DOLPHIN/nautilus_dolphin/test_tp_sweep.py

"""TP Sweep — Champion 5s System, 85–120bps in 2bp steps.

Motivation:
  - Noise experiment: 99bps sub-optimal; 96% of sigma=1bp seeds beat baseline (+4.3% ROI avg)
  - Dynamic TP experiment: random TP variation positive (E[ROI]≈48.8% vs baseline 44.89%)
  - Expected optimum: 103–108bps. Gain: +3–6% ROI.

Sweep: fixed_tp_pct in [0.0085, 0.0087, ..., 0.0120] — 19 values × 55-day champion window.
Full engine stack (all layers). ACB, OB, MC-Forewarner identical to champion.
Seed=42 throughout (deterministic; cross-TP comparisons are apples-to-apples).

Saves:
  run_logs/tp_sweep_{TS}.csv     (one row per TP value: tp_bps, roi, pf, dd, sharpe, wr, n_trades)
  run_logs/tp_sweep_{TS}.json    (full summary + best config)
"""
import sys, time, json, csv
sys.stdout.reconfigure(encoding='utf-8', errors='replace')
from pathlib import Path
from datetime import datetime
import numpy as np
import pandas as pd

sys.path.insert(0, str(Path(__file__).parent))

print("Compiling numba kernels...")
t0c = time.time()
from nautilus_dolphin.nautilus.alpha_asset_selector import compute_irp_nb, compute_ars_nb, rank_assets_irp_nb
from nautilus_dolphin.nautilus.alpha_bet_sizer import compute_sizing_nb
from nautilus_dolphin.nautilus.alpha_signal_generator import check_dc_nb
from nautilus_dolphin.nautilus.ob_features import (
    OBFeatureEngine, compute_imbalance_nb, compute_depth_1pct_nb,
    compute_depth_quality_nb, compute_fill_probability_nb, compute_spread_proxy_nb,
    compute_depth_asymmetry_nb, compute_imbalance_persistence_nb,
    compute_withdrawal_velocity_nb, compute_market_agreement_nb, compute_cascade_signal_nb,
)
from nautilus_dolphin.nautilus.ob_provider import MockOBProvider
_p = np.array([1.0, 2.0, 3.0], dtype=np.float64)
compute_irp_nb(_p, -1); compute_ars_nb(1.0, 0.5, 0.01)
rank_assets_irp_nb(np.ones((10, 2), dtype=np.float64), 8, -1, 5, 500.0, 20, 0.20)
compute_sizing_nb(-0.03, -0.02, -0.05, 3.0, 0.5, 5.0, 0.20, True, True, 0.0,
                  np.zeros(4, dtype=np.int64), np.zeros(4, dtype=np.int64),
                  np.zeros(5, dtype=np.float64), 0, -1, 0.01, 0.04)
check_dc_nb(_p, 3, 1, 0.75)
_b = np.array([100.0, 200.0, 300.0, 400.0, 500.0], dtype=np.float64)
_a = np.array([110.0, 190.0, 310.0, 390.0, 510.0], dtype=np.float64)
compute_imbalance_nb(_b, _a); compute_depth_1pct_nb(_b, _a)
compute_depth_quality_nb(210.0, 200.0); compute_fill_probability_nb(1.0)
compute_spread_proxy_nb(_b, _a); compute_depth_asymmetry_nb(_b, _a)
compute_imbalance_persistence_nb(np.array([0.1, -0.1], dtype=np.float64), 2)
compute_withdrawal_velocity_nb(np.array([100.0, 110.0], dtype=np.float64), 1)
compute_market_agreement_nb(np.array([0.1, -0.05], dtype=np.float64), 2)
compute_cascade_signal_nb(np.array([-0.05, -0.15], dtype=np.float64), 2, -0.10)
print(f"  JIT: {time.time()-t0c:.1f}s")

from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine
from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
from mc.mc_ml import DolphinForewarner

VBT_DIR      = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache")
DATE_START   = '2025-12-31'
DATE_END     = '2026-02-25'
META_COLS    = {'timestamp', 'scan_number', 'v50_lambda_max_velocity', 'v150_lambda_max_velocity',
                'v300_lambda_max_velocity', 'v750_lambda_max_velocity', 'vel_div',
                'instability_50', 'instability_150'}
MC_MODELS_DIR = str(Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\nautilus_dolphin\mc_results\models"))
MC_BASE_CFG = {
    'trial_id': 0, 'vel_div_threshold': -0.020, 'vel_div_extreme': -0.050,
    'use_direction_confirm': True, 'dc_lookback_bars': 7,
    'dc_min_magnitude_bps': 0.75, 'dc_skip_contradicts': True,
    'dc_leverage_boost': 1.00, 'dc_leverage_reduce': 0.50,
    'vd_trend_lookback': 10, 'min_leverage': 0.50, 'max_leverage': 5.00,
    'leverage_convexity': 3.00, 'fraction': 0.20, 'use_alpha_layers': True,
    'use_dynamic_leverage': True, 'fixed_tp_pct': 0.0099, 'stop_pct': 1.00,
    'max_hold_bars': 120, 'use_sp_fees': True, 'use_sp_slippage': True,
    'sp_maker_entry_rate': 0.62, 'sp_maker_exit_rate': 0.50,
    'use_ob_edge': True, 'ob_edge_bps': 5.00, 'ob_confirm_rate': 0.40,
    'ob_imbalance_bias': -0.09, 'ob_depth_scale': 1.00,
    'use_asset_selection': True, 'min_irp_alignment': 0.45, 'lookback': 100,
    'acb_beta_high': 0.80, 'acb_beta_low': 0.20, 'acb_w750_threshold_pct': 60,
}
BASE_ENGINE_KWARGS = dict(
    initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05,
    min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0,
    fraction=0.20, stop_pct=1.0, max_hold_bars=120,
    use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75,
    dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5,
    use_asset_selection=True, min_irp_alignment=0.45,
    use_sp_fees=True, use_sp_slippage=True,
    sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.50,
    use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40,
    lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42,
)
OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]

# ── Load shared state once ──────────────────────────────────────────────────────
print("\nLoading MC-Forewarner...")
forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR)

parquet_files = sorted(
    p for p in VBT_DIR.glob("*.parquet")
    if 'catalog' not in str(p) and DATE_START <= p.stem <= DATE_END
)
date_strings = [pf.stem for pf in parquet_files]
print(f"Dates: {len(parquet_files)} ({date_strings[0]} to {date_strings[-1]})")

# ACB (shared across all runs — w750 threshold is data-derived, not TP-dependent)
acb_master = AdaptiveCircuitBreaker()
acb_master.preload_w750(date_strings)
print(f"ACB w750 p60: {acb_master._w750_threshold:.6f}")

# Vol calibration
all_vols = []
for pf in parquet_files[:2]:
    df = pd.read_parquet(pf)
    if 'BTCUSDT' not in df.columns: continue
    pr = df['BTCUSDT'].values
    for i in range(60, len(pr)):
        seg = pr[max(0,i-50):i]
        if len(seg)<10: continue
        v = float(np.std(np.diff(seg)/seg[:-1]))
        if v > 0: all_vols.append(v)
vol_p60 = float(np.percentile(all_vols, 60))
print(f"Vol p60: {vol_p60:.6f}")

# Pre-load data
print(f"Pre-loading {len(parquet_files)} parquets...")
t_load = time.time()
pq_data = {}
for pf in parquet_files:
    df = pd.read_parquet(pf)
    ac = [c for c in df.columns if c not in META_COLS]
    bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
    dv = np.full(len(df), np.nan)
    if bp is not None:
        for i in range(50, len(bp)):
            seg = bp[max(0,i-50):i]
            if len(seg)<10: continue
            dv[i] = float(np.std(np.diff(seg)/seg[:-1]))
    pq_data[pf.stem] = (df, ac, dv)
print(f"  Done in {time.time()-t_load:.1f}s")

# OB engine (shared)
_mock_ob = MockOBProvider(
    imbalance_bias=-0.09, depth_scale=1.0, assets=OB_ASSETS,
    imbalance_biases={"BTCUSDT": -0.086, "ETHUSDT": -0.092,
                      "BNBUSDT": +0.05, "SOLUSDT": +0.05},
)
ob_eng = OBFeatureEngine(_mock_ob)
ob_eng.preload_date("mock", OB_ASSETS)

# ── TP sweep values ─────────────────────────────────────────────────────────────
TP_VALUES_BPS = list(range(85, 122, 2))   # 85,87,...,121 → 19 values
print(f"\nTP sweep: {TP_VALUES_BPS[0]}–{TP_VALUES_BPS[-1]} bps, {len(TP_VALUES_BPS)} steps")
print(f"Baseline (champion): 99bps")

results = []
t_sweep_start = time.time()

for i, tp_bps in enumerate(TP_VALUES_BPS):
    tp_pct = tp_bps / 10000.0
    kw = dict(BASE_ENGINE_KWARGS, fixed_tp_pct=tp_pct)

    engine = NDAlphaEngine(**kw)
    engine.set_ob_engine(ob_eng)
    engine.set_acb(acb_master)
    engine.set_mc_forewarner(forewarner, MC_BASE_CFG)
    engine.set_esoteric_hazard_multiplier(0.0)

    dstats = []
    for ds in date_strings:
        df, acols, dvol = pq_data[ds]
        vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60, False)
        r = engine.process_day(ds, df, acols, vol_regime_ok=vol_ok)
        dstats.append({'pnl': r.get('pnl', 0.0), 'capital': r.get('capital', 25000.0),
                       'trades': r.get('trades', 0)})

    tr = engine.trade_history
    wins   = [t for t in tr if t.pnl_absolute > 0]
    losses = [t for t in tr if t.pnl_absolute <= 0]
    gw = sum(t.pnl_absolute for t in wins)
    gl = abs(sum(t.pnl_absolute for t in losses))
    roi    = (engine.capital - 25000) / 25000 * 100
    pf     = gw / gl if gl > 0 else 999.0
    wr     = len(wins) / len(tr) * 100 if tr else 0.0
    pnls   = np.array([s['pnl'] for s in dstats])
    sharpe = float(pnls.mean() / pnls.std() * np.sqrt(252)) if pnls.std() > 0 else 0.0
    caps   = [s['capital'] for s in dstats]
    peak   = 25000.0; max_dd = 0.0
    for c in caps:
        if c > peak: peak = c
        dd = (peak - c) / peak * 100
        if dd > max_dd: max_dd = dd
    tp_hits  = engine.tp_exits
    mh_exits = engine.hold_exits
    tp_rate  = tp_hits / len(tr) * 100 if tr else 0.0

    row = {'tp_bps': tp_bps, 'roi': roi, 'pf': pf, 'dd': max_dd, 'sharpe': sharpe,
           'wr': wr, 'n_trades': len(tr), 'tp_hits': tp_hits, 'mh_exits': mh_exits,
           'tp_rate_pct': tp_rate}
    results.append(row)

    marker = " ← BASELINE" if tp_bps == 99 else ""
    print(f"  TP={tp_bps:3d}bps  ROI={roi:+6.2f}%  PF={pf:.4f}  DD={max_dd:5.2f}%  "
          f"Sh={sharpe:.3f}  WR={wr:.1f}%  T={len(tr)}  TP%={tp_rate:.1f}%{marker}")

elapsed = time.time() - t_sweep_start

# ── Analysis ────────────────────────────────────────────────────────────────────
best_roi    = max(results, key=lambda r: r['roi'])
best_pf     = max(results, key=lambda r: r['pf'])
best_sharpe = max(results, key=lambda r: r['sharpe'])
baseline    = next(r for r in results if r['tp_bps'] == 99)

print(f"\n{'='*70}")
print(f"  TP SWEEP COMPLETE  ({elapsed/60:.1f} min)")
print(f"{'='*70}")
print(f"  Baseline (99bps):  ROI={baseline['roi']:+.2f}%  PF={baseline['pf']:.4f}  "
      f"Sh={baseline['sharpe']:.3f}  DD={baseline['dd']:.2f}%")
print(f"  Best ROI:   {best_roi['tp_bps']}bps → ROI={best_roi['roi']:+.2f}%  "
      f"ΔROI={best_roi['roi']-baseline['roi']:+.2f}%")
print(f"  Best PF:    {best_pf['tp_bps']}bps → PF={best_pf['pf']:.4f}  "
      f"ΔPF={best_pf['pf']-baseline['pf']:+.4f}")
print(f"  Best Sharpe:{best_sharpe['tp_bps']}bps → Sh={best_sharpe['sharpe']:.3f}  "
      f"ΔSh={best_sharpe['sharpe']-baseline['sharpe']:+.3f}")
print()
print(f"  Full table:")
print(f"  {'TP':>6}  {'ROI':>7}  {'PF':>7}  {'DD':>6}  {'Sharpe':>7}  {'WR':>5}  {'TP%':>5}")
for r in results:
    mk = " *" if r['tp_bps'] == best_roi['tp_bps'] else ("  " if r['tp_bps'] != 99 else " B")
    print(f"  {r['tp_bps']:>4}bps  {r['roi']:>+6.2f}%  {r['pf']:>7.4f}  {r['dd']:>5.2f}%  "
          f"{r['sharpe']:>7.3f}  {r['wr']:>4.1f}%  {r['tp_rate_pct']:>4.1f}%{mk}")

# ── 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'tp_sweep_{ts}.csv', 'w', newline='') as f:
    w = csv.DictWriter(f, fieldnames=list(results[0].keys()))
    w.writeheader(); w.writerows(results)

summary = {
    'experiment': 'tp_sweep_5s_champion',
    'date_range': f'{DATE_START}_to_{DATE_END}',
    'tp_range_bps': [TP_VALUES_BPS[0], TP_VALUES_BPS[-1]],
    'tp_step_bps': 2,
    'n_steps': len(TP_VALUES_BPS),
    'baseline_tp_bps': 99,
    'baseline': baseline,
    'best_roi': best_roi,
    'best_pf': best_pf,
    'best_sharpe': best_sharpe,
    'delta_roi_best_vs_baseline': best_roi['roi'] - baseline['roi'],
    'elapsed_s': elapsed,
    'run_ts': ts,
    'all_results': results,
}
with open(run_dir / f'tp_sweep_{ts}.json', 'w') as f:
    json.dump(summary, f, indent=2)

print(f"\nSaved: run_logs/tp_sweep_{ts}.csv + .json")
print(f"Best TP: {best_roi['tp_bps']}bps (ΔROI={best_roi['roi']-baseline['roi']:+.2f}%)")