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
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nautilus_dolphin/test_forewarner_reality.py
Executable file
95
nautilus_dolphin/test_forewarner_reality.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 numpy as np
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from pathlib import Path
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# Adjust paths
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PROJECT_ROOT = Path(__file__).resolve().parent
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sys.path.insert(0, str(PROJECT_ROOT))
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sys.path.insert(0, str(PROJECT_ROOT.parent / 'external_factors'))
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from mc.mc_ml import DolphinForewarner
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from mc.mc_sampler import MCSampler
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from mc.mc_executor import MCExecutor
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MODELS_DIR = PROJECT_ROOT / "mc_results" / "models"
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def run_reality_check():
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print("======================================================================")
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print("FOREWARNER REALITY CHECK: PREDICTION VS. ACTUAL PAPER TRADE")
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print("======================================================================")
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forewarner = DolphinForewarner(models_dir=str(MODELS_DIR))
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sampler = MCSampler(base_seed=4242)
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executor = MCExecutor(verbose=False)
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print("[1/3] Generating random parameter gamuts to find extremums...")
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trials = sampler.generate_trials(n_samples_per_switch=20, max_trials=1000)
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worst_red = None
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best_green = None
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for trial in trials:
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report = forewarner.assess(trial)
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# Look for a really bad RED
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if report.catastrophic_probability >= 0.25 or report.envelope_score < -2.0:
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if worst_red is None or report.envelope_score < worst_red['report'].envelope_score:
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worst_red = {'trial': trial, 'report': report}
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# Look for a really good GREEN
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if report.champion_probability >= 0.8 and report.envelope_score > 3.0:
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if best_green is None or report.envelope_score > best_green['report'].envelope_score:
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best_green = {'trial': trial, 'report': report}
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if not worst_red:
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print("Couldn't find a deep RED config in this sample, taking the lowest envelope score.")
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reports = [(t, forewarner.assess(t)) for t in trials]
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reports.sort(key=lambda x: x[1].envelope_score)
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worst_red = {'trial': reports[0][0], 'report': reports[0][1]}
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if not best_green:
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print("Couldn't find a deep GREEN config in this sample, taking the highest envelope score.")
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reports = [(t, forewarner.assess(t)) for t in trials]
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reports.sort(key=lambda x: x[1].envelope_score, reverse=True)
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best_green = {'trial': reports[0][0], 'report': reports[0][1]}
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print("\n[2/3] Found Extremums. Running Full Vectorized Backtests (Reality)...")
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# Run RED
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print("\n--- RUNNING 'WORST RED' CONFIGURATION ---")
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print(f"Prediction: Envelope Score: {worst_red['report'].envelope_score:.3f} | Catastrophic Prob: {worst_red['report'].catastrophic_probability:.1%} | ROI: {worst_red['report'].predicted_roi:.1f}%")
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print(f"Key Triggers: max_leverage={worst_red['trial'].max_leverage:.2f}, fraction={worst_red['trial'].fraction:.2f}, tp={worst_red['trial'].fixed_tp_pct:.4f}")
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red_actual = executor.execute_trial(worst_red['trial'])
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print(f"\nACTUAL PIPELINE RESULT (RED):")
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print(f" Final ROI: {red_actual.roi_pct:.2f}%")
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print(f" Max Drawdown: {red_actual.max_drawdown_pct:.2f}%")
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print(f" Sharpe Ratio: {red_actual.sharpe_ratio:.2f}")
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print(f" Win Rate: {red_actual.win_rate*100:.1f}%")
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print(f" Trades: {red_actual.n_trades}")
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if red_actual.max_drawdown_pct > 30.0 or red_actual.roi_pct < 0:
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print(" VERDICT: ML Model correctly identified a CATASTROPHIC blowout run.")
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else:
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print(" VERDICT: ML Model was overly pessimistic.")
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# Run GREEN
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print("\n--- RUNNING 'BEST GREEN' CONFIGURATION ---")
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print(f"Prediction: Envelope Score: {best_green['report'].envelope_score:.3f} | Champion Prob: {best_green['report'].champion_probability:.1%} | ROI: {best_green['report'].predicted_roi:.1f}%")
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print(f"Key Triggers: max_leverage={best_green['trial'].max_leverage:.2f}, fraction={best_green['trial'].fraction:.2f}, tp={best_green['trial'].fixed_tp_pct:.4f}")
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green_actual = executor.execute_trial(best_green['trial'])
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print(f"\nACTUAL PIPELINE RESULT (GREEN):")
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print(f" Final ROI: {green_actual.roi_pct:.2f}%")
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print(f" Max Drawdown: {green_actual.max_drawdown_pct:.2f}%")
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print(f" Sharpe Ratio: {green_actual.sharpe_ratio:.2f}")
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print(f" Win Rate: {green_actual.win_rate*100:.1f}%")
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print(f" Trades: {green_actual.n_trades}")
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if green_actual.roi_pct > 30.0 and green_actual.max_drawdown_pct < 20.0:
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print(" VERDICT: ML Model correctly identified a SAFE, HIGH-YIELD champion run.")
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else:
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print(" VERDICT: ML Model was overly optimistic.")
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if __name__ == "__main__":
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run_reality_check()
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