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
387
nautilus_dolphin/mc/mc_executor.py
Executable file
387
nautilus_dolphin/mc/mc_executor.py
Executable file
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"""
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Monte Carlo Trial Executor
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==========================
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Trial execution harness for running backtests with parameter configurations.
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This module interfaces with the Nautilus-Dolphin system to run backtests
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with sampled parameter configurations and extract metrics.
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Reference: MONTE_CARLO_SYSTEM_ENVELOPE_SPEC.md Section 5
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"""
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import time
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from typing import Dict, List, Optional, Any, Tuple
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from pathlib import Path
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from datetime import datetime
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import numpy as np
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from .mc_sampler import MCTrialConfig
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from .mc_validator import MCValidator, ValidationResult
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from .mc_metrics import MCMetrics, MCTrialResult
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class MCExecutor:
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"""
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Monte Carlo Trial Executor.
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Runs backtests for parameter configurations and extracts metrics.
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"""
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def __init__(
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self,
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initial_capital: float = 25000.0,
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data_period: Tuple[str, str] = ('2025-12-31', '2026-02-18'),
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preflight_bars: int = 500,
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preflight_min_trades: int = 2,
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verbose: bool = False
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):
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"""
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Initialize the executor.
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Parameters
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----------
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initial_capital : float
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Starting capital for backtests
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data_period : Tuple[str, str]
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(start_date, end_date) for backtest
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preflight_bars : int
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Bars for preflight check (V4)
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preflight_min_trades : int
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Minimum trades for preflight to pass
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verbose : bool
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Print detailed execution info
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"""
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self.initial_capital = initial_capital
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self.data_period = data_period
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self.preflight_bars = preflight_bars
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self.preflight_min_trades = preflight_min_trades
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self.verbose = verbose
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self.validator = MCValidator(verbose=verbose)
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self.metrics = MCMetrics(initial_capital=initial_capital)
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# Try to import Nautilus-Dolphin components
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self._init_nd_components()
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def _init_nd_components(self):
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"""Initialize Nautilus-Dolphin components if available."""
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self.nd_available = False
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try:
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# Import key components from Nautilus-Dolphin
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from nautilus_dolphin.nautilus.strategy_config import DolphinStrategyConfig
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from nautilus_dolphin.nautilus.backtest_runner import run_backtest
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self.DolphinStrategyConfig = DolphinStrategyConfig
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self.run_nd_backtest = run_backtest
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self.nd_available = True
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if self.verbose:
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print("[OK] Nautilus-Dolphin components loaded")
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except ImportError as e:
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if self.verbose:
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print(f"[WARN] Nautilus-Dolphin not available: {e}")
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print("[WARN] Will use simulation mode for testing")
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def execute_trial(
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self,
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config: MCTrialConfig,
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skip_validation: bool = False
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) -> MCTrialResult:
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"""
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Execute a single MC trial.
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Parameters
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----------
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config : MCTrialConfig
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Trial configuration
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skip_validation : bool
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Skip validation (if already validated)
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Returns
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-------
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MCTrialResult
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Complete trial result with metrics
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"""
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start_time = time.time()
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# Step 1: Validation (V1-V4)
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if not skip_validation:
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validation = self.validator.validate(config)
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if not validation.is_valid():
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result = MCTrialResult(
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trial_id=config.trial_id,
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config=config,
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status=validation.status.value,
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error_message=validation.reject_reason
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)
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result.execution_time_sec = time.time() - start_time
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return result
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# Step 2: Preflight check (V4 lightweight)
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preflight_passed, preflight_msg = self._run_preflight(config)
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if not preflight_passed:
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result = MCTrialResult(
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trial_id=config.trial_id,
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config=config,
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status='PREFLIGHT_FAIL',
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error_message=preflight_msg
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)
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result.execution_time_sec = time.time() - start_time
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return result
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# Step 3: Full backtest
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try:
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if self.nd_available:
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trades, daily_pnls, date_stats, signal_stats = self._run_nd_backtest(config)
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else:
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trades, daily_pnls, date_stats, signal_stats = self._run_simulated_backtest(config)
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# Step 4: Compute metrics
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execution_time = time.time() - start_time
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result = self.metrics.compute(
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config, trades, daily_pnls, date_stats, signal_stats, execution_time
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)
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if self.verbose:
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print(f" Trial {config.trial_id}: ROI={result.roi_pct:.2f}%, "
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f"Trades={result.n_trades}, Sharpe={result.sharpe_ratio:.2f}")
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return result
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except Exception as e:
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if self.verbose:
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print(f" Trial {config.trial_id}: ERROR - {e}")
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result = MCTrialResult(
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trial_id=config.trial_id,
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config=config,
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status='ERROR',
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error_message=str(e)
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)
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result.execution_time_sec = time.time() - start_time
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return result
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def _run_preflight(self, config: MCTrialConfig) -> Tuple[bool, str]:
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"""
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Run lightweight preflight check (V4).
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Returns (passed, message).
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"""
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# Check for extreme values that would cause issues
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# Fraction too small
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if config.fraction < 0.02:
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return False, f"FRACTION_TOO_SMALL: {config.fraction}"
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# Leverage range issues
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leverage_range = config.max_leverage - config.min_leverage
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if leverage_range < 0.5 and config.leverage_convexity > 2.0:
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return False, f"NARROW_RANGE_HIGH_CONVEXITY"
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# Hold period too short
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if config.max_hold_bars < config.vd_trend_lookback + 10:
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return False, f"HOLD_TOO_SHORT"
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# TP/SL ratio check
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tp_sl_ratio = config.fixed_tp_pct / (config.stop_pct / 100)
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if tp_sl_ratio > 10:
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return False, f"TP_SL_RATIO_EXTREME: {tp_sl_ratio}"
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return True, "OK"
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def _run_nd_backtest(
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self,
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config: MCTrialConfig
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) -> Tuple[List[Dict], List[float], List[Dict], Dict[str, Any]]:
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"""
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Run actual Nautilus-Dolphin backtest.
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Returns (trades, daily_pnls, date_stats, signal_stats).
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"""
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# Convert MC config to ND config
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nd_config = self._mc_to_nd_config(config)
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# Run backtest
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backtest_result = self.run_nd_backtest(nd_config)
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# Extract results
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trades = backtest_result.get('trades', [])
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daily_pnls = backtest_result.get('daily_pnls', [])
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date_stats = backtest_result.get('date_stats', [])
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signal_stats = backtest_result.get('signal_stats', {})
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return trades, daily_pnls, date_stats, signal_stats
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def _mc_to_nd_config(self, config: MCTrialConfig) -> Dict[str, Any]:
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"""Convert MC trial config to Nautilus-Dolphin config."""
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return {
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'venue': 'BINANCE_FUTURES',
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'environment': 'BACKTEST',
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'trader_id': f'DOLPHIN-MC-{config.trial_id}',
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'strategy': {
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'venue': 'BINANCE_FUTURES',
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'direction': 'SHORT',
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'vel_div_threshold': config.vel_div_threshold,
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'vel_div_extreme': config.vel_div_extreme,
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'max_leverage': config.max_leverage,
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'min_leverage': config.min_leverage,
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'leverage_convexity': config.leverage_convexity,
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'capital_fraction': config.fraction,
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'max_hold_bars': config.max_hold_bars,
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'tp_bps': int(config.fixed_tp_pct * 10000),
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'fixed_tp_pct': config.fixed_tp_pct,
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'stop_pct': config.stop_pct,
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'use_trailing': False,
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'irp_alignment_min': config.min_irp_alignment,
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'lookback': config.lookback,
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'excluded_assets': ['TUSDUSDT', 'USDCUSDT'],
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'acb_enabled': True,
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'max_concurrent_positions': 1,
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'daily_loss_limit_pct': 10.0,
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'use_sp_fees': config.use_sp_fees,
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'use_sp_slippage': config.use_sp_slippage,
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'sp_maker_fill_rate': config.sp_maker_entry_rate,
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'sp_maker_exit_rate': config.sp_maker_exit_rate,
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'use_ob_edge': config.use_ob_edge,
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'ob_edge_bps': config.ob_edge_bps,
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'ob_confirm_rate': config.ob_confirm_rate,
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'ob_imbalance_bias': config.ob_imbalance_bias,
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'ob_depth_scale': config.ob_depth_scale,
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'use_direction_confirm': config.use_direction_confirm,
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'dc_lookback_bars': config.dc_lookback_bars,
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'dc_min_magnitude_bps': config.dc_min_magnitude_bps,
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'dc_skip_contradicts': config.dc_skip_contradicts,
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'dc_leverage_boost': config.dc_leverage_boost,
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'dc_leverage_reduce': config.dc_leverage_reduce,
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'use_alpha_layers': config.use_alpha_layers,
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'use_dynamic_leverage': config.use_dynamic_leverage,
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'acb_beta_high': config.acb_beta_high,
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'acb_beta_low': config.acb_beta_low,
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'acb_w750_threshold_pct': config.acb_w750_threshold_pct,
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},
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'data_catalog': {
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'eigenvalues_dir': '../eigenvalues',
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'catalog_path': 'nautilus_dolphin/catalog',
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'start_date': self.data_period[0],
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'end_date': self.data_period[1],
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'assets': [
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'BTCUSDT', 'ETHUSDT', 'ADAUSDT', 'SOLUSDT', 'DOTUSDT',
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'AVAXUSDT', 'MATICUSDT', 'LINKUSDT', 'UNIUSDT', 'ATOMUSDT'
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],
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},
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}
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def _run_simulated_backtest(
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self,
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config: MCTrialConfig
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) -> Tuple[List[Dict], List[float], List[Dict], Dict[str, Any]]:
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"""
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Run simulated backtest for testing without Nautilus.
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This produces realistic-looking results based on parameter configuration
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without actually running a full backtest.
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"""
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# Number of trades based on vel_div_threshold (lower = more trades)
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base_trades = 500
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threshold_factor = abs(-0.02 / config.vel_div_threshold)
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n_trades = int(base_trades * threshold_factor * np.random.uniform(0.8, 1.2))
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n_trades = max(20, min(2000, n_trades))
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# Win rate based on parameters
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base_wr = 0.48
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if config.use_direction_confirm:
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base_wr += 0.05
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if config.use_ob_edge:
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base_wr += 0.02
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win_rate = np.clip(base_wr + np.random.normal(0, 0.05), 0.3, 0.7)
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# Generate trades
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trades = []
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n_wins = int(n_trades * win_rate)
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n_losses = n_trades - n_wins
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for i in range(n_trades):
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is_win = i < n_wins
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if is_win:
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pnl_pct = np.random.exponential(0.008) + 0.002
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pnl = pnl_pct * self.initial_capital * config.fraction * config.max_leverage
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exit_type = 'tp' if np.random.random() < 0.7 else 'hold'
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else:
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pnl_pct = -np.random.exponential(0.006) - 0.001
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pnl = pnl_pct * self.initial_capital * config.fraction * config.max_leverage
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exit_type = np.random.choice(['stop', 'hold'], p=[0.3, 0.7])
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trades.append({
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'pnl': pnl,
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'pnl_pct': pnl_pct,
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'exit_type': exit_type,
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'bars_held': np.random.randint(10, config.max_hold_bars),
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'asset': np.random.choice(['BTCUSDT', 'ETHUSDT', 'SOLUSDT', 'ADAUSDT']),
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})
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# Shuffle trades
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np.random.shuffle(trades)
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# Generate daily P&Ls (48 days)
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daily_pnls = []
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date_stats = []
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trades_per_day = len(trades) // 48
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for day in range(48):
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day_trades = trades[day * trades_per_day:(day + 1) * trades_per_day]
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day_pnl = sum(t['pnl'] for t in day_trades)
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daily_pnls.append(day_pnl)
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date_str = f'2026-01-{day % 31 + 1:02d}' if day < 31 else f'2026-02-{day - 30:02d}'
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date_stats.append({
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'date': date_str,
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'pnl': day_pnl,
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})
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# Signal stats
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signal_stats = {
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'dc_skip_rate': 0.1 if config.use_direction_confirm else 0.0,
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'ob_skip_rate': 0.05 if config.use_ob_edge else 0.0,
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'dc_confirm_rate': 0.7 if config.use_direction_confirm else 0.0,
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'irp_match_rate': 0.6 if config.use_asset_selection else 0.0,
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'entry_attempt_rate': 0.3,
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'signal_to_trade_rate': len(trades) / (48 * 1000), # Approximate
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}
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return trades, daily_pnls, date_stats, signal_stats
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def execute_batch(
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self,
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configs: List[MCTrialConfig],
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progress_interval: int = 10
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) -> List[MCTrialResult]:
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"""
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Execute a batch of trials.
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Parameters
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----------
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configs : List[MCTrialConfig]
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Trial configurations
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progress_interval : int
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Print progress every N trials
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Returns
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-------
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List[MCTrialResult]
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Results for all trials
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"""
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results = []
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total = len(configs)
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for i, config in enumerate(configs):
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result = self.execute_trial(config)
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results.append(result)
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if (i + 1) % progress_interval == 0 or i == total - 1:
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print(f" Progress: {i+1}/{total} ({(i+1)/total*100:.1f}%)")
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return results
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Reference in New Issue
Block a user