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
578
nautilus_dolphin/test_pf_mtf_5s_1m.py
Executable file
578
nautilus_dolphin/test_pf_mtf_5s_1m.py
Executable file
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"""MTF 5s + 1m Combined Experiment — Orthogonal Leverage Modulation.
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Hypothesis: 1m vel_div (klines-derived, orthogonal to 5s) acts as an INDEPENDENT
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second-order leverage modulator. When the 1m signal also fires bearishly on the
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same day as the 5s system, it's a genuinely rare joint event with higher conviction.
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Architecture (Iron Rule preserved — 1m never touches entry gating):
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- Baseline: champion 5s engine on 55-day window (vbt_cache, Dec31-Feb25)
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- MTF: same engine + MTFBoostACB wrapper that multiplies ACB boost by
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a 1m-alignment factor derived from vbt_cache_klines for same dates
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MTFBoostACB mechanism:
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- Compute per-day: align_frac = fraction of 1m bars with vel_div < VD_1M_THRESHOLD
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(VD_1M_THRESHOLD = -0.50, the klines p~7 threshold, matching signal selectivity)
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- MTF multiplier: 1.0 + MTF_MAX_BOOST * min(align_frac / ALIGN_NORM, 1.0)
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MTF_MAX_BOOST = 0.15 (up to +15% leverage boost)
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ALIGN_NORM = 0.15 (15% of 1m bars bearish = max boost day)
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- Final ACB boost (MTF) = ACB_boost_base * mtf_mult
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- Result feeds into engine's 3-scale formula: regime_size_mult = ACB_boost * (1 + beta * strength^3)
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- abs_max_leverage = 6.0 ceiling unchanged — MTF can only push within it
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Expected (from prior analysis):
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- Cross-corr r<0.013 at all lags → signals are orthogonal alpha sources
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- MTF boost should add +3-5% ROI, WR lift 49%→52-54%
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- Joint firing is genuinely rare (both p~7) → selective, not noise
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Timescale comparison:
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- 5s bars: 1 bar per 5s → 17,280 bars/day, typical entries ~40/day
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- 1m bars: 1 bar per 1min → 1,440 bars/day, used as DAILY aggregate only
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Saved outputs:
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- run_logs/mtf_5s_1m_{TS}.json (summary: baseline vs MTF)
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- run_logs/mtf_daily_baseline_{TS}.csv (per-day stats, baseline)
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- run_logs/mtf_daily_mtf_{TS}.csv (per-day stats, MTF)
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- run_logs/mtf_alignment_{TS}.csv (per-day 1m alignment + mtf_mult)
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"""
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import sys, time, math, 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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# ── JIT warmup ─────────────────────────────────────────────────────────────────
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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, ACBConfig
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from mc.mc_ml import DolphinForewarner
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# ── Paths ───────────────────────────────────────────────────────────────────────
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VBT_5S_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache")
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VBT_1M_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines")
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DATE_START = '2025-12-31'
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DATE_END = '2026-02-25' # 55-day champion window
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# ── MTF parameters ─────────────────────────────────────────────────────────────
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VD_1M_THRESHOLD = -0.50 # klines p~7 (matches 5s p~7 selectivity)
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MTF_MAX_BOOST = 0.15 # max additional boost fraction (+15%)
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ALIGN_NORM = 0.15 # 15% of 1m bars bearish = max-boost day
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# typical: ~7% → +7% boost; extreme: 15%+ → +15%
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# ── Champion engine parameters (frozen — do NOT change) ─────────────────────────
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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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ENGINE_KWARGS = dict(
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initial_capital=25000.0, 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=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,
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'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': 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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OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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# ── MTFBoostACB: ACB wrapper that injects 1m alignment as leverage modulator ─────
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class MTFBoostACB:
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"""Wraps AdaptiveCircuitBreaker, multiplying per-date boost by 1m alignment factor.
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Iron Rule preserved: 1m signal NEVER touches entry gating (vel_div threshold).
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Only affects position SIZING via ACB boost channel.
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MTF boost formula:
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align_frac = fraction of 1m bars where vel_div < VD_1M_THRESHOLD
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mtf_mult = 1.0 + MTF_MAX_BOOST * min(align_frac / ALIGN_NORM, 1.0)
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info['boost'] *= mtf_mult # rest of ACB info unchanged
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"""
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def __init__(self, base_acb, klines_align: dict,
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max_boost: float = MTF_MAX_BOOST,
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align_norm: float = ALIGN_NORM):
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self._acb = base_acb
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self._klines_align = klines_align # date_str -> align_frac [0, 1]
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self._max_boost = max_boost
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self._align_norm = align_norm
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self.mtf_log = {} # date_str -> (align_frac, mtf_mult, final_boost)
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def __getattr__(self, name):
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return getattr(self._acb, name)
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def get_dynamic_boost_for_date(self, date_str: str, ob_engine=None) -> dict:
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info = dict(self._acb.get_dynamic_boost_for_date(date_str, ob_engine=ob_engine))
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align = self._klines_align.get(date_str, 0.0)
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mtf_mult = 1.0 + self._max_boost * min(align / self._align_norm, 1.0) if self._align_norm > 0 else 1.0
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info['boost'] *= mtf_mult
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info['mtf_align'] = align
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info['mtf_mult'] = mtf_mult
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self.mtf_log[date_str] = (align, mtf_mult, info['boost'])
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return info
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# ── Compute engine results helper ───────────────────────────────────────────────
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def compute_metrics(dstats, initial_cap=25000.0):
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capitals = [s['capital'] for s in dstats]
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pnls = [s['pnl'] for s in dstats]
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final_cap = capitals[-1] if capitals else initial_cap
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roi = (final_cap - initial_cap) / initial_cap * 100
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peak = initial_cap; 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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return roi, max_dd, sharpe, final_cap
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def compute_trade_metrics(trade_history):
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wins = [t for t in trade_history if t.pnl_absolute > 0]
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losses = [t for t in trade_history 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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pf = gw / gl if gl > 0 else float('inf')
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wr = len(wins) / len(trade_history) * 100 if trade_history else 0.0
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avg_bars = float(np.mean([t.bars_held for t in trade_history])) if trade_history else 0.0
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avg_win = float(np.mean([t.pnl_pct for t in wins]) * 100) if wins else 0.0
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avg_loss = float(np.mean([t.pnl_pct for t in losses]) * 100) if losses else 0.0
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return pf, wr, avg_bars, avg_win, avg_loss, len(trade_history)
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# ── Load MC-Forewarner ──────────────────────────────────────────────────────────
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print("\nLoading MC-Forewarner...")
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forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR)
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print(" MC-Forewarner ready")
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# ── Load 5s champion parquet files ─────────────────────────────────────────────
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parquet_5s = sorted(
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p for p in VBT_5S_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_5s]
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print(f"\n5s champion parquets: {len(parquet_5s)} dates ({date_strings[0]} to {date_strings[-1]})")
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# ── Vol p60 calibration (from first 2 5s files, champion standard) ──────────────
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all_vols = []
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for pf in parquet_5s[: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 (5s calibration): {vol_p60:.6f}")
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# ── Pre-load 5s parquet data ────────────────────────────────────────────────────
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print(f"\nPre-loading {len(parquet_5s)} 5s parquet files...")
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t_load = time.time()
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pq_data_5s = {}
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for pf in parquet_5s:
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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_5s[pf.stem] = (df, ac, dv)
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print(f" Done in {time.time()-t_load:.1f}s")
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# ── Load 1m klines and compute per-day alignment fraction ──────────────────────
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print(f"\nComputing 1m alignment fractions (VD threshold {VD_1M_THRESHOLD})...")
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klines_align = {} # date_str -> align_frac [0, 1]
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klines_stats = {} # date_str -> {n_bars, n_bearish, align_frac, vd_median, vd_p5, vd_p95}
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missing_1m = []
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for ds in date_strings:
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pf_1m = VBT_1M_DIR / f"{ds}.parquet"
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if not pf_1m.exists():
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klines_align[ds] = 0.0
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missing_1m.append(ds)
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continue
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try:
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df_1m = pd.read_parquet(pf_1m)
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if 'vel_div' not in df_1m.columns:
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klines_align[ds] = 0.0
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missing_1m.append(ds)
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continue
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vd = df_1m['vel_div'].dropna().values
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n_valid = len(vd)
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n_bearish = int(np.sum(vd < VD_1M_THRESHOLD))
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align_frac = n_bearish / n_valid if n_valid > 0 else 0.0
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klines_align[ds] = align_frac
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klines_stats[ds] = {
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'n_bars': n_valid,
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'n_bearish': n_bearish,
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'align_frac': align_frac,
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'vd_median': float(np.median(vd)),
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'vd_p5': float(np.percentile(vd, 5)),
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'vd_p95': float(np.percentile(vd, 95)),
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}
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except Exception as e:
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print(f" WARNING: could not load 1m parquet for {ds}: {e}")
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klines_align[ds] = 0.0
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missing_1m.append(ds)
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if missing_1m:
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print(f" WARNING: {len(missing_1m)} dates missing 1m klines data: {missing_1m[:5]}...")
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else:
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print(f" All {len(date_strings)} dates have 1m klines data")
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# Alignment summary
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aligns = [klines_align[ds] for ds in date_strings]
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mtf_mults = [1.0 + MTF_MAX_BOOST * min(a / ALIGN_NORM, 1.0) for a in aligns]
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print(f"\n 1m Alignment (fraction bearish bars):")
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print(f" Mean: {np.mean(aligns):.3f} (expected ~0.07 = p7 of distribution)")
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print(f" Std: {np.std(aligns):.3f}")
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print(f" Min: {np.min(aligns):.3f}")
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print(f" Max: {np.max(aligns):.3f}")
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print(f" MTF multiplier range: [{min(mtf_mults):.3f}, {max(mtf_mults):.3f}]")
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print(f" MTF multiplier mean: {np.mean(mtf_mults):.3f}")
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high_align = [(ds, a) for ds, a in klines_align.items() if a >= 0.12]
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high_align.sort(key=lambda x: -x[1])
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print(f" High-alignment days (>=12%): {len(high_align)}")
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for ds, a in high_align[:5]:
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mult = 1.0 + MTF_MAX_BOOST * min(a / ALIGN_NORM, 1.0)
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print(f" {ds}: align={a:.3f} → mtf_mult={mult:.3f}x")
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# ── Build OB engine (real-calibrated MockOBProvider) ──────────────────────────
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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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# ── ACB initialization (shared base, cloned for each run) ─────────────────────
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def build_acb(date_strings):
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acb = AdaptiveCircuitBreaker()
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acb.preload_w750(date_strings)
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return acb
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||||
|
||||
|
||||
# ── Engine runner ──────────────────────────────────────────────────────────────
|
||||
def run_engine(label, acb, pq_data, date_strings, vol_p60):
|
||||
print(f"\n{'='*65}")
|
||||
print(f" RUN: {label}")
|
||||
print(f"{'='*65}")
|
||||
t0 = time.time()
|
||||
|
||||
engine = NDAlphaEngine(**ENGINE_KWARGS)
|
||||
engine.set_ob_engine(ob_eng)
|
||||
engine.set_acb(acb)
|
||||
engine.set_mc_forewarner(forewarner, MC_BASE_CFG)
|
||||
engine.set_esoteric_hazard_multiplier(0.0)
|
||||
|
||||
dstats = []
|
||||
for ds in date_strings:
|
||||
if ds not in pq_data:
|
||||
continue
|
||||
df, acols, dvol = pq_data[ds]
|
||||
vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60, False)
|
||||
result = engine.process_day(ds, df, acols, vol_regime_ok=vol_ok)
|
||||
dstats.append({
|
||||
'date': ds,
|
||||
'pnl': result.get('pnl', 0.0),
|
||||
'capital': result.get('capital', ENGINE_KWARGS['initial_capital']),
|
||||
'trades': result.get('trades', 0),
|
||||
'boost': result.get('boost', 1.0),
|
||||
'beta': result.get('beta', 0.0),
|
||||
'mc_status': result.get('mc_status', 'OK'),
|
||||
})
|
||||
|
||||
elapsed = time.time() - t0
|
||||
roi, max_dd, sharpe, final_cap = compute_metrics(dstats)
|
||||
pf, wr, avg_bars, avg_win, avg_loss, n_trades = compute_trade_metrics(engine.trade_history)
|
||||
mid = len(dstats) // 2
|
||||
h1 = sum(s['pnl'] for s in dstats[:mid])
|
||||
h2 = sum(s['pnl'] for s in dstats[mid:])
|
||||
h2h1 = h2 / h1 if h1 != 0 else float('nan')
|
||||
|
||||
print(f" ROI: {roi:+.2f}%")
|
||||
print(f" PF: {pf:.4f}")
|
||||
print(f" DD: {max_dd:.2f}%")
|
||||
print(f" Sharpe: {sharpe:.3f}")
|
||||
print(f" WR: {wr:.1f}% (N={n_trades})")
|
||||
print(f" AvgWin: {avg_win:+.3f}% AvgLoss: {avg_loss:+.3f}%")
|
||||
print(f" AvgBars: {avg_bars:.1f}")
|
||||
print(f" Capital: ${final_cap:,.2f}")
|
||||
print(f" H1 P&L: ${h1:+,.2f}")
|
||||
print(f" H2 P&L: ${h2:+,.2f}")
|
||||
print(f" H2/H1: {h2h1:.3f}")
|
||||
print(f" Runtime: {elapsed:.1f}s")
|
||||
|
||||
return {
|
||||
'label': label, 'roi': roi, 'pf': pf, 'dd': max_dd, 'sharpe': sharpe,
|
||||
'wr': wr, 'n_trades': n_trades, 'avg_bars': avg_bars,
|
||||
'avg_win': avg_win, 'avg_loss': avg_loss, 'final_capital': final_cap,
|
||||
'h1_pnl': h1, 'h2_pnl': h2, 'h2h1': h2h1, 'elapsed_s': elapsed,
|
||||
}, dstats, engine.trade_history
|
||||
|
||||
|
||||
# ── RUN 1: BASELINE (standard ACB, no 1m conditioning) ────────────────────────
|
||||
print(f"\nInitializing ACB (baseline)...")
|
||||
acb_base = build_acb(date_strings)
|
||||
print(f" w750 p60 threshold: {acb_base._w750_threshold:.6f}")
|
||||
print(f" Dates with w750: {sum(1 for v in acb_base._w750_vel_cache.values() if v != 0.0)}/{len(date_strings)}")
|
||||
|
||||
base_result, base_dstats, base_trades = run_engine(
|
||||
"BASELINE (5s only, no 1m)", acb_base, pq_data_5s, date_strings, vol_p60
|
||||
)
|
||||
|
||||
# ── RUN 2: MTF (ACB boost × 1m alignment factor) ──────────────────────────────
|
||||
print(f"\nInitializing ACB (MTF — wrapping with 1m alignment)...")
|
||||
acb_base2 = build_acb(date_strings)
|
||||
mtf_acb = MTFBoostACB(acb_base2, klines_align, max_boost=MTF_MAX_BOOST, align_norm=ALIGN_NORM)
|
||||
print(f" MTF_MAX_BOOST = {MTF_MAX_BOOST:.2f} ALIGN_NORM = {ALIGN_NORM:.2f}")
|
||||
|
||||
mtf_result, mtf_dstats, mtf_trades = run_engine(
|
||||
f"MTF (5s × 1m align, max_boost={MTF_MAX_BOOST:.2f})", mtf_acb, pq_data_5s, date_strings, vol_p60
|
||||
)
|
||||
|
||||
# ── DELTA ANALYSIS ─────────────────────────────────────────────────────────────
|
||||
print(f"\n{'═'*65}")
|
||||
print(f" MTF vs BASELINE DELTA")
|
||||
print(f"{'═'*65}")
|
||||
delta_roi = mtf_result['roi'] - base_result['roi']
|
||||
delta_pf = mtf_result['pf'] - base_result['pf']
|
||||
delta_dd = mtf_result['dd'] - base_result['dd']
|
||||
delta_sharpe = mtf_result['sharpe'] - base_result['sharpe']
|
||||
delta_wr = mtf_result['wr'] - base_result['wr']
|
||||
delta_n = mtf_result['n_trades'] - base_result['n_trades']
|
||||
delta_h2h1 = mtf_result['h2h1'] - base_result['h2h1']
|
||||
|
||||
print(f" ΔROI: {delta_roi:+.2f}% pp ({base_result['roi']:+.2f}% → {mtf_result['roi']:+.2f}%)")
|
||||
print(f" ΔPF: {delta_pf:+.4f} ({base_result['pf']:.4f} → {mtf_result['pf']:.4f})")
|
||||
print(f" ΔDD: {delta_dd:+.2f}% pp ({base_result['dd']:.2f}% → {mtf_result['dd']:.2f}%)")
|
||||
print(f" ΔSharpe: {delta_sharpe:+.3f} ({base_result['sharpe']:.3f} → {mtf_result['sharpe']:.3f})")
|
||||
print(f" ΔWR: {delta_wr:+.1f}% pp ({base_result['wr']:.1f}% → {mtf_result['wr']:.1f}%)")
|
||||
print(f" ΔTrades: {delta_n:+d} ({base_result['n_trades']} → {mtf_result['n_trades']})")
|
||||
print(f" ΔH2/H1: {delta_h2h1:+.3f} ({base_result['h2h1']:.3f} → {mtf_result['h2h1']:.3f})")
|
||||
|
||||
efficiency_gain = delta_roi / base_result['roi'] * 100 if base_result['roi'] != 0 else 0
|
||||
dd_cost = delta_dd / base_result['dd'] * 100 if base_result['dd'] != 0 else 0
|
||||
print(f"\n Efficiency (ΔROI as % of baseline): {efficiency_gain:+.1f}%")
|
||||
print(f" DD cost (ΔDD as % of baseline): {dd_cost:+.1f}%")
|
||||
roi_per_dd = delta_roi / delta_dd if delta_dd != 0 else float('inf')
|
||||
print(f" ROI/DD ratio (Δ): {roi_per_dd:.2f}")
|
||||
|
||||
# MTF boost log summary
|
||||
print(f"\n 1m Alignment → MTF multiplier summary:")
|
||||
log_items = sorted(mtf_acb.mtf_log.items())
|
||||
boost_by_date = {ds: info for ds, info in zip(date_strings,
|
||||
[mtf_acb.mtf_log.get(ds, (0, 1.0, 1.0)) for ds in date_strings])}
|
||||
high_mult_days = [(ds, a, m, b) for ds, (a, m, b) in mtf_acb.mtf_log.items() if m > 1.05]
|
||||
high_mult_days.sort(key=lambda x: -x[2])
|
||||
print(f" High MTF multiplier days (>1.05x): {len(high_mult_days)}")
|
||||
for ds, a, m, b in high_mult_days[:8]:
|
||||
print(f" {ds}: align={a:.3f} → mtf_mult={m:.3f}x final_boost={b:.3f}x")
|
||||
|
||||
# ── Per-day delta table (top winners and losers) ────────────────────────────────
|
||||
base_by_date = {s['date']: s for s in base_dstats}
|
||||
mtf_by_date = {s['date']: s for s in mtf_dstats}
|
||||
day_deltas = []
|
||||
for ds in date_strings:
|
||||
if ds in base_by_date and ds in mtf_by_date:
|
||||
dp = mtf_by_date[ds]['pnl'] - base_by_date[ds]['pnl']
|
||||
day_deltas.append((ds, dp, mtf_acb.mtf_log.get(ds, (0.0, 1.0, 1.0))))
|
||||
|
||||
day_deltas.sort(key=lambda x: -abs(x[1]))
|
||||
print(f"\n Largest per-day P&L differences (MTF - Baseline):")
|
||||
for ds, dp, (a, m, b) in day_deltas[:10]:
|
||||
print(f" {ds}: ΔPNL={dp:+.0f} align={a:.3f} mtf_mult={m:.3f}x")
|
||||
|
||||
# ── Sub-period breakdown ────────────────────────────────────────────────────────
|
||||
def sub_period_roi(dstats, start, end, label, cap_start):
|
||||
sub = [s for s in dstats if start <= s['date'] <= end]
|
||||
if not sub: return
|
||||
roi_s = (sub[-1]['capital'] - cap_start) / cap_start * 100
|
||||
t_s = sum(s['trades'] for s in sub)
|
||||
print(f" {label:30s}: ROI={roi_s:+.1f}% T={t_s}")
|
||||
|
||||
print(f"\n Sub-period comparison:")
|
||||
print(f" {'Period':<30} Baseline MTF")
|
||||
periods = [
|
||||
('Jan 2026', '2026-01-01', '2026-01-31'),
|
||||
('Feb 2026', '2026-02-01', '2026-02-25'),
|
||||
]
|
||||
for label, s, e in periods:
|
||||
b_sub = [x for x in base_dstats if s <= x['date'] <= e]
|
||||
m_sub = [x for x in mtf_dstats if s <= x['date'] <= e]
|
||||
if b_sub and m_sub:
|
||||
# Find cap at start of period
|
||||
all_b = [x for x in base_dstats if x['date'] < s]
|
||||
b_cap0 = all_b[-1]['capital'] if all_b else ENGINE_KWARGS['initial_capital']
|
||||
all_m = [x for x in mtf_dstats if x['date'] < s]
|
||||
m_cap0 = all_m[-1]['capital'] if all_m else ENGINE_KWARGS['initial_capital']
|
||||
b_roi = (b_sub[-1]['capital'] - b_cap0) / b_cap0 * 100
|
||||
m_roi = (m_sub[-1]['capital'] - m_cap0) / m_cap0 * 100
|
||||
delta = m_roi - b_roi
|
||||
b_t = sum(x['trades'] for x in b_sub)
|
||||
m_t = sum(x['trades'] for x in m_sub)
|
||||
print(f" {label:30s}: {b_roi:+5.1f}% (T={b_t}) {m_roi:+5.1f}% (T={m_t}) Δ={delta:+.1f}%")
|
||||
|
||||
# ── Statistical note ────────────────────────────────────────────────────────────
|
||||
print(f"\n Statistical note:")
|
||||
print(f" Trade count: baseline={base_result['n_trades']} MTF={mtf_result['n_trades']}")
|
||||
print(f" (Same entries — MTF only changes sizing, not entry decisions.)")
|
||||
print(f" Iron Rule preserved: 1m signal NEVER gated entries.")
|
||||
print(f" 1m vel_div cross-corr with 5s: max |r|<0.013 at all lags (orthogonal)")
|
||||
|
||||
# ── Save results ───────────────────────────────────────────────────────────────
|
||||
ts = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
run_dir = Path(__file__).parent / 'run_logs'
|
||||
run_dir.mkdir(exist_ok=True)
|
||||
|
||||
summary = {
|
||||
'experiment': 'mtf_5s_1m_combined',
|
||||
'date_range': f'{DATE_START}_to_{DATE_END}',
|
||||
'mtf_params': {
|
||||
'vd_1m_threshold': VD_1M_THRESHOLD,
|
||||
'mtf_max_boost': MTF_MAX_BOOST,
|
||||
'align_norm': ALIGN_NORM,
|
||||
},
|
||||
'baseline': base_result,
|
||||
'mtf': mtf_result,
|
||||
'delta': {
|
||||
'roi_pp': delta_roi,
|
||||
'pf': delta_pf,
|
||||
'dd_pp': delta_dd,
|
||||
'sharpe': delta_sharpe,
|
||||
'wr_pp': delta_wr,
|
||||
'n_trades': delta_n,
|
||||
'h2h1': delta_h2h1,
|
||||
'efficiency_pct': efficiency_gain,
|
||||
},
|
||||
'alignment_stats': {
|
||||
'n_dates': len(date_strings),
|
||||
'n_missing_1m': len(missing_1m),
|
||||
'align_mean': float(np.mean(aligns)),
|
||||
'align_std': float(np.std(aligns)),
|
||||
'align_min': float(np.min(aligns)),
|
||||
'align_max': float(np.max(aligns)),
|
||||
'mtf_mult_mean': float(np.mean(mtf_mults)),
|
||||
'mtf_mult_max': float(np.max(mtf_mults)),
|
||||
'n_high_align': len(high_mult_days),
|
||||
},
|
||||
'engine_kwargs': ENGINE_KWARGS,
|
||||
'run_ts': ts,
|
||||
}
|
||||
with open(run_dir / f'mtf_5s_1m_{ts}.json', 'w') as f:
|
||||
json.dump(summary, f, indent=2, default=str)
|
||||
|
||||
# Save alignment CSV
|
||||
with open(run_dir / f'mtf_alignment_{ts}.csv', 'w', newline='') as f:
|
||||
w = csv.writer(f)
|
||||
w.writerow(['date', 'align_frac', 'n_bearish', 'n_bars', 'mtf_mult',
|
||||
'vd_median', 'vd_p5', 'vd_p95'])
|
||||
for ds in date_strings:
|
||||
st = klines_stats.get(ds, {})
|
||||
a = klines_align.get(ds, 0.0)
|
||||
m = 1.0 + MTF_MAX_BOOST * min(a / ALIGN_NORM, 1.0) if ALIGN_NORM > 0 else 1.0
|
||||
w.writerow([
|
||||
ds, f'{a:.4f}',
|
||||
st.get('n_bearish', 0), st.get('n_bars', 0),
|
||||
f'{m:.4f}',
|
||||
f'{st.get("vd_median", 0):.4f}',
|
||||
f'{st.get("vd_p5", 0):.4f}',
|
||||
f'{st.get("vd_p95", 0):.4f}',
|
||||
])
|
||||
|
||||
# Save daily CSVs
|
||||
for label, dstats in [('baseline', base_dstats), ('mtf', mtf_dstats)]:
|
||||
with open(run_dir / f'mtf_daily_{label}_{ts}.csv', 'w', newline='') as f:
|
||||
w = csv.writer(f)
|
||||
w.writerow(['date', 'pnl', 'capital', 'trades', 'boost', 'beta', 'mc_status'])
|
||||
for s in dstats:
|
||||
w.writerow([s['date'], f'{s["pnl"]:.4f}', f'{s["capital"]:.4f}',
|
||||
s['trades'], f'{s["boost"]:.4f}', f'{s["beta"]:.4f}', s['mc_status']])
|
||||
|
||||
print(f"\nResults saved:")
|
||||
print(f" {run_dir}/mtf_5s_1m_{ts}.json")
|
||||
print(f" {run_dir}/mtf_daily_baseline_{ts}.csv")
|
||||
print(f" {run_dir}/mtf_daily_mtf_{ts}.csv")
|
||||
print(f" {run_dir}/mtf_alignment_{ts}.csv")
|
||||
|
||||
print(f"\n{'='*65}")
|
||||
print(f" MTF EXPERIMENT COMPLETE")
|
||||
print(f"{'='*65}")
|
||||
print(f" Hypothesis: 1m orthogonal conditioning improves 5s alpha")
|
||||
if delta_roi > 0 and delta_dd <= 2.0:
|
||||
verdict = "CONFIRMED — positive ROI with controlled DD"
|
||||
elif delta_roi > 0 and delta_dd > 2.0:
|
||||
verdict = "PARTIAL — positive ROI but elevated DD; needs calibration"
|
||||
elif delta_roi <= 0:
|
||||
verdict = "REJECTED — 1m conditioning does not improve 5s ROI"
|
||||
else:
|
||||
verdict = "INCONCLUSIVE"
|
||||
print(f" Verdict: {verdict}")
|
||||
print(f" ΔROI: {delta_roi:+.2f}% ΔDD: {delta_dd:+.2f}% ΔSharpe: {delta_sharpe:+.3f}")
|
||||
print(f"{'='*65}")
|
||||
Reference in New Issue
Block a user