initial: import DOLPHIN baseline 2026-04-21 from dolphinng5_predict working tree
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
This commit is contained in:
hjnormey
284
nautilus_dolphin/posture_5s_dvol_gate_test.py
Executable file
284
nautilus_dolphin/posture_5s_dvol_gate_test.py
Executable file
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"""Compare 5s posture backtest: dvol Q1 gated vs ungated.
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Hypothesis: days where dvol_btc < 47.5 show -0.79pp SHORT edge historically.
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Gating them to NONE should improve PF by removing noisy low-dvol days
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driven only by extreme fng (fng 6-17 in Jan 2026).
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Runs both variants in a single pass and compares.
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"""
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import sys, time, gc
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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from pathlib import Path
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_here = Path(__file__).parent
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sys.path.insert(0, str(_here)) # nautilus_dolphin/
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sys.path.insert(0, str(_here.parent)) # project root
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from pathlib import Path
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from collections import defaultdict
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import numpy as np
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import pandas as pd
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from nautilus_dolphin.nautilus.macro_posture_switcher import (
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MacroPostureSwitcher, Posture
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)
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VBT_DIR_5S = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache")
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EIGEN_PATH = Path(r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues")
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LOG_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\nautilus_dolphin\run_logs")
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ENTRY_T = 0.020
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MAX_HOLD = 240
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EXF_KEYS = ['dvol_btc', 'fng', 'funding_btc', 'taker']
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def load_exf(date_str):
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defaults = {'dvol_btc': 50.0, 'fng': 50.0, 'funding_btc': 0.0, 'taker': 1.0}
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dp = EIGEN_PATH / date_str
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if not dp.exists():
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return defaults
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files = sorted(dp.glob('scan_*__Indicators.npz'))[:5]
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if not files:
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return defaults
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buckets = defaultdict(list)
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for f in files:
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try:
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d = np.load(f, allow_pickle=True)
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if 'api_names' not in d:
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continue
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names = list(d['api_names'])
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vals = d['api_indicators']
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for k in EXF_KEYS:
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if k in names:
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v = float(vals[names.index(k)])
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if np.isfinite(v):
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buckets[k].append(v)
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except Exception:
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pass
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out = dict(defaults)
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for k, vs in buckets.items():
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if vs:
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out[k] = float(np.median(vs))
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return out
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parquet_files = sorted(VBT_DIR_5S.glob("*.parquet"))
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parquet_files = [p for p in parquet_files if 'catalog' not in str(p)]
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total = len(parquet_files)
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# Two switchers: baseline (no dvol gate) vs gated (dvol < 47.5 → NONE)
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switcher_base = MacroPostureSwitcher(
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enable_long_posture=True,
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rvol_pause_thresh=0.000203,
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rvol_strong_thresh=0.000337,
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dvol_none_below=0.0, # UNGATED
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)
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switcher_gate = MacroPostureSwitcher(
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enable_long_posture=True,
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rvol_pause_thresh=0.000203,
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rvol_strong_thresh=0.000337,
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dvol_none_below=47.5, # GATED — dvol Q1 → NONE
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)
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# Pass 1: prev-day rvol
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print("Pass 1: lag-1 rvol...")
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t0 = time.time()
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day_rvol = {}
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day_btcret = {}
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for pf in parquet_files:
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ds = pf.stem
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try:
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df = pd.read_parquet(pf, columns=['BTCUSDT'])
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except Exception:
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continue
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btc = df['BTCUSDT'].values.astype(np.float64)
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btc = btc[np.isfinite(btc) & (btc > 0)]
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if len(btc) < 2:
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continue
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log_r = np.diff(np.log(btc))
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day_rvol[ds] = float(np.std(log_r))
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day_btcret[ds] = float((btc[-1] - btc[0]) / btc[0])
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dates_sorted = sorted(day_rvol.keys())
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prev_rvol = {d: day_rvol.get(dates_sorted[i-1]) if i > 0 else None for i, d in enumerate(dates_sorted)}
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prev_btcret = {d: day_btcret.get(dates_sorted[i-1]) if i > 0 else None for i, d in enumerate(dates_sorted)}
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print(f" done: {time.time()-t0:.1f}s")
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# Accumulators for both variants
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def make_acc():
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return {'wins': 0, 'losses': 0, 'gw': 0.0, 'gl': 0.0, 'n': 0,
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'equity': 1.0, 'equity_curve': [1.0], 'active': 0, 'paused': 0,
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'day_rets': [], 'day_rows': []}
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acc_base = make_acc()
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acc_gate = make_acc()
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print("Pass 2: crossover simulation...")
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for i, pf in enumerate(parquet_files):
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ds = pf.stem
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pr = prev_rvol.get(ds)
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pb = prev_btcret.get(ds)
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exf = load_exf(ds)
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for acc, sw in [(acc_base, switcher_base), (acc_gate, switcher_gate)]:
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decision = sw.decide(
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dvol_btc=exf['dvol_btc'], fng=exf['fng'],
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funding_btc=exf['funding_btc'],
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realized_vol=pr, btc_day_return=pb,
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)
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if decision.posture == Posture.NONE:
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acc['paused'] += 1
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acc['day_rows'].append({
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'date': ds, 'posture': 'NONE', 'dvol': exf['dvol_btc'],
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'fng': exf['fng'], 'fear': round(decision.fear_score, 3),
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'n': 0, 'day_ret': 0.0,
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})
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continue
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acc['active'] += 1
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# Load data (only once per file — reuse for both variants)
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# We'll load separately but it's fine for a 56-file test
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try:
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df = pd.read_parquet(pf)
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except Exception:
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acc['paused'] += 1
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continue
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if 'vel_div' not in df.columns or 'BTCUSDT' not in df.columns:
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acc['paused'] += 1
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continue
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vd = df['vel_div'].values.astype(np.float64)
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btc = df['BTCUSDT'].values.astype(np.float64)
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vd = np.where(np.isfinite(vd), vd, 0.0)
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btc = np.where(np.isfinite(btc) & (btc > 0), btc, np.nan)
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n = len(btc)
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if n < MAX_HOLD + 5:
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acc['paused'] += 1
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del vd, btc, df
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continue
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del df
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pos = decision.posture
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smult = decision.size_mult
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if pos == Posture.SHORT:
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entry_mask = (vd >= ENTRY_T) & np.isfinite(btc)
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cross_back = (vd <= -ENTRY_T)
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sign = -1
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else:
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entry_mask = (vd <= -ENTRY_T) & np.isfinite(btc)
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cross_back = (vd >= ENTRY_T)
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sign = +1
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day_rets_sized = []
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for t in range(n - MAX_HOLD):
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if not entry_mask[t]:
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continue
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ep = btc[t]
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if not np.isfinite(ep) or ep <= 0:
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continue
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exit_bar = MAX_HOLD
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for k in range(1, MAX_HOLD + 1):
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tb = t + k
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if tb >= n:
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exit_bar = k; break
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if cross_back[tb]:
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exit_bar = k; break
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tb = t + exit_bar
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if tb >= n:
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continue
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xp = btc[tb]
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if not np.isfinite(xp) or xp <= 0:
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continue
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raw_ret = sign * (xp - ep) / ep
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sized_ret = raw_ret * smult
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day_rets_sized.append((raw_ret, sized_ret))
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del vd, btc, entry_mask, cross_back
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n_t = len(day_rets_sized)
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if n_t == 0:
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acc['day_rows'].append({'date': ds, 'posture': pos.value,
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'dvol': exf['dvol_btc'], 'fng': exf['fng'],
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'fear': round(decision.fear_score, 3), 'n': 0, 'day_ret': 0.0})
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continue
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wins = sum(1 for r, _ in day_rets_sized if r >= 0)
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losses = n_t - wins
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gw = sum(r for r, _ in day_rets_sized if r >= 0)
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gl = sum(abs(r) for r, _ in day_rets_sized if r < 0)
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day_ret = sum(s for _, s in day_rets_sized)
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acc['wins'] += wins; acc['losses'] += losses
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acc['gw'] += gw; acc['gl'] += gl; acc['n'] += n_t
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day_ret_clamped = max(-0.5, min(day_ret, 2.0))
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acc['equity'] *= (1 + day_ret_clamped)
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acc['equity_curve'].append(acc['equity'])
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acc['day_rets'].append(day_ret)
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pf_d = gw / gl if gl > 0 else 999.0
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acc['day_rows'].append({
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'date': ds, 'posture': pos.value, 'dvol': round(exf['dvol_btc'], 1),
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'fng': round(exf['fng'], 1), 'fear': round(decision.fear_score, 3),
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'n': n_t, 'wins': wins, 'losses': losses,
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'pf': round(pf_d, 4), 'day_ret': round(day_ret, 6),
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})
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if (i + 1) % 10 == 0:
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gc.collect()
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elapsed = time.time() - t0
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print(f"Done: {elapsed:.1f}s\n")
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# ── Report ──────────────────────────────────────────────────────────────────
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def report(label, acc):
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n = acc['n']
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pf = acc['gw'] / acc['gl'] if acc['gl'] > 0 else 999.0
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wr = acc['wins'] / n * 100 if n > 0 else 0.0
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ec = np.array(acc['equity_curve'])
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roi = (ec[-1] - 1.0) * 100
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running_max = np.maximum.accumulate(ec)
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dd = float(np.max((running_max - ec) / running_max)) * 100
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dr = np.array(acc['day_rets'])
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sharpe = float(np.mean(dr) / np.std(dr) * np.sqrt(252)) if len(dr) > 1 and np.std(dr) > 0 else 0.0
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print(f" {label}")
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print(f" Active/Paused: {acc['active']} / {acc['paused']}")
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print(f" N trades: {n:,} | WR: {wr:.2f}% | PF: {pf:.4f}")
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print(f" ROI: {roi:+.2f}% | MaxDD: {dd:.2f}% | Sharpe: {sharpe:.3f}")
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print(f" Equity: {ec[-1]:.4f}x")
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return pf, wr, n
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print("=" * 60)
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print(" 5s Posture Backtest — dvol Q1 Gate Comparison")
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print("=" * 60)
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pf_b, wr_b, n_b = report("BASELINE (no dvol gate)", acc_base)
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print()
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pf_g, wr_g, n_g = report("GATED (dvol < 47.5 → NONE)", acc_gate)
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print()
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print(f" Delta PF: {pf_g - pf_b:+.4f} ({'+' if pf_g > pf_b else ''}{(pf_g/pf_b - 1)*100:.1f}% change)")
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print(f" Delta WR: {wr_g - wr_b:+.2f}pp")
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print(f" Trades removed: {n_b - n_g:,}")
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# ── Per-day detail for gated variant: show which days got removed ──────────
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print(f"\n Days gated to NONE in variant (dvol < 47.5):")
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gated_days = [r for r in acc_gate['day_rows'] if r['posture'] == 'NONE' and r.get('dvol', 99) < 47.5]
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# Cross-ref with baseline to find what their day_ret WAS
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base_by_date = {r['date']: r for r in acc_base['day_rows']}
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removed = []
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for r in gated_days:
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b = base_by_date.get(r['date'])
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if b and b['posture'] != 'NONE':
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removed.append((r['date'], b.get('dvol', 0), b.get('fng', 0),
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b.get('fear', 0), b.get('pf', 0), b.get('day_ret', 0)))
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removed.sort(key=lambda x: x[5]) # sort by day_ret
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print(f" {'Date':<12} {'dvol':>5} {'fng':>4} {'fear':>6} {'PF_base':>7} {'ret_base':>9}")
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print(f" {'-'*55}")
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for date, dvol, fng, fear, pf_d, ret in removed:
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marker = " ◄ LOSS" if ret < -0.01 else (" ◄ WIN" if ret > 0.01 else "")
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print(f" {date:<12} {dvol:>5.1f} {fng:>4.0f} {fear:>6.3f} {pf_d:>7.4f} {ret:>+9.4f}{marker}")
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win_days = sum(1 for _, _, _, _, _, r in removed if r > 0)
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loss_days = sum(1 for _, _, _, _, _, r in removed if r < 0)
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net_ret = sum(r for _, _, _, _, _, r in removed)
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print(f"\n Removed: {len(removed)} days | Wins: {win_days} Losses: {loss_days} | Net ret removed: {net_ret:+.4f}")
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