VIOLET V3a: V-TYPES alpha-kernel wrappers (selector/sizer/exit-v7) + parity/drift guards

L1 pure-alpha layer: wrap BLUE's live AlphaAssetSelector/AlphaBetSizer/
AlphaExitEngineV7 behind V-TYPES boundaries (wrap, not reimplement). max_leverage
is a required explicit param (live default 5.0 / blue_parity 8.0 / recorded 9.0);
smoke + tests confirm max_leverage=9.0 reproduces recorded sizing exactly
(notional_fraction 0.20x9=1.8 = recorded our_leverage max). 7 tests pass.
Exchange-agnostic: conviction leverage sizes quantity; exchange-lev map is L3.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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2026-06-13 17:39:44 +02:00
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"""VIOLET V3a: V-TYPES-bounded wrappers over BLUE's LIVE alpha kernels.
L1 (PURE ALPHA) of the V3 three-layer architecture: decide + size *literally as
BLUE* by WRAPPING — never reimplementing — the same production kernels that
``prod/nautilus_event_trader.py`` (``DolphinLiveTrader``) runs:
- ``AlphaAssetSelector`` (alpha_asset_selector.py) — IRP selection, BIBLE §5
- ``AlphaBetSizer`` (alpha_bet_sizer.py) — cubic-convex conviction
leverage + alpha fraction §6
- ``AlphaExitEngineV7`` (alpha_exit_v7_engine.py) — live multi-leg exit (as-is)
``prod/clean_arch/dita_v2/blue_parity.py`` (PinkAssetPicker/PinkAlphaSizer) is the
REFERENCE for the wrap discipline (observe/dedupe-on-scan), NOT authoritative:
it caps ``max_leverage`` at 8.0 while the live kernel *default* is 5.0 and the
recorded conviction distribution reaches 9.0. Therefore ``max_leverage`` (and the
other sizer knobs) are REQUIRED explicit parameters here — the V3d parity harness
pins them from the recorded data; nothing in VIOLET hardcodes a value the way the
drifted wrapper did.
DUAL-LEVERAGE: the conviction leverage produced here SIZES THE QUANTITY (the
internal/conviction side). The exchange-leverage mapping (``prod/bingx/leverage.py``,
max-3x cubic) is L3 (tradeability), applied downstream — NEVER in this module.
This module is exchange-agnostic by construction.
"""
from __future__ import annotations
import sys
from pathlib import Path
from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple
from pydantic import Field
from .domain import StrictModel, Symbol, typed
_PROJECT_ROOT = Path(__file__).resolve().parents[3]
# ── refined alpha scalars (L1, exchange-agnostic) ─────────────────────────────
# ConvictionLeverage is the bet-sizer's internal leverage — it sizes the
# quantity. It is NOT exchange leverage. Range mirrors the kernel's own
# [min_leverage, max_leverage] envelope; we keep a generous finite ceiling and
# let the kernel's clamp be authoritative (the parity harness verifies it).
ConvictionLeverage = Annotated[float, Field(ge=0.0, le=64.0, allow_inf_nan=False)]
Fraction = Annotated[float, Field(ge=0.0, le=1.0, allow_inf_nan=False)]
VelDiv = Annotated[float, Field(allow_inf_nan=False)]
Side = Literal["LONG", "SHORT"]
def _import_blue_alpha() -> Tuple[Any, Any, Any]:
"""Import BLUE's live selector/sizer/exit-v7 kernels.
Mirrors blue_parity._import_blue_modules: the real package lives at
<root>/nautilus_dolphin/nautilus_dolphin/; BLUE runs with
<root>/nautilus_dolphin on sys.path.
"""
try:
from nautilus_dolphin.nautilus import ( # type: ignore
alpha_asset_selector, alpha_bet_sizer, alpha_exit_v7_engine,
)
except ImportError:
for p in (str(_PROJECT_ROOT / "nautilus_dolphin"), str(_PROJECT_ROOT)):
if p not in sys.path:
sys.path.insert(0, p)
sys.modules.pop("nautilus_dolphin", None)
from nautilus_dolphin.nautilus import ( # type: ignore
alpha_asset_selector, alpha_bet_sizer, alpha_exit_v7_engine,
)
return alpha_asset_selector, alpha_bet_sizer, alpha_exit_v7_engine
# ── L1 value objects (the pure-alpha output, exchange-agnostic) ────────────────
class AssetPick(StrictModel):
"""One IRP selection result — the asset the alpha would trade this scan."""
asset: Symbol
side: Side
ars_score: float
alignment: float
class SizeDecision(StrictModel):
"""Bet-sizer output. ``notional_fraction = fraction * conviction_leverage``
is the realized notional/capital (== the recorded ``our_leverage``); it is
the conviction side of the dual-leverage and is exchange-agnostic."""
fraction: Fraction
conviction_leverage: ConvictionLeverage
notional_fraction: float = Field(ge=0.0, allow_inf_nan=False)
bucket_idx: int = Field(ge=0, le=3)
strength_score: float = Field(ge=0.0, allow_inf_nan=False)
signal_bucket: str
# ── wrappers ───────────────────────────────────────────────────────────────────
class VioletAssetSelector:
"""Wraps BLUE's ``AlphaAssetSelector`` — IRP ranking over the scan universe.
Faithful to blue_parity.PinkAssetPicker semantics: per-asset price history
accumulated from scan payloads, ranked at signal time; no-fallback (§5.5).
"""
def __init__(self, lookback_horizon: int = 0, min_alignment: float = 0.0):
sel_mod, _, _ = _import_blue_alpha()
self._mod = sel_mod
self.lookback = int(lookback_horizon) if lookback_horizon > 0 else int(sel_mod.IRP_LOOKBACK)
self.min_alignment = float(min_alignment)
self._selector = sel_mod.AlphaAssetSelector(lookback_horizon=self.lookback)
@typed
def pick(self, market_data: dict[str, list[float]], regime_direction: int) -> Optional[AssetPick]:
"""Rank the warm universe; return the first survivor as a typed pick.
``market_data``: per-asset price history (>= lookback+1 prices).
``regime_direction``: -1 short regime, +1 long.
"""
if not market_data:
return None
rankings = self._selector.rank_assets(market_data, regime_direction=int(regime_direction))
expected = "SHORT" if int(regime_direction) == -1 else "LONG"
for r in rankings:
if self.min_alignment > 0 and r.metrics.alignment < self.min_alignment:
continue
if r.action != expected:
continue
return AssetPick(
asset=str(r.asset).upper(),
side=r.action,
ars_score=float(r.ars_score),
alignment=float(r.metrics.alignment),
)
return None
class VioletBetSizer:
"""Wraps BLUE's ``AlphaBetSizer`` — cubic-convex conviction leverage + fraction.
All sizer knobs are EXPLICIT (no hidden defaults): the live kernel default
``max_leverage`` is 5.0, blue_parity passes 8.0, and recorded conviction
reaches 9.0 — so the caller (and the parity harness) must pass the value
pinned from live BLUE, never inherit a drifted default.
"""
def __init__(
self,
*,
base_fraction: float,
min_leverage: float,
max_leverage: float,
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_alpha()
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.max_leverage = float(max_leverage)
@typed
def calculate(
self, *, capital: float, vel_div: float,
vel_div_trend: float = 0.0, trade_direction: int = -1,
) -> SizeDecision:
raw = self._sizer.calculate_size(
capital=float(capital), vel_div=float(vel_div),
vel_div_trend=float(vel_div_trend), trade_direction=int(trade_direction),
)
fraction = float(raw["fraction"])
leverage = float(raw["leverage"])
breakdown = raw.get("breakdown", {})
return SizeDecision(
fraction=fraction,
conviction_leverage=leverage,
notional_fraction=fraction * leverage, # == our_leverage
bucket_idx=int(raw.get("bucket_idx", 3)),
strength_score=float(breakdown.get("strength_score", 0.0)),
signal_bucket=str(breakdown.get("signal_bucket", "")),
)
def record_trade(self, bucket_idx: int, pnl: float) -> None:
"""Feed realized PnL back into the alpha layers (bucket boost/streak)."""
self._sizer.record_trade(int(bucket_idx), float(pnl))
class VioletExitEngine:
"""Wraps BLUE's live ``AlphaExitEngineV7`` (multi-leg exit). Run AS-IS first
(operator mandate); refactor/promote/demote later. Exchange-agnostic: emits
an exit decision dict from the kernel, not a venue order."""
def __init__(self, *, bar_duration_sec: float = 11.0, **engine_kwargs: Any):
_, _, exit_mod = _import_blue_alpha()
self._mod = exit_mod
self._engine = exit_mod.AlphaExitEngineV7(
bar_duration_sec=bar_duration_sec, **engine_kwargs,
)
def make_context(self, *args: Any, **kwargs: Any) -> Any:
return self._engine.make_context(*args, **kwargs)
@typed
def evaluate(self, context: Any) -> dict:
"""Return the kernel's exit decision dict verbatim (parity-faithful)."""
return self._engine.evaluate(context)