"""Stochastic resonance + noise injection experiment. Question: can controlled randomness at specific points in a near-threshold system improve performance? Three mechanistic hypotheses tested: H1 — STOCHASTIC RESONANCE on vel_div signal (col: 'vel_div') Bars hovering just above threshold (-0.02) occasionally fire with noise. SR predicts: optimal sigma ≈ mean(distance_to_threshold) for near-miss bars. Sigmas: 0.001 / 0.003 / 0.005 / 0.010 (5% / 15% / 25% / 50% of |threshold|) H2 — PRICE DITHER on asset execution prices Avoid fill clustering at round price levels. Small multiplicative noise. Sigmas: 0.0001 (1bp) / 0.0005 (5bp) H3 — TP TARGET DITHER per-run Sensitivity analysis: does TP=99bps sit at a local optimum? Sigma: 0.0001 (±1bp 1-sigma band around 0.0099) Stats: Mann-Whitney U vs baseline, Cohen's d, 95% bootstrap CI on ROI delta. Results saved incrementally → run_logs/noise_exp_YYYYMMDD_HHMMSS.csv """ import sys, time, math, csv, os sys.stdout.reconfigure(encoding='utf-8', errors='replace') from pathlib import Path from datetime import datetime from scipy.stats import mannwhitneyu import numpy as np import pandas as pd HCM = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict") sys.path.insert(0, str(HCM / "nautilus_dolphin")) VBT_DIR = HCM / "vbt_cache" MC_MODELS_DIR= str(HCM / "nautilus_dolphin" / "mc_results" / "models") LOG_DIR = HCM / "nautilus_dolphin" / "run_logs" LOG_DIR.mkdir(exist_ok=True) # ── experiment parameters ─────────────────────────────────────────────────── N_SEEDS = 25 # per (noise_type, sigma) — 25 gives ~80% power to detect 3% ROI shift CONFIGS = [ # (label, noise_type, sigma) ("baseline", "none", 0.0), ("sr_5pct", "signal_sr", 0.001), ("sr_15pct", "signal_sr", 0.003), ("sr_25pct", "signal_sr", 0.005), ("sr_50pct", "signal_sr", 0.010), ("price_1bp", "price_dither", 0.0001), ("price_5bp", "price_dither", 0.0005), ("tp_1bp", "tp_dither", 0.0001), ] BASELINE_LABEL = "baseline" # ── engine config (exact champion) ────────────────────────────────────────── META_COLS = {'timestamp', 'scan_number', 'v50_lambda_max_velocity', 'v150_lambda_max_velocity', 'v300_lambda_max_velocity', 'v750_lambda_max_velocity', 'vel_div', 'instability_50', 'instability_150'} ENGINE_KWARGS = dict( initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05, min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0, fraction=0.20, fixed_tp_pct=0.0099, stop_pct=1.0, max_hold_bars=120, use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75, dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5, use_asset_selection=True, min_irp_alignment=0.45, use_sp_fees=True, use_sp_slippage=True, sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.50, use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40, lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42, ) MC_BASE_CFG = { 'trial_id': 0, 'vel_div_threshold': -0.020, 'vel_div_extreme': -0.050, 'use_direction_confirm': True, 'dc_lookback_bars': 7, 'dc_min_magnitude_bps': 0.75, 'dc_skip_contradicts': True, 'dc_leverage_boost': 1.00, 'dc_leverage_reduce': 0.50, 'vd_trend_lookback': 10, 'min_leverage': 0.50, 'max_leverage': 5.00, 'leverage_convexity': 3.00, 'fraction': 0.20, 'use_alpha_layers': True, 'use_dynamic_leverage': True, 'fixed_tp_pct': 0.0099, 'stop_pct': 1.00, 'max_hold_bars': 120, 'use_sp_fees': True, 'use_sp_slippage': True, 'sp_maker_entry_rate': 0.62, 'sp_maker_exit_rate': 0.50, 'use_ob_edge': True, 'ob_edge_bps': 5.00, 'ob_confirm_rate': 0.40, 'ob_imbalance_bias': -0.09, 'ob_depth_scale': 1.00, 'use_asset_selection': True, 'min_irp_alignment': 0.45, 'lookback': 100, 'acb_beta_high': 0.80, 'acb_beta_low': 0.20, 'acb_w750_threshold_pct': 60, } # ── JIT warmup (one-time) ──────────────────────────────────────────────────── print("JIT warmup...", end='', flush=True) t_jit = time.time() from nautilus_dolphin.nautilus.alpha_asset_selector import compute_irp_nb, compute_ars_nb, rank_assets_irp_nb from nautilus_dolphin.nautilus.alpha_bet_sizer import compute_sizing_nb from nautilus_dolphin.nautilus.alpha_signal_generator import check_dc_nb from nautilus_dolphin.nautilus.ob_features import ( OBFeatureEngine, compute_imbalance_nb, compute_depth_1pct_nb, compute_depth_quality_nb, compute_fill_probability_nb, compute_spread_proxy_nb, compute_depth_asymmetry_nb, compute_imbalance_persistence_nb, compute_withdrawal_velocity_nb, compute_market_agreement_nb, compute_cascade_signal_nb, ) from nautilus_dolphin.nautilus.ob_provider import MockOBProvider _p = np.array([1.0, 2.0, 3.0], dtype=np.float64) compute_irp_nb(_p, -1); compute_ars_nb(1.0, 0.5, 0.01) rank_assets_irp_nb(np.ones((10, 2), dtype=np.float64), 8, -1, 5, 500.0, 20, 0.20) compute_sizing_nb(-0.03, -0.02, -0.05, 3.0, 0.5, 5.0, 0.20, True, True, 0.0, np.zeros(4, np.int64), np.zeros(4, np.int64), np.zeros(5, np.float64), 0, -1, 0.01, 0.04) check_dc_nb(_p, 3, 1, 0.75) _b = np.array([100., 200., 300., 400., 500.], dtype=np.float64) _a = np.array([110., 190., 310., 390., 510.], dtype=np.float64) compute_imbalance_nb(_b, _a); compute_depth_1pct_nb(_b, _a) compute_depth_quality_nb(210., 200.); compute_fill_probability_nb(1.0) compute_spread_proxy_nb(_b, _a); compute_depth_asymmetry_nb(_b, _a) compute_imbalance_persistence_nb(np.array([0.1, -0.1], dtype=np.float64), 2) compute_withdrawal_velocity_nb(np.array([100., 110.], dtype=np.float64), 1) compute_market_agreement_nb(np.array([0.1, -0.05], dtype=np.float64), 2) compute_cascade_signal_nb(np.array([-0.05, -0.15], dtype=np.float64), 2, -0.10) print(f" {time.time()-t_jit:.1f}s") from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker from mc.mc_ml import DolphinForewarner # ── load shared infrastructure (one-time) ─────────────────────────────────── print("Loading MC-Forewarner...", end='', flush=True) forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR) print(" OK") parquet_files = sorted([p for p in VBT_DIR.glob("*.parquet") if 'catalog' not in str(p)]) date_strings = [pf.stem for pf in parquet_files] print("Initializing ACB...", end='', flush=True) acb = AdaptiveCircuitBreaker() acb.preload_w750(date_strings) print(f" OK (w750 p60={acb._w750_threshold:.6f})") OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] _mock_ob = MockOBProvider( imbalance_bias=-0.09, depth_scale=1.0, assets=OB_ASSETS, imbalance_biases={"BTCUSDT": -0.086, "ETHUSDT": -0.092, "BNBUSDT": +0.05, "SOLUSDT": +0.05}, ) ob_eng = OBFeatureEngine(_mock_ob) ob_eng.preload_date("mock", OB_ASSETS) # ── pre-load all parquet data ──────────────────────────────────────────────── print("Pre-loading parquet data...", end='', flush=True) all_vols = [] for pf in parquet_files[:2]: df = pd.read_parquet(pf) if 'BTCUSDT' not in df.columns: continue pr = df['BTCUSDT'].values for i in range(60, len(pr)): seg = pr[max(0, i-50):i] if len(seg) >= 10: v = float(np.std(np.diff(seg) / seg[:-1])) if v > 0: all_vols.append(v) vol_p60 = float(np.percentile(all_vols, 60)) pq_data = {} for pf in parquet_files: df = pd.read_parquet(pf) ac = [c for c in df.columns if c not in META_COLS] bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None dv = np.full(len(df), np.nan) if bp is not None: for i in range(50, len(bp)): seg = bp[max(0, i-50):i] if len(seg) >= 10: dv[i] = float(np.std(np.diff(seg) / seg[:-1])) pq_data[pf.stem] = (df, ac, dv) print(f" {len(pq_data)} days") # also log near-threshold vel_div distribution (SR calibration info) all_vd = [] for ds, (df, ac, dv) in pq_data.items(): if 'vel_div' in df.columns: vd_vals = df['vel_div'].dropna().values all_vd.extend(vd_vals[(vd_vals > -0.05) & (vd_vals < 0.0)]) all_vd = np.array(all_vd) near_thresh = all_vd[(all_vd > -0.025) & (all_vd < -0.015)] print(f" vel_div near-threshold (-0.025 to -0.015): N={len(near_thresh)}, " f"mean={np.mean(near_thresh):+.5f}, σ={np.std(near_thresh):.5f}") print(f" SR optimal sigma ≈ mean distance to threshold: " f"{float(np.mean(np.abs(near_thresh - (-0.02)))):.4f}") # ── engine runner ──────────────────────────────────────────────────────────── def run_engine(data_dict, engine_kw, vol_p60_val): eng = NDAlphaEngine(**engine_kw) eng.set_ob_engine(ob_eng) eng.set_acb(acb) eng.set_mc_forewarner(forewarner, MC_BASE_CFG) eng.set_esoteric_hazard_multiplier(0.0) dstats = [] for pf in parquet_files: ds = pf.stem df, acols, dvol = data_dict[ds] vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60_val, False) stats = eng.process_day(ds, df, acols, vol_regime_ok=vol_ok) dstats.append({**stats, 'cap': eng.capital}) tr = eng.trade_history wins = [t for t in tr if t.pnl_absolute > 0] loss = [t for t in tr if t.pnl_absolute <= 0] gw = sum(t.pnl_absolute for t in wins) if wins else 0.0 gl = abs(sum(t.pnl_absolute for t in loss)) if loss else 0.0 roi = (eng.capital - 25000.0) / 25000.0 * 100 pff = gw / gl if gl > 0 else 999.0 dr = np.array([s['pnl'] / 25000.0 * 100 for s in dstats]) sh = float(np.mean(dr) / np.std(dr) * np.sqrt(365)) if np.std(dr) > 0 else 0.0 pk = 25000.0; mdd = 0.0 for s in dstats: pk = max(pk, s['cap']); mdd = max(mdd, (pk - s['cap']) / pk * 100) wr = len(wins) / len(tr) * 100 if tr else 0.0 return dict(roi=roi, pf=pff, dd=mdd, sharpe=sh, wr=wr, trades=len(tr), capital=eng.capital) # ── noise application ──────────────────────────────────────────────────────── def apply_noise(noise_type, sigma, seed_val): """Return (data_dict_noisy, engine_kw_noisy).""" rng = np.random.default_rng(seed_val) ekw = dict(ENGINE_KWARGS) if noise_type == "none": return pq_data, ekw if noise_type == "tp_dither": tp_noise = float(rng.normal(0, sigma)) ekw = dict(ENGINE_KWARGS) ekw['fixed_tp_pct'] = max(0.003, ENGINE_KWARGS['fixed_tp_pct'] + tp_noise) return pq_data, ekw # no data modification # signal_sr or price_dither — need data copies noisy = {} for ds, (df, ac, dvol) in pq_data.items(): df2 = df.copy() if noise_type == "signal_sr": if 'vel_div' in df2.columns: noise = rng.normal(0, sigma, len(df2)).astype(np.float32) df2['vel_div'] = df2['vel_div'] + noise elif noise_type == "price_dither": for col in ac: if col in df2.columns: noise = rng.normal(0, sigma, len(df2)) df2[col] = df2[col] * (1.0 + noise) # recompute dvol from dithered BTC prices if 'BTCUSDT' in df2.columns: bp = df2['BTCUSDT'].values dv2 = np.full(len(df2), np.nan) for i in range(50, len(bp)): seg = bp[max(0, i-50):i] if len(seg) >= 10: dv2[i] = float(np.std(np.diff(seg) / seg[:-1])) dvol = dv2 noisy[ds] = (df2, ac, dvol) return noisy, ekw # ── incremental CSV output ─────────────────────────────────────────────────── run_ts = datetime.now().strftime("%Y%m%d_%H%M%S") out_path = LOG_DIR / f"noise_exp_{run_ts}.csv" FIELDS = ['label', 'noise_type', 'sigma', 'seed', 'roi', 'pf', 'dd', 'sharpe', 'wr', 'trades', 'capital', 'elapsed_s'] with open(out_path, 'w', newline='') as f: csv.writer(f).writerow(FIELDS) def append_row(row_dict): with open(out_path, 'a', newline='') as f: csv.writer(f).writerow([row_dict[k] for k in FIELDS]) # ── main experiment loop ───────────────────────────────────────────────────── print(f"\n{'='*65}") print(f" NOISE EXPERIMENT — {len(CONFIGS)} configs × up to {N_SEEDS} seeds") print(f" Output: {out_path.name}") print(f"{'='*65}") all_results = {} # label → list of roi values (for final stats) t_exp_start = time.time() completed = 0 total_runs = 1 + (len(CONFIGS) - 1) * N_SEEDS # baseline=1, others=N_SEEDS for label, noise_type, sigma in CONFIGS: n = 1 if noise_type == "none" else N_SEEDS rois = [] print(f"\n [{label}] noise={noise_type} σ={sigma} n={n}") for seed_i in range(n): t0 = time.time() data_d, eng_kw = apply_noise(noise_type, sigma, seed_val=seed_i + 1000) result = run_engine(data_d, eng_kw, vol_p60) elapsed = time.time() - t0 rois.append(result['roi']) row = dict(label=label, noise_type=noise_type, sigma=sigma, seed=seed_i, elapsed_s=round(elapsed, 1), **{k: round(result[k], 4) for k in result}) append_row(row) completed += 1 eta_s = (time.time() - t_exp_start) / completed * (total_runs - completed) print(f" seed={seed_i:2d} ROI={result['roi']:+6.2f}% PF={result['pf']:.3f}" f" DD={result['dd']:.2f}% T={result['trades']}" f" [{elapsed:.0f}s | ETA {eta_s/60:.0f}min]") all_results[label] = rois # ── final analysis ─────────────────────────────────────────────────────────── print(f"\n{'='*65}") print(f" RESULTS SUMMARY") print(f"{'='*65}") baseline_rois = all_results.get(BASELINE_LABEL, [44.89]) b_roi = float(np.mean(baseline_rois)) print(f" {'Label':<14} {'σ':>8} {'E[ROI]':>8} {'±std':>7} {'ΔROI':>7} " f"{'Cohen_d':>8} {'MW_p':>6} {'Beat%':>6} {'E[PF]':>6} {'E[T]':>6}") print(f" {'-'*90}") for label, noise_type, sigma in CONFIGS: rois = all_results[label] df_res = pd.read_csv(out_path) sub = df_res[df_res['label'] == label] mean_roi = float(np.mean(rois)) std_roi = float(np.std(rois)) if len(rois) > 1 else 0.0 delta = mean_roi - b_roi mean_pf = float(sub['pf'].mean()) mean_t = float(sub['trades'].mean()) beat_pct = float(np.mean(np.array(rois) > b_roi)) * 100 if len(rois) > 1 else (100 if mean_roi > b_roi else 0) # Cohen's d vs baseline if len(rois) > 1 and len(baseline_rois) > 1: pooled_std = math.sqrt((np.var(rois) + np.var(baseline_rois)) / 2) cohens_d = delta / pooled_std if pooled_std > 0 else 0.0 else: cohens_d = 0.0 # Mann-Whitney U vs baseline if len(rois) > 1 and len(baseline_rois) > 1: try: _, mw_p = mannwhitneyu(rois, baseline_rois, alternative='two-sided') except Exception: mw_p = 1.0 else: mw_p = 1.0 print(f" {label:<14} {sigma:>8.4f} {mean_roi:>+7.2f}% {std_roi:>6.2f}%" f" {delta:>+6.2f}% {cohens_d:>+8.3f} {mw_p:>6.3f} {beat_pct:>5.1f}%" f" {mean_pf:>6.3f} {mean_t:>6.0f}") print(f"{'='*65}") print(f"\n Interpretation guide:") print(f" ΔROI > 0 & MW_p < 0.10 & Cohen_d > 0.3 → promising signal") print(f" SR optimal sigma ≈ mean near-threshold distance (see calibration above)") print(f"\n Full results → {out_path}") print(f" Total time: {(time.time()-t_exp_start)/60:.1f} min")