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
371
nautilus_dolphin/test_sharpe_adaptive_p2.py
Executable file
371
nautilus_dolphin/test_sharpe_adaptive_p2.py
Executable file
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"""Part 2 only — Adaptive Sharpe Monitor Prototype (55-day champion window).
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Part 1 (static fraction sweep) already complete. Results:
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frac=0.16: ROI=+44.90% PF=1.1559 DD=11.86% Sh=2.617 <- Best PF & Sharpe
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frac=0.18: ROI=+51.02% PF=1.1524 DD=13.39% Sh=2.554
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frac=0.20: ROI=+57.18% PF=1.1487 DD=14.94% Sh=2.490 <- BASELINE
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frac=0.22: ROI=+63.38% PF=1.1450 DD=16.50% Sh=2.426
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frac=0.24: ROI=+69.59% PF=1.1413 DD=18.07% Sh=2.361
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frac=0.26: ROI=+75.79% PF=1.1376 DD=19.65% Sh=2.295
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frac=0.28: ROI=+81.96% PF=1.1338 DD=21.24% Sh=2.230
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frac=0.30: ROI=+88.08% PF=1.1301 DD=22.84% Sh=2.165 <- Best ROI
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Pattern: +0.02 fraction -> +6.2% ROI, +1.57% DD, -0.003 PF, -0.065 Sharpe
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Implication: higher static fraction buys ROI at cost of Sharpe/PF/DD.
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Adaptive monitor goal: get ROI of higher-fraction days WITHOUT the DD cost.
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Adaptive Sharpe Monitor:
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Rolling 20-day realized Sharpe -> fraction multiplier (EWMA smoothed).
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Elastic ceiling: soft=1.20x, apex=1.35x. Ceiling expands when
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ACB boost + MC GREEN + low drawdown align (mirrors leverage 5x->6x doctrine).
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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(-0.03, -0.02, -0.05, 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")
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DATE_START = '2025-12-31'
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DATE_END = '2026-02-25'
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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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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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BASE_FRACTION = 0.20
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INITIAL_CAPITAL = 25000.0
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MULT_SOFT_CEILING = 1.20
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MULT_APEX_CEILING = 1.35
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SHARPE_LOOKBACK = 20
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EWMA_ALPHA = 0.18 # ~5-day half-life
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BASE_ENGINE_KWARGS = dict(
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initial_capital=INITIAL_CAPITAL, vel_div_threshold=-0.02, 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=BASE_FRACTION, fixed_tp_pct=0.0095, 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_BASE_CFG = {
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'trial_id': 0, 'vel_div_threshold': -0.020, 'vel_div_extreme': -0.050,
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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': BASE_FRACTION,
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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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OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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# ── Elastic ceiling formula ───────────────────────────────────────────────────────
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def compute_sizing_mult(rolling_sharpe, acb_boost_float, mc_status, current_drawdown):
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"""
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Returns (fraction_mult, effective_ceiling).
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Base mult: piecewise-linear on rolling Sharpe.
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Elastic ceiling: soft=1.20, apex=1.35. Expands when ACB-boost + MC-GREEN
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+ low drawdown align. Mirrors leverage doctrine (5x soft-cap -> 6x apex).
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"""
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if rolling_sharpe < 1.5:
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base_mult = 0.85
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elif rolling_sharpe < 2.0:
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base_mult = 0.85 + (rolling_sharpe - 1.5) / 0.5 * 0.10
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elif rolling_sharpe < 2.5:
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base_mult = 0.95 + (rolling_sharpe - 2.0) / 0.5 * 0.05
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elif rolling_sharpe < 3.0:
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base_mult = 1.00 + (rolling_sharpe - 2.5) / 0.5 * 0.10
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elif rolling_sharpe < 3.5:
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base_mult = 1.10 + (rolling_sharpe - 3.0) / 0.5 * 0.10
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else:
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base_mult = 1.25
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# Elastic ceiling expansion score [0,1]
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ceiling_headroom = MULT_APEX_CEILING - MULT_SOFT_CEILING # 0.15
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score = 0.0
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if acb_boost_float >= 1.55: score += 0.50
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elif acb_boost_float >= 1.35: score += 0.25
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if mc_status == 'GREEN': score += 0.30
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elif mc_status == 'ORANGE': score -= 0.60
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if current_drawdown < 0.03: score += 0.20
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elif current_drawdown > 0.10: score -= 0.40
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score = max(0.0, min(1.0, score))
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effective_ceiling = MULT_SOFT_CEILING + score * ceiling_headroom
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# Hard safety overrides
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if mc_status == 'ORANGE': effective_ceiling = min(effective_ceiling, 1.10)
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if current_drawdown > 0.12: effective_ceiling = min(effective_ceiling, 1.00)
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effective_ceiling = min(effective_ceiling, MULT_APEX_CEILING)
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return min(base_mult, effective_ceiling), effective_ceiling
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# ── Shared setup ─────────────────────────────────────────────────────────────────
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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 = AdaptiveCircuitBreaker()
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acb.preload_w750(date_strings)
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all_vols = []
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for pf in parquet_files[:2]:
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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))
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print(f"Vol p60: {vol_p60:.6f}")
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print(f"Pre-loading {len(parquet_files)} parquets...")
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pq_data = {}
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for pf in 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 i in range(50, len(bp)):
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seg = bp[max(0, i-50):i]
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if len(seg) < 10: continue
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dv[i] = float(np.std(np.diff(seg)/seg[:-1]))
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pq_data[pf.stem] = (df, ac, dv)
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print(f" Done")
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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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# ════════════════════════════════════════════════════════════════════════════════
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# Run: Adaptive Sharpe Monitor
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# ════════════════════════════════════════════════════════════════════════════════
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print(f"\n{'='*70}")
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print(f" ADAPTIVE SHARPE MONITOR — 55-day run")
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print(f" Soft ceiling={MULT_SOFT_CEILING}x Apex ceiling={MULT_APEX_CEILING}x")
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print(f" Rolling window={SHARPE_LOOKBACK}d EWMA alpha={EWMA_ALPHA}")
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print(f"{'='*70}\n")
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engine = NDAlphaEngine(**BASE_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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daily_pnl = []
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daily_log = []
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ewma_mult = 1.0
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peak_cap = INITIAL_CAPITAL
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t_run = time.time()
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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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# Rolling Sharpe (annualised) from prior days
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if len(daily_pnl) >= 5:
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window = np.array(daily_pnl[-SHARPE_LOOKBACK:])
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roll_sh = float(window.mean() / window.std() * np.sqrt(252)) if window.std() > 0 else 0.0
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else:
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roll_sh = 0.0
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cur_dd = (peak_cap - engine.capital) / peak_cap if peak_cap > 0 else 0.0
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# ACB boost for this date (float)
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_acb_info = acb.get_boost_for_date(ds) if hasattr(acb, 'get_boost_for_date') else {}
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acb_boost_today = _acb_info.get('boost', 1.0) if isinstance(_acb_info, dict) else float(_acb_info)
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raw_mult, eff_ceiling = compute_sizing_mult(roll_sh, acb_boost_today, 'GREEN', cur_dd)
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# EWMA smoothing — prevents day-to-day whipsaw
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ewma_mult = EWMA_ALPHA * raw_mult + (1.0 - EWMA_ALPHA) * ewma_mult
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ewma_mult = max(0.80, min(ewma_mult, eff_ceiling))
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# Apply to engine live
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engine.bet_sizer.base_fraction = BASE_FRACTION * ewma_mult
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r = engine.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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pnl_today = r.get('pnl', 0.0)
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daily_pnl.append(pnl_today)
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if engine.capital > peak_cap:
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peak_cap = engine.capital
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daily_log.append({
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'date': ds,
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'pnl': round(pnl_today, 2),
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'capital': round(engine.capital, 2),
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'trades': r.get('trades', 0),
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'rolling_sharpe': round(roll_sh, 3),
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'acb_boost': round(acb_boost_today, 4),
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'raw_mult': round(raw_mult, 4),
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'ewma_mult': round(ewma_mult, 4),
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'effective_ceiling': round(eff_ceiling, 4),
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'applied_fraction': round(BASE_FRACTION * ewma_mult, 4),
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'drawdown_pct': round(cur_dd * 100, 2),
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})
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print(f" {ds} PnL={pnl_today:+8.1f} Cap={engine.capital:10.0f} "
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f"RollSh={roll_sh:6.3f} ACBboost={acb_boost_today:.3f} "
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f"Mult(ewma)={ewma_mult:.4f} Ceil={eff_ceiling:.4f} "
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f"Frac={BASE_FRACTION*ewma_mult:.4f} DD={cur_dd*100:.1f}%")
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sys.stdout.flush()
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# ── Stats ─────────────────────────────────────────────────────────────────────────
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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 - INITIAL_CAPITAL) / INITIAL_CAPITAL * 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(daily_pnl)
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sharpe = float(pnls.mean() / pnls.std() * np.sqrt(252)) if pnls.std() > 0 else 0.0
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pk = INITIAL_CAPITAL; max_dd = 0.0
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running = INITIAL_CAPITAL
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for p in daily_pnl:
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running += p
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if running > pk: pk = running
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dd = (pk - running) / pk * 100.0
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if dd > max_dd: max_dd = dd
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fracs = [r['applied_fraction'] for r in daily_log]
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ceilings = [r['effective_ceiling'] for r in daily_log]
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BASELINE = {'roi': 57.18, 'pf': 1.1487, 'dd': 14.94, 'sharpe': 2.490}
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print(f"\n{'='*70}")
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print(f" ADAPTIVE SHARPE MONITOR — RESULT")
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print(f"{'='*70}")
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print(f" ROI: {roi:+.2f}% (baseline 0.20-fixed: +{BASELINE['roi']:.2f}% delta={roi-BASELINE['roi']:+.2f}%)")
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print(f" PF: {pf:.4f} (baseline: {BASELINE['pf']:.4f} delta={pf-BASELINE['pf']:+.4f})")
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print(f" DD: {max_dd:.2f}% (baseline: {BASELINE['dd']:.2f}% delta={max_dd-BASELINE['dd']:+.2f}%)")
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print(f" Sharpe: {sharpe:.3f} (baseline: {BASELINE['sharpe']:.3f} delta={sharpe-BASELINE['sharpe']:+.3f})")
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print(f" WR: {wr:.1f}% Trades: {len(tr)}")
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print(f" Applied fraction: mean={np.mean(fracs):.4f} min={np.min(fracs):.4f} max={np.max(fracs):.4f}")
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print(f" Elastic ceiling: mean={np.mean(ceilings):.4f} max={np.max(ceilings):.4f} "
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f"days-at-apex={sum(1 for c in ceilings if c >= MULT_APEX_CEILING-0.001)}/{len(ceilings)}")
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print(f" Runtime: {time.time()-t_run:.0f}s")
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|
||||
# ── Save ─────────────────────────────────────────────────────────────────────────
|
||||
ts = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
run_dir = Path(__file__).parent / 'run_logs'
|
||||
run_dir.mkdir(exist_ok=True)
|
||||
|
||||
with open(run_dir / f'sharpe_adaptive_{ts}.csv', 'w', newline='') as f:
|
||||
w = csv.DictWriter(f, fieldnames=list(daily_log[0].keys()))
|
||||
w.writeheader(); w.writerows(daily_log)
|
||||
|
||||
PART1_RESULTS = [
|
||||
{'fraction':0.16,'roi':44.90,'pf':1.1559,'dd':11.86,'sharpe':2.617,'wr':49.6,'n_trades':2138},
|
||||
{'fraction':0.18,'roi':51.02,'pf':1.1524,'dd':13.39,'sharpe':2.554,'wr':49.6,'n_trades':2138},
|
||||
{'fraction':0.20,'roi':57.18,'pf':1.1487,'dd':14.94,'sharpe':2.490,'wr':49.6,'n_trades':2138},
|
||||
{'fraction':0.22,'roi':63.38,'pf':1.1450,'dd':16.50,'sharpe':2.426,'wr':49.6,'n_trades':2138},
|
||||
{'fraction':0.24,'roi':69.59,'pf':1.1413,'dd':18.07,'sharpe':2.361,'wr':49.6,'n_trades':2138},
|
||||
{'fraction':0.26,'roi':75.79,'pf':1.1376,'dd':19.65,'sharpe':2.295,'wr':49.6,'n_trades':2138},
|
||||
{'fraction':0.28,'roi':81.96,'pf':1.1338,'dd':21.24,'sharpe':2.230,'wr':49.6,'n_trades':2138},
|
||||
{'fraction':0.30,'roi':88.08,'pf':1.1301,'dd':22.84,'sharpe':2.165,'wr':49.6,'n_trades':2138},
|
||||
]
|
||||
|
||||
summary = {
|
||||
'experiment': 'fraction_sweep_and_sharpe_adaptive_55day',
|
||||
'date_range': f'{DATE_START}_to_{DATE_END}',
|
||||
'base_fraction': BASE_FRACTION, 'fixed_tp_pct': 0.0095,
|
||||
'kelly_anchor': {'mean_pnl_pct': 0.051, 'sigma_pct': 0.908,
|
||||
'full_kelly_frac': 0.062, 'half_kelly_frac': 0.031},
|
||||
'elastic_ceiling': {
|
||||
'soft_ceiling': MULT_SOFT_CEILING, 'apex_ceiling': MULT_APEX_CEILING,
|
||||
'sharpe_lookback_days': SHARPE_LOOKBACK, 'ewma_alpha': EWMA_ALPHA,
|
||||
},
|
||||
'part1_static_sweep': PART1_RESULTS,
|
||||
'part1_observation': (
|
||||
'ROI scales linearly with fraction (+6.2% per +0.02). '
|
||||
'BUT Sharpe and PF DECREASE with higher fraction. '
|
||||
'Higher static fraction = more capital at risk per same signal quality. '
|
||||
'Adaptive goal: capture ROI of high-fraction on strong Sharpe days '
|
||||
'while reducing on weak Sharpe days to protect DD.'
|
||||
),
|
||||
'part2_adaptive': {
|
||||
'roi': roi, 'pf': pf, 'dd': max_dd, 'sharpe': sharpe,
|
||||
'wr': wr, 'n_trades': len(tr),
|
||||
'delta_roi_vs_baseline': roi - BASELINE['roi'],
|
||||
'delta_sharpe_vs_baseline': sharpe - BASELINE['sharpe'],
|
||||
'delta_dd_vs_baseline': max_dd - BASELINE['dd'],
|
||||
'fraction_mean': float(np.mean(fracs)),
|
||||
'fraction_min': float(np.min(fracs)),
|
||||
'fraction_max': float(np.max(fracs)),
|
||||
'ceiling_mean': float(np.mean(ceilings)),
|
||||
'ceiling_max': float(np.max(ceilings)),
|
||||
'days_at_apex_ceiling': int(sum(1 for c in ceilings if c >= MULT_APEX_CEILING - 0.001)),
|
||||
},
|
||||
'run_ts': ts,
|
||||
}
|
||||
with open(run_dir / f'fraction_sharpe_{ts}.json', 'w') as f:
|
||||
json.dump(summary, f, indent=2)
|
||||
|
||||
print(f"\nSaved:")
|
||||
print(f" run_logs/sharpe_adaptive_{ts}.csv")
|
||||
print(f" run_logs/fraction_sharpe_{ts}.json")
|
||||
Reference in New Issue
Block a user