308 lines
15 KiB
Python
308 lines
15 KiB
Python
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"""1m Klines System Calibration — max_hold_bars × abs_max_lev sweep.
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Problem: 1m system has DD=31.69% (elevated). Cause: 2-hour max hold (120 bars at 1min)
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× avg leverage 2.57x — long holds amplify adverse periods.
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Sweep 1: max_hold_bars in [30, 45, 60, 90, 120] (with abs_max_lev=5.0)
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Sweep 2: abs_max_lev in [3.0, 4.0, 5.0, 6.0] (with best max_hold from sweep 1)
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Combined grid: 5 × 4 = 20 runs on 795-day klines window.
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Full engine stack identical to klines_2y_experiment (ACBv6 + OB + MC-Forewarner + EsoF neutral).
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Thresholds: vel_div_threshold=-0.50, vel_div_extreme=-1.25 (klines-adapted).
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Saves: run_logs/1m_calib_{TS}.csv + .json
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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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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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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, 'use_alpha_layers': True,
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'use_dynamic_leverage': True, 'fixed_tp_pct': 0.0099, 'stop_pct': 1.00,
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'max_hold_bars': 120, '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=-0.50, vel_div_extreme=-1.25, # klines-adapted
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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,
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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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# ── Shared state ───────────────────────────────────────────────────────────────
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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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# Vol calibration (first 5 dates for klines)
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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 (klines): {vol_p60:.6f}")
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print(f"Pre-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) % 200 == 0:
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print(f" {i+1}/{len(parquet_files)} loaded...")
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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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acb_master = AdaptiveCircuitBreaker()
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acb_master.preload_w750(date_strings) # returns all-zero for klines (no NPZ files)
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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_master._w750_vel_cache[ds] = float(v750.median())
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_w750 = [v for v in acb_master._w750_vel_cache.values() if v != 0.0]
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if _w750:
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acb_master._w750_threshold = float(np.percentile(_w750, acb_master.config.W750_THRESHOLD_PCT))
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print(f"ACB w750 p60 (klines): {acb_master._w750_threshold:.6f}")
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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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# ── Sweep grid ─────────────────────────────────────────────────────────────────
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MAX_HOLD_SWEEP = [30, 45, 60, 90, 120] # bars (=minutes at 1m timescale)
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ABS_MAX_LEV_SWEEP = [3.0, 4.0, 5.0, 6.0]
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print(f"\n{'='*75}")
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print(f" 1m CALIBRATION SWEEP: max_hold × abs_max_lev")
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print(f" max_hold_bars: {MAX_HOLD_SWEEP}")
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print(f" abs_max_lev: {ABS_MAX_LEV_SWEEP}")
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print(f" Total runs: {len(MAX_HOLD_SWEEP) * len(ABS_MAX_LEV_SWEEP)}")
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print(f" Baseline: max_hold=120 abs_max_lev=5.0 (795-day: ROI=+172.34% DD=31.69%)")
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print(f"{'='*75}")
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def run_klines(max_hold, abs_max_lev):
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kw = dict(BASE_ENGINE_KWARGS, max_hold_bars=max_hold, abs_max_leverage=abs_max_lev)
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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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all_daily = []
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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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all_daily.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) / 25000 * 100
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pf = gw / gl if gl > 0 else 999.0
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wr = len(wins) / len(tr) * 100 if tr else 0.0
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pnls = np.array([s['pnl'] for s in all_daily])
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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 all_daily]
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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
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if dd > max_dd: max_dd = dd
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tp_rate = engine.tp_exits / len(tr) * 100 if tr else 0.0
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avg_lev = float(np.mean([t.leverage for t in tr])) if tr else 0.0
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avg_bars = float(np.mean([t.bars_held for t in tr])) if tr else 0.0
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h1 = [r for r in all_daily if pq_data and date_strings[all_daily.index(r)] < '2025-01-01']
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h2 = [r for r in all_daily if pq_data and date_strings[all_daily.index(r)] >= '2025-01-01']
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# simpler h1/h2 split by index
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mid = len(all_daily) // 2
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h1_roi = sum(s['pnl'] for s in all_daily[:mid]) / 25000 * 100
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h2_roi = sum(s['pnl'] for s in all_daily[mid:]) / 25000 * 100
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h2h1 = h2_roi / h1_roi if h1_roi != 0 else float('nan')
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return {
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'max_hold_bars': max_hold, 'abs_max_lev': abs_max_lev,
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'max_hold_min': max_hold, # 1min bars → minutes
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'roi': roi, 'pf': pf, 'dd': max_dd, 'sharpe': sharpe, 'wr': wr,
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'n_trades': len(tr), 'tp_rate_pct': tp_rate, 'avg_lev': avg_lev,
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'avg_bars_held': avg_bars, 'h1_roi': h1_roi, 'h2_roi': h2_roi, 'h2h1': h2h1,
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}
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results = []
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t_sweep_start = time.time()
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run_n = 0
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for max_hold in MAX_HOLD_SWEEP:
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for abs_max_lev in ABS_MAX_LEV_SWEEP:
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run_n += 1
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t0r = time.time()
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baseline_mark = " ← BASELINE" if (max_hold == 120 and abs_max_lev == 5.0) else ""
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print(f"\n[{run_n}/{len(MAX_HOLD_SWEEP)*len(ABS_MAX_LEV_SWEEP)}] "
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f"max_hold={max_hold}min abs_max_lev={abs_max_lev}x{baseline_mark}")
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row = run_klines(max_hold, abs_max_lev)
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results.append(row)
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elapsed_r = time.time() - t0r
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print(f" ROI={row['roi']:+.2f}% PF={row['pf']:.4f} DD={row['dd']:.2f}% "
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f"Sh={row['sharpe']:.3f} WR={row['wr']:.1f}% T={row['n_trades']} "
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f"TP%={row['tp_rate_pct']:.1f}% AvgLev={row['avg_lev']:.2f}x "
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f"AvgBars={row['avg_bars_held']:.1f} [{elapsed_r:.0f}s]")
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total_elapsed = time.time() - t_sweep_start
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# ── Analysis ────────────────────────────────────────────────────────────────────
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baseline = next(r for r in results if r['max_hold_bars'] == 120 and r['abs_max_lev'] == 5.0)
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best_roi = max(results, key=lambda r: r['roi'])
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best_sharpe = max(results, key=lambda r: r['sharpe'])
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best_dd = min(results, key=lambda r: r['dd'])
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|
# Best risk-adjusted: highest ROI with DD < 25%
|
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|
|
viable = [r for r in results if r['dd'] <= 25.0]
|
|||
|
|
best_viable = max(viable, key=lambda r: r['roi']) if viable else best_roi
|
|||
|
|
|
|||
|
|
print(f"\n{'='*75}")
|
|||
|
|
print(f" 1m CALIBRATION SWEEP COMPLETE ({total_elapsed/60:.1f} min)")
|
|||
|
|
print(f"{'='*75}")
|
|||
|
|
print(f" Baseline (hold=120m lev=5x): ROI={baseline['roi']:+.2f}% PF={baseline['pf']:.4f} "
|
|||
|
|
f"DD={baseline['dd']:.2f}% Sh={baseline['sharpe']:.3f}")
|
|||
|
|
print(f" Best ROI: hold={best_roi['max_hold_bars']}m lev={best_roi['abs_max_lev']}x "
|
|||
|
|
f"→ ROI={best_roi['roi']:+.2f}% DD={best_roi['dd']:.2f}%")
|
|||
|
|
print(f" Best Sharpe: hold={best_sharpe['max_hold_bars']}m lev={best_sharpe['abs_max_lev']}x "
|
|||
|
|
f"→ Sh={best_sharpe['sharpe']:.3f} DD={best_sharpe['dd']:.2f}%")
|
|||
|
|
print(f" Min DD: hold={best_dd['max_hold_bars']}m lev={best_dd['abs_max_lev']}x "
|
|||
|
|
f"→ DD={best_dd['dd']:.2f}% ROI={best_dd['roi']:+.2f}%")
|
|||
|
|
print(f" Best viable (DD≤25%): hold={best_viable['max_hold_bars']}m "
|
|||
|
|
f"lev={best_viable['abs_max_lev']}x → ROI={best_viable['roi']:+.2f}% "
|
|||
|
|
f"DD={best_viable['dd']:.2f}% Sh={best_viable['sharpe']:.3f}")
|
|||
|
|
|
|||
|
|
print(f"\n Grid summary (ROI | DD):")
|
|||
|
|
print(f" {'':>12}", end='')
|
|||
|
|
for lev in ABS_MAX_LEV_SWEEP:
|
|||
|
|
print(f" lev={lev:.0f}x ", end='')
|
|||
|
|
print()
|
|||
|
|
for mh in MAX_HOLD_SWEEP:
|
|||
|
|
print(f" hold={mh:3d}min ", end='')
|
|||
|
|
for lev in ABS_MAX_LEV_SWEEP:
|
|||
|
|
row = next(r for r in results if r['max_hold_bars'] == mh and r['abs_max_lev'] == lev)
|
|||
|
|
mk = '*' if (row == best_viable) else (' ' if (mh != 120 or lev != 5.0) else 'B')
|
|||
|
|
print(f" {row['roi']:+6.1f}%/{row['dd']:4.1f}%{mk} ", end='')
|
|||
|
|
print()
|
|||
|
|
|
|||
|
|
# ── 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'1m_calib_{ts}.csv', 'w', newline='') as f:
|
|||
|
|
w = csv.DictWriter(f, fieldnames=list(results[0].keys()))
|
|||
|
|
w.writeheader(); w.writerows(results)
|
|||
|
|
|
|||
|
|
summary = {
|
|||
|
|
'experiment': '1m_klines_calibration_sweep',
|
|||
|
|
'date_range': f'{DATE_START}_to_{DATE_END}',
|
|||
|
|
'max_hold_sweep': MAX_HOLD_SWEEP,
|
|||
|
|
'abs_max_lev_sweep': ABS_MAX_LEV_SWEEP,
|
|||
|
|
'baseline': baseline,
|
|||
|
|
'best_roi': best_roi,
|
|||
|
|
'best_sharpe': best_sharpe,
|
|||
|
|
'best_dd_reduction': best_dd,
|
|||
|
|
'best_viable_dd25': best_viable,
|
|||
|
|
'elapsed_s': total_elapsed,
|
|||
|
|
'run_ts': ts,
|
|||
|
|
'all_results': results,
|
|||
|
|
}
|
|||
|
|
with open(run_dir / f'1m_calib_{ts}.json', 'w') as f:
|
|||
|
|
json.dump(summary, f, indent=2)
|
|||
|
|
|
|||
|
|
print(f"\nSaved: run_logs/1m_calib_{ts}.csv + .json")
|