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.
505 lines
19 KiB
Python
Executable File
505 lines
19 KiB
Python
Executable File
"""
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Nautilus-Dolphin Backtest — Arrow NG5 Edition
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===============================================
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Runs the full Nautilus-Dolphin backtest using DOLPHIN NG5 Arrow IPC
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scan files instead of legacy JSON/VBT-cache Parquet files.
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Supports two modes:
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--mode catalog : Arrow → Nautilus ParquetDataCatalog → BacktestNode
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--mode stream : Arrow → streaming ArrowEigenvalueDataAdapter (VBT path)
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Usage (catalog mode = production recommended):
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python run_nd_backtest_arrow.py
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--arrow-scans "C:/.../correlation_arb512/arrow_scans"
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--mode catalog
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--assets BTCUSDT,ETHUSDT
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--start-date 2026-02-25
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--end-date 2026-02-25
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Usage (stream mode = direct data validation):
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python run_nd_backtest_arrow.py
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--arrow-scans "C:/.../correlation_arb512/arrow_scans"
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--mode stream
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--assets BTCUSDT
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Author: Antigravity / DOLPHIN NG5 migration
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Date: 2026-02-25
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"""
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import os
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import sys
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import json
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import asyncio
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from datetime import datetime
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from pathlib import Path
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from typing import Optional, Dict, Any, List
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import logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s | %(levelname)-8s | %(message)s',
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datefmt='%H:%M:%S',
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)
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logger = logging.getLogger(__name__)
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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# ─── Arrow adapter (always available) ─────────────────────────────────────────
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from nautilus_dolphin.nautilus.arrow_data_adapter import (
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ArrowEigenvalueDataAdapter,
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ArrowBacktestDataLoader,
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ArrowToParquetBatchConverter,
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)
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from nautilus_dolphin.nautilus.arrow_parquet_catalog_builder import ArrowNautilusCatalogBuilder
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# ─── Nautilus (required for catalog/backtest modes) ────────────────────────────
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try:
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from nautilus_trader.backtest.node import BacktestNode
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from nautilus_trader.backtest.config import (
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BacktestRunConfig,
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BacktestEngineConfig,
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BacktestVenueConfig,
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BacktestDataConfig,
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)
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from nautilus_trader.config import ImportableStrategyConfig
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from nautilus_trader.execution.config import ImportableExecAlgorithmConfig
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from nautilus_trader.persistence.catalog import ParquetDataCatalog
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from nautilus_trader.model.data import QuoteTick
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from nautilus_trader.risk.config import RiskEngineConfig
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from nautilus_trader.cache.config import CacheConfig
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NAUTILUS_AVAILABLE = True
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logger.info("[OK] nautilus_trader imports successful")
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except ImportError as e:
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logger.warning(f"[WARN] nautilus_trader not available: {e}")
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NAUTILUS_AVAILABLE = False
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from nautilus_dolphin.nautilus.strategy_config import (
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create_tight_3_3_config,
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DolphinStrategyConfig,
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)
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import pandas as pd
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# ──────────────────────────────────────────────────────────────────────────────
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class NDBacktestArrow:
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"""
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Runs Nautilus-Dolphin backtest using DOLPHIN NG5 Arrow IPC output.
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Supports two data paths:
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1. ``catalog`` – Arrow→catalog→BacktestNode (full Nautilus VBT pipeline)
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2. ``stream`` – Arrow→adapter→manual signal loop (validation / debug)
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"""
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def __init__(
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self,
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arrow_scans_dir: str,
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output_dir: str = "backtest_results_arrow",
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venue: str = "BINANCE_FUTURES",
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):
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self.arrow_scans_dir = Path(arrow_scans_dir)
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self.output_dir = Path(output_dir)
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self.venue_str = venue
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self.output_dir.mkdir(parents=True, exist_ok=True)
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logger.info(f"[OK] NDBacktestArrow initialized")
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logger.info(f" Arrow scans: {self.arrow_scans_dir}")
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logger.info(f" Output: {self.output_dir}")
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# ─── Catalog mode ────────────────────────────────────────────────────────
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def prepare_catalog(
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self,
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assets: List[str],
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start_date: str,
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end_date: str,
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) -> str:
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catalog_dir = self.output_dir / "catalog"
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builder = ArrowNautilusCatalogBuilder(
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arrow_scans_dir=str(self.arrow_scans_dir),
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catalog_output_dir=str(catalog_dir),
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venue=self.venue_str,
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)
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return builder.build_catalog(
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assets=assets,
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start_date=start_date,
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end_date=end_date,
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)
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def run_backtest_catalog(
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self,
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catalog_path: str,
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assets: List[str],
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start_date: str,
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end_date: str,
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strategy_config: Optional[DolphinStrategyConfig] = None,
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) -> Dict[str, Any]:
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"""Full Nautilus BacktestNode run (VBT pipeline preserved)."""
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if not NAUTILUS_AVAILABLE:
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raise RuntimeError("nautilus_trader is required for catalog mode")
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if strategy_config is None:
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strategy_config = create_tight_3_3_config()
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logger.info("=" * 70)
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logger.info("CONFIGURING NAUTILUS BACKTEST (Arrow→Catalog mode)")
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logger.info("=" * 70)
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max_leverage = getattr(strategy_config, 'max_leverage', 2.5)
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venue_config = BacktestVenueConfig(
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name=self.venue_str,
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oms_type="NETTING",
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account_type="MARGIN",
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base_currency="USDT",
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starting_balances=["100000 USDT"],
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default_leverage=str(max_leverage),
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)
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data_configs = []
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for asset in assets:
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instrument_id = f"{asset}.{self.venue_str}"
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data_configs.append(BacktestDataConfig(
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catalog_path=catalog_path,
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data_cls="nautilus_trader.model.data:QuoteTick",
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instrument_id=instrument_id,
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))
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data_configs.append(BacktestDataConfig(
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catalog_path=catalog_path,
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data_cls="nautilus_trader.model.data:QuoteTick",
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instrument_id=f"{asset}.SIGNAL.{self.venue_str}",
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))
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nautilus_strategy_config = ImportableStrategyConfig(
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strategy_path="nautilus_dolphin.nautilus.strategy:DolphinExecutionStrategy",
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config_path="nautilus_dolphin.nautilus.strategy_config:DolphinStrategyConfig",
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config=strategy_config.dict(),
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)
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exec_algorithm_config = ImportableExecAlgorithmConfig(
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exec_algorithm_path="nautilus_dolphin.nautilus.smart_exec_algorithm:SmartExecAlgorithm",
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config_path="nautilus_trader.execution.config:ExecAlgorithmConfig",
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config={
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'exec_algorithm_id': "SMART_EXEC",
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'entry_timeout_sec': 25,
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'entry_abort_threshold_bps': 5.0,
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'exit_timeout_sec': 10,
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'maker_fee_rate': 0.0002,
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'taker_fee_rate': 0.0005,
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},
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)
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engine_config = BacktestEngineConfig(
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strategies=[nautilus_strategy_config],
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exec_algorithms=[exec_algorithm_config],
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risk_engine=RiskEngineConfig(bypass=True),
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cache=CacheConfig(tick_capacity=1_000_000, bar_capacity=100_000),
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)
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run_config = BacktestRunConfig(
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venues=[venue_config],
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data=data_configs,
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engine=engine_config,
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chunk_size=None,
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raise_exception=True,
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dispose_on_completion=False,
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)
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logger.info("=" * 70)
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logger.info("RUNNING BACKTEST")
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logger.info("=" * 70)
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try:
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node = BacktestNode(configs=[run_config])
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node.run()
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engine = node.get_engine(run_config.id)
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result = engine.get_result()
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if result:
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try:
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trades = self._extract_trades_from_result(result)
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except Exception:
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trades = self._extract_trades(engine)
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else:
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trades = self._extract_trades(engine)
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metrics = self._compute_metrics(trades)
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self._enrich_from_nautilus_result(result, metrics)
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except Exception as e:
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logger.error(f"[FAIL] Backtest failed: {e}")
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import traceback; logger.error(traceback.format_exc())
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raise
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return self._save_and_report(trades, metrics, assets, start_date, end_date, "catalog")
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# ─── Stream mode (validation / paper-trade path) ─────────────────────────
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def run_backtest_stream(
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self,
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assets: List[str],
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start_date: str,
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end_date: str,
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) -> Dict[str, Any]:
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"""
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Stream Arrow files through the adapter and collect signal statistics.
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Does NOT use the Nautilus BacktestNode — this is a direct signal-loop
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validation mode useful for confirming data parity vs. JSON.
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"""
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logger.info("=" * 70)
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logger.info("RUNNING SIGNAL-LOOP BACKTEST (Arrow stream mode)")
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logger.info("=" * 70)
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adapter = ArrowEigenvalueDataAdapter(
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arrow_scans_dir=str(self.arrow_scans_dir),
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venue=self.venue_str,
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assets=assets,
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)
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start = datetime.strptime(start_date, '%Y-%m-%d')
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end_dt = datetime.strptime(end_date, '%Y-%m-%d')
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adapter.load_date_range(start, end_dt)
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all_signals = []
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total_bars = 0
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for bars, signals in adapter:
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total_bars += len(bars)
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all_signals.extend(signals)
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metrics = self._signal_loop_metrics(all_signals, assets)
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logger.info(f"[Stream] Total scans: {len(adapter._scan_files)}")
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logger.info(f"[Stream] Total bars: {total_bars}")
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logger.info(f"[Stream] Total signals: {len(all_signals)}")
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return self._save_and_report([], metrics, assets, start_date, end_date, "stream",
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extra={'total_bars': total_bars,
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'total_signals': len(all_signals)})
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# ─── Helpers ──────────────────────────────────────────────────────────────
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def _extract_trades(self, engine) -> list:
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return [
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{
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"trade_id": str(p.id),
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"instrument_id": str(p.instrument_id),
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"entry_time": str(pd.to_datetime(p.ts_opened, unit='ns', utc=True)),
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"exit_time": str(pd.to_datetime(p.ts_closed, unit='ns', utc=True)),
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"entry_price": float(p.avg_px_open),
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"exit_price": float(p.avg_px_close),
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"direction": str(p.side),
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"quantity": float(p.quantity),
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"pnl": float(p.realized_pnl),
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}
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for p in engine.cache.positions_closed()
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]
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def _extract_trades_from_result(self, result) -> list:
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return [
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{
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"trade_id": str(p.id),
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"instrument_id": str(p.instrument_id),
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"entry_time": str(pd.to_datetime(p.ts_opened, unit='ns', utc=True)),
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"exit_time": str(pd.to_datetime(p.ts_closed, unit='ns', utc=True)),
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"entry_price": float(p.avg_px_open),
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"exit_price": float(p.avg_px_close),
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"direction": str(p.side),
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"quantity": float(p.quantity),
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"pnl": float(p.realized_pnl),
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}
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for p in result.positions()
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]
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def _compute_metrics(self, trades: list) -> Dict[str, Any]:
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if not trades:
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return {"win_rate": 0, "total_pnl": 0, "roi": 0,
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"winning_trades": 0, "losing_trades": 0,
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"max_drawdown": 0, "sharpe": 0}
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pnls = [t.get('pnl', 0) for t in trades]
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winners = [p for p in pnls if p > 0]
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cumulative = pd.Series(pnls).cumsum()
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rolling_max = cumulative.cummax()
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drawdown = (cumulative - rolling_max)
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max_dd = float(drawdown.min())
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import numpy as np
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returns = pd.Series(pnls) / 100_000
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sharpe = float(returns.mean() / returns.std() * (252 * 17_280) ** 0.5) if returns.std() > 0 else 0.0
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return {
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"win_rate": len(winners) / len(trades),
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"total_pnl": round(sum(pnls), 2),
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"roi": sum(pnls) / 100_000,
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"winning_trades": len(winners),
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"losing_trades": len(trades) - len(winners),
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"max_drawdown": max_dd,
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"sharpe": sharpe,
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}
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def _signal_loop_metrics(self, signals: list, assets: List[str]) -> Dict[str, Any]:
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import numpy as np
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if not signals:
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return {"signal_count": 0}
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vel_divs = [s.get('vel_div', 0) for s in signals]
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longs = sum(1 for vd in vel_divs if vd > 0)
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shorts = sum(1 for vd in vel_divs if vd < 0)
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return {
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"signal_count": len(signals),
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"long_signals": longs,
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"short_signals": shorts,
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"mean_vel_div": float(np.mean(vel_divs)),
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"std_vel_div": float(np.std(vel_divs)),
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}
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def _enrich_from_nautilus_result(self, result, metrics: dict):
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if result is None:
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return
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if hasattr(result, 'stats_pnls'):
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stats = result.stats_pnls.get('USDT', {}) or next(iter(result.stats_pnls.values()), {})
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for nd_key, our_key in [
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('PnL (total)', 'total_pnl'),
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('PnL% (total)', 'roi'),
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('Win Rate', 'win_rate'),
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]:
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if nd_key in stats:
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try: metrics[our_key] = float(stats[nd_key])
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except (TypeError, ValueError): pass
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def _save_and_report(
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self,
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trades: list,
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metrics: Dict[str, Any],
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assets: List[str],
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start_date: str,
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end_date: str,
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mode: str,
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extra: Optional[dict] = None,
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) -> Dict[str, Any]:
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result = {
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"timestamp": datetime.now().isoformat(),
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"mode": mode,
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"data_source": "arrow_ng5",
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"backtest_params": {
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"assets": assets,
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"start_date": start_date,
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"end_date": end_date,
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"venue": self.venue_str,
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},
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"trades": trades,
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"metrics": metrics,
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"trade_count": len(trades),
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}
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if extra:
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result.update(extra)
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ts = datetime.now().strftime('%Y%m%d_%H%M%S')
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out_file = self.output_dir / f"nd_arrow_backtest_{mode}_{ts}.json"
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with open(out_file, 'w') as f:
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json.dump(result, f, indent=2, default=str)
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logger.info("=" * 70)
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logger.info("BACKTEST RESULTS (Arrow NG5)")
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logger.info("=" * 70)
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logger.info(f"Mode: {mode}")
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logger.info(f"Trades: {len(trades)}")
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logger.info(f"Win Rate: {metrics.get('win_rate', 0):.2%}")
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logger.info(f"Total P&L: ${metrics.get('total_pnl', 0):,.2f}")
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logger.info(f"ROI: {metrics.get('roi', 0):.2%}")
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logger.info(f"Max DD: ${metrics.get('max_drawdown', 0):,.2f}")
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logger.info(f"Sharpe: {metrics.get('sharpe', 0):.3f}")
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logger.info(f"Results: {out_file}")
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logger.info("=" * 70)
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return result
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|
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# ──────────────────────────────────────────────────────────────────────────────
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async def main():
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import argparse
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|
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parser = argparse.ArgumentParser(
|
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description="Nautilus-Dolphin Arrow NG5 Backtest Runner"
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)
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parser.add_argument(
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"--arrow-scans", required=True,
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help="Path to NG5 arrow_scans directory, e.g. .../correlation_arb512/arrow_scans",
|
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)
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parser.add_argument(
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"--mode", choices=["catalog", "stream"], default="catalog",
|
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help="catalog=full Nautilus VBT, stream=signal-loop validation",
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)
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parser.add_argument("--assets", default="BTCUSDT", help="Comma-separated assets")
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parser.add_argument("--start-date", default="2026-02-25", help="Start date YYYY-MM-DD")
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||
parser.add_argument("--end-date", default="2026-02-25", help="End date YYYY-MM-DD")
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||
parser.add_argument("--output-dir", default="backtest_results_arrow")
|
||
parser.add_argument("--venue", default="BINANCE_FUTURES")
|
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parser.add_argument(
|
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"--reference-file", default=None,
|
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help="Legacy JSON backtest results for comparison (optional)",
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)
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args = parser.parse_args()
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assets = [a.strip() for a in args.assets.split(",")]
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runner = NDBacktestArrow(
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arrow_scans_dir=args.arrow_scans,
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output_dir=args.output_dir,
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venue=args.venue,
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)
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if args.mode == "catalog":
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||
catalog_path = runner.prepare_catalog(
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assets=assets,
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start_date=args.start_date,
|
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end_date=args.end_date,
|
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)
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results = runner.run_backtest_catalog(
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catalog_path=catalog_path,
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assets=assets,
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start_date=args.start_date,
|
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end_date=args.end_date,
|
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)
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||
else:
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results = runner.run_backtest_stream(
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assets=assets,
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start_date=args.start_date,
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end_date=args.end_date,
|
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)
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# Optional comparison with legacy JSON results
|
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if args.reference_file and os.path.exists(args.reference_file):
|
||
logger.info("=" * 70)
|
||
logger.info("COMPARISON vs. LEGACY JSON BACKTEST")
|
||
logger.info("=" * 70)
|
||
with open(args.reference_file, 'r') as f:
|
||
ref = json.load(f)
|
||
|
||
for key, label in [
|
||
('trade_count', 'Trades'),
|
||
('metrics.win_rate', 'Win Rate'),
|
||
('metrics.roi', 'ROI'),
|
||
('metrics.total_pnl', 'Total P&L'),
|
||
]:
|
||
parts = key.split('.')
|
||
ref_val = ref
|
||
ng5_val = results
|
||
for p in parts:
|
||
ref_val = ref_val.get(p, 0) if isinstance(ref_val, dict) else 0
|
||
ng5_val = ng5_val.get(p, 0) if isinstance(ng5_val, dict) else 0
|
||
|
||
if isinstance(ref_val, float) and abs(ref_val) < 10:
|
||
logger.info(f"{label:15s}: Legacy={ref_val:.4%}, NG5={ng5_val:.4%}, Δ={abs(ng5_val - ref_val):.4%}")
|
||
else:
|
||
logger.info(f"{label:15s}: Legacy={ref_val}, NG5={ng5_val}, Δ={abs(ng5_val - ref_val):.2f}")
|
||
|
||
return results
|
||
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(main())
|