blue_parity: hyphen-tolerant price_of (first add of module)

PinkAssetPicker/PinkAlphaSizer (BLUE-parity IRP picker + sizer, 2026-06-10)
plus the 2026-06-11 fix: price_of falls back to the dehyphenated symbol —
reconcile/fill paths write venue format (FET-USDT) into the slot while the
scan universe keys are Binance-style (FETUSDT); without the fallback an
adopted slot had no price and TP/SL/MAX_HOLD never evaluated (the unmanaged
FET position incident).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

prod/clean_arch/dita_v2/blue_parity.py

@@ -0,0 +1,254 @@
+"""BLUE-parity alpha components for the PINK DITAv2 runtime.
+
+PINK must trade the SAME universe with the SAME sizing curve as BLUE
+(operator mandate 2026-06-10). This module wraps BLUE's exact production
+kernels — no reimplementation, no drift:
+
+  - ``AlphaAssetSelector`` (nautilus_dolphin/nautilus/alpha_asset_selector.py)
+    IRP ranking over the scan universe (SYSTEM BIBLE §5). The scan payload
+    carries ``assets`` + ``asset_prices`` for the full ~50-asset universe on
+    every scan; PINK accumulates per-asset price history here and ranks at
+    signal time, exactly like BLUE's engine does.
+
+  - ``AlphaBetSizer`` (nautilus_dolphin/nautilus/alpha_bet_sizer.py)
+    Cubic-convex dynamic leverage + alpha-layer fraction (SYSTEM BIBLE §6).
+    Replaces the DITAv2 stub formula that saturated at max_leverage on every
+    entry (confidence = |vdiv/threshold| ≥ 1 whenever an ENTER is possible).
+
+Both wrappers are observe()-driven and dedupe on scan_number so the 1 s poll
+loop cannot poison histories with repeated scans.
+
+DUAL-LEVERAGE INVARIANT (prod/docs/FRACTIONAL_LEVERAGE_TO_BINGX_FIX.md,
+prod/bingx/leverage.py): the fractional leverage produced here is STRATEGY
+conviction — it sizes the quantity. At-exchange leverage is derived from it
+at the venue boundary via map_internal_conviction_to_exchange_leverage()
+(linear [0.5, 9.0] → [1, cap], bankers rounding, security cap).
+"""
+
+from __future__ import annotations
+
+import logging
+import sys
+from collections import deque
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+LOGGER = logging.getLogger("PinkBlueParity")
+
+_PROJECT_ROOT = str(Path(__file__).resolve().parents[3])
+
+
+def _import_blue_modules() -> Tuple[Any, Any]:
+    """Import BLUE's selector/sizer modules, adding their root if needed.
+
+    The real package lives at <root>/nautilus_dolphin/nautilus_dolphin/ —
+    BLUE runs with <root>/nautilus_dolphin on sys.path (see
+    nautilus_event_trader.py's proxy_boost_engine import).
+    """
+    try:
+        from nautilus_dolphin.nautilus import alpha_asset_selector, alpha_bet_sizer
+    except ImportError:
+        pkg_root = str(Path(_PROJECT_ROOT) / "nautilus_dolphin")
+        for p in (pkg_root, _PROJECT_ROOT):
+            if p not in sys.path:
+                sys.path.insert(0, p)
+        # A namespace-package hit from <root>/nautilus_dolphin (no __init__)
+        # can shadow the real package — drop it so the retry resolves cleanly.
+        sys.modules.pop("nautilus_dolphin", None)
+        from nautilus_dolphin.nautilus import alpha_asset_selector, alpha_bet_sizer
+    return alpha_asset_selector, alpha_bet_sizer
+
+
+class PinkAssetPicker:
+    """Accumulate universe price history from scan payloads; rank via BLUE IRP.
+
+    BLUE semantics preserved:
+      - IRP lookback default 50 bars (alpha_asset_selector.IRP_LOOKBACK).
+      - Kernel gates (noise>500, latency>20, alignment<0.20) apply inside
+        rank_assets_irp_nb; min_irp_alignment=0.0 matches BLUE gold spec.
+      - No fallback asset when ranking returns no candidate (Bible §5.5:
+        "No match → return None").
+    """
+
+    def __init__(self, lookback: int = 0, min_irp_alignment: float = 0.0):
+        selector_mod, _ = _import_blue_modules()
+        self._selector_mod = selector_mod
+        self.lookback = int(lookback) if lookback > 0 else int(selector_mod.IRP_LOOKBACK)
+        self.min_irp_alignment = float(min_irp_alignment)
+        self._selector = selector_mod.AlphaAssetSelector(lookback_horizon=self.lookback)
+        # Per-asset rolling price history. lookback+1 prices = lookback returns.
+        self._history: Dict[str, deque] = {}
+        self._last_price: Dict[str, float] = {}
+        self._last_scan_number: int = -1
+        self.scans_observed: int = 0
+
+    # ── feeding ──────────────────────────────────────────────────────────────
+
+    def observe(self, scan_payload: Optional[dict], scan_number: int) -> bool:
+        """Append one bar per NEW scan. Returns True if the bar was accepted."""
+        payload = scan_payload or {}
+        sn = int(scan_number or 0)
+        if sn <= self._last_scan_number:
+            return False  # duplicate / stale scan — poll loop runs faster than scans
+        assets = payload.get("assets") or []
+        prices = payload.get("asset_prices") or []
+        if not (isinstance(assets, list) and isinstance(prices, list) and assets):
+            return False
+        maxlen = self.lookback + 1
+        for asset, price in zip(assets, prices):
+            try:
+                px = float(price)
+            except (TypeError, ValueError):
+                continue
+            if px <= 0:
+                continue
+            sym = str(asset).upper()
+            hist = self._history.get(sym)
+            if hist is None:
+                hist = deque(maxlen=maxlen)
+                self._history[sym] = hist
+            hist.append(px)
+            self._last_price[sym] = px
+        self._last_scan_number = sn
+        self.scans_observed += 1
+        return True
+
+    # ── queries ──────────────────────────────────────────────────────────────
+
+    @property
+    def warm(self) -> bool:
+        """True once at least one asset has a full lookback window."""
+        need = self.lookback + 1
+        return any(len(h) >= need for h in self._history.values())
+
+    def price_of(self, asset: str) -> Optional[float]:
+        key = str(asset).upper()
+        px = self._last_price.get(key)
+        if px is None:
+            # Venue-format tolerance: reconcile/fill paths write the BingX
+            # symbol ("FET-USDT") into the slot, while the scan universe keys
+            # are Binance-style ("FETUSDT"). Without this fallback an open
+            # slot adopted from the exchange has no price and every policy
+            # step degrades to HOLD — TP/SL/MAX_HOLD never fire (2026-06-11).
+            px = self._last_price.get(key.replace("-", ""))
+        return px
+
+    def pick(self, direction: int = -1) -> Optional[Tuple[str, float, float]]:
+        """Rank the warm universe; return (asset, last_price, ars) or None.
+
+        ``direction`` is the regime direction (-1 = short regime; PINK is
+        short-only). BLUE walks the ranked list applying the alignment and
+        direction gates; first survivor wins.
+        """
+        need = self.lookback + 1
+        market_data: Dict[str, List[float]] = {
+            sym: list(h) for sym, h in self._history.items() if len(h) >= need
+        }
+        if not market_data:
+            return None
+        try:
+            rankings = self._selector.rank_assets(market_data, regime_direction=int(direction))
+        except Exception as exc:
+            LOGGER.warning("IRP ranking failed: %s — no asset picked", exc)
+            return None
+        expected_action = "SHORT" if int(direction) == -1 else "LONG"
+        for r in rankings:
+            if self.min_irp_alignment > 0 and r.metrics.alignment < self.min_irp_alignment:
+                continue
+            if r.action != expected_action:
+                continue
+            px = self._last_price.get(r.asset)
+            if px is None or px <= 0:
+                continue
+            return r.asset, px, float(r.ars_score)
+        return None
+
+
+class PinkAlphaSizer:
+    """BLUE's AlphaBetSizer with vel_div-trend tracking and trade feedback.
+
+    calculate_size() output is BLUE-identical:
+      strength_score = clamp((threshold − vel_div)/(threshold − extreme), 0, 1)
+      eff_leverage   = min_lev + strength³ × (max_lev − min_lev)   (convexity 3)
+      eff_fraction   = alpha layers (bucket boost, streak, trend, extreme)
+
+    vd_trend = vel_div[-1] − vel_div[-10] over the last 10 SCANS (Bible §6.4),
+    maintained here via observe(); the decision layer only passes capital and
+    the current vel_div.
+    """
+
+    def __init__(
+        self,
+        *,
+        base_fraction: float = 0.20,
+        min_leverage: float = 0.5,
+        max_leverage: float = 8.0,
+        leverage_convexity: float = 3.0,
+        vel_div_threshold: float = -0.02,
+        vel_div_extreme: float = -0.05,
+        use_dynamic_leverage: bool = True,
+        use_alpha_layers: bool = True,
+    ):
+        _, sizer_mod = _import_blue_modules()
+        self._sizer = sizer_mod.AlphaBetSizer(
+            base_fraction=base_fraction,
+            min_leverage=min_leverage,
+            max_leverage=max_leverage,
+            leverage_convexity=leverage_convexity,
+            vel_div_threshold=vel_div_threshold,
+            vel_div_extreme=vel_div_extreme,
+            use_dynamic_leverage=use_dynamic_leverage,
+            use_alpha_layers=use_alpha_layers,
+        )
+        self.min_leverage = float(min_leverage)
+        self.max_leverage = float(max_leverage)
+        self._vd_history: deque = deque(maxlen=10)
+        self._last_scan_number: int = -1
+        # Bucket of the most recent calculate_size(); frozen by note_entry()
+        # so record_close() credits the right bucket even after later scans.
+        self._pending_bucket: int = 3
+        self._open_bucket: Optional[int] = None
+
+    def observe(self, vel_div: Any, scan_number: int) -> None:
+        sn = int(scan_number or 0)
+        if sn <= self._last_scan_number:
+            return
+        try:
+            vd = float(vel_div)
+        except (TypeError, ValueError):
+            return
+        self._vd_history.append(vd)
+        self._last_scan_number = sn
+
+    @property
+    def vd_trend(self) -> float:
+        if len(self._vd_history) >= 10:
+            return float(self._vd_history[-1] - self._vd_history[0])
+        return 0.0
+
+    def calculate_size(self, *, capital: float, vel_div: float) -> dict:
+        result = self._sizer.calculate_size(
+            capital=float(capital),
+            vel_div=float(vel_div),
+            vel_div_trend=self.vd_trend,
+            trade_direction=-1,
+        )
+        self._pending_bucket = int(result.get("bucket_idx", 3))
+        return result
+
+    # ── trade feedback (bucket boost / streak multiplier inputs) ─────────────
+
+    def note_entry(self) -> None:
+        """An ENTER sized by the last calculate_size() was actually submitted."""
+        self._open_bucket = self._pending_bucket
+
+    def record_close(self, pnl: float) -> None:
+        """Feed realized PnL of the closed trade back into the alpha layers."""
+        bucket = self._open_bucket
+        self._open_bucket = None
+        if bucket is None:
+            return
+        try:
+            self._sizer.record_trade(int(bucket), float(pnl))
+        except Exception as exc:
+            LOGGER.warning("sizer record_trade failed: %s", exc)