364 lines
16 KiB
Python
364 lines
16 KiB
Python
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"""
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exp13_model_sweep.py — Multi-model exp13 test harness.
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For each model in the registry:
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1. Auto-identify proxy_B dim (highest |r| correlation with raw proxy_B signal)
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2. Validate calibration (always-positive in 56-day window required for exp13 scaling)
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3. Run exp13 Phase 1 (14-day screening) + Phase 2 (full 56-day, top-k configs)
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4. Save per-model results to exp13_sweep_<tag>_results.json
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5. Print final comparison table across all models
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All tests use IDENTICAL configs/window/threshold to exp13 v2 (the CONFIRMED baseline).
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Threshold: Calmar > 7.83 (102% of D_LIQ_GOLD baseline 7.67 in the 56-day window)
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Reference: v2 BOB — 9/20 PASS, best dROI=+4.59pp, best Calmar=7.87
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Usage (from nautilus_dolphin/ dir):
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python dvae/exp13_model_sweep.py # all available models in registry
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python dvae/exp13_model_sweep.py --models v4 # single model
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python dvae/exp13_model_sweep.py --models v4 v5 v6 # explicit list
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python dvae/exp13_model_sweep.py --probe_only # dim probe only, no backtest
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python dvae/exp13_model_sweep.py --subset 14 --top_k 20 # explicit Phase 1/2 params
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Adding new models (v5, v6, v7, v8):
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1. Transfer model JSON from DOLPHIN Linux to models/convnext_dvae_ML/
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2. Uncomment (or add) the entry in MODEL_REGISTRY below
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3. Re-run this script
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"""
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import sys, os, time, json, importlib, argparse
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import io
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace', line_buffering=True)
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import numpy as np
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import pandas as pd
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent.parent
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DVAE_DIR = ROOT / 'nautilus_dolphin' / 'dvae'
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sys.path.insert(0, str(ROOT / 'nautilus_dolphin'))
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# ── Model registry ─────────────────────────────────────────────────────────────
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# Slot in v5/v6/v7/v8 when transferred from DOLPHIN Linux — just uncomment.
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MODEL_REGISTRY = {
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'v2_bob': ROOT / 'nautilus_dolphin' / 'dvae' / 'convnext_model_v2.json',
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'v4': ROOT / 'models' / 'convnext_dvae_ML' / 'convnext_model_v4_ep22_best.json',
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'v5': ROOT / 'models' / 'dolphin_training' / 'winning_models' / 'v5_ep28_best_total_loss.json',
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'v6': ROOT / 'models' / 'dolphin_training' / 'winning_models' / 'v6_ep8_best_val_loss.json',
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'v7': ROOT / 'models' / 'dolphin_training' / 'winning_models' / 'v7_ep10_best_generalization.json',
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# v8: step=2 training (1.2M windows), only 2 epochs — val=34.92. Experimental.
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'v8': ROOT / 'models' / 'dolphin_training' / 'dvae' / 'v8_step2.json',
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# v6.5 (ANOMALOUS — DO NOT USE: broken during training per researcher note)
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}
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KLINES_DIR = ROOT / 'vbt_cache_klines'
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DATE_START = '2025-12-31'
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DATE_END = '2026-02-25'
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CALMAR_THR = 7.83 # 102% of D_LIQ_GOLD baseline 7.67
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FEATURE_COLS = [
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'v50_lambda_max_velocity', 'v150_lambda_max_velocity',
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'v300_lambda_max_velocity', 'v750_lambda_max_velocity',
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'vel_div', 'instability_50', 'instability_150',
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]
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T_WIN = 32
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# ── Proxy_B dim identification ─────────────────────────────────────────────────
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from dvae.convnext_dvae import ConvNeXtVAE
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def _load_model(path: Path):
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with open(path) as f:
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meta = json.load(f)
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arch = meta['architecture']
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m = ConvNeXtVAE(
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C_in=arch['C_in'], T_in=arch['T_in'],
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z_dim=arch['z_dim'], base_ch=arch['base_ch'],
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n_blocks=arch.get('n_blocks', 3), seed=42,
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)
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m.load(str(path))
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nm = np.array(meta['norm_mean']) if 'norm_mean' in meta else None
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ns = np.array(meta['norm_std']) if 'norm_std' in meta else None
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return m, nm, ns, meta
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def _build_probe_set():
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"""Sample probe windows from 56-day window; shared across all models."""
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files = sorted(KLINES_DIR.glob('*.parquet'))
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period = [f for f in files if DATE_START <= f.stem[:10] <= DATE_END]
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rng = np.random.default_rng(42)
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probes_raw, proxy_B_vals = [], []
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step = max(1, len(period) // 60)
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for f in period[::step]:
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try:
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df = pd.read_parquet(f, columns=FEATURE_COLS).dropna()
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if len(df) < T_WIN + 10: continue
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mid = len(df) // 2
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pos = int(rng.integers(max(0, mid-30), min(len(df)-T_WIN, mid+30)))
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arr = df[FEATURE_COLS].values[pos:pos+T_WIN].astype(np.float64)
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proxy_B = (arr[:, 5] - arr[:, 3]).reshape(-1, 1)
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exf = np.zeros((T_WIN, 3), dtype=np.float64)
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arr11 = np.concatenate([arr, proxy_B, exf], axis=1).T # (11, T)
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if not np.isfinite(arr11).all(): continue
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probes_raw.append(arr11)
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proxy_B_vals.append(float(proxy_B.mean()))
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except Exception:
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pass
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return np.stack(probes_raw), np.array(proxy_B_vals)
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def probe_model(tag: str, path: Path, probes_raw: np.ndarray,
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proxy_B_arr: np.ndarray) -> dict:
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"""Identify proxy_B dim and report calibration for one model."""
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print(f"\n{'='*60}")
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print(f" PROBE: {tag} ({path.name})")
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print(f"{'='*60}")
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model, nm, ns, meta = _load_model(path)
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ep = meta.get('epoch', '?')
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val = meta.get('val_loss', 0.0)
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print(f" epoch={ep} val_loss={val:.5f}")
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probes = probes_raw.copy()
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if nm is not None:
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probes = (probes - nm[None, :, None]) / ns[None, :, None]
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np.clip(probes, -6., 6., out=probes)
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z_mu, z_logvar = model.encode(probes)
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z_std = z_mu.std(0)
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corrs = []
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for d in range(z_mu.shape[1]):
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if z_std[d] > 0.01:
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r = float(np.corrcoef(z_mu[:, d], proxy_B_arr)[0, 1])
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if np.isfinite(r):
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corrs.append((abs(r), r, d))
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corrs.sort(reverse=True)
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best_abs_r, best_r, best_dim = corrs[0] if corrs else (0.0, 0.0, -1)
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z_best = z_mu[:, best_dim]
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z_min, z_max = float(z_best.min()), float(z_best.max())
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always_pos = z_min > 0
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always_neg = z_max < 0
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if always_pos: calib = 'ALWAYS_POSITIVE'
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elif always_neg: calib = 'ALWAYS_NEGATIVE'
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else: calib = f'MIXED[{z_min:+.3f},{z_max:+.3f}]'
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q75, q25 = np.percentile(proxy_B_arr, 75), np.percentile(proxy_B_arr, 25)
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z_hi = float(z_best[proxy_B_arr >= q75].mean())
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z_lo = float(z_best[proxy_B_arr <= q25].mean())
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sep = abs(z_hi - z_lo)
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# Also find best POSITIVELY correlated dim (same sign as v2 z[13])
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pos_corrs = [(abs_r, r, d) for abs_r, r, d in corrs if r > 0]
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pos_dim = pos_corrs[0][2] if pos_corrs else best_dim
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pos_r = pos_corrs[0][1] if pos_corrs else best_r
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usable = always_pos and best_abs_r > 0.5
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print(f" proxy_B dim : z[{best_dim}] r={best_r:+.4f} "
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f"(best |r|) best positive: z[{pos_dim}] r={pos_r:+.4f}")
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print(f" Top-5 : " + ' '.join(f'z[{d}]={r:+.3f}' for _,r,d in corrs[:5]))
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print(f" Calibration : {calib} sep={sep:.4f}")
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print(f" Usable : {'YES ✓' if usable else 'CAUTION ⚠ (will skip exp13 sweep)'}")
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return {
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'tag': tag, 'path': str(path),
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'epoch': ep, 'val_loss': val,
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'proxy_B_dim': best_dim, 'proxy_B_r': best_r,
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'proxy_B_dim_pos': pos_dim, 'proxy_B_r_pos': pos_r,
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'calibration': calib, 'always_positive': always_pos,
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'separation': sep, 'top5': [(r,d) for _,r,d in corrs[:5]],
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}
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# ── exp13 runner per model ─────────────────────────────────────────────────────
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def run_exp13_for_model(probe: dict, subset_days: int, top_k: int,
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only_config: str = None) -> dict:
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"""Patch MODEL_1M + PROXY_B_DIM into exp13, call main(), read results JSON."""
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import dvae.exp13_multiscale_sweep as e13
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importlib.reload(e13) # fresh state: clears cached signals from prior model
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# Patch module-level constants AFTER reload
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e13.MODEL_1M = Path(probe['path'])
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e13.PROXY_B_DIM = probe['proxy_B_dim']
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out_file = ROOT / f"exp13_sweep_{probe['tag']}_results.json"
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e13.OUT_FILE = out_file
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# Patch sys.argv so argparse inside main() picks up our params
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sys.argv = [
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'exp13_multiscale_sweep.py',
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'--subset', str(subset_days),
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'--top_k', str(top_k),
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'--skip_sets', 'B,Bp', # skip 5s sets (no 5s model per model variant)
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]
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if only_config:
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sys.argv += ['--only_config', only_config, '--skip_5s']
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print(f"\n{'━'*60}")
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print(f" EXP13: {probe['tag']} z[{probe['proxy_B_dim']}] r={probe['proxy_B_r']:+.4f}")
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print(f" subset={subset_days}d top_k={top_k} out={out_file.name}")
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print(f"{'━'*60}")
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t0 = time.time()
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e13.main()
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elapsed = time.time() - t0
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# Read saved results
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if not out_file.exists():
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print(f"[ERROR] Results file not written: {out_file}")
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return {'tag': probe['tag'], 'error': 'no results file'}
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with open(out_file) as f:
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raw = json.load(f)
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baseline_full = raw.get('phase2', {}).get('baseline_full') or raw.get('phase1_results', [{}])[0]
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p2_list = raw.get('phase2', {}).get('results', [])
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if not p2_list:
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# full run (subset=0): use phase1_results as p2
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p2_list = raw.get('phase1_results', [])
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baseline_cal = (raw.get('phase2', {}).get('baseline_full') or {}).get('Calmar', 0.0)
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n_pass = sum(1 for r in p2_list if r.get('Calmar', 0) > CALMAR_THR)
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best = max(p2_list, key=lambda r: r.get('Calmar', 0)) if p2_list else {}
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base_roi = (raw.get('phase2', {}).get('baseline_full') or {}).get('ROI', 0.0)
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return {
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'tag': probe['tag'],
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'val_loss': probe['val_loss'],
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'proxy_B_dim': probe['proxy_B_dim'],
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'proxy_B_r': probe['proxy_B_r'],
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'calibration': probe['calibration'],
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'baseline_calmar': baseline_cal,
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'baseline_roi': base_roi,
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'n_phase2': len(p2_list),
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'n_pass': n_pass,
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'best_config': best.get('name', '?'),
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'best_roi': best.get('ROI', 0.0),
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'best_calmar': best.get('Calmar', 0.0),
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'best_droi': best.get('ROI', 0.0) - base_roi,
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'best_ddd': best.get('DD', 0.0) - (raw.get('phase2', {}).get('baseline_full') or {}).get('DD', 0.0),
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'elapsed_s': round(elapsed),
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'results_file': str(out_file),
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}
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# ── Comparison tables ──────────────────────────────────────────────────────────
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def _print_probe_table(probes: dict):
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print(f"\n{'='*72}")
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print(f" MODEL PROBE SUMMARY")
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print(f"{'='*72}")
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print(f" {'Tag':10s} {'ValLoss':>8s} {'Dim':>5s} {'r':>7s} {'Sep':>6s} {'Calibration':22s} OK?")
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print(f" {'-'*72}")
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for tag, p in probes.items():
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ok = '✓' if p['always_positive'] and abs(p['proxy_B_r']) > 0.5 else '⚠'
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print(f" {tag:10s} {p['val_loss']:8.4f} "
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f"z[{p['proxy_B_dim']:2d}] {p['proxy_B_r']:+7.4f} "
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f"{p['separation']:6.4f} {p['calibration']:22s} {ok}")
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def _print_comparison_table(results: list):
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if not results:
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return
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print(f"\n{'='*84}")
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print(f" EXP13 MULTI-MODEL FINAL COMPARISON")
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print(f" Threshold: Calmar > {CALMAR_THR} | Reference: v2_BOB — 9/20 PASS dROI=+4.59pp Cal=7.87")
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print(f"{'='*84}")
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print(f" {'Tag':10s} {'ValLoss':>8s} {'Dim':>5s} {'r':>7s} "
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f"{'Pass':>6s} {'BestCal':>8s} {'BestdROI':>9s} {'BestdDD':>8s} Best Config")
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print(f" {'-'*84}")
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for r in sorted(results, key=lambda x: x.get('best_calmar', 0), reverse=True):
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pass_str = f"{r['n_pass']:2d}/{r['n_phase2']}"
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flag = '✓' if r['n_pass'] > 0 else ' '
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print(f" {r['tag']:10s} {r['val_loss']:8.4f} "
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f"z[{r['proxy_B_dim']:2d}] {r['proxy_B_r']:+7.4f} "
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f"{pass_str:>6s} {flag} {r['best_calmar']:8.3f} "
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f"{r['best_droi']:+9.2f}pp {r['best_ddd']:+8.2f}pp {r['best_config']}")
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print(f" {'─'*84}")
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print(f" {'v2_bob REF':10s} {'18.0024':>8s} z[13] {'+0.9332':>7s} "
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f"{'9/20':>6s} ✓ {'7.870':>8s} {'+4.59':>9s}pp {'0.00':>8s}pp A_P5_M2_W1_a0.5")
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# ── Main ───────────────────────────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser(description='Multi-model exp13 test harness')
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parser.add_argument('--models', nargs='+', default=None,
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help='Model tags to run (default: all available)')
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parser.add_argument('--probe_only', action='store_true',
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help='Dim probe only — skip exp13 sweep')
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parser.add_argument('--subset', type=int, default=14,
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help='Phase 1 days (default 14)')
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parser.add_argument('--top_k', type=int, default=20,
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help='Phase 2 top-k configs (default 20)')
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parser.add_argument('--fast_check', type=str, default='',
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help='Skip Phase 1; run just this config on full window. '
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'Default known-winner: A_P5_M2_W1_a0.5')
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args = parser.parse_args()
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if args.fast_check == 'winner':
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args.fast_check = 'A_P5_M2_W1_a0.5' # shorthand
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# Select models
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tags = args.models or list(MODEL_REGISTRY.keys())
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available = {t: MODEL_REGISTRY[t] for t in tags
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if t in MODEL_REGISTRY and Path(MODEL_REGISTRY[t]).exists()}
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skipped = [t for t in tags if t not in MODEL_REGISTRY]
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missing = [t for t in MODEL_REGISTRY if t not in available and
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t in (args.models or list(MODEL_REGISTRY.keys()))]
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if skipped: print(f"[WARN] Unknown tags: {skipped}")
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if missing: print(f"[WARN] File not found (transfer from DOLPHIN Linux): {missing}")
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if not available:
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print("No model files found. Check MODEL_REGISTRY or --models flag."); return
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print(f"\n{'━'*60}")
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print(f" EXP13 MULTI-MODEL SWEEP")
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print(f" Models : {list(available.keys())}")
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print(f" Window : {DATE_START} → {DATE_END}")
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print(f" Threshold: Calmar > {CALMAR_THR}")
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print(f" Phase1 : {args.subset} days Phase2 top-k: {args.top_k}")
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print(f"{'━'*60}")
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# Build shared probe set
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print(f"\nBuilding probe set ({DATE_START}→{DATE_END})...")
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probes_raw, proxy_B_arr = _build_probe_set()
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print(f" {len(probes_raw)} windows proxy_B: μ={proxy_B_arr.mean():+.4f} σ={proxy_B_arr.std():.4f}")
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|
# Step 1: probe all models
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probe_reports = {tag: probe_model(tag, path, probes_raw, proxy_B_arr)
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|
for tag, path in available.items()}
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|
_print_probe_table(probe_reports)
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|
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|
|
if args.probe_only:
|
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|
return
|
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|
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|
|
# Step 2: run exp13 for each usable model
|
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|
|
sweep_results = []
|
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|
for tag, probe in probe_reports.items():
|
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|
if not probe['always_positive']:
|
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|
print(f"\n[SKIP] {tag}: calib={probe['calibration']} — not always-positive")
|
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|
continue
|
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|
|
try:
|
|||
|
|
summary = run_exp13_for_model(probe, args.subset, args.top_k,
|
|||
|
|
only_config=args.fast_check or None)
|
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|
|
sweep_results.append(summary)
|
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|
|
except Exception as ex:
|
|||
|
|
import traceback
|
|||
|
|
print(f"\n[ERROR] {tag}: {ex}")
|
|||
|
|
traceback.print_exc()
|
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|
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|
|||
|
|
_print_comparison_table(sweep_results)
|
|||
|
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|
|||
|
|
# Save combined summary
|
|||
|
|
out = ROOT / 'exp13_model_sweep_results.json'
|
|||
|
|
with open(out, 'w') as f:
|
|||
|
|
json.dump({
|
|||
|
|
'probes': probe_reports,
|
|||
|
|
'sweep': sweep_results,
|
|||
|
|
'threshold': CALMAR_THR,
|
|||
|
|
'window': {'start': DATE_START, 'end': DATE_END},
|
|||
|
|
'ref_v2_bob': {'n_pass': 9, 'best_droi': 4.59, 'best_calmar': 7.87},
|
|||
|
|
}, f, indent=2, default=str)
|
|||
|
|
print(f"\nSummary → {out}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
main()
|