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
225
nautilus_dolphin/crossover_5s_test.py
Executable file
225
nautilus_dolphin/crossover_5s_test.py
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"""Crossover Scalp — 5s Gold Standard Data
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==========================================
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Option 4: Port vel_div crossover to 5s resolution.
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Signal: vel_div <= -ENTRY_T → LONG
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Exit: vel_div >= +ENTRY_T (mean-reversion complete)
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OR MAX_HOLD bars reached (safety cap)
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1 bar = ~5 seconds on this dataset.
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Legacy optimal hold: 120 bars × 5s = 600s = 10 min.
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Sweep:
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ENTRY_T = [0.020, 0.050, 0.100, 0.200]
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MAX_HOLD = [10, 20, 60, 120, 240] bars (50s, 100s, 5m, 10m, 20m)
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Compare PF vs 1m klines crossover result (PF=1.007 ungated).
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Output:
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run_logs/crossover_5s_YYYYMMDD_HHMMSS.csv
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run_logs/crossover_5s_top_YYYYMMDD_HHMMSS.txt
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Runtime: ~10s
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"""
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import sys, time, csv, gc
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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from pathlib import Path
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from datetime import datetime
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from collections import defaultdict
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import numpy as np
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import pandas as pd
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VBT_DIR_5S = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache")
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LOG_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\nautilus_dolphin\run_logs")
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# Sweep parameters
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ENTRY_Ts = [0.020, 0.050, 0.100, 0.200]
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MAX_HOLDS = [10, 20, 60, 120, 240] # bars (× 5s = real seconds)
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# stats[(entry_t, max_hold)] = {wins, losses, gw, gl, n, total_hold}
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stats = defaultdict(lambda: {'wins': 0, 'losses': 0, 'gw': 0.0, 'gl': 0.0, 'n': 0, 'total_hold': 0})
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# Also track per-date stats for PF per file
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daily_rows = []
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parquet_files = sorted(VBT_DIR_5S.glob("*.parquet"))
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parquet_files = [p for p in parquet_files if 'catalog' not in str(p)]
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total = len(parquet_files)
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print(f"Files: {total} (5s gold standard data)")
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print(f"Entry Ts: {ENTRY_Ts}")
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print(f"MaxHold: {MAX_HOLDS} bars (×5s = {[h*5 for h in MAX_HOLDS]}s)")
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print()
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t0 = time.time()
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# Control baseline: fraction of bars where BTCUSDT moves ±0.95% within 120 bars
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ctrl_stats = defaultdict(lambda: {'dn': 0, 'up': 0, 'n': 0})
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for i, pf in enumerate(parquet_files):
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ds = pf.stem
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try:
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df = pd.read_parquet(pf)
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except Exception:
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continue
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if 'vel_div' not in df.columns or 'BTCUSDT' not in df.columns:
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continue
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vd = df['vel_div'].values.astype(np.float64)
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btc = df['BTCUSDT'].values.astype(np.float64)
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vd = np.where(np.isfinite(vd), vd, 0.0)
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btc = np.where(np.isfinite(btc) & (btc > 0), btc, np.nan)
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n = len(btc)
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del df
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MAX_H = max(MAX_HOLDS)
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if n < MAX_H + 5:
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del vd, btc
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continue
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# cross_back[t] = True when vel_div has returned to >= +ENTRY_T (computed per threshold)
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# Iterate per ENTRY_T
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for entry_t in ENTRY_Ts:
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entry_mask = (vd <= -entry_t) & np.isfinite(btc)
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cross_back = (vd >= entry_t)
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# Build trades for all max_holds at once
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# For each entry, find the first cross_back within each max_hold window
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# Vectorized approach: build the trade list once, then tally by max_hold
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# trade list: (exit_bar_first_crossover, ret_at_crossover, ret_at_each_hold[])
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# Since MAX_HOLDS is [10,20,60,120,240], we find crossover for max_hold=240 first
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# then earlier exits apply to smaller max_hold caps too
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for t in range(n - MAX_H):
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if not entry_mask[t]:
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continue
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ep = btc[t]
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if not np.isfinite(ep) or ep <= 0:
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continue
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# Find first crossover bar
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first_cross = MAX_H # default: no crossover within max window
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for k in range(1, MAX_H + 1):
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tb = t + k
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if tb >= n:
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first_cross = k
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break
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if cross_back[tb]:
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first_cross = k
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break
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for max_hold in MAX_HOLDS:
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# Actual exit: min(first_cross, max_hold)
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exit_bar = min(first_cross, max_hold)
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tb = t + exit_bar
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if tb >= n:
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continue
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xp = btc[tb]
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if not np.isfinite(xp) or xp <= 0:
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continue
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ret = (xp - ep) / ep # LONG return
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key = (entry_t, max_hold)
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s = stats[key]
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if ret >= 0:
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s['wins'] += 1
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s['gw'] += ret
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else:
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s['losses'] += 1
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s['gl'] += abs(ret)
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s['n'] += 1
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s['total_hold'] += exit_bar
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del entry_mask, cross_back
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del vd, btc
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if (i + 1) % 10 == 0:
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gc.collect()
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print(f" [{i+1}/{total}] {ds} {time.time()-t0:.0f}s")
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elapsed = time.time() - t0
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print(f"\nPass complete: {elapsed:.0f}s\n")
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# ─── Results Table ─────────────────────────────────────────────────────────────
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rows = []
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for entry_t in ENTRY_Ts:
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for max_hold in MAX_HOLDS:
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key = (entry_t, max_hold)
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s = stats.get(key, {'wins': 0, 'losses': 0, 'gw': 0.0, 'gl': 0.0, 'n': 0, 'total_hold': 0})
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n_t = s['wins'] + s['losses']
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if n_t == 0:
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continue
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pf = s['gw'] / s['gl'] if s['gl'] > 0 else (999.0 if s['gw'] > 0 else float('nan'))
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wr = s['wins'] / n_t * 100
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avg_hold = s['total_hold'] / n_t
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avg_win = s['gw'] / s['wins'] if s['wins'] > 0 else 0.0
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avg_loss = s['gl'] / s['losses'] if s['losses'] > 0 else 0.0
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hold_sec = avg_hold * 5 # 5s per bar
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rows.append({
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'entry_t': entry_t,
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'max_hold_bars': max_hold,
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'max_hold_sec': max_hold * 5,
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'n_trades': n_t,
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'pf': round(pf, 4),
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'wr': round(wr, 3),
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'avg_hold_bars': round(avg_hold, 2),
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'avg_hold_sec': round(hold_sec, 1),
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'avg_win_pct': round(avg_win * 100, 4),
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'avg_loss_pct': round(avg_loss * 100, 4),
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'gross_win': round(s['gw'], 6),
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'gross_loss': round(s['gl'], 6),
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})
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# ─── Console ──────────────────────────────────────────────────────────────────
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print(f"{'EntryT':>8} {'MaxH':>5} {'MaxSec':>6} {'N':>8} {'PF':>7} {'WR%':>6} {'AvgH_s':>7} {'AvgW%':>7} {'AvgL%':>7}")
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print("-" * 90)
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for r in rows:
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marker = " ◄" if r['pf'] > 1.01 else ""
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print(f" T={r['entry_t']:.3f} {r['max_hold_bars']:>5}b {r['max_hold_sec']:>5}s "
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f"{r['n_trades']:>8,} {r['pf']:>7.4f} {r['wr']:>6.2f}% "
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f"{r['avg_hold_sec']:>7.1f}s {r['avg_win_pct']:>7.4f}% {r['avg_loss_pct']:>7.4f}%{marker}")
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# Highlight best
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best = max(rows, key=lambda r: r['pf']) if rows else None
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if best:
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print(f"\n Best: T={best['entry_t']:.3f} MaxH={best['max_hold_bars']}b ({best['max_hold_sec']}s) "
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f"PF={best['pf']:.4f} WR={best['wr']:.1f}% AvgHold={best['avg_hold_sec']:.0f}s "
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f"N={best['n_trades']:,}")
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# ─── Comparison with 1m ────────────────────────────────────────────────────────
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print(f"\n{'='*50}")
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print(f" 1m KLINES REFERENCE (ungated crossover):")
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print(f" PF=1.0073 N=1,005,665 AvgHold=2.2 bars (2.2 min)")
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print(f" BEST3 (9h,12h,18h): PF=1.0429 N=127,760")
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print(f" 5s GOLD STANDARD ({total} days, 2025-12-31 to 2026-02-26):")
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# ─── Save ──────────────────────────────────────────────────────────────────────
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LOG_DIR.mkdir(exist_ok=True)
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ts_str = datetime.now().strftime("%Y%m%d_%H%M%S")
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out_csv = LOG_DIR / f"crossover_5s_{ts_str}.csv"
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if rows:
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with open(out_csv, 'w', newline='') as f:
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w = csv.DictWriter(f, fieldnames=rows[0].keys())
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w.writeheader(); w.writerows(rows)
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print(f"\n → {out_csv}")
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out_txt = LOG_DIR / f"crossover_5s_top_{ts_str}.txt"
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with open(out_txt, 'w', encoding='utf-8') as f:
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f.write(f"Crossover Scalp — 5s Gold Standard Data\n")
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f.write(f"56 days 2025-12-31 to 2026-02-26\n")
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f.write(f"Runtime: {elapsed:.0f}s\n\n")
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f.write(f"{'EntryT':>8} {'MaxH':>5} {'MaxSec':>6} {'N':>8} {'PF':>7} {'WR%':>6} "
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f"{'AvgH_s':>7} {'AvgW%':>7} {'AvgL%':>7}\n")
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f.write("-" * 90 + "\n")
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for r in rows:
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f.write(f" T={r['entry_t']:.3f} {r['max_hold_bars']:>5}b {r['max_hold_sec']:>5}s "
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f"{r['n_trades']:>8,} {r['pf']:>7.4f} {r['wr']:>6.2f}% "
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f"{r['avg_hold_sec']:>7.1f}s {r['avg_win_pct']:>7.4f}% {r['avg_loss_pct']:>7.4f}%\n")
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f.write(f"\n1m reference (ungated): PF=1.0073 BEST3: PF=1.0429\n")
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print(f" → {out_txt}")
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print(f"\n Runtime: {elapsed:.0f}s")
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print(f" KEY: PF > 1.01 on 5s with decent N = potential real edge at 5s resolution.")
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print(f" AvgHold short (< 30s) = micro scalp viable. AvgHold > 60s = slow mean reversion.")
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