429 lines
17 KiB
Python
429 lines
17 KiB
Python
|
"""DOLPHIN Backtest Prefect Flow — NDAlphaEngine (Gold Standard Path).
|
||
|
|
|
||
|
|
Wraps test_pf_dynamic_beta_validate.py as a Prefect flow.
|
||
|
|
Results stored in Hazelcast + run_logs/REGISTRY.md + date-partitioned dir.
|
||
|
|
|
||
|
|
GOLD STANDARD (55-day Dec31-Feb25, CURRENT parquet state, seed=42, TP=95bps):
|
||
|
|
ROI=+66.26%, PF=1.175, DD=14.94%, Sharpe=3.62*, WR=49.6%, Trades=2143
|
||
|
|
File: nautilus_dolphin/run_logs/summary_20260307_222506.json
|
||
|
|
*Sharpe=3.62 is N=55 artifact (95% CI [-2.2, +8.1]). Economic Sharpe ~2.5-3.0.
|
||
|
|
SUPERSEDED: summary_20260307_163401.json (+54.67%, T=2145) — different Feb25 parquet state.
|
||
|
|
|
||
|
|
IMPORTANT: Uses NDAlphaEngine directly — NOT dolphin_vbt_real.py.
|
||
|
|
dolphin_vbt_real.py is a parallel implementation and cannot match the gold
|
||
|
|
standard byte-for-byte. Only this NDAlphaEngine path achieves 1e-6 parity.
|
||
|
|
"""
|
||
|
|
import os
|
||
|
|
import sys
|
||
|
|
import json
|
||
|
|
import csv
|
||
|
|
import time
|
||
|
|
from pathlib import Path
|
||
|
|
from datetime import datetime, timezone
|
||
|
|
from typing import Optional
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pandas as pd
|
||
|
|
from prefect import flow, task, get_run_logger
|
||
|
|
from prefect.artifacts import create_markdown_artifact
|
||
|
|
|
||
|
|
os.environ.setdefault('PREFECT_API_URL', 'http://localhost:4200/api')
|
||
|
|
|
||
|
|
# ── Paths ────────────────────────────────────────────────────────────────────
|
||
|
|
HCM_DIR = Path(__file__).parent.parent
|
||
|
|
NAUTILUS_DIR = HCM_DIR / 'nautilus_dolphin'
|
||
|
|
VBT_DIR = HCM_DIR / 'vbt_cache'
|
||
|
|
REGISTRY_PATH = HCM_DIR / 'run_logs' / 'REGISTRY.md'
|
||
|
|
MC_MODELS_DIR = str(NAUTILUS_DIR / 'mc_results' / 'models')
|
||
|
|
HZ_HOST = 'localhost:5701'
|
||
|
|
HZ_CLUSTER = 'dolphin'
|
||
|
|
|
||
|
|
sys.path.insert(0, str(NAUTILUS_DIR))
|
||
|
|
|
||
|
|
# ── Canonical champion ENGINE_KWARGS — matches test_pf_dynamic_beta_validate.py ─
|
||
|
|
# NOTE: abs_max_leverage is NOT passed here — the engine default (6.0) applies,
|
||
|
|
# which is correct for the current gold standard (summary_20260307_222506.json,
|
||
|
|
# ROI=+66.26%, T=2143). The old +54.67% reference used a different Feb25 parquet
|
||
|
|
# state and is superseded. Do not add abs_max_leverage=6.0 explicitly — it's already
|
||
|
|
# the engine default and explicitly passing it is redundant.
|
||
|
|
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=0.0095, 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-Forewarner config (frozen champion) ───────────────────────────────────
|
||
|
|
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': 0.0095,
|
||
|
|
'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,
|
||
|
|
}
|
||
|
|
|
||
|
|
# ── OB calibration — real Binance observation 2025-01-15 ─────────────────────
|
||
|
|
OB_ASSETS = ['BTCUSDT', 'ETHUSDT', 'BNBUSDT', 'SOLUSDT']
|
||
|
|
OB_IMBALANCE_BIASES = {
|
||
|
|
'BTCUSDT': -0.086, # sell pressure, confirms SHORT
|
||
|
|
'ETHUSDT': -0.092, # sell pressure, confirms SHORT
|
||
|
|
'BNBUSDT': +0.05, # mild buy, mild contradict
|
||
|
|
'SOLUSDT': +0.05, # mild buy, mild contradict
|
||
|
|
}
|
||
|
|
|
||
|
|
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',
|
||
|
|
}
|
||
|
|
|
||
|
|
GOLD = dict(roi=66.26, pf=1.175, dd=14.94, sharpe=3.62, wr=49.6, trades=2143)
|
||
|
|
|
||
|
|
|
||
|
|
# ── Tasks ────────────────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
@task(name='load-parquet-data', retries=1)
|
||
|
|
def load_parquet_data(date_from: Optional[str], date_to: Optional[str]) -> dict:
|
||
|
|
"""Load vbt_cache parquets.
|
||
|
|
|
||
|
|
CRITICAL: ACB preload and vol_p60 use ALL available parquets (no date filter),
|
||
|
|
matching test_pf_dynamic_beta_validate.py exactly. The date filter applies only
|
||
|
|
to the backtest loop. This is what the gold standard run did.
|
||
|
|
"""
|
||
|
|
log = get_run_logger()
|
||
|
|
|
||
|
|
# ALL parquets — for ACB calibration and vol_p60 (no date filter)
|
||
|
|
all_parquet_files = sorted(VBT_DIR.glob('*.parquet'))
|
||
|
|
all_parquet_files = [p for p in all_parquet_files if 'catalog' not in p.name]
|
||
|
|
|
||
|
|
# Date-filtered subset — for actual backtest loop
|
||
|
|
loop_files = all_parquet_files[:]
|
||
|
|
if date_from:
|
||
|
|
loop_files = [p for p in loop_files if p.stem >= date_from]
|
||
|
|
if date_to:
|
||
|
|
loop_files = [p for p in loop_files if p.stem <= date_to]
|
||
|
|
|
||
|
|
if not loop_files:
|
||
|
|
raise ValueError(f'No parquets in {VBT_DIR} for {date_from} to {date_to}')
|
||
|
|
|
||
|
|
log.info(f'All parquets in vbt_cache: {len(all_parquet_files)} '
|
||
|
|
f'({all_parquet_files[0].stem} to {all_parquet_files[-1].stem})')
|
||
|
|
log.info(f'Backtest window: {len(loop_files)} dates '
|
||
|
|
f'({loop_files[0].stem} to {loop_files[-1].stem})')
|
||
|
|
|
||
|
|
# vol_p60: from first 2 of ALL parquets — matches test_pf_dynamic_beta_validate.py
|
||
|
|
all_vols = []
|
||
|
|
for pf in all_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:
|
||
|
|
continue
|
||
|
|
v = float(np.std(np.diff(seg) / seg[:-1]))
|
||
|
|
if v > 0:
|
||
|
|
all_vols.append(v)
|
||
|
|
vol_p60 = float(np.percentile(all_vols, 60)) if all_vols else 0.0
|
||
|
|
log.info(f'vol_p60={vol_p60:.6f}')
|
||
|
|
|
||
|
|
# Load loop-window parquets into memory + per-bar vol regime signal
|
||
|
|
pq_data = {}
|
||
|
|
for pf in loop_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:
|
||
|
|
continue
|
||
|
|
dv[i] = float(np.std(np.diff(seg) / seg[:-1]))
|
||
|
|
pq_data[pf.stem] = (df, ac, dv)
|
||
|
|
|
||
|
|
log.info(f'Data loaded: {len(pq_data)} dates in memory')
|
||
|
|
return {
|
||
|
|
'pq_data': pq_data,
|
||
|
|
'parquet_stems': [p.stem for p in loop_files],
|
||
|
|
'all_stems': [p.stem for p in all_parquet_files], # for ACB preload
|
||
|
|
'vol_p60': vol_p60,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
@task(name='run-nd-backtest', timeout_seconds=900)
|
||
|
|
def run_nd_backtest(data: dict) -> dict:
|
||
|
|
"""Run NDAlphaEngine — exact match to test_pf_dynamic_beta_validate.py."""
|
||
|
|
log = get_run_logger()
|
||
|
|
|
||
|
|
from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine
|
||
|
|
from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
|
||
|
|
from nautilus_dolphin.nautilus.ob_features import OBFeatureEngine
|
||
|
|
from nautilus_dolphin.nautilus.ob_provider import MockOBProvider
|
||
|
|
from mc.mc_ml import DolphinForewarner
|
||
|
|
|
||
|
|
pq_data = data['pq_data']
|
||
|
|
parquet_stems = data['parquet_stems']
|
||
|
|
vol_p60 = data['vol_p60']
|
||
|
|
|
||
|
|
all_stems = data['all_stems'] # ALL parquets — for ACB calibration
|
||
|
|
|
||
|
|
log.info('Initialising ACB v6...')
|
||
|
|
acb = AdaptiveCircuitBreaker()
|
||
|
|
acb.preload_w750(all_stems) # ALL stems — matches test_pf_dynamic_beta_validate.py
|
||
|
|
log.info(f' w750 threshold (p60): {acb._w750_threshold:.6f}')
|
||
|
|
|
||
|
|
log.info('Loading MC-Forewarner...')
|
||
|
|
forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR)
|
||
|
|
|
||
|
|
log.info('Building OB engine (4D MockOBProvider, real-calibrated)...')
|
||
|
|
mock_ob = MockOBProvider(
|
||
|
|
imbalance_bias=-0.09, depth_scale=1.0, assets=OB_ASSETS,
|
||
|
|
imbalance_biases=OB_IMBALANCE_BIASES,
|
||
|
|
)
|
||
|
|
ob_eng = OBFeatureEngine(mock_ob)
|
||
|
|
ob_eng.preload_date('mock', OB_ASSETS)
|
||
|
|
|
||
|
|
log.info('Assembling stack: ACBv6 + OB 4D + MC-Forewarner + EsoF(neutral)...')
|
||
|
|
engine = NDAlphaEngine(**ENGINE_KWARGS)
|
||
|
|
engine.set_ob_engine(ob_eng)
|
||
|
|
engine.set_acb(acb)
|
||
|
|
engine.set_mc_forewarner(forewarner, MC_BASE_CFG)
|
||
|
|
engine.set_esoteric_hazard_multiplier(0.0)
|
||
|
|
engine._bar_log_enabled = False # off for flow performance
|
||
|
|
|
||
|
|
t0 = time.time()
|
||
|
|
dstats = []
|
||
|
|
for stem in parquet_stems:
|
||
|
|
df, acols, dvol = pq_data[stem]
|
||
|
|
vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60, False)
|
||
|
|
stats = engine.process_day(stem, df, acols, vol_regime_ok=vol_ok)
|
||
|
|
dstats.append({**stats, 'cap': engine.capital})
|
||
|
|
elapsed = time.time() - t0
|
||
|
|
|
||
|
|
# ── Metrics — identical computation to test_pf_dynamic_beta_validate.py ──
|
||
|
|
tr = engine.trade_history
|
||
|
|
wins = [t for t in tr if t.pnl_absolute > 0]
|
||
|
|
loses = [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 loses)) if loses else 0.0
|
||
|
|
|
||
|
|
roi = (engine.capital - 25000.0) / 25000.0 * 100.0
|
||
|
|
pf = gw / gl if gl > 0 else 999.0
|
||
|
|
wr = len(wins) / len(tr) * 100.0 if tr else 0.0
|
||
|
|
dr_all = np.array([s['pnl'] / 25000.0 * 100.0 for s in dstats])
|
||
|
|
sharpe = (float(np.mean(dr_all) / np.std(dr_all) * np.sqrt(365))
|
||
|
|
if np.std(dr_all) > 0 else 0.0)
|
||
|
|
|
||
|
|
peak_cap = 25000.0
|
||
|
|
max_dd = 0.0
|
||
|
|
for s in dstats:
|
||
|
|
peak_cap = max(peak_cap, s['cap'])
|
||
|
|
max_dd = max(max_dd, (peak_cap - s['cap']) / peak_cap * 100.0)
|
||
|
|
|
||
|
|
exit_ctr = {}
|
||
|
|
for t in tr:
|
||
|
|
exit_ctr[t.exit_reason] = exit_ctr.get(t.exit_reason, 0) + 1
|
||
|
|
|
||
|
|
trade_records = []
|
||
|
|
for t in tr:
|
||
|
|
trade_records.append({
|
||
|
|
'date': getattr(t, 'date_str', ''),
|
||
|
|
'entry_bar': t.entry_bar,
|
||
|
|
'exit_bar': t.exit_bar,
|
||
|
|
'bars_held': t.bars_held,
|
||
|
|
'direction': t.direction,
|
||
|
|
'leverage': round(t.leverage, 6),
|
||
|
|
'pnl_pct': round(t.pnl_pct, 8),
|
||
|
|
'pnl_abs': round(t.pnl_absolute, 4),
|
||
|
|
'exit_reason': t.exit_reason,
|
||
|
|
})
|
||
|
|
|
||
|
|
metrics = {
|
||
|
|
'roi_pct': round(roi, 4),
|
||
|
|
'pf': round(pf, 4),
|
||
|
|
'max_dd_pct': round(max_dd, 4),
|
||
|
|
'sharpe': round(sharpe, 4),
|
||
|
|
'wr_pct': round(wr, 4),
|
||
|
|
'trades': len(tr),
|
||
|
|
'wins': len(wins),
|
||
|
|
'losses': len(loses),
|
||
|
|
'capital_final': round(engine.capital, 2),
|
||
|
|
'exit_breakdown': exit_ctr,
|
||
|
|
'mc_red_days': sum(1 for s in dstats if s.get('mc_status') == 'RED'),
|
||
|
|
'mc_orange_days': sum(1 for s in dstats if s.get('mc_status') == 'ORANGE'),
|
||
|
|
'elapsed_sec': round(elapsed, 1),
|
||
|
|
'n_dates': len(parquet_stems),
|
||
|
|
'date_from': parquet_stems[0] if parquet_stems else '',
|
||
|
|
'date_to': parquet_stems[-1] if parquet_stems else '',
|
||
|
|
'delta_roi': round(roi - GOLD['roi'], 4),
|
||
|
|
'delta_pf': round(pf - GOLD['pf'], 4),
|
||
|
|
'delta_trades': len(tr) - GOLD['trades'],
|
||
|
|
}
|
||
|
|
|
||
|
|
log.info(f'ROI={roi:+.2f}% PF={pf:.3f} DD={max_dd:.2f}% Sh={sharpe:.2f} '
|
||
|
|
f'WR={wr:.1f}% T={len(tr)} ({elapsed:.0f}s)')
|
||
|
|
log.info(f'vs Gold: ΔROI={metrics["delta_roi"]:+.2f}pp '
|
||
|
|
f'ΔPF={metrics["delta_pf"]:+.3f} ΔT={metrics["delta_trades"]:+d}')
|
||
|
|
|
||
|
|
return {'metrics': metrics, 'trade_records': trade_records}
|
||
|
|
|
||
|
|
|
||
|
|
@task(name='report-results', retries=2)
|
||
|
|
def report_results(result: dict, date_from: str, date_to: str) -> str:
|
||
|
|
"""Write to date-partitioned run_logs/, Hazelcast, REGISTRY.md."""
|
||
|
|
log = get_run_logger()
|
||
|
|
m = result['metrics']
|
||
|
|
tr = result['trade_records']
|
||
|
|
|
||
|
|
run_ts = datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')
|
||
|
|
run_hm = datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M')
|
||
|
|
run_day = datetime.now(timezone.utc).strftime('%Y-%m-%d')
|
||
|
|
|
||
|
|
validated = abs(m['delta_roi']) < 5.0 and abs(m['delta_pf']) < 0.05
|
||
|
|
status = 'VALIDATED' if validated else 'DIVERGED'
|
||
|
|
|
||
|
|
# 1. Date-partitioned output
|
||
|
|
output_dir = HCM_DIR / 'run_logs' / run_day / f'nd_backtest_{run_ts}'
|
||
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
||
|
|
|
||
|
|
summary = {
|
||
|
|
'script': 'vbt_backtest_flow (NDAlphaEngine)',
|
||
|
|
'timestamp': run_ts,
|
||
|
|
'engine_kwargs': {k: v for k, v in ENGINE_KWARGS.items()},
|
||
|
|
'results': m,
|
||
|
|
'gold_ref': GOLD,
|
||
|
|
'status': status,
|
||
|
|
'output_dir': str(output_dir),
|
||
|
|
}
|
||
|
|
(output_dir / 'summary.json').write_text(json.dumps(summary, indent=2))
|
||
|
|
|
||
|
|
if tr:
|
||
|
|
with open(output_dir / 'trades.csv', 'w', newline='') as f:
|
||
|
|
w = csv.DictWriter(f, fieldnames=list(tr[0].keys()))
|
||
|
|
w.writeheader()
|
||
|
|
w.writerows(tr)
|
||
|
|
|
||
|
|
log.info(f'Output: {output_dir}')
|
||
|
|
|
||
|
|
# 2. Hazelcast
|
||
|
|
try:
|
||
|
|
import hazelcast
|
||
|
|
client = hazelcast.HazelcastClient(
|
||
|
|
cluster_name=HZ_CLUSTER, cluster_members=[HZ_HOST])
|
||
|
|
imap = client.get_map('vbt_metrics_history').blocking()
|
||
|
|
imap.put(f'nd_{run_ts}', json.dumps({**m, 'output_dir': str(output_dir)}))
|
||
|
|
client.shutdown()
|
||
|
|
log.info(f'HZ OK → vbt_metrics_history[nd_{run_ts}]')
|
||
|
|
except Exception as e:
|
||
|
|
log.warning(f'HZ reporting failed (non-fatal): {e}')
|
||
|
|
|
||
|
|
# 3. REGISTRY.md
|
||
|
|
try:
|
||
|
|
key_params = f'seed=42 tp=95bps abs_max_lev=6.0 {date_from}→{date_to}'
|
||
|
|
res_str = (f'ROI={m["roi_pct"]:+.2f}% PF={m["pf"]:.3f} '
|
||
|
|
f'T={m["trades"]} DD={m["max_dd_pct"]:.1f}% Sh={m["sharpe"]:.2f}')
|
||
|
|
row = [run_hm, 'nd_backtest_flow', key_params, res_str, status, str(output_dir)]
|
||
|
|
with open(REGISTRY_PATH, 'a', encoding='utf-8') as f:
|
||
|
|
f.write(f'| {" | ".join(row)} |\n')
|
||
|
|
log.info('REGISTRY.md updated')
|
||
|
|
except Exception as e:
|
||
|
|
log.error(f'REGISTRY update failed: {e}')
|
||
|
|
|
||
|
|
return str(output_dir)
|
||
|
|
|
||
|
|
|
||
|
|
# ── Flow ─────────────────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
@flow(
|
||
|
|
name='dolphin-nd-backtest',
|
||
|
|
description=(
|
||
|
|
'NDAlphaEngine backtest — gold-standard code path. '
|
||
|
|
'1e-6 parity with test_pf_dynamic_beta_validate.py. '
|
||
|
|
'Default: 55-day champion window Dec31→Feb25.'
|
||
|
|
),
|
||
|
|
log_prints=True,
|
||
|
|
)
|
||
|
|
def vbt_backtest_flow(
|
||
|
|
date_from: str = '2025-12-31',
|
||
|
|
date_to: str = '2026-02-25',
|
||
|
|
):
|
||
|
|
log = get_run_logger()
|
||
|
|
log.info(f'=== DOLPHIN NDAlphaEngine Backtest {date_from} → {date_to} ===')
|
||
|
|
log.info(f'Gold: ROI=+{GOLD["roi"]}% PF={GOLD["pf"]} T={GOLD["trades"]}')
|
||
|
|
|
||
|
|
data = load_parquet_data(date_from=date_from, date_to=date_to)
|
||
|
|
result = run_nd_backtest(data)
|
||
|
|
output = report_results(result, date_from=date_from, date_to=date_to)
|
||
|
|
|
||
|
|
m = result['metrics']
|
||
|
|
validated = abs(m['delta_roi']) < 5.0 and abs(m['delta_pf']) < 0.05
|
||
|
|
|
||
|
|
create_markdown_artifact(
|
||
|
|
key='nd-backtest-result',
|
||
|
|
markdown=f"""
|
||
|
|
## NDAlphaEngine Backtest: {date_from} → {date_to}
|
||
|
|
|
||
|
|
| Metric | Result | Gold Standard | Delta |
|
||
|
|
|--------|--------|--------------|-------|
|
||
|
|
| ROI | {m['roi_pct']:+.2f}% | +{GOLD['roi']}% | {m['delta_roi']:+.2f}pp |
|
||
|
|
| PF | {m['pf']:.3f} | {GOLD['pf']} | {m['delta_pf']:+.3f} |
|
||
|
|
| DD | {m['max_dd_pct']:.2f}% | {GOLD['dd']}% | — |
|
||
|
|
| Sharpe | {m['sharpe']:.2f} | {GOLD['sharpe']} | — |
|
||
|
|
| WR | {m['wr_pct']:.1f}% | {GOLD['wr']}% | — |
|
||
|
|
| Trades | {m['trades']} | {GOLD['trades']} | {m['delta_trades']:+d} |
|
||
|
|
|
||
|
|
**Status**: {'VALIDATED' if validated else 'DIVERGED'}
|
||
|
|
**Output**: `{output}`
|
||
|
|
**MC**: {m['mc_red_days']} RED / {m['mc_orange_days']} ORANGE
|
||
|
|
**Exits**: {m['exit_breakdown']}
|
||
|
|
""",
|
||
|
|
description=f'NDAlphaEngine {date_from}→{date_to}',
|
||
|
|
)
|
||
|
|
|
||
|
|
return m
|
||
|
|
|
||
|
|
|
||
|
|
# ── Entry point ───────────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
import argparse
|
||
|
|
p = argparse.ArgumentParser()
|
||
|
|
p.add_argument('--date-from', default='2025-12-31')
|
||
|
|
p.add_argument('--date-to', default='2026-02-25')
|
||
|
|
p.add_argument('--register', action='store_true')
|
||
|
|
args = p.parse_args()
|
||
|
|
|
||
|
|
if args.register:
|
||
|
|
dep = vbt_backtest_flow.to_deployment(
|
||
|
|
name='dolphin-nd-champion',
|
||
|
|
tags=['backtest', 'ndengine', 'champion'],
|
||
|
|
work_pool_name='dolphin',
|
||
|
|
)
|
||
|
|
dep.apply()
|
||
|
|
print('Registered: dolphin-nd-champion')
|
||
|
|
else:
|
||
|
|
vbt_backtest_flow(date_from=args.date_from, date_to=args.date_to)
|