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
179
prod/nautilus_native_gold_repro.py
Executable file
179
prod/nautilus_native_gold_repro.py
Executable file
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import os
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import sys
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import json
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import logging
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import gc
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import math
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import uuid
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from pathlib import Path
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from datetime import datetime, timezone
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import numpy as np
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import pandas as pd
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from tqdm import tqdm
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# --- Project Paths ---
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BASE_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
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sys.path.insert(0, str(BASE_DIR))
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sys.path.insert(0, str(BASE_DIR / 'nautilus_dolphin'))
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from nautilus_trader.backtest.engine import BacktestEngine, BacktestEngineConfig
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from nautilus_trader.model.identifiers import Venue, InstrumentId
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from nautilus_trader.model.currencies import USDT
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from nautilus_trader.model.objects import Money
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from nautilus_trader.model.data import BarType, Bar
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from nautilus_trader.model.enums import AccountType, OmsType
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from nautilus_trader.model.instruments import Instrument
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from nautilus_dolphin.nautilus.dolphin_actor import DolphinActor
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from nautilus_dolphin.nautilus.proxy_boost_engine import create_boost_engine
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# --- [GOLD STANDARD REPRO CONFIGURATION] ---
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NATIVE_GOLD_CFG = dict(
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initial_capital=25000.0,
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vel_div_threshold=-0.02, vel_div_extreme=-0.05,
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min_leverage=0.5, max_leverage=8.0,
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leverage_convexity=3.0, fraction=0.20,
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fixed_tp_pct=0.0095, stop_pct=1.0, max_hold_bars=250,
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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=1.0, dc_leverage_reduce=0.5,
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use_asset_selection=True, min_irp_alignment=0.0,
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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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boost_mode='d_liq',
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)
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# --- Helpers ---
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from prod.nautilus_native_backtest import _make_instrument, _make_bar, _FEATURE_STORE
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def run_gold_repro():
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print(f"\n[!] LAUNCHING GOLD REPRO V12 (THE FORTRESS): T=2155 | ROI=181.81% (Target)")
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data_dir = BASE_DIR / "vbt_cache"
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# Process exactly 56 days as per Gold Standard
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parquet_files = sorted(data_dir.glob("202*.parquet"))[0:56]
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if not parquet_files:
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print(f"ERROR: No parquet files found in {data_dir}")
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return
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venue = Venue("BINANCE")
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config = BacktestEngineConfig(trader_id="GOLD-REPRO-012")
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engine = BacktestEngine(config=config)
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# Starting capital
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engine.add_venue(venue, OmsType.NETTING, AccountType.MARGIN, [Money(25000.0, USDT)])
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actor_cfg = {
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'initial_capital': 25000.0,
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'paper_trade': {'initial_capital': 25000.0},
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'native_mode': True,
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'registered_assets': [],
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'engine_cfg': NATIVE_GOLD_CFG
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}
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_FEATURE_STORE.clear()
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all_bars = []
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df0 = pd.read_parquet(parquet_files[0])
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excluded = {'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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assets = [c for c in df0.columns if c not in excluded]
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actor_cfg['registered_assets'] = assets
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bar_type_map = {}
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for sym in assets:
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inst = _make_instrument(sym, venue)
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if inst:
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engine.add_instrument(inst)
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bt_str = f"{sym}.BINANCE-5-SECOND-LAST-EXTERNAL"
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bar_type_map[sym] = BarType.from_str(bt_str)
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print(f"[*] Pre-loading {len(parquet_files)} days ({len(assets)} assets)...")
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global_row_idx = 0
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for pf in tqdm(parquet_files):
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df = pd.read_parquet(pf)
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columns = df.columns.tolist()
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day_str = pf.stem
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day_dt = datetime.strptime(day_str, '%Y-%m-%d').replace(tzinfo=timezone.utc)
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day_start_ns = int(day_dt.timestamp() * 1e9)
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vd_idx = columns.index('vel_div') if 'vel_div' in columns else -1
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v50_idx = columns.index('v50_lambda_max_velocity') if 'v50_lambda_max_velocity' in columns else -1
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v750_idx = columns.index('v750_lambda_max_velocity') if 'v750_lambda_max_velocity' in columns else -1
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inst50_idx = columns.index('instability_50') if 'instability_50' in columns else -1
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asset_indices = {sym: columns.index(sym) for sym in assets if sym in columns}
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data = df.values.tolist()
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for i, row in enumerate(data):
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ts_ns = day_start_ns + i * 5 * 1_000_000_000
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vol_ok = (global_row_idx >= 100)
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# FORTRESS FIX: Sanitize features. Replace NaNs with reasonable defaults.
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def _safe_float(val, default=0.0):
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if val is None: return default
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try:
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f = float(val)
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return f if math.isfinite(f) else default
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except:
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return default
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_FEATURE_STORE[ts_ns] = {
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'vel_div': _safe_float(row[vd_idx], 0.0) if vd_idx != -1 else 0.0,
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'v50': _safe_float(row[v50_idx], 0.0) if v50_idx != -1 else 0.0,
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'v750': _safe_float(row[v750_idx], 0.0) if v750_idx != -1 else 0.0,
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'inst50': _safe_float(row[inst50_idx], 0.0) if inst50_idx != -1 else 0.0,
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'vol_ok': vol_ok,
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'row_i': i,
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}
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# Non-BTC assets first
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for sym in assets:
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if sym == 'BTCUSDT': continue
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idx = asset_indices.get(sym)
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if idx is not None:
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p = row[idx]
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if p is not None and p > 0 and math.isfinite(p):
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bar = _make_bar(bar_type_map[sym], float(p), ts_ns)
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if bar: all_bars.append(bar)
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# BTC last (The "Heartbeat" of DolphinActor)
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if 'BTCUSDT' in asset_indices:
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p = row[asset_indices['BTCUSDT']]
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if p is not None and p > 0 and math.isfinite(p):
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bar = _make_bar(bar_type_map['BTCUSDT'], float(p), ts_ns)
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if bar: all_bars.append(bar)
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global_row_idx += 1
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del df, data
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gc.collect()
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print(f"[RAM] Pre-load complete. {len(all_bars):,} bars synthesized.")
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actor = DolphinActor(actor_cfg)
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engine.add_strategy(actor)
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# Inject all bars into the engine queue
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engine.add_data(all_bars)
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print(f"[NATIVE] Executing Gold-Repro V12 ({len(all_bars):,} bars)...")
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t0 = datetime.now()
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engine.run()
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dur = (datetime.now() - t0).total_seconds()
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stats = actor.engine.get_performance_summary()
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print("\n" + "="*72)
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print(f" GOLD REPRO RESULTS (V12 - FORTRESS)")
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print(f" ROI : {stats['total_return']*100:+.2f}%")
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print(f" Trades : {stats['total_trades']}")
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print(f" Final Capital : ${stats['final_capital']:,.2f}")
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print(f" Elapsed : {dur/60:.1f} min")
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print("="*72)
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if __name__ == "__main__":
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os.environ['PYTHONIOENCODING'] = 'utf-8'
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run_gold_repro()
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