163 lines
6.8 KiB
Python
163 lines
6.8 KiB
Python
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import sys
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with open('nautilus_dolphin/nautilus/alpha_orchestrator.py', 'r', encoding='utf-8') as f:
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text = f.read()
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addition = """
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def set_acb(self, acb) -> None:
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\"\"\"Inject AdaptiveCircuitBreaker for per-date boost + dynamic beta.\"\"\"
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self._acb = acb
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def set_mc_forewarner(self, forewarner, mc_base_cfg: dict) -> None:
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\"\"\"Inject DolphinForewarner + frozen champion config for per-date envelope gate.\"\"\"
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self._forewarner = forewarner
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self._mc_base_cfg = dict(mc_base_cfg)
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# ------------------------------------------------------------------
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# Internal helpers — ACB 3-scale meta-boost
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# ------------------------------------------------------------------
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def _strength_cubic(self, vel_div: float) -> float:
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\"\"\"Normalised signal strength in [0,1]^convexity — mirrors bet_sizer formula.\"\"\"
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if vel_div >= self.signal_gen.threshold:
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return 0.0
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raw = (self.signal_gen.threshold - vel_div) / (self.signal_gen.threshold - self.signal_gen.extreme)
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return min(1.0, max(0.0, raw)) ** self.bet_sizer.convexity
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def _update_regime_size_mult(self, vel_div: float) -> None:
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\"\"\"Compute regime_size_mult from ACB day state + current vel_div strength.
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3-scale formula: base_boost × (1 + β × strength³) × mc_scale
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β>0 gate is doctrinal: applies whenever eigenvalue-velocity regime is active,
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independent of whether ACB has macro signals (base_boost may still be 1.0).
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\"\"\"
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if self._day_beta > 0:
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ss = self._strength_cubic(vel_div)
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self.regime_size_mult = self._day_base_boost * (1.0 + self._day_beta * ss) * self._day_mc_scale
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else:
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self.regime_size_mult = self._day_base_boost * self._day_mc_scale
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# ------------------------------------------------------------------
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# process_day — canonical day runner (params live here, not in tests)
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# ------------------------------------------------------------------
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def process_day(
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self,
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date_str: str,
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df,
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asset_columns: list,
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vol_regime_ok = None,
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) -> dict:
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\"\"\"Run one full trading day. All per-date regime setup is internal.
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Parameters
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----------
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date_str : str
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Date string matching ACB w750 cache key, e.g. '2026-01-14'.
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df : pd.DataFrame
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Parquet bar data: must contain 'vel_div' column + asset price columns.
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asset_columns : List[str]
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Non-meta column names (asset price columns only).
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vol_regime_ok : Optional[np.ndarray]
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Per-bar boolean array (len == len(df)). If None, bars 0-99 are treated
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as warm-up (vol_ok=False); bar 100+ treated as True.
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Returns
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-------
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dict
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date, pnl, capital, boost, beta, mc_status, trades
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\"\"\"
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import numpy as np
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cap_start = self.capital
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self.regime_direction = -1 # SHORT-biased (invariant of champion)
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self.regime_dd_halt = False
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self._day_mc_scale = 1.0
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self._day_mc_status = 'OK'
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# 1. ACB per-date: dynamic beta + base boost (OB Sub-4 modulates beta)
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if hasattr(self, '_acb') and self._acb is not None:
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acb_info = self._acb.get_dynamic_boost_for_date(date_str, ob_engine=self.ob_engine)
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self._day_base_boost = acb_info['boost']
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self._day_beta = acb_info['beta']
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else:
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self._day_base_boost = 1.0
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self._day_beta = 0.0
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# 2. MC-Forewarner per-date envelope gate
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if hasattr(self, '_forewarner') and self._forewarner is not None and self._mc_base_cfg is not None:
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mc_cfg = dict(self._mc_base_cfg)
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mc_cfg['max_leverage'] = self.base_max_leverage * self._day_base_boost
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mc_report = self._forewarner.assess_config_dict(mc_cfg)
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mc_red = mc_report.catastrophic_probability > 0.25 or mc_report.envelope_score < -1.0
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mc_orange = (not mc_red) and (
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mc_report.envelope_score < 0 or mc_report.catastrophic_probability > 0.10
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)
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self._day_mc_status = 'RED' if mc_red else ('ORANGE' if mc_orange else 'OK')
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self._day_mc_scale = 0.5 if mc_orange else 1.0
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if mc_red:
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self.regime_dd_halt = True
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# 3. Bar loop
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n_before = len(self.trade_history)
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bid = 0
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for ri in range(len(df)):
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row = df.iloc[ri]
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vd = row.get('vel_div')
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if vd is None or not np.isfinite(float(vd)):
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self._global_bar_idx += 1; bid += 1; continue
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v50_raw = row.get('v50_lambda_max_velocity')
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v750_raw = row.get('v750_lambda_max_velocity')
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v50_val = float(v50_raw) if (v50_raw is not None and np.isfinite(float(v50_raw))) else 0.0
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v750_val = float(v750_raw) if (v750_raw is not None and np.isfinite(float(v750_raw))) else 0.0
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prices = {}
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for ac in asset_columns:
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p = row.get(ac)
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if p is not None and p > 0 and np.isfinite(p):
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prices[ac] = float(p)
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if ac not in self._price_histories:
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self._price_histories[ac] = []
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self._price_histories[ac].append(float(p))
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if len(self._price_histories[ac]) > 500:
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self._price_histories[ac] = self._price_histories[ac][-200:]
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if not prices:
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self._global_bar_idx += 1; bid += 1; continue
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vrok = bool(vol_regime_ok[ri]) if vol_regime_ok is not None else (bid >= 100)
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# Compute regime_size_mult from ACB day state + current signal strength
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self._update_regime_size_mult(float(vd))
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self.process_bar(
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bar_idx=self._global_bar_idx,
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vel_div=float(vd),
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prices=prices,
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vol_regime_ok=vrok,
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price_histories=self._price_histories,
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v50_vel=v50_val,
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v750_vel=v750_val,
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)
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self._global_bar_idx += 1
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bid += 1
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return {
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'date': date_str,
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'pnl': self.capital - cap_start,
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'capital': self.capital,
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'boost': self._day_base_boost,
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'beta': self._day_beta,
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'mc_status': self._day_mc_status,
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'trades': len(self.trade_history) - n_before,
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}
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"""
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if "def process_day" not in text:
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with open('nautilus_dolphin/nautilus/alpha_orchestrator.py', 'a', encoding='utf-8') as f:
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f.write(addition)
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print("Patched alpha_orchestrator.py successfully.")
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else:
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print("Already patched.")
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