01c19662cb
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.
327 lines
14 KiB
Python
Executable File
327 lines
14 KiB
Python
Executable File
"""LONG Validation — 2Y Klines, Mirror Hypothesis.
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Hypothesis: vel_div = w50_vel - w150_vel is symmetric.
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SHORT (champion): vel_div <= -0.50 → covariance structure breaking DOWN → SHORT
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LONG (this run): vel_div >= +0.50 → covariance structure breaking UP → LONG
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Same engine stack, same 795-day dataset, same sizing, same ACB/OB/MC.
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Only change: regime_direction = +1 (LONG), threshold sign flipped.
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If PF_long is comparable to PF_short (1.148), the mirror hypothesis holds and
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a bidirectional system becomes the path to smooth cashflow.
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"""
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import sys, time, json, csv
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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from pathlib import Path
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from datetime import datetime
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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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sys.path.insert(0, str(Path(__file__).parent))
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print("Compiling numba kernels...")
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t0c = time.time()
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from nautilus_dolphin.nautilus.alpha_asset_selector import compute_irp_nb, compute_ars_nb, rank_assets_irp_nb
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from nautilus_dolphin.nautilus.alpha_bet_sizer import compute_sizing_nb
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from nautilus_dolphin.nautilus.alpha_signal_generator import check_dc_nb
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from nautilus_dolphin.nautilus.ob_features import (
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OBFeatureEngine, compute_imbalance_nb, compute_depth_1pct_nb,
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compute_depth_quality_nb, compute_fill_probability_nb, compute_spread_proxy_nb,
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compute_depth_asymmetry_nb, compute_imbalance_persistence_nb,
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compute_withdrawal_velocity_nb, compute_market_agreement_nb, compute_cascade_signal_nb,
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)
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from nautilus_dolphin.nautilus.ob_provider import MockOBProvider
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_p = np.array([1.0, 2.0, 3.0], dtype=np.float64)
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compute_irp_nb(_p, -1); compute_ars_nb(1.0, 0.5, 0.01)
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rank_assets_irp_nb(np.ones((10, 2), dtype=np.float64), 8, -1, 5, 500.0, 20, 0.20)
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compute_sizing_nb(+0.55, -0.50, -1.25, 3.0, 0.5, 5.0, 0.20, True, True, 0.0,
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np.zeros(4, dtype=np.int64), np.zeros(4, dtype=np.int64),
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np.zeros(5, dtype=np.float64), 0, -1, 0.01, 0.04)
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check_dc_nb(_p, 3, 1, 0.75)
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_b = np.array([100.0, 200.0, 300.0, 400.0, 500.0], dtype=np.float64)
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_a = np.array([110.0, 190.0, 310.0, 390.0, 510.0], dtype=np.float64)
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compute_imbalance_nb(_b, _a); compute_depth_1pct_nb(_b, _a)
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compute_depth_quality_nb(210.0, 200.0); compute_fill_probability_nb(1.0)
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compute_spread_proxy_nb(_b, _a); compute_depth_asymmetry_nb(_b, _a)
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compute_imbalance_persistence_nb(np.array([0.1, -0.1], dtype=np.float64), 2)
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compute_withdrawal_velocity_nb(np.array([100.0, 110.0], dtype=np.float64), 1)
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compute_market_agreement_nb(np.array([0.1, -0.05], dtype=np.float64), 2)
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compute_cascade_signal_nb(np.array([-0.05, -0.15], dtype=np.float64), 2, -0.10)
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print(f" JIT: {time.time()-t0c:.1f}s")
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from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine
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from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
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from mc.mc_ml import DolphinForewarner
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VBT_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines")
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DATE_START = '2024-01-01'
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DATE_END = '2026-03-05'
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META_COLS = {'timestamp', 'scan_number', 'v50_lambda_max_velocity', 'v150_lambda_max_velocity',
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'v300_lambda_max_velocity', 'v750_lambda_max_velocity', 'vel_div',
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'instability_50', 'instability_150'}
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# LONG mirror thresholds — set directly on signal_gen after construction (long_threshold is separate from SHORT)
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# Mirror of SHORT: short=-0.02/-0.05 → long=+0.02/+0.05
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VD_THRESHOLD_LONG = +0.02
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VD_EXTREME_LONG = +0.05
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ENGINE_KWARGS = dict(
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initial_capital=25000.0,
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vel_div_threshold=-0.02, # SHORT threshold (unused when direction=+1, but must not corrupt signal_gen)
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vel_div_extreme=-0.05,
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min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0,
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fraction=0.20, fixed_tp_pct=0.0099, stop_pct=1.0, max_hold_bars=120,
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use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75,
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dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5,
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use_asset_selection=True, min_irp_alignment=0.45,
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use_sp_fees=True, use_sp_slippage=True,
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sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.50,
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use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40,
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lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42,
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)
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MC_MODELS_DIR = str(Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\nautilus_dolphin\mc_results\models"))
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MC_BASE_CFG = {
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'trial_id': 0, 'vel_div_threshold': -0.02, 'vel_div_extreme': -0.05,
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'use_direction_confirm': True, 'dc_lookback_bars': 7,
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'dc_min_magnitude_bps': 0.75, 'dc_skip_contradicts': True,
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'dc_leverage_boost': 1.00, 'dc_leverage_reduce': 0.50,
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'vd_trend_lookback': 10, 'min_leverage': 0.50, 'max_leverage': 5.00,
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'leverage_convexity': 3.00, 'fraction': 0.20,
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'use_alpha_layers': True, 'use_dynamic_leverage': True,
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'fixed_tp_pct': 0.0099, 'stop_pct': 1.00, 'max_hold_bars': 120,
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'use_sp_fees': True, 'use_sp_slippage': True,
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'sp_maker_entry_rate': 0.62, 'sp_maker_exit_rate': 0.50,
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'use_ob_edge': True, 'ob_edge_bps': 5.00, 'ob_confirm_rate': 0.40,
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'ob_imbalance_bias': -0.09, 'ob_depth_scale': 1.00,
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'use_asset_selection': True, 'min_irp_alignment': 0.45, 'lookback': 100,
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'acb_beta_high': 0.80, 'acb_beta_low': 0.20, 'acb_w750_threshold_pct': 60,
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}
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print("\nLoading MC-Forewarner...")
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forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR)
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parquet_files = sorted(
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p for p in VBT_DIR.glob("*.parquet")
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if 'catalog' not in str(p) and DATE_START <= p.stem <= DATE_END
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)
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date_strings = [pf.stem for pf in parquet_files]
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print(f"Dates: {len(parquet_files)} ({date_strings[0]} to {date_strings[-1]})")
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# ACB
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acb = AdaptiveCircuitBreaker()
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acb.preload_w750(date_strings)
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print(f"\nACB w750 p60: {acb._w750_threshold:.6f}")
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# Vol p60
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all_vols = []
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for pf in parquet_files[:5]:
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df = pd.read_parquet(pf)
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if 'BTCUSDT' not in df.columns: continue
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pr = df['BTCUSDT'].values
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for i in range(60, len(pr)):
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seg = pr[max(0,i-50):i]
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if len(seg)<10: continue
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v = float(np.std(np.diff(seg)/seg[:-1]))
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if v > 0: all_vols.append(v)
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vol_p60 = float(np.percentile(all_vols, 60)) if all_vols else 1e-4
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print(f"Vol p60: {vol_p60:.6f}")
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# Pre-load
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print(f"\nPre-loading {len(parquet_files)} parquets...")
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t_load = time.time()
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pq_data = {}
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for i, pf in enumerate(parquet_files):
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df = pd.read_parquet(pf)
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ac = [c for c in df.columns if c not in META_COLS]
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bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
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dv = np.full(len(df), np.nan)
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if bp is not None:
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for j in range(50, len(bp)):
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seg = bp[max(0,j-50):j]
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if len(seg)<10: continue
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dv[j] = float(np.std(np.diff(seg)/seg[:-1]))
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pq_data[pf.stem] = (df, ac, dv)
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if (i+1) % 100 == 0:
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print(f" {i+1}/{len(parquet_files)}...")
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print(f" Done in {time.time()-t_load:.1f}s")
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# ACB w750 from klines parquet
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for ds, (df, _, _) in pq_data.items():
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if 'v750_lambda_max_velocity' in df.columns:
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v750 = df['v750_lambda_max_velocity'].dropna()
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if len(v750) > 0:
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acb._w750_vel_cache[ds] = float(v750.median())
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_w750 = [v for v in acb._w750_vel_cache.values() if v != 0.0]
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if _w750:
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acb._w750_threshold = float(np.percentile(_w750, acb.config.W750_THRESHOLD_PCT))
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print(f"ACB w750 p60 (klines): {acb._w750_threshold:.6f}")
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# OB
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OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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_mock_ob = MockOBProvider(
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imbalance_bias=+0.09, depth_scale=1.0, assets=OB_ASSETS, # LONG: positive imbalance bias
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imbalance_biases={"BTCUSDT": +0.086, "ETHUSDT": +0.092,
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"BNBUSDT": -0.05, "SOLUSDT": -0.05},
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)
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ob_eng = OBFeatureEngine(_mock_ob)
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ob_eng.preload_date("mock", OB_ASSETS)
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print(f"\n=== 2Y KLINES LONG VALIDATION: vel_div >= +{VD_THRESHOLD_LONG} ===")
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print(f" Period: {DATE_START} to {DATE_END} ({len(parquet_files)} days)")
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print(f" Threshold: +{VD_THRESHOLD_LONG} (mirror of SHORT -{VD_THRESHOLD_LONG})")
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print(f" SHORT baseline: ROI=+172.34% PF=1.1482 DD=31.69% Sh=0.982 WR=58.88%")
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t0 = time.time()
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engine = NDAlphaEngine(**ENGINE_KWARGS)
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engine.set_ob_engine(ob_eng)
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engine.set_acb(acb)
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engine.set_mc_forewarner(forewarner, MC_BASE_CFG)
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engine.set_esoteric_hazard_multiplier(0.0)
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# Wire LONG thresholds into signal generator (long_threshold is separate from vel_div_threshold)
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engine.signal_gen.long_threshold = VD_THRESHOLD_LONG
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engine.signal_gen.long_extreme = VD_EXTREME_LONG
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all_daily = []
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for date_str in date_strings:
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if date_str not in pq_data:
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continue
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df, asset_cols, dvol_arr = pq_data[date_str]
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vol_ok = np.where(np.isfinite(dvol_arr), dvol_arr > vol_p60, False)
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result = engine.process_day(date_str, df, asset_cols, vol_regime_ok=vol_ok, direction=+1)
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all_daily.append({
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'date': date_str,
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'pnl': result.get('pnl', 0.0),
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'capital': result.get('capital', ENGINE_KWARGS['initial_capital']),
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'trades': result.get('trades', 0),
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'boost': result.get('boost', 1.0),
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'beta': result.get('beta', 0.0),
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'mc_status': result.get('mc_status', 'OK'),
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})
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if len(all_daily) % 50 == 0:
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roi = (all_daily[-1]['capital'] - 25000) / 25000 * 100
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ntrades = sum(r['trades'] for r in all_daily[-50:])
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print(f" [{date_str}] Day {len(all_daily)}/{len(date_strings)} | ROI={roi:+.1f}% | Last-50d T={ntrades}")
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t_elapsed = time.time() - t0
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all_trades = [
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{'pnl': t.pnl_absolute, 'pnl_pct': t.pnl_pct*100, 'asset': t.asset,
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'bars_held': t.bars_held, 'exit_reason': t.exit_reason, 'leverage': t.leverage}
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for t in engine.trade_history
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]
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capitals = [r['capital'] for r in all_daily]
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pnls = [r['pnl'] for r in all_daily]
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final_cap = capitals[-1] if capitals else 25000.0
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roi = (final_cap - 25000) / 25000 * 100
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peak = 25000.0; max_dd = 0.0
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for c in capitals:
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if c > peak: peak = c
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dd = (peak - c) / peak * 100
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if dd > max_dd: max_dd = dd
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pnl_arr = np.array(pnls)
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sharpe = float(pnl_arr.mean() / pnl_arr.std() * np.sqrt(252)) if pnl_arr.std() > 0 else 0.0
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wins = [t for t in all_trades if t['pnl'] > 0]
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losses = [t for t in all_trades if t['pnl'] <= 0]
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gw = sum(t['pnl'] for t in wins)
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gl = abs(sum(t['pnl'] for t in losses))
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pf = gw / gl if gl > 0 else float('inf')
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wr = len(wins) / len(all_trades) * 100 if all_trades else 0.0
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n_trades = len(all_trades)
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h1_pnl = sum(r['pnl'] for r in all_daily if r['date'] < '2025-01-01')
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h2_pnl = sum(r['pnl'] for r in all_daily if r['date'] >= '2025-01-01')
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h2h1 = h2_pnl / h1_pnl if h1_pnl != 0 else float('nan')
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tp_rate = engine.tp_exits / n_trades * 100 if n_trades else 0.0
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avg_lev = float(np.mean([t['leverage'] for t in all_trades])) if all_trades else 0.0
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monthly_buckets = defaultdict(list)
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for r in all_daily: monthly_buckets[r['date'][:7]].append(r)
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print(f"\n{'='*65}")
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print(f" 2Y KLINES LONG VALIDATION ({DATE_START} to {DATE_END})")
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print(f"{'='*65}")
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print(f" ROI: {roi:+.2f}%")
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print(f" PF: {pf:.4f}")
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print(f" Max DD: {max_dd:.2f}%")
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print(f" Sharpe: {sharpe:.3f}")
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print(f" Win Rate: {wr:.1f}% (W={len(wins)} L={len(losses)})")
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print(f" Trades: {n_trades:,} ({n_trades/len(all_daily):.1f}/day avg)")
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print(f" TP rate: {tp_rate:.1f}%")
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print(f" Avg lev: {avg_lev:.2f}x")
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print(f" H1 (2024): {h1_pnl/25000*100:+.2f}%")
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print(f" H2 (2025+): {h2_pnl/25000*100:+.2f}%")
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print(f" H2/H1: {h2h1:.3f}x")
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print(f" Runtime: {t_elapsed/60:.1f} min")
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print(f"{'='*65}")
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print(f" SHORT baseline: ROI=+172.34% PF=1.1482 DD=31.69% Sh=0.982 WR=58.88% T=3042")
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print(f" LONG result: ROI={roi:+.2f}% PF={pf:.4f} DD={max_dd:.2f}% Sh={sharpe:.3f} WR={wr:.1f}% T={n_trades}")
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if pf > 1.05:
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verdict = "CONFIRMED — LONG vel_div signal has edge. Bidirectional system viable."
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elif pf > 1.00:
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verdict = "MARGINAL — weak LONG edge. Further calibration needed before bidirectional."
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else:
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verdict = "REJECTED — LONG vel_div has no edge at +0.50 threshold. Mirror hypothesis fails."
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print(f"\n Mirror hypothesis: {verdict}")
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print(f"\n Quarterly breakdown:")
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q = len(all_daily) // 4
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for qi, (qa, qb) in enumerate([(0,q),(q,2*q),(2*q,3*q),(3*q,len(all_daily))]):
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sl = all_daily[qa:qb]
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if not sl: continue
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ic = all_daily[qa-1]['capital'] if qa > 0 else 25000.0
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r_s = (sl[-1]['capital'] - ic) / ic * 100
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t_s = sum(s['trades'] for s in sl)
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print(f" Q{qi+1} ({sl[0]['date']} – {sl[-1]['date']}): ROI={r_s:+.1f}% T={t_s}")
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print(f"\n Monthly breakdown:")
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for mo in sorted(monthly_buckets):
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sl_m = monthly_buckets[mo]
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idx0 = next(i for i,r in enumerate(all_daily) if r['date'] == sl_m[0]['date'])
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ic_m = all_daily[idx0-1]['capital'] if idx0 > 0 else 25000.0
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r_m = (sl_m[-1]['capital'] - ic_m) / ic_m * 100
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t_m = sum(s['trades'] for s in sl_m)
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print(f" {mo}: ROI={r_m:+.1f}% T={t_m}")
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# Save
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ts = datetime.now().strftime('%Y%m%d_%H%M%S')
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run_dir = Path(__file__).parent / 'run_logs'
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run_dir.mkdir(exist_ok=True)
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summary = {
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'experiment': '2y_klines_long_validation',
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'date_range': f'{DATE_START}_to_{DATE_END}',
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'direction': 'LONG',
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'vd_threshold': VD_THRESHOLD_LONG,
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'vd_extreme': VD_EXTREME_LONG,
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'roi_pct': roi, 'pf': pf, 'max_dd_pct': max_dd, 'sharpe': sharpe,
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'win_rate_pct': wr, 'n_trades': n_trades, 'n_days': len(all_daily),
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'trades_per_day': n_trades/len(all_daily) if all_daily else 0,
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'h1_roi_pct': h1_pnl/25000*100, 'h2_roi_pct': h2_pnl/25000*100, 'h2h1': h2h1,
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'tp_rate_pct': tp_rate, 'avg_leverage': avg_lev,
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'mirror_hypothesis_verdict': verdict,
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'short_baseline': {'roi': 172.34, 'pf': 1.1482, 'dd': 31.69, 'sharpe': 0.982,
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'wr': 58.88, 'n_trades': 3042},
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'engine_kwargs': ENGINE_KWARGS,
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'runtime_s': t_elapsed, 'run_ts': ts,
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}
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with open(run_dir / f'klines_2y_long_{ts}.json', 'w') as f:
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json.dump(summary, f, indent=2)
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with open(run_dir / f'klines_2y_long_daily_{ts}.csv', 'w', newline='') as f:
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w = csv.writer(f)
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w.writerow(['date','pnl','capital','trades','boost','beta','mc_status'])
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for r in all_daily:
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w.writerow([r['date'],f"{r['pnl']:.4f}",f"{r['capital']:.4f}",
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r['trades'],f"{r['boost']:.4f}",f"{r['beta']:.4f}",r['mc_status']])
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print(f"\nSaved: run_logs/klines_2y_long_{ts}.json + _daily_{ts}.csv")
|