96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
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import sys
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from pathlib import Path
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import json
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import numpy as np
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import time
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HCM_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
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sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin'))
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sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin' / 'dvae'))
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# We'll monkeypatch run_backtest to add progress bars
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import exp_shared
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def replicate():
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print("="*60)
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print("EXACT REPLICATION OF D_LIQ_GOLD PERFECT MAKER (181.81%)")
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print("="*60)
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exp_shared.ensure_jit()
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perfect_kwargs = {
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'seed': 42,
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'sp_maker_entry_rate': 1.0,
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'sp_maker_exit_rate': 1.0,
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'use_sp_fees': True,
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'use_sp_slippage': False,
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}
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print("Running D_liq (8x/9x) configuration in PERFECT MAKER mode...")
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# Using the monkeypatched version that prints daily progress to avoid 'locked up' appearance
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from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
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# Manually running loop here for transparency
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from exp_shared import load_data, ENGINE_KWARGS, MC_BASE_CFG
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from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
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import pandas as pd
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import gc
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d = load_data()
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kw = ENGINE_KWARGS.copy()
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kw.update(perfect_kwargs)
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acb = AdaptiveCircuitBreaker()
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acb.preload_w750(d['date_strings'])
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eng = create_d_liq_engine(**kw)
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eng.set_ob_engine(d['ob_eng'])
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eng.set_acb(acb)
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daily_caps, daily_pnls = [], []
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total_days = len(d['parquet_files'])
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t_start = time.time()
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for i, pf in enumerate(d['parquet_files']):
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ds = pf.stem
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# Print progress every 5 days or first/last
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if i == 0 or i == total_days-1 or (i+1) % 10 == 0:
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elapsed = time.time() - t_start
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print(f" Day {i+1}/{total_days}: {ds} | Cap: ${eng.capital:,.2f} | Trades: {len(eng.trade_history)} | Elapsed: {elapsed:.0f}s")
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df = pd.read_parquet(pf)
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acols = [c for c in df.columns if c not in exp_shared.META_COLS]
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bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
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dvol = np.full(len(df), np.nan)
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if bp is not None:
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diffs = np.zeros(len(bp), dtype=np.float64)
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diffs[1:] = np.diff(bp) / bp[:-1]
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for j in range(50, len(bp)):
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dvol[j] = np.std(diffs[j-50:j])
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cap_before = eng.capital
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vol_ok = np.where(np.isfinite(dvol), dvol > d['vol_p60'], False)
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eng.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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daily_caps.append(eng.capital)
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daily_pnls.append(eng.capital - cap_before)
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del df
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gc.collect()
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tr = eng.trade_history
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n = len(tr)
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roi = (eng.capital - 25000.0) / 25000.0 * 100.0
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print("\nREPLICATION RESULTS:")
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print(f" ROI: {roi:.2f}% (Target: 181.81%)")
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print(f" Trades: {n} (Target: 2155)")
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print(f" Final Cap: ${eng.capital:,.2f}")
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if abs(roi - 181.81) < 0.2:
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print("\nGOLD CERTIFICATION: SUCCESS ✓")
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else:
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print(f"\nGOLD CERTIFICATION: FAILED ✗ (Diff: {roi-181.81:+.2f}pp)")
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if __name__ == "__main__":
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replicate()
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