01c19662cb
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
477 lines
22 KiB
Python
Executable File
477 lines
22 KiB
Python
Executable File
"""Dynamic TP Experiment — per-trade TP variation strategies.
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Background
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----------
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Noise experiment (2026-03-05) showed fixed TP=99bps is sub-optimal:
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96% of seeds in ±3bp band beat baseline (+4.3% ΔROI, std=2.17%).
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Mechanism: bar-close execution already gives +22bps average overshoot (mean TP exit = 1.21%
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not 0.99%). Trades that hit TP at ~53 bars average still have 67 bars of slack — momentum
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continues past 99bps more often than it reverses.
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Three TP strategies tested (all per-trade, not per-run):
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S0 baseline — fixed 99bps for all trades (deterministic reference)
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S1a random_3bp — Uniform[96.5, 102.5]bps per trade (anti-detection, ±3bp)
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S1b random_7bp — Uniform[92, 106]bps per trade (wider anti-detection radius)
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S2a eigen_2bp — TP += 2bp * signal_strength (stronger vel_div → wider TP)
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S2b eigen_4bp — TP += 4bp * signal_strength
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S2c eigen_6bp — TP += 6bp * signal_strength
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S3a regime_boost — TP += 5bp * (acb_boost - 1.0) (high-boost day → wider TP)
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S3b regime_vol — TP += 3bp * (regime_mult - 1.0) (high-regime → wider TP)
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S4 combined — eigen_3bp + regime_2bp + random_jitter_1bp (meta-bias)
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Logging
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-------
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Per-trade CSV: all engine state + entry features + tp_target_used + tp_strategy
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Daily CSV: date, pnl, capital, boost, beta, mc_status
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Summary JSON: all hyperparams + stats per strategy
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Branch: experiment/dynamic-tp (no modifications to core engine files)
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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 typing import Optional, Dict, List
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import numpy as np
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import pandas as pd
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HCM = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
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sys.path.insert(0, str(HCM / "nautilus_dolphin"))
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VBT_DIR = HCM / "vbt_cache"
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MC_MODELS_DIR = str(HCM / "nautilus_dolphin" / "mc_results" / "models")
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LOG_DIR = HCM / "nautilus_dolphin" / "run_logs"
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LOG_DIR.mkdir(exist_ok=True)
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# ── champion engine config (exact) ──────────────────────────────────────────
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META_COLS = {'timestamp', 'scan_number', 'v50_lambda_max_velocity',
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'v150_lambda_max_velocity', 'v300_lambda_max_velocity',
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'v750_lambda_max_velocity', 'vel_div', 'instability_50', 'instability_150'}
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BASE_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_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': 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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# ── JIT warmup ───────────────────────────────────────────────────────────────
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print("JIT warmup...", end='', flush=True)
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t_jit = 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, np.int64), np.zeros(4, np.int64), np.zeros(5, 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., 200., 300., 400., 500.], dtype=np.float64)
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_a = np.array([110., 190., 310., 390., 510.], 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., 200.); 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., 110.], 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" {time.time()-t_jit:.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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# ── DynamicTPEngine — subclass, no core file modification ───────────────────
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class DynamicTPEngine(NDAlphaEngine):
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"""NDAlphaEngine subclass that applies per-trade dynamic TP strategies.
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Injection point: overrides _try_entry() to set exit_manager.fixed_tp_pct
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immediately before position open. Restores base TP afterward.
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Records per-trade: tp_target, entry_vel_div, entry_boost, entry_beta,
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entry_regime_mult, entry_leverage, entry_v50_vel, entry_v750_vel.
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"""
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def configure_tp_strategy(self, strategy: str, params: dict, seed: int = 0):
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self._tp_strategy = strategy
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self._tp_params = params
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self._base_tp = self.exit_manager.fixed_tp_pct
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self._tp_record: Dict[str, dict] = {} # trade_id → entry metadata
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self._rng_tp = np.random.default_rng(seed)
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self._vd_threshold = self.vel_div_threshold # -0.02
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self._vd_extreme = self.vel_div_extreme # -0.05
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def _compute_tp(self, vel_div: float) -> float:
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base = self._base_tp
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strat = self._tp_strategy
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p = self._tp_params
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if strat == 'baseline':
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return base
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elif strat == 'random_uniform':
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# Anti-detection: uniform within ±radius
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r = p['radius']
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return float(self._rng_tp.uniform(base - r, base + r))
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elif strat == 'eigen_biased':
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# Signal strength in [0,1]: 0 = at threshold, 1 = at extreme
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strength = min(1.0, max(0.0,
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(self._vd_threshold - vel_div) /
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(self._vd_threshold - self._vd_extreme)
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))
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return base + p['k'] * strength
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elif strat == 'regime_biased':
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# ACB boost contribution
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boost_delta = max(0.0, getattr(self, '_day_base_boost', 1.0) - 1.0)
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regime_delta = max(0.0, getattr(self, 'regime_size_mult', 1.0) - 1.0)
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return base + p['alpha'] * boost_delta + p['gamma'] * regime_delta
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elif strat == 'combined':
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# Eigenvalue component
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strength = min(1.0, max(0.0,
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(self._vd_threshold - vel_div) /
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(self._vd_threshold - self._vd_extreme)
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))
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eigen_part = p['k_eigen'] * strength
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# Regime component
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boost_delta = max(0.0, getattr(self, '_day_base_boost', 1.0) - 1.0)
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regime_part = p['k_regime'] * boost_delta
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# Random jitter (anti-detection noise on top of signal-derived TP)
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jitter = float(self._rng_tp.normal(0, p['jitter_sigma']))
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return base + eigen_part + regime_part + jitter
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return base
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def _try_entry(self, bar_idx: int, vel_div: float, prices: Dict[str, float],
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price_histories, v50_vel: float = 0.0, v750_vel: float = 0.0):
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# Compute and inject per-trade TP before position opens
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dynamic_tp = float(np.clip(self._compute_tp(vel_div),
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0.003, 0.030)) # hard bounds: 30bps–300bps
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self.exit_manager.fixed_tp_pct = dynamic_tp
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# Record pre-entry state for logging
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pre_n_trades = len(self.trade_history)
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pre_pos = self.position
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result = super()._try_entry(bar_idx, vel_div, prices, price_histories,
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v50_vel, v750_vel)
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# If a new position was opened, tag it
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if self.position is not None and self.position is not pre_pos:
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tid = self.position.trade_id
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if tid not in self._tp_record:
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self._tp_record[tid] = {
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'tp_target_bps' : round(dynamic_tp * 10000, 3),
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'entry_vel_div' : round(vel_div, 6),
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'entry_v50_vel' : round(v50_vel, 8),
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'entry_v750_vel' : round(v750_vel, 8),
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'entry_boost' : round(getattr(self, '_day_base_boost', 1.0), 4),
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'entry_beta' : round(getattr(self, '_day_beta', 0.0), 2),
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'entry_regime' : round(getattr(self, 'regime_size_mult', 1.0), 4),
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'signal_strength': round(min(1.0, max(0.0,
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(self._vd_threshold - vel_div) /
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(self._vd_threshold - self._vd_extreme))), 4),
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}
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return result
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# ── shared infrastructure (loaded once) ─────────────────────────────────────
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print("Loading MC-Forewarner...", end='', flush=True)
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forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR)
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print(" OK")
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parquet_files = sorted([p for p in VBT_DIR.glob("*.parquet") if 'catalog' not in str(p)])
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date_strings = [pf.stem for pf in parquet_files]
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print("Initializing ACB...", end='', flush=True)
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acb = AdaptiveCircuitBreaker()
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acb.preload_w750(date_strings)
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print(f" OK (p60={acb._w750_threshold:.6f})")
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OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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_mock_ob = MockOBProvider(
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imbalance_bias=-0.09, depth_scale=1.0, assets=OB_ASSETS,
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imbalance_biases={"BTCUSDT": -0.086, "ETHUSDT": -0.092,
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"BNBUSDT": +0.05, "SOLUSDT": +0.05},
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)
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ob_eng = OBFeatureEngine(_mock_ob)
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ob_eng.preload_date("mock", OB_ASSETS)
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print("Pre-loading parquet data...", end='', flush=True)
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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:
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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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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:
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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" {len(pq_data)} days")
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# ── run one engine configuration ─────────────────────────────────────────────
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def run_one(strategy: str, params: dict, seed: int, label: str) -> dict:
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eng = DynamicTPEngine(**BASE_ENGINE_KWARGS)
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eng.set_ob_engine(ob_eng)
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eng.set_acb(acb)
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eng.set_mc_forewarner(forewarner, MC_BASE_CFG)
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eng.set_esoteric_hazard_multiplier(0.0)
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eng.configure_tp_strategy(strategy, params, seed=seed)
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dstats = []
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for pf in parquet_files:
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ds = pf.stem
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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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stats = eng.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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dstats.append({**stats, 'cap': eng.capital})
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tr = eng.trade_history
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wins = [t for t in tr if t.pnl_absolute > 0]
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loss = [t for t in tr if t.pnl_absolute <= 0]
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gw = sum(t.pnl_absolute for t in wins) if wins else 0.0
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gl = abs(sum(t.pnl_absolute for t in loss)) if loss else 0.0
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roi = (eng.capital - 25000.0) / 25000.0 * 100
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pff = gw / gl if gl > 0 else 999.0
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dr = np.array([s['pnl'] / 25000.0 * 100 for s in dstats])
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sh = float(np.mean(dr) / np.std(dr) * np.sqrt(365)) if np.std(dr) > 0 else 0.0
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pk = 25000.0; mdd = 0.0
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for s in dstats:
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pk = max(pk, s['cap']); mdd = max(mdd, (pk - s['cap']) / pk * 100)
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wr = len(wins) / len(tr) * 100 if tr else 0.0
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tp_exits = sum(1 for t in tr if t.exit_reason == 'FIXED_TP')
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mh_exits = sum(1 for t in tr if t.exit_reason == 'MAX_HOLD')
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# TP stats from record
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tp_targets = [eng._tp_record.get(t.trade_id, {}).get('tp_target_bps', 99.0) for t in tr]
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tp_arr = np.array(tp_targets)
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return dict(
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label=label, strategy=strategy, seed=seed,
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roi=roi, pf=pff, dd=mdd, sharpe=sh, wr=wr, trades=len(tr), capital=eng.capital,
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tp_exits=tp_exits, mh_exits=mh_exits,
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tp_mean_bps=float(np.mean(tp_arr)), tp_std_bps=float(np.std(tp_arr)),
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tp_min_bps=float(np.min(tp_arr)), tp_max_bps=float(np.max(tp_arr)),
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_engine=eng, _dstats=dstats,
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)
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# ── experiment plan ───────────────────────────────────────────────────────────
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N_RANDOM_SEEDS = 15
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EXPERIMENTS = []
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# S0 baseline
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EXPERIMENTS.append(('baseline', 'baseline', {}, [0]))
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# S1a random ±3bp
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EXPERIMENTS.append(('random_3bp', 'random_uniform', {'radius': 0.0003}, list(range(N_RANDOM_SEEDS))))
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# S1b random ±7bp
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EXPERIMENTS.append(('random_7bp', 'random_uniform', {'radius': 0.0007}, list(range(N_RANDOM_SEEDS))))
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# S2 eigenvalue-biased (deterministic — 1 seed each)
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for k_bps, kname in [(0.0002, 'eigen_2bp'), (0.0004, 'eigen_4bp'), (0.0006, 'eigen_6bp')]:
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EXPERIMENTS.append((kname, 'eigen_biased', {'k': k_bps}, [0]))
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# S3 regime-biased (deterministic)
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EXPERIMENTS.append(('regime_boost', 'regime_biased', {'alpha': 0.0005, 'gamma': 0.0000}, [0]))
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EXPERIMENTS.append(('regime_vol', 'regime_biased', {'alpha': 0.0003, 'gamma': 0.0002}, [0]))
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# S4 combined (random component → 10 seeds)
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EXPERIMENTS.append(('combined', 'combined',
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{'k_eigen': 0.0003, 'k_regime': 0.0002, 'jitter_sigma': 0.0001},
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list(range(N_RANDOM_SEEDS))))
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total_runs = sum(len(seeds) for _, _, _, seeds in EXPERIMENTS)
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# ── output files ──────────────────────────────────────────────────────────────
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run_ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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sum_path = LOG_DIR / f"dyntp_summary_{run_ts}.csv"
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trade_path= LOG_DIR / f"dyntp_trades_{run_ts}.csv"
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daily_path= LOG_DIR / f"dyntp_daily_{run_ts}.csv"
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SUM_FIELDS = ['label','strategy','seed','roi','pf','dd','sharpe','wr','trades','capital',
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'tp_exits','mh_exits','tp_mean_bps','tp_std_bps','tp_min_bps','tp_max_bps','elapsed_s']
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TRADE_FIELDS = ['label','strategy','seed','trade_id','asset','direction',
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'entry_price','exit_price','entry_bar','exit_bar','bars_held',
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'leverage','notional','pnl_pct_pct','pnl_absolute','exit_reason','bucket_idx',
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'tp_target_bps','entry_vel_div','entry_v50_vel','entry_v750_vel',
|
||
'entry_boost','entry_beta','entry_regime','signal_strength']
|
||
DAILY_FIELDS = ['label','strategy','seed','date','pnl','capital','dd_pct','boost','beta','mc_status','trades']
|
||
|
||
with open(sum_path, 'w', newline='') as f: csv.writer(f).writerow(SUM_FIELDS)
|
||
with open(trade_path, 'w', newline='') as f: csv.writer(f).writerow(TRADE_FIELDS)
|
||
with open(daily_path, 'w', newline='') as f: csv.writer(f).writerow(DAILY_FIELDS)
|
||
|
||
def append_summary(r, elapsed):
|
||
with open(sum_path, 'a', newline='') as f:
|
||
csv.writer(f).writerow([
|
||
r['label'], r['strategy'], r['seed'],
|
||
round(r['roi'],4), round(r['pf'],4), round(r['dd'],4),
|
||
round(r['sharpe'],4), round(r['wr'],4), r['trades'],
|
||
round(r['capital'],4), r['tp_exits'], r['mh_exits'],
|
||
round(r['tp_mean_bps'],3), round(r['tp_std_bps'],3),
|
||
round(r['tp_min_bps'],3), round(r['tp_max_bps'],3),
|
||
round(elapsed,1),
|
||
])
|
||
|
||
def append_trades(r):
|
||
eng = r['_engine']
|
||
with open(trade_path, 'a', newline='') as f:
|
||
cw = csv.writer(f)
|
||
for t in eng.trade_history:
|
||
meta = eng._tp_record.get(t.trade_id, {})
|
||
cw.writerow([
|
||
r['label'], r['strategy'], r['seed'],
|
||
t.trade_id, t.asset, t.direction,
|
||
f"{t.entry_price:.6f}", f"{t.exit_price:.6f}",
|
||
t.entry_bar, t.exit_bar, t.bars_held,
|
||
f"{t.leverage:.4f}", f"{t.notional:.4f}",
|
||
f"{t.pnl_pct*100:.6f}", f"{t.pnl_absolute:.4f}",
|
||
t.exit_reason, t.bucket_idx,
|
||
meta.get('tp_target_bps', 99.0),
|
||
meta.get('entry_vel_div', ''),
|
||
meta.get('entry_v50_vel', ''),
|
||
meta.get('entry_v750_vel', ''),
|
||
meta.get('entry_boost', ''),
|
||
meta.get('entry_beta', ''),
|
||
meta.get('entry_regime', ''),
|
||
meta.get('signal_strength', ''),
|
||
])
|
||
|
||
def append_daily(r):
|
||
eng = r['_engine']
|
||
with open(daily_path, 'a', newline='') as f:
|
||
cw = csv.writer(f)
|
||
pk = 25000.0
|
||
for s in r['_dstats']:
|
||
pk = max(pk, s['cap'])
|
||
cw.writerow([
|
||
r['label'], r['strategy'], r['seed'],
|
||
s['date'], f"{s['pnl']:.4f}", f"{s['cap']:.4f}",
|
||
f"{(pk-s['cap'])/pk*100:.4f}",
|
||
f"{s['boost']:.4f}", f"{s['beta']:.2f}",
|
||
s['mc_status'], s['trades'],
|
||
])
|
||
|
||
# ── main loop ────────────────────────────────────────────────────────────────
|
||
print(f"\n{'='*65}")
|
||
print(f" DYNAMIC TP EXPERIMENT — {total_runs} total runs")
|
||
print(f" Baseline TP: 99bps | Branch: experiment/dynamic-tp")
|
||
print(f"{'='*65}\n")
|
||
|
||
all_results = {}
|
||
completed = 0
|
||
t_exp = time.time()
|
||
|
||
for label, strategy, params, seeds in EXPERIMENTS:
|
||
n = len(seeds)
|
||
print(f" [{label}] strategy={strategy} params={params} n={n}")
|
||
all_results[label] = []
|
||
|
||
for seed in seeds:
|
||
t0 = time.time()
|
||
r = run_one(strategy, params, seed, label)
|
||
elapsed = time.time() - t0
|
||
all_results[label].append(r)
|
||
completed += 1
|
||
eta = (time.time() - t_exp) / completed * (total_runs - completed)
|
||
|
||
print(f" seed={seed:2d} ROI={r['roi']:+6.2f}% PF={r['pf']:.3f}"
|
||
f" DD={r['dd']:.2f}% T={r['trades']}"
|
||
f" TP_hits={r['tp_exits']} TP_mean={r['tp_mean_bps']:.1f}bps"
|
||
f" [{elapsed:.0f}s | ETA {eta/60:.0f}min]")
|
||
|
||
append_summary(r, elapsed)
|
||
append_trades(r)
|
||
append_daily(r)
|
||
|
||
# ── final analysis ────────────────────────────────────────────────────────────
|
||
print(f"\n{'='*65}")
|
||
print(f" RESULTS — Dynamic TP Strategies vs Baseline")
|
||
print(f"{'='*65}")
|
||
|
||
b_rois = [r['roi'] for r in all_results.get('baseline', [])]
|
||
b_roi = b_rois[0] if b_rois else 44.89
|
||
b_tp = all_results.get('baseline', [{}])[0].get('tp_exits', 301)
|
||
|
||
print(f"\n {'Label':<14} {'E[ROI]':>8} {'ΔROI':>7} {'E[PF]':>6} "
|
||
f"{'E[T]':>5} {'TP_exits':>8} {'TP_mean':>8} {'TP_std':>7} {'Beat%':>6}")
|
||
print(f" {'-'*85}")
|
||
|
||
for label, strategy, params, seeds in EXPERIMENTS:
|
||
results = all_results[label]
|
||
rois = [r['roi'] for r in results]
|
||
pfs = [r['pf'] for r in results]
|
||
ts = [r['trades'] for r in results]
|
||
tpe = [r['tp_exits'] for r in results]
|
||
tpmn = [r['tp_mean_bps'] for r in results]
|
||
tpsd = [r['tp_std_bps'] for r in results]
|
||
|
||
mean_roi = float(np.mean(rois))
|
||
delta = mean_roi - b_roi
|
||
mean_pf = float(np.mean(pfs))
|
||
mean_t = float(np.mean(ts))
|
||
mean_tpe = float(np.mean(tpe))
|
||
mean_tpmn = float(np.mean(tpmn))
|
||
mean_tpsd = float(np.mean(tpsd))
|
||
beat_pct = float(np.mean(np.array(rois) > b_roi)) * 100 if len(rois) > 1 else (100.0 if mean_roi > b_roi else 0.0)
|
||
|
||
print(f" {label:<14} {mean_roi:>+7.2f}% {delta:>+6.2f}% {mean_pf:>6.3f} "
|
||
f"{mean_t:>5.0f} {mean_tpe:>8.0f} {mean_tpmn:>7.1f}bps "
|
||
f"{mean_tpsd:>6.1f}bps {beat_pct:>5.1f}%")
|
||
|
||
print(f"\n{'='*65}")
|
||
print(f" Interpretation:")
|
||
print(f" random_*bp: Does randomisation itself help (anti-detection + luck)?")
|
||
print(f" eigen_*bp: Does signal-strength-proportional TP add edge?")
|
||
print(f" regime_*: Does ACB-boost-proportional TP add edge?")
|
||
print(f" combined: Does the meta-bias composite beat individual components?")
|
||
print(f"\n Key question: do TP exits increase (wider TP → fewer TP hits)?")
|
||
print(f" Or decrease (smarter TP → more precise captures)?")
|
||
print(f"\n Files: {sum_path.name} | {trade_path.name} | {daily_path.name}")
|
||
print(f" Total time: {(time.time()-t_exp)/60:.1f} min")
|