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.
319 lines
15 KiB
Python
Executable File
319 lines
15 KiB
Python
Executable File
"""TP Sweep — 795-Day Klines Dataset, 85–121bps in 2bp steps.
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Purpose: Validate that 95bps TP optimality (confirmed on 55-day NG3 5s bear window)
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holds across broader regime sample (2 years: bull, bear, sideways, ranging).
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Dataset: vbt_cache_klines/ 2024-01-01 to 2026-03-05 (~795 1-min parquets).
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Thresholds: adapted for 1-min timescale (vel_div distribution ~23x wider than 5s NG3).
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vel_div_threshold=-0.50 (champion NG3: -0.02, same ~7th pctile signal rate)
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vel_div_extreme =-1.25 (champion NG3: -0.05, same 2.5x ratio)
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Full engine stack: ACBv6 + OB 4D (MockOB) + MC-Forewarner + EsoF(neutral).
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Seed=42 throughout. ACB w750 populated from klines parquet v750 column.
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Saves:
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run_logs/tp_sweep_klines_{TS}.csv (one row per TP: tp_bps, roi, pf, dd, sharpe, wr, n_trades, tp_rate_pct)
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run_logs/tp_sweep_klines_{TS}.json (full summary + best config)
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Expected runtime: ~4-6 hrs (795 dates × 19 TP steps × full engine stack).
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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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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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# ── Config ──────────────────────────────────────────────────────────────────────
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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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# Thresholds adapted for 1-min timescale (klines vel_div ~23x wider than NG3 5s)
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VD_THRESHOLD = -0.50 # p~7 (champion NG3: -0.02)
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VD_EXTREME = -1.25 # p~2 (champion NG3: -0.05)
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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,
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# MC-Forewarner trained on champion thresholds — pass those for correct risk envelope
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'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': 0.0095, '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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BASE_ENGINE_KWARGS = dict(
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initial_capital=25000.0,
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vel_div_threshold=VD_THRESHOLD,
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vel_div_extreme=VD_EXTREME,
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min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0,
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fraction=0.20, 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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OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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# ── Load shared state ────────────────────────────────────────────────────────────
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print("\nLoading MC-Forewarner trained models...")
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forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR)
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print(" MC-Forewarner ready")
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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"\nKlines parquet files: {len(parquet_files)} dates ({date_strings[0]} to {date_strings[-1]})")
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# ACB init — w750 will be overridden from parquet v750 column below
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print("\nInitializing ACB v6...")
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acb_master = AdaptiveCircuitBreaker()
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acb_master.preload_w750(date_strings)
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# Vol calibration from first 5 dates
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print("\nCalibrating vol p60 from first 5 dates...")
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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 all parquets
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print(f"\nPre-loading {len(parquet_files)} parquet files (this takes a few minutes)...")
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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" Loaded {i+1}/{len(parquet_files)} dates...")
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print(f" Done in {time.time()-t_load:.1f}s")
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# Override ACB w750 cache from klines v750 column (NG3 NPZ not available for 2024-2025)
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print("\nPopulating ACB w750 cache from klines v750_lambda_max_velocity...")
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for date_str, (df, _, _) in pq_data.items():
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if 'v750_lambda_max_velocity' in df.columns:
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v750_vals = df['v750_lambda_max_velocity'].dropna()
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if len(v750_vals) > 0:
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acb_master._w750_vel_cache[date_str] = float(v750_vals.median())
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_w750_vals = [v for v in acb_master._w750_vel_cache.values() if v != 0.0]
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if _w750_vals:
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acb_master._w750_threshold = float(np.percentile(_w750_vals, acb_master.config.W750_THRESHOLD_PCT))
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print(f" w750 klines p60 threshold: {acb_master._w750_threshold:.6f}")
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print(f" Dates with klines w750 data: {len(_w750_vals)}/{len(date_strings)}")
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else:
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print(" WARNING: no klines w750 data — ACB beta will be constant 0.2")
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# OB engine (shared, reset per TP run)
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_mock_ob = MockOBProvider(
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imbalance_bias=-0.09, depth_scale=1.0, assets=OB_ASSETS,
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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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# ── TP sweep values ──────────────────────────────────────────────────────────────
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TP_VALUES_BPS = list(range(85, 122, 2)) # 85,87,...,121 → 19 values
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BASELINE_BPS = 95 # current champion (was 99 pre-sweep)
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print(f"\n{'='*70}")
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print(f" TP SWEEP — 795-DAY KLINES")
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print(f" Range: {TP_VALUES_BPS[0]}–{TP_VALUES_BPS[-1]} bps, {len(TP_VALUES_BPS)} steps")
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print(f" Baseline reference: {BASELINE_BPS}bps (current 55-day champion)")
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print(f" Dates: {date_strings[0]} to {date_strings[-1]} ({len(date_strings)} days)")
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print(f"{'='*70}\n")
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results = []
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t_sweep_start = time.time()
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for step_i, tp_bps in enumerate(TP_VALUES_BPS):
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tp_pct = tp_bps / 10000.0
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kw = dict(BASE_ENGINE_KWARGS, fixed_tp_pct=tp_pct)
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engine = NDAlphaEngine(**kw)
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engine.set_ob_engine(ob_eng)
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engine.set_acb(acb_master)
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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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dstats = []
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for ds in date_strings:
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df, acols, dvol = pq_data[ds]
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vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60, False)
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r = engine.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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dstats.append({'pnl': r.get('pnl', 0.0), 'capital': r.get('capital', 25000.0),
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'trades': r.get('trades', 0)})
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tr = engine.trade_history
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wins = [t for t in tr if t.pnl_absolute > 0]
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losses = [t for t in tr if t.pnl_absolute <= 0]
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gw = sum(t.pnl_absolute for t in wins)
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gl = abs(sum(t.pnl_absolute for t in losses))
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roi = (engine.capital - 25000.0) / 25000.0 * 100.0
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pf = gw / gl if gl > 0 else 999.0
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wr = len(wins) / len(tr) * 100.0 if tr else 0.0
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pnls = np.array([s['pnl'] for s in dstats])
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sharpe = float(pnls.mean() / pnls.std() * np.sqrt(252)) if pnls.std() > 0 else 0.0
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caps = [s['capital'] for s in dstats]
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peak = 25000.0; max_dd = 0.0
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for c in caps:
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if c > peak: peak = c
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dd = (peak - c) / peak * 100.0
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if dd > max_dd: max_dd = dd
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tp_hits = engine.tp_exits
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mh_exits = engine.hold_exits
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tp_rate = tp_hits / len(tr) * 100.0 if tr else 0.0
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elapsed_step = time.time() - t_sweep_start
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row = {'tp_bps': tp_bps, 'roi': roi, 'pf': pf, 'dd': max_dd, 'sharpe': sharpe,
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'wr': wr, 'n_trades': len(tr), 'tp_hits': tp_hits, 'mh_exits': mh_exits,
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'tp_rate_pct': tp_rate}
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results.append(row)
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marker = f" <- BASELINE ({BASELINE_BPS}bps)" if tp_bps == BASELINE_BPS else ""
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print(f" [{step_i+1:2d}/{len(TP_VALUES_BPS)}] TP={tp_bps:3d}bps "
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f"ROI={roi:+7.2f}% PF={pf:.4f} DD={max_dd:5.2f}% "
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f"Sh={sharpe:.3f} WR={wr:.1f}% T={len(tr):5d} TP%={tp_rate:.1f}%"
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f" ({elapsed_step/60:.0f}min){marker}")
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sys.stdout.flush()
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elapsed_total = time.time() - t_sweep_start
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# ── Analysis ─────────────────────────────────────────────────────────────────────
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best_roi = max(results, key=lambda r: r['roi'])
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best_pf = max(results, key=lambda r: r['pf'])
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best_sharpe = max(results, key=lambda r: r['sharpe'])
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baseline = next((r for r in results if r['tp_bps'] == BASELINE_BPS), results[0])
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print(f"\n{'='*70}")
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print(f" TP SWEEP KLINES COMPLETE ({elapsed_total/60:.1f} min, {len(date_strings)} days)")
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print(f"{'='*70}")
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print(f" Baseline ({BASELINE_BPS}bps): ROI={baseline['roi']:+.2f}% PF={baseline['pf']:.4f} "
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f"Sh={baseline['sharpe']:.3f} DD={baseline['dd']:.2f}% T={baseline['n_trades']}")
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print(f" Best ROI: {best_roi['tp_bps']}bps → ROI={best_roi['roi']:+.2f}% "
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f"ΔROI={best_roi['roi']-baseline['roi']:+.2f}%")
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print(f" Best PF: {best_pf['tp_bps']}bps → PF={best_pf['pf']:.4f} "
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f"ΔPF={best_pf['pf']-baseline['pf']:+.4f}")
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print(f" Best Sharpe: {best_sharpe['tp_bps']}bps → Sh={best_sharpe['sharpe']:.3f} "
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f"ΔSh={best_sharpe['sharpe']-baseline['sharpe']:+.3f}")
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print()
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print(f" {'TP':>6} {'ROI':>8} {'PF':>7} {'DD':>6} {'Sharpe':>7} {'WR':>5} {'TP%':>5} {'Trades':>7}")
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for r in results:
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mk = " *BEST_ROI" if r['tp_bps'] == best_roi['tp_bps'] else (
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f" *BASELINE" if r['tp_bps'] == BASELINE_BPS else "")
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print(f" {r['tp_bps']:>4}bps {r['roi']:>+7.2f}% {r['pf']:>7.4f} {r['dd']:>5.2f}% "
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f"{r['sharpe']:>7.3f} {r['wr']:>4.1f}% {r['tp_rate_pct']:>4.1f}% {r['n_trades']:>7}{mk}")
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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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csv_path = run_dir / f'tp_sweep_klines_{ts}.csv'
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json_path = run_dir / f'tp_sweep_klines_{ts}.json'
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with open(csv_path, 'w', newline='') as f:
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w = csv.DictWriter(f, fieldnames=list(results[0].keys()))
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w.writeheader(); w.writerows(results)
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summary = {
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'experiment': 'tp_sweep_klines_795day',
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'date_range': f'{DATE_START}_to_{DATE_END}',
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'n_dates': len(date_strings),
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'tp_range_bps': [TP_VALUES_BPS[0], TP_VALUES_BPS[-1]],
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'tp_step_bps': 2,
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'n_steps': len(TP_VALUES_BPS),
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'baseline_tp_bps': BASELINE_BPS,
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'vd_threshold': VD_THRESHOLD,
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'vd_extreme': VD_EXTREME,
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'vol_p60': vol_p60,
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'baseline': baseline,
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'best_roi': best_roi,
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'best_pf': best_pf,
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'best_sharpe': best_sharpe,
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'delta_roi_best_vs_baseline': best_roi['roi'] - baseline['roi'],
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'elapsed_s': elapsed_total,
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'run_ts': ts,
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'all_results': results,
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}
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with open(json_path, 'w') as f:
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json.dump(summary, f, indent=2)
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print(f"\nSaved:")
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print(f" {csv_path}")
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print(f" {json_path}")
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verdict = "95bps HOLDS on 795-day" if best_roi['tp_bps'] == 95 else f"Optimal is {best_roi['tp_bps']}bps on 795-day — review blue.yml"
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print(f"\nVerdict: {verdict}")
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