    curve_steepener, curve_flattener

#24 Liquidation Waterfall (live/monitors/liq_waterfall.py):
  Cross-margin liquidation order prediction.
  - Margin ratio tracking (equity / maintenance margin)
  - Danger/critical level classification
  - Asset liquidation priority: maintenance / book_liquidity ratio
    (least liquid asset relative to margin = dumped first)
  - Strategy output: widen_spreads on target, tighten on rest

#31 Spoof Detector (microstructure/spoof_detector.py):
  Adversarial ML-style spoofing pattern recognition.
  - Rule 1: Large order far from mid, cancelled immediately
  - Rule 2: Cancel right before trade approaches price level
  - Rule 3: Oversized order with no fill within short lifetime
  - Spoof probability (rolling window ratio)
  - Cancel-to-fill ratio monitoring

live/monitors/hlp_vault.py

@@ -0,0 +1,269 @@
+"""
+HLP (Hyperliquidity Provider) Vault monitoring.
+
+Tracks Hyperliquid's native protocol-level market-making vault at
+address 0xfefefefefefefefefefefefefefefefefefefefe.
+
+HLP acts as counterparty to all user trades. When it absorbs toxic flow
+or becomes directionally overextended, it must rebalance — creating
+predictable market impact that can be traded.
+
+Signals:
+  - fade_short: HLP is too short → expect buying rebalance → go long
+  - fade_long:  HLP is too long → expect selling rebalance → go short
+  - neutral: HLP delta is balanced, safe to provide liquidity alongside
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from collections import deque
+from typing import Optional
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+HLP_ADDRESS = "0xfefefefefefefefefefefefefefefefefefefefe"
+TESTNET_API = "https://api.hyperliquid-testnet.xyz/info"
+MAINNET_API = "https://api.hyperliquid.xyz/info"
+
+
+class HlpVaultMonitor:
+    """Monitor HLP vault state — delta, PnL, rebalancing pressure, toxicity."""
+
+    def __init__(
+        self,
+        testnet: bool = True,
+        overextended_threshold: float = 5.0,    # notional in $M before overextended
+        history_window: int = 1000,
+    ):
+        self._api_url = TESTNET_API if testnet else MAINNET_API
+        self._overextended_threshold = overextended_threshold * 1_000_000  # convert to USD
+        self._testnet = testnet
+
+        # Per-coin state
+        self._positions: dict[str, dict] = {}      # coin → {side, szi, entry_px, upnl}
+        self._mark_prices: dict[str, float] = {}    # coin → mark price
+        self._oracle_prices: dict[str, float] = {}  # coin → oracle price
+        self._funding_rates: dict[str, float] = {}  # coin → funding rate
+        self._open_interest: dict[str, float] = {}  # coin → OI
+
+        # History
+        self._delta_history: dict[str, deque] = {}  # coin → deque of (time, notional)
+        self._pnl_history: list[dict] = []
+        self._history_window = history_window
+
+        self._last_update: float = 0
+        self._update_count: int = 0
+
+    # ── Core update ──────────────────────────────────────────
+
+    def _api_post(self, payload: dict) -> dict:
+        """Make a POST request to HL info API (testable via mock)."""
+        resp = requests.post(self._api_url, json=payload, timeout=10)
+        resp.raise_for_status()
+        return resp.json()
+
+    def update(self):
+        """Fetch latest HLP state from Hyperliquid API.
+
+        Makes two calls:
+          1. metaAndAssetCtxs → mark prices, funding, OI
+          2. clearinghouseState(HLP_ADDRESS) → positions, PnL
+        """
+        now = time.time()
+
+        # Fetch market data
+        try:
+            meta_and_ctx = self._api_post({"type": "metaAndAssetCtxs"})
+            if isinstance(meta_and_ctx, list) and len(meta_and_ctx) >= 2:
+                universe = meta_and_ctx[0].get("universe", [])
+                ctxs = meta_and_ctx[1]
+                for i, asset in enumerate(universe):
+                    name = asset.get("name", "")
+                    if name and i < len(ctxs):
+                        self._mark_prices[name] = float(ctxs[i].get("markPx", 0))
+                        self._oracle_prices[name] = float(ctxs[i].get("oraclePx", 0))
+                        self._funding_rates[name] = float(ctxs[i].get("funding", 0))
+                        self._open_interest[name] = float(ctxs[i].get("openInterest", 0))
+        except Exception as e:
+            logger.warning("HLP meta fetch error: %s", e)
+
+        # Fetch HLP positions
+        try:
+            ch_state = self._api_post({
+                "type": "clearinghouseState",
+                "user": HLP_ADDRESS,
+            })
+            self._parse_positions(ch_state, now)
+        except Exception as e:
+            logger.warning("HLP clearinghouse fetch error: %s", e)
+
+        self._last_update = now
+        self._update_count += 1
+
+    def _parse_positions(self, data: dict, now: float):
+        """Parse clearinghouseState response into per-coin positions."""
+        asset_positions = data.get("assetPositions", [])
+        new_positions: dict[str, dict] = {}
+
+        for ap in asset_positions:
+            pos = ap.get("position", {})
+            if not pos:
+                continue
+            coin = pos.get("coin", "")
+            if not coin:
+                continue
+            szi = float(pos.get("szi", 0))
+            entry_px = float(pos.get("entryPx", 0))
+            upnl = float(pos.get("unrealizedPnl", 0))
+            side = pos.get("side", "")  # "A" = short, "B" = long
+
+            # HLP short is side="A", long is side="B"
+            signed_szi = -szi if side == "A" else szi
+
+            new_positions[coin] = {
+                "side": side,
+                "szi": szi,
+                "signed_szi": signed_szi,
+                "entry_px": entry_px,
+                "unrealized_pnl": upnl,
+                "mark_px": self._mark_prices.get(coin, entry_px),
+                "notional_usd": abs(szi) * self._mark_prices.get(coin, entry_px),
+            }
+
+            # Update delta history
+            if coin not in self._delta_history:
+                self._delta_history[coin] = deque(maxlen=self._history_window)
+            self._delta_history[coin].append({
+                "t": now,
+                "signed_szi": signed_szi,
+                "notional_usd": new_positions[coin]["notional_usd"],
+                "upnl": upnl,
+            })
+
+        self._positions = new_positions
+        self._pnl_history.append({
+            "t": now,
+            "total_upnl": sum(p["unrealized_pnl"] for p in new_positions.values()),
+            "asset_count": len(new_positions),
+        })
+        if len(self._pnl_history) > self._history_window:
+            self._pnl_history = self._pnl_history[-self._history_window:]
+
+    # ── Queries ──────────────────────────────────────────────
+
+    def position(self, coin: str) -> float:
+        """Signed position for a coin (positive = long)."""
+        pos = self._positions.get(coin.upper(), {})
+        return pos.get("signed_szi", 0.0)
+
+    def delta_exposure(self) -> dict:
+        """Delta exposure per coin with notional and PnL."""
+        result = {}
+        for coin, pos in self._positions.items():
+            result[coin] = {
+                "signed_size": pos["signed_szi"],
+                "notional_usd": round(pos["notional_usd"], 2),
+                "unrealized_pnl": round(pos["unrealized_pnl"], 2),
+                "side": "long" if pos["side"] == "B" else "short",
+            }
+        return result
+
+    def is_overextended(self, coin: str) -> bool:
+        """Check if HLP delta on this coin exceeds the threshold."""
+        pos = self._positions.get(coin.upper(), {})
+        notional = pos.get("notional_usd", 0)
+        return notional > self._overextended_threshold
+
+    def overextended_assets(self) -> list[str]:
+        """List of assets where HLP is overextended."""
+        return [c for c in self._positions if self.is_overextended(c)]
+
+    def rebalancing_signal(self, coin: str) -> dict:
+        """Generate a trading signal based on HLP rebalancing pressure.
+
+        Returns:
+            signal: "fade_short" | "fade_long" | "neutral"
+            direction: -1 (short) | 0 | 1 (long) for the TRADE direction
+            overextended: whether HLP is over capacity
+        """
+        pos = self._positions.get(coin.upper(), {})
+        if not pos:
+            return {"signal": "neutral", "direction": 0, "overextended": False, "reason": "no_position"}
+
+        signed = pos["signed_szi"]
+        is_over = self.is_overextended(coin)
+
+        if not is_over:
+            return {"signal": "neutral", "direction": 0, "overextended": False, "reason": "balanced"}
+
+        # HLP is short (side=A, signed_szi negative) → it will need to buy to rebalance
+        # We should fade the short (go long)
+        if signed < -0.001:
+            return {
+                "signal": "fade_short",
+                "direction": 1,
+                "overextended": True,
+                "reason": f"HLP short {abs(signed):.2f} units, expect buying rebalance",
+                "notional_usd": round(pos["notional_usd"], 2),
+            }
+        # HLP is long (side=B, signed_szi positive) → it will need to sell to rebalance
+        # We should fade the long (go short)
+        elif signed > 0.001:
+            return {
+                "signal": "fade_long",
+                "direction": -1,
+                "overextended": True,
+                "reason": f"HLP long {signed:.2f} units, expect selling rebalance",
+                "notional_usd": round(pos["notional_usd"], 2),
+            }
+        else:
+            return {"signal": "neutral", "direction": 0, "overextended": False, "reason": "flat"}
+
+    def toxicity_score(self) -> float:
+        """Estimate how much toxic flow HLP is absorbing.
+
+        Higher score = HLP is losing money = informed traders are beating it.
+        Range: 0 (healthy) to 1 (toxic).
+
+        Uses: unrealized PnL / total notional as a proxy.
+        """
+        total_notional = sum(p["notional_usd"] for p in self._positions.values())
+        total_upnl = sum(p["unrealized_pnl"] for p in self._positions.values())
+
+        if total_notional <= 0:
+            return 0.0
+
+        # Negative PnL → toxic score > 0
+        # Positive PnL → toxic score 0 (healthy)
+        loss_ratio = max(0.0, -total_upnl / total_notional)
+        toxicity = min(1.0, loss_ratio * 10)  # scale: 10% loss = 1.0 toxicity
+        return round(toxicity, 4)
+
+    def delta_history(self, coin: str) -> list[dict]:
+        """Historical delta trace for a coin."""
+        return list(self._delta_history.get(coin.upper(), []))
+
+    # ── Summary ──────────────────────────────────────────────
+
+    def summary(self) -> dict:
+        """One-shot summary of HLP state for dashboard/monitoring."""
+        assets = list(self._positions.keys())
+        total_delta = sum(p["notional_usd"] for p in self._positions.values())
+        overextended = self.overextended_assets()
+        signals = {coin: self.rebalancing_signal(coin) for coin in assets}
+
+        return {
+            "assets_tracked": len(assets),
+            "total_delta_usd": round(total_delta, 2),
+            "total_delta_m": round(total_delta / 1_000_000, 2),
+            "toxicity_score": self.toxicity_score(),
+            "overextended_assets": overextended,
+            "signals": signals,
+            "positions": self.delta_exposure(),
+            "last_update": self._last_update,
+            "update_count": self._update_count,
+        }

live/monitors/liq_waterfall.py

@@ -0,0 +1,168 @@
+"""
+Cross-margin liquidation waterfall prediction.
+
+When a whale's cross-margin portfolio approaches liquidation, the
+Hyperliquid liquidation engine selects which asset to dump first based
+on maintenance margin requirements and order book liquidity.
+
+By monitoring large cross-margin accounts via clearinghouseState,
+we can predict which asset gets liquidated first and position accordingly:
+  - Widen spreads on the predicted liquidation asset
+  - Tighten spreads on non-liquidation assets
+  - Pre-position for the post-liquidation bounce
+"""
+
+from __future__ import annotations
+
+import logging
+from collections import deque
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+class LiquidationWaterfall:
+    """Predict the order of cross-margin liquidations for large accounts."""
+
+    def __init__(
+        self,
+        danger_margin_ratio: float = 1.2,     # margin_ratio < this = danger
+        critical_margin_ratio: float = 1.05,   # margin_ratio < this = imminent
+        maintenance_margin_pct: float = 0.03,   # 3% maintenance
+        book_depth_window: int = 10,            # levels to estimate liquidity
+    ):
+        self._danger = danger_margin_ratio
+        self._critical = critical_margin_ratio
+        self._mm_pct = maintenance_margin_pct
+        self._depth_window = book_depth_window
+
+        self._accounts: dict[str, dict] = {}   # address → positions, equity
+        self._book_depths: dict[str, dict] = {} # coin → {bid_depth, ask_depth}
+        self._history: deque = deque(maxlen=1000)
+
+    # ── Data feed ────────────────────────────────────────────
+
+    def update_account(self, address: str, positions: list[dict], margin_balance: float):
+        """Update a tracked account's positions and margin."""
+        self._accounts[address] = {
+            "positions": positions,
+            "margin_balance": margin_balance,
+            "margin_ratio": self._compute_margin_ratio(positions, margin_balance),
+        }
+
+    def update_book_depth(self, coin: str, bid_depth: float, ask_depth: float):
+        """Update estimated order book depth for a coin."""
+        self._book_depths[coin.upper()] = {
+            "bid_depth": bid_depth,
+            "ask_depth": ask_depth,
+        }
+
+    # ── Risk assessment ─────────────────────────────────────
+
+    def _compute_margin_ratio(
+        self, positions: list[dict], margin_balance: float
+    ) -> float:
+        """Compute margin ratio = equity / maintenance_margin."""
+        if not positions or margin_balance <= 0:
+            return float("inf")
+
+        total_mm = 0.0
+        for pos in positions:
+            size = abs(float(pos.get("szi", 0)))
+            px = float(pos.get("entryPx", pos.get("markPx", 0)))
+            total_mm += size * px * self._mm_pct
+
+        return margin_balance / total_mm if total_mm > 0 else float("inf")
+
+    def at_risk_accounts(self) -> list[dict]:
+        """List accounts approaching liquidation."""
+        risky = []
+        for addr, acct in self._accounts.items():
+            ratio = acct["margin_ratio"]
+            if ratio < self._danger:
+                level = "critical" if ratio < self._critical else "danger"
+                risky.append({
+                    "address": addr[:10] + "...",
+                    "margin_ratio": round(ratio, 3),
+                    "level": level,
+                    "positions": len(acct["positions"]),
+                })
+        return sorted(risky, key=lambda r: r["margin_ratio"])
+
+    def predict_liquidation_order(self, address: str) -> list[dict]:
+        """Predict which assets get liquidated first for a given account.
+
+        Returns assets ranked by liquidation priority (first to go = top).
+        Uses: maintenance_margin_requirement / book_liquidity ratio.
+        Higher ratio = less liquid relative to margin cost = dumped first.
+        """
+        acct = self._accounts.get(address)
+        if not acct:
+            return []
+
+        positions = acct["positions"]
+        ranked = []
+
+        for pos in positions:
+            coin = pos.get("coin", "").upper()
+            size = abs(float(pos.get("szi", 0)))
+            px = float(pos.get("entryPx", pos.get("markPx", 0)))
+
+            maintenance = size * px * self._mm_pct
+
+            # Liquidity: how much the book can absorb before significant slippage
+            book = self._book_depths.get(coin, {})
+            side = "buy" if float(pos.get("szi", 0)) < 0 else "sell"
+            depth = book.get("bid_depth" if side == "buy" else "ask_depth", size * px * 0.1)
+
+            # Liquidation priority score: higher = dumped first
+            liquidity_ratio = maintenance / max(depth, 1e-8)
+            ranked.append({
+                "coin": coin,
+                "size": size,
+                "notional_usd": round(size * px, 2),
+                "maintenance_usd": round(maintenance, 2),
+                "liquidity_ratio": round(liquidity_ratio, 4),
+                "predicted_first": False,  # set below
+            })
+
+        # Sort by liquidity ratio (highest = least liquid = dumped first)
+        ranked.sort(key=lambda r: r["liquidity_ratio"], reverse=True)
+        if ranked:
+            ranked[0]["predicted_first"] = True
+
+        return ranked
+
+    def signal(self, address: str) -> dict:
+        """Generate trading signal based on predicted liquidation waterfall.
+
+        If an account is in danger and we can predict the liquidation order:
+          - Asset predicted to be dumped first → widen spreads, go short
+          - Other assets in the portfolio → tighten spreads (safer to quote)
+        """
+        acct = self._accounts.get(address)
+        if not acct or acct["margin_ratio"] > self._danger:
+            return {"action": "none", "reason": "account_safe"}
+
+        order = self.predict_liquidation_order(address)
+        if not order:
+            return {"action": "none", "reason": "no_positions"}
+
+        first_asset = order[0]
+        return {
+            "action": "position",
+            "reason": f"liquidation_imminent_{acct['margin_ratio']:.2f}",
+            "margin_ratio": round(acct["margin_ratio"], 3),
+            "liquidation_target": first_asset["coin"],
+            "strategy": {
+                first_asset["coin"]: "widen_spreads_2x",
+                **{r["coin"]: "tighten_spreads" for r in order[1:]},
+            },
+            "predicted_order": [r["coin"] for r in order],
+        }
+
+    def summary(self) -> dict:
+        return {
+            "at_risk_accounts": self.at_risk_accounts(),
+            "tracked_accounts": len(self._accounts),
+        }

live/monitors/term_structure.py

@@ -0,0 +1,197 @@
+"""
+Term structure monitor — perp vs quarterly vs bi-quarterly futures basis.
+
+Hyperliquid offers perpetual (funding-based), quarterly, and bi-quarterly
+futures contracts. The basis curve (perp→quarterly→bi-quarterly) contains
+information about market expectations and can be traded.
+
+Anomalies:
+  - Perp funding deeply negative but quarterly basis remains steep →
+    go long perp (collect funding), short quarterly (lock basis)
+  - Quarterly futures converging to perp at expiration →
+    calendar spread mean-reversion
+  - Bi-quarterly premium over quarterly deviating from fair value →
+    curve steepener/flattener trades
+"""
+
+from __future__ import annotations
+
+from collections import deque
+from typing import Optional
+
+
+class TermStructureMonitor:
+    """Monitor perp/futures term structure for arbitrage opportunities.
+
+    Tracks:
+      - Perp funding rate and mark price
+      - Quarterly futures price
+      - Bi-quarterly futures price (if available)
+      - Basis spreads: quarterly-perp, biq-quarterly, biq-perp
+      - Calendar spread mean-reversion
+    """
+
+    def __init__(
+        self,
+        funding_window: int = 1440,    # 24h of 1-min samples
+        basis_window: int = 100,
+    ):
+        self._funding_window = funding_window
+        self._basis_window = basis_window
+
+        self._perp_prices: dict[str, deque[float]] = {}
+        self._quarterly_prices: dict[str, deque[float]] = {}
+        self._biq_prices: dict[str, deque[float]] = {}
+        self._funding_rates: dict[str, deque[float]] = {}
+
+        self._funding_epoch_seconds: int = 8 * 3600
+        self._quarterly_expiry_days: int = 90
+        self._biq_expiry_days: int = 180
+
+    # ── Data feed ────────────────────────────────────────────
+
+    def update_perp(self, coin: str, price: float, funding_rate: float):
+        c = coin.upper()
+        self._perp_prices.setdefault(c, deque(maxlen=self._basis_window)).append(price)
+        self._funding_rates.setdefault(c, deque(maxlen=self._funding_window)).append(funding_rate)
+
+    def update_quarterly(self, coin: str, price: float):
+        self._quarterly_prices.setdefault(coin.upper(), deque(maxlen=self._basis_window)).append(price)
+
+    def update_biq(self, coin: str, price: float):
+        self._biq_prices.setdefault(coin.upper(), deque(maxlen=self._basis_window)).append(price)
+
+    # ── Basis computation ────────────────────────────────────
+
+    def quarterly_perp_basis(self, coin: str) -> dict | None:
+        """Basis between quarterly future and perpetual."""
+        q = list(self._quarterly_prices.get(coin.upper(), []))
+        p = list(self._perp_prices.get(coin.upper(), []))
+        min_len = min(len(q), len(p))
+        if min_len < 2:
+            return None
+
+        qq = q[-min_len:]
+        pp = p[-min_len:]
+        basis_bps = [(qq[i] - pp[i]) / pp[i] * 10000 for i in range(min_len) if pp[i] > 0]
+
+        if not basis_bps:
+            return None
+
+        return {
+            "current_bps": round(basis_bps[-1], 2),
+            "mean_bps": round(sum(basis_bps) / len(basis_bps), 2),
+            "std_bps": round(_std(basis_bps), 2),
+            "z_score": round((basis_bps[-1] - sum(basis_bps) / len(basis_bps)) / max(_std(basis_bps), 0.01), 2),
+            "n_samples": len(basis_bps),
+        }
+
+    def biq_quarterly_basis(self, coin: str) -> dict | None:
+        """Basis between bi-quarterly and quarterly futures (curve steepness)."""
+        bq = list(self._biq_prices.get(coin.upper(), []))
+        q = list(self._quarterly_prices.get(coin.upper(), []))
+        min_len = min(len(bq), len(q))
+        if min_len < 2:
+            return None
+
+        bb = bq[-min_len:]
+        qq = q[-min_len:]
+        basis_bps = [(bb[i] - qq[i]) / qq[i] * 10000 for i in range(min_len) if qq[i] > 0]
+
+        if not basis_bps:
+            return None
+
+        return {
+            "current_bps": round(basis_bps[-1], 2),
+            "mean_bps": round(sum(basis_bps) / len(basis_bps), 2),
+            "std_bps": round(_std(basis_bps), 2),
+            "z_score": round((basis_bps[-1] - sum(basis_bps) / len(basis_bps)) / max(_std(basis_bps), 0.01), 2),
+            "n_samples": len(basis_bps),
+        }
+
+    def fair_quarterly_price(self, coin: str, risk_free_annual: float = 0.05) -> dict | None:
+        """Compute fair quarterly price from perp via interest rate parity."""
+        p = list(self._perp_prices.get(coin.upper(), []))
+        if not p:
+            return None
+
+        perp_px = p[-1]
+        days = self._quarterly_expiry_days
+        funding = list(self._funding_rates.get(coin.upper(), []))
+        avg_funding = sum(funding[-100:]) / max(len(funding[-100:]), 1) if funding else 0
+
+        # Fair quarterly = perp * (1 + (r + avg_funding) * days/365)
+        carry_rate = risk_free_annual + avg_funding * 3 * 365  # annualize 8h funding
+        fair_px = perp_px * (1 + carry_rate * days / 365)
+
+        return {
+            "perp_price": perp_px,
+            "fair_quarterly": round(fair_px, 2),
+            "carry_rate_annual_pct": round(carry_rate * 100, 2),
+            "days_to_expiry": days,
+        }
+
+    def signal(self, coin: str) -> dict:
+        """Generate term-structure trading signal.
+
+        Returns:
+            signal: "buy_basis", "sell_basis", "curve_steepener", "curve_flattener", "none"
+        """
+        c = coin.upper()
+        qp = self.quarterly_perp_basis(c)
+        bq = self.biq_quarterly_basis(c)
+        fair = self.fair_quarterly_price(c)
+
+        signals = []
+
+        # Check quarterly-perp basis anomalies
+        if qp and abs(qp["z_score"]) > 2.0:
+            if qp["z_score"] > 0:
+                signals.append({
+                    "signal": "sell_basis",
+                    "reason": f"Quarterly {qp['z_score']:.1f}σ rich vs perp",
+                    "confidence": min(1.0, abs(qp["z_score"]) / 4.0),
+                })
+            else:
+                signals.append({
+                    "signal": "buy_basis",
+                    "reason": f"Quarterly {qp['z_score']:.1f}σ cheap vs perp",
+                    "confidence": min(1.0, abs(qp["z_score"]) / 4.0),
+                })
+
+        # Check curve steepness
+        if bq and abs(bq["z_score"]) > 2.0:
+            if bq["z_score"] > 0:
+                signals.append({
+                    "signal": "curve_flattener",
+                    "reason": f"BiQ {bq['z_score']:.1f}σ rich vs quarterly",
+                    "confidence": min(1.0, abs(bq["z_score"]) / 4.0),
+                })
+            else:
+                signals.append({
+                    "signal": "curve_steepener",
+                    "reason": f"BiQ {bq['z_score']:.1f}σ cheap vs quarterly",
+                    "confidence": min(1.0, abs(bq["z_score"]) / 4.0),
+                })
+
+        result = {
+            "coin": c,
+            "signals": signals,
+            "primary_signal": signals[0]["signal"] if signals else "none",
+            "quarterly_perp_basis": qp,
+            "biq_quarterly_basis": bq,
+            "fair_quarterly": fair,
+        }
+
+        return result
+
+    def summary(self) -> dict:
+        return {coin: self.signal(coin) for coin in self._perp_prices}
+
+
+def _std(vals: list[float]) -> float:
+    """Population standard deviation."""
+    if len(vals) < 2:
+        return 0.0
+    mean = sum(vals) / len(vals)
+    return (sum((v - mean) ** 2 for v in vals) / len(vals)) ** 0.5