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>
215 lines
9.0 KiB
Python
215 lines
9.0 KiB
Python
"""VIOLET V3a: V-TYPES-bounded wrappers over BLUE's LIVE alpha kernels.
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L1 (PURE ALPHA) of the V3 three-layer architecture: decide + size *literally as
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BLUE* by WRAPPING — never reimplementing — the same production kernels that
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``prod/nautilus_event_trader.py`` (``DolphinLiveTrader``) runs:
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- ``AlphaAssetSelector`` (alpha_asset_selector.py) — IRP selection, BIBLE §5
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- ``AlphaBetSizer`` (alpha_bet_sizer.py) — cubic-convex conviction
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leverage + alpha fraction §6
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- ``AlphaExitEngineV7`` (alpha_exit_v7_engine.py) — live multi-leg exit (as-is)
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``prod/clean_arch/dita_v2/blue_parity.py`` (PinkAssetPicker/PinkAlphaSizer) is the
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REFERENCE for the wrap discipline (observe/dedupe-on-scan), NOT authoritative:
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it caps ``max_leverage`` at 8.0 while the live kernel *default* is 5.0 and the
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recorded conviction distribution reaches 9.0. Therefore ``max_leverage`` (and the
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other sizer knobs) are REQUIRED explicit parameters here — the V3d parity harness
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pins them from the recorded data; nothing in VIOLET hardcodes a value the way the
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drifted wrapper did.
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DUAL-LEVERAGE: the conviction leverage produced here SIZES THE QUANTITY (the
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internal/conviction side). The exchange-leverage mapping (``prod/bingx/leverage.py``,
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max-3x cubic) is L3 (tradeability), applied downstream — NEVER in this module.
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This module is exchange-agnostic by construction.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple
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from pydantic import Field
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from .domain import StrictModel, Symbol, typed
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_PROJECT_ROOT = Path(__file__).resolve().parents[3]
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# ── refined alpha scalars (L1, exchange-agnostic) ─────────────────────────────
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# ConvictionLeverage is the bet-sizer's internal leverage — it sizes the
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# quantity. It is NOT exchange leverage. Range mirrors the kernel's own
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# [min_leverage, max_leverage] envelope; we keep a generous finite ceiling and
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# let the kernel's clamp be authoritative (the parity harness verifies it).
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ConvictionLeverage = Annotated[float, Field(ge=0.0, le=64.0, allow_inf_nan=False)]
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Fraction = Annotated[float, Field(ge=0.0, le=1.0, allow_inf_nan=False)]
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VelDiv = Annotated[float, Field(allow_inf_nan=False)]
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Side = Literal["LONG", "SHORT"]
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def _import_blue_alpha() -> Tuple[Any, Any, Any]:
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"""Import BLUE's live selector/sizer/exit-v7 kernels.
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Mirrors blue_parity._import_blue_modules: the real package lives at
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<root>/nautilus_dolphin/nautilus_dolphin/; BLUE runs with
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<root>/nautilus_dolphin on sys.path.
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"""
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try:
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from nautilus_dolphin.nautilus import ( # type: ignore
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alpha_asset_selector, alpha_bet_sizer, alpha_exit_v7_engine,
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)
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except ImportError:
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for p in (str(_PROJECT_ROOT / "nautilus_dolphin"), str(_PROJECT_ROOT)):
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if p not in sys.path:
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sys.path.insert(0, p)
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sys.modules.pop("nautilus_dolphin", None)
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from nautilus_dolphin.nautilus import ( # type: ignore
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alpha_asset_selector, alpha_bet_sizer, alpha_exit_v7_engine,
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)
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return alpha_asset_selector, alpha_bet_sizer, alpha_exit_v7_engine
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# ── L1 value objects (the pure-alpha output, exchange-agnostic) ────────────────
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class AssetPick(StrictModel):
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"""One IRP selection result — the asset the alpha would trade this scan."""
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asset: Symbol
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side: Side
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ars_score: float
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alignment: float
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class SizeDecision(StrictModel):
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"""Bet-sizer output. ``notional_fraction = fraction * conviction_leverage``
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is the realized notional/capital (== the recorded ``our_leverage``); it is
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the conviction side of the dual-leverage and is exchange-agnostic."""
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fraction: Fraction
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conviction_leverage: ConvictionLeverage
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notional_fraction: float = Field(ge=0.0, allow_inf_nan=False)
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bucket_idx: int = Field(ge=0, le=3)
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strength_score: float = Field(ge=0.0, allow_inf_nan=False)
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signal_bucket: str
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# ── wrappers ───────────────────────────────────────────────────────────────────
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class VioletAssetSelector:
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"""Wraps BLUE's ``AlphaAssetSelector`` — IRP ranking over the scan universe.
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Faithful to blue_parity.PinkAssetPicker semantics: per-asset price history
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accumulated from scan payloads, ranked at signal time; no-fallback (§5.5).
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"""
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def __init__(self, lookback_horizon: int = 0, min_alignment: float = 0.0):
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sel_mod, _, _ = _import_blue_alpha()
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self._mod = sel_mod
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self.lookback = int(lookback_horizon) if lookback_horizon > 0 else int(sel_mod.IRP_LOOKBACK)
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self.min_alignment = float(min_alignment)
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self._selector = sel_mod.AlphaAssetSelector(lookback_horizon=self.lookback)
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@typed
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def pick(self, market_data: dict[str, list[float]], regime_direction: int) -> Optional[AssetPick]:
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"""Rank the warm universe; return the first survivor as a typed pick.
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``market_data``: per-asset price history (>= lookback+1 prices).
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``regime_direction``: -1 short regime, +1 long.
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"""
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if not market_data:
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return None
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rankings = self._selector.rank_assets(market_data, regime_direction=int(regime_direction))
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expected = "SHORT" if int(regime_direction) == -1 else "LONG"
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for r in rankings:
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if self.min_alignment > 0 and r.metrics.alignment < self.min_alignment:
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continue
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if r.action != expected:
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continue
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return AssetPick(
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asset=str(r.asset).upper(),
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side=r.action,
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ars_score=float(r.ars_score),
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alignment=float(r.metrics.alignment),
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)
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return None
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class VioletBetSizer:
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"""Wraps BLUE's ``AlphaBetSizer`` — cubic-convex conviction leverage + fraction.
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All sizer knobs are EXPLICIT (no hidden defaults): the live kernel default
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``max_leverage`` is 5.0, blue_parity passes 8.0, and recorded conviction
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reaches 9.0 — so the caller (and the parity harness) must pass the value
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pinned from live BLUE, never inherit a drifted default.
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"""
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def __init__(
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self,
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*,
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base_fraction: float,
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min_leverage: float,
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max_leverage: float,
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leverage_convexity: float = 3.0,
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vel_div_threshold: float = -0.02,
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vel_div_extreme: float = -0.05,
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use_dynamic_leverage: bool = True,
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use_alpha_layers: bool = True,
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):
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_, sizer_mod, _ = _import_blue_alpha()
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self._sizer = sizer_mod.AlphaBetSizer(
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base_fraction=base_fraction,
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min_leverage=min_leverage,
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max_leverage=max_leverage,
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leverage_convexity=leverage_convexity,
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vel_div_threshold=vel_div_threshold,
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vel_div_extreme=vel_div_extreme,
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use_dynamic_leverage=use_dynamic_leverage,
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use_alpha_layers=use_alpha_layers,
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)
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self.max_leverage = float(max_leverage)
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@typed
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def calculate(
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self, *, capital: float, vel_div: float,
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vel_div_trend: float = 0.0, trade_direction: int = -1,
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) -> SizeDecision:
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raw = self._sizer.calculate_size(
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capital=float(capital), vel_div=float(vel_div),
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vel_div_trend=float(vel_div_trend), trade_direction=int(trade_direction),
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)
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fraction = float(raw["fraction"])
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leverage = float(raw["leverage"])
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breakdown = raw.get("breakdown", {})
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return SizeDecision(
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fraction=fraction,
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conviction_leverage=leverage,
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notional_fraction=fraction * leverage, # == our_leverage
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bucket_idx=int(raw.get("bucket_idx", 3)),
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strength_score=float(breakdown.get("strength_score", 0.0)),
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signal_bucket=str(breakdown.get("signal_bucket", "")),
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)
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def record_trade(self, bucket_idx: int, pnl: float) -> None:
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"""Feed realized PnL back into the alpha layers (bucket boost/streak)."""
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self._sizer.record_trade(int(bucket_idx), float(pnl))
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class VioletExitEngine:
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"""Wraps BLUE's live ``AlphaExitEngineV7`` (multi-leg exit). Run AS-IS first
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(operator mandate); refactor/promote/demote later. Exchange-agnostic: emits
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an exit decision dict from the kernel, not a venue order."""
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def __init__(self, *, bar_duration_sec: float = 11.0, **engine_kwargs: Any):
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_, _, exit_mod = _import_blue_alpha()
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self._mod = exit_mod
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self._engine = exit_mod.AlphaExitEngineV7(
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bar_duration_sec=bar_duration_sec, **engine_kwargs,
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)
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def make_context(self, *args: Any, **kwargs: Any) -> Any:
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return self._engine.make_context(*args, **kwargs)
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@typed
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def evaluate(self, context: Any) -> dict:
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"""Return the kernel's exit decision dict verbatim (parity-faithful)."""
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return self._engine.evaluate(context)
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