"""MTF 5s + 1m Combined Experiment — Orthogonal Leverage Modulation. Hypothesis: 1m vel_div (klines-derived, orthogonal to 5s) acts as an INDEPENDENT second-order leverage modulator. When the 1m signal also fires bearishly on the same day as the 5s system, it's a genuinely rare joint event with higher conviction. Architecture (Iron Rule preserved — 1m never touches entry gating): - Baseline: champion 5s engine on 55-day window (vbt_cache, Dec31-Feb25) - MTF: same engine + MTFBoostACB wrapper that multiplies ACB boost by a 1m-alignment factor derived from vbt_cache_klines for same dates MTFBoostACB mechanism: - Compute per-day: align_frac = fraction of 1m bars with vel_div < VD_1M_THRESHOLD (VD_1M_THRESHOLD = -0.50, the klines p~7 threshold, matching signal selectivity) - MTF multiplier: 1.0 + MTF_MAX_BOOST * min(align_frac / ALIGN_NORM, 1.0) MTF_MAX_BOOST = 0.15 (up to +15% leverage boost) ALIGN_NORM = 0.15 (15% of 1m bars bearish = max boost day) - Final ACB boost (MTF) = ACB_boost_base * mtf_mult - Result feeds into engine's 3-scale formula: regime_size_mult = ACB_boost * (1 + beta * strength^3) - abs_max_leverage = 6.0 ceiling unchanged — MTF can only push within it Expected (from prior analysis): - Cross-corr r<0.013 at all lags → signals are orthogonal alpha sources - MTF boost should add +3-5% ROI, WR lift 49%→52-54% - Joint firing is genuinely rare (both p~7) → selective, not noise Timescale comparison: - 5s bars: 1 bar per 5s → 17,280 bars/day, typical entries ~40/day - 1m bars: 1 bar per 1min → 1,440 bars/day, used as DAILY aggregate only Saved outputs: - run_logs/mtf_5s_1m_{TS}.json (summary: baseline vs MTF) - run_logs/mtf_daily_baseline_{TS}.csv (per-day stats, baseline) - run_logs/mtf_daily_mtf_{TS}.csv (per-day stats, MTF) - run_logs/mtf_alignment_{TS}.csv (per-day 1m alignment + mtf_mult) """ import sys, time, math, json, csv sys.stdout.reconfigure(encoding='utf-8', errors='replace') from pathlib import Path from datetime import datetime from collections import defaultdict import numpy as np import pandas as pd sys.path.insert(0, str(Path(__file__).parent)) # ── JIT warmup ───────────────────────────────────────────────────────────────── print("Compiling numba kernels...") t0c = 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, dtype=np.int64), np.zeros(4, dtype=np.int64), np.zeros(5, dtype=np.float64), 0, -1, 0.01, 0.04) check_dc_nb(_p, 3, 1, 0.75) _b = np.array([100.0, 200.0, 300.0, 400.0, 500.0], dtype=np.float64) _a = np.array([110.0, 190.0, 310.0, 390.0, 510.0], dtype=np.float64) compute_imbalance_nb(_b, _a); compute_depth_1pct_nb(_b, _a) compute_depth_quality_nb(210.0, 200.0); 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([100.0, 110.0], 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" JIT: {time.time() - t0c:.1f}s") from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker, ACBConfig from mc.mc_ml import DolphinForewarner # ── Paths ─────────────────────────────────────────────────────────────────────── VBT_5S_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache") VBT_1M_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines") DATE_START = '2025-12-31' DATE_END = '2026-02-25' # 55-day champion window # ── MTF parameters ───────────────────────────────────────────────────────────── VD_1M_THRESHOLD = -0.50 # klines p~7 (matches 5s p~7 selectivity) MTF_MAX_BOOST = 0.15 # max additional boost fraction (+15%) ALIGN_NORM = 0.15 # 15% of 1m bars bearish = max-boost day # typical: ~7% → +7% boost; extreme: 15%+ → +15% # ── Champion engine parameters (frozen — do NOT change) ───────────────────────── 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_MODELS_DIR = str(Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\nautilus_dolphin\mc_results\models")) 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, } OB_ASSETS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] # ── MTFBoostACB: ACB wrapper that injects 1m alignment as leverage modulator ───── class MTFBoostACB: """Wraps AdaptiveCircuitBreaker, multiplying per-date boost by 1m alignment factor. Iron Rule preserved: 1m signal NEVER touches entry gating (vel_div threshold). Only affects position SIZING via ACB boost channel. MTF boost formula: align_frac = fraction of 1m bars where vel_div < VD_1M_THRESHOLD mtf_mult = 1.0 + MTF_MAX_BOOST * min(align_frac / ALIGN_NORM, 1.0) info['boost'] *= mtf_mult # rest of ACB info unchanged """ def __init__(self, base_acb, klines_align: dict, max_boost: float = MTF_MAX_BOOST, align_norm: float = ALIGN_NORM): self._acb = base_acb self._klines_align = klines_align # date_str -> align_frac [0, 1] self._max_boost = max_boost self._align_norm = align_norm self.mtf_log = {} # date_str -> (align_frac, mtf_mult, final_boost) def __getattr__(self, name): return getattr(self._acb, name) def get_dynamic_boost_for_date(self, date_str: str, ob_engine=None) -> dict: info = dict(self._acb.get_dynamic_boost_for_date(date_str, ob_engine=ob_engine)) align = self._klines_align.get(date_str, 0.0) mtf_mult = 1.0 + self._max_boost * min(align / self._align_norm, 1.0) if self._align_norm > 0 else 1.0 info['boost'] *= mtf_mult info['mtf_align'] = align info['mtf_mult'] = mtf_mult self.mtf_log[date_str] = (align, mtf_mult, info['boost']) return info # ── Compute engine results helper ─────────────────────────────────────────────── def compute_metrics(dstats, initial_cap=25000.0): capitals = [s['capital'] for s in dstats] pnls = [s['pnl'] for s in dstats] final_cap = capitals[-1] if capitals else initial_cap roi = (final_cap - initial_cap) / initial_cap * 100 peak = initial_cap; max_dd = 0.0 for c in capitals: if c > peak: peak = c dd = (peak - c) / peak * 100 if dd > max_dd: max_dd = dd pnl_arr = np.array(pnls) sharpe = float(pnl_arr.mean() / pnl_arr.std() * np.sqrt(252)) if pnl_arr.std() > 0 else 0.0 return roi, max_dd, sharpe, final_cap def compute_trade_metrics(trade_history): wins = [t for t in trade_history if t.pnl_absolute > 0] losses = [t for t in trade_history if t.pnl_absolute <= 0] gw = sum(t.pnl_absolute for t in wins) gl = abs(sum(t.pnl_absolute for t in losses)) pf = gw / gl if gl > 0 else float('inf') wr = len(wins) / len(trade_history) * 100 if trade_history else 0.0 avg_bars = float(np.mean([t.bars_held for t in trade_history])) if trade_history else 0.0 avg_win = float(np.mean([t.pnl_pct for t in wins]) * 100) if wins else 0.0 avg_loss = float(np.mean([t.pnl_pct for t in losses]) * 100) if losses else 0.0 return pf, wr, avg_bars, avg_win, avg_loss, len(trade_history) # ── Load MC-Forewarner ────────────────────────────────────────────────────────── print("\nLoading MC-Forewarner...") forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR) print(" MC-Forewarner ready") # ── Load 5s champion parquet files ───────────────────────────────────────────── parquet_5s = sorted( p for p in VBT_5S_DIR.glob("*.parquet") if 'catalog' not in str(p) and DATE_START <= p.stem <= DATE_END ) date_strings = [pf.stem for pf in parquet_5s] print(f"\n5s champion parquets: {len(parquet_5s)} dates ({date_strings[0]} to {date_strings[-1]})") # ── Vol p60 calibration (from first 2 5s files, champion standard) ────────────── all_vols = [] for pf in parquet_5s[: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)) print(f"Vol p60 (5s calibration): {vol_p60:.6f}") # ── Pre-load 5s parquet data ──────────────────────────────────────────────────── print(f"\nPre-loading {len(parquet_5s)} 5s parquet files...") t_load = time.time() pq_data_5s = {} for pf in parquet_5s: 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_5s[pf.stem] = (df, ac, dv) print(f" Done in {time.time()-t_load:.1f}s") # ── Load 1m klines and compute per-day alignment fraction ────────────────────── print(f"\nComputing 1m alignment fractions (VD threshold {VD_1M_THRESHOLD})...") klines_align = {} # date_str -> align_frac [0, 1] klines_stats = {} # date_str -> {n_bars, n_bearish, align_frac, vd_median, vd_p5, vd_p95} missing_1m = [] for ds in date_strings: pf_1m = VBT_1M_DIR / f"{ds}.parquet" if not pf_1m.exists(): klines_align[ds] = 0.0 missing_1m.append(ds) continue try: df_1m = pd.read_parquet(pf_1m) if 'vel_div' not in df_1m.columns: klines_align[ds] = 0.0 missing_1m.append(ds) continue vd = df_1m['vel_div'].dropna().values n_valid = len(vd) n_bearish = int(np.sum(vd < VD_1M_THRESHOLD)) align_frac = n_bearish / n_valid if n_valid > 0 else 0.0 klines_align[ds] = align_frac klines_stats[ds] = { 'n_bars': n_valid, 'n_bearish': n_bearish, 'align_frac': align_frac, 'vd_median': float(np.median(vd)), 'vd_p5': float(np.percentile(vd, 5)), 'vd_p95': float(np.percentile(vd, 95)), } except Exception as e: print(f" WARNING: could not load 1m parquet for {ds}: {e}") klines_align[ds] = 0.0 missing_1m.append(ds) if missing_1m: print(f" WARNING: {len(missing_1m)} dates missing 1m klines data: {missing_1m[:5]}...") else: print(f" All {len(date_strings)} dates have 1m klines data") # Alignment summary aligns = [klines_align[ds] for ds in date_strings] mtf_mults = [1.0 + MTF_MAX_BOOST * min(a / ALIGN_NORM, 1.0) for a in aligns] print(f"\n 1m Alignment (fraction bearish bars):") print(f" Mean: {np.mean(aligns):.3f} (expected ~0.07 = p7 of distribution)") print(f" Std: {np.std(aligns):.3f}") print(f" Min: {np.min(aligns):.3f}") print(f" Max: {np.max(aligns):.3f}") print(f" MTF multiplier range: [{min(mtf_mults):.3f}, {max(mtf_mults):.3f}]") print(f" MTF multiplier mean: {np.mean(mtf_mults):.3f}") high_align = [(ds, a) for ds, a in klines_align.items() if a >= 0.12] high_align.sort(key=lambda x: -x[1]) print(f" High-alignment days (>=12%): {len(high_align)}") for ds, a in high_align[:5]: mult = 1.0 + MTF_MAX_BOOST * min(a / ALIGN_NORM, 1.0) print(f" {ds}: align={a:.3f} → mtf_mult={mult:.3f}x") # ── Build OB engine (real-calibrated MockOBProvider) ────────────────────────── _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) # ── ACB initialization (shared base, cloned for each run) ───────────────────── def build_acb(date_strings): acb = AdaptiveCircuitBreaker() acb.preload_w750(date_strings) return acb # ── Engine runner ────────────────────────────────────────────────────────────── def run_engine(label, acb, pq_data, date_strings, vol_p60): print(f"\n{'='*65}") print(f" RUN: {label}") print(f"{'='*65}") t0 = time.time() 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) dstats = [] for ds in date_strings: if ds not in pq_data: continue df, acols, dvol = pq_data[ds] vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60, False) result = engine.process_day(ds, df, acols, vol_regime_ok=vol_ok) dstats.append({ 'date': ds, 'pnl': result.get('pnl', 0.0), 'capital': result.get('capital', ENGINE_KWARGS['initial_capital']), 'trades': result.get('trades', 0), 'boost': result.get('boost', 1.0), 'beta': result.get('beta', 0.0), 'mc_status': result.get('mc_status', 'OK'), }) elapsed = time.time() - t0 roi, max_dd, sharpe, final_cap = compute_metrics(dstats) pf, wr, avg_bars, avg_win, avg_loss, n_trades = compute_trade_metrics(engine.trade_history) mid = len(dstats) // 2 h1 = sum(s['pnl'] for s in dstats[:mid]) h2 = sum(s['pnl'] for s in dstats[mid:]) h2h1 = h2 / h1 if h1 != 0 else float('nan') print(f" ROI: {roi:+.2f}%") print(f" PF: {pf:.4f}") print(f" DD: {max_dd:.2f}%") print(f" Sharpe: {sharpe:.3f}") print(f" WR: {wr:.1f}% (N={n_trades})") print(f" AvgWin: {avg_win:+.3f}% AvgLoss: {avg_loss:+.3f}%") print(f" AvgBars: {avg_bars:.1f}") print(f" Capital: ${final_cap:,.2f}") print(f" H1 P&L: ${h1:+,.2f}") print(f" H2 P&L: ${h2:+,.2f}") print(f" H2/H1: {h2h1:.3f}") print(f" Runtime: {elapsed:.1f}s") return { 'label': label, 'roi': roi, 'pf': pf, 'dd': max_dd, 'sharpe': sharpe, 'wr': wr, 'n_trades': n_trades, 'avg_bars': avg_bars, 'avg_win': avg_win, 'avg_loss': avg_loss, 'final_capital': final_cap, 'h1_pnl': h1, 'h2_pnl': h2, 'h2h1': h2h1, 'elapsed_s': elapsed, }, dstats, engine.trade_history # ── RUN 1: BASELINE (standard ACB, no 1m conditioning) ──────────────────────── print(f"\nInitializing ACB (baseline)...") acb_base = build_acb(date_strings) print(f" w750 p60 threshold: {acb_base._w750_threshold:.6f}") print(f" Dates with w750: {sum(1 for v in acb_base._w750_vel_cache.values() if v != 0.0)}/{len(date_strings)}") base_result, base_dstats, base_trades = run_engine( "BASELINE (5s only, no 1m)", acb_base, pq_data_5s, date_strings, vol_p60 ) # ── RUN 2: MTF (ACB boost × 1m alignment factor) ────────────────────────────── print(f"\nInitializing ACB (MTF — wrapping with 1m alignment)...") acb_base2 = build_acb(date_strings) mtf_acb = MTFBoostACB(acb_base2, klines_align, max_boost=MTF_MAX_BOOST, align_norm=ALIGN_NORM) print(f" MTF_MAX_BOOST = {MTF_MAX_BOOST:.2f} ALIGN_NORM = {ALIGN_NORM:.2f}") mtf_result, mtf_dstats, mtf_trades = run_engine( f"MTF (5s × 1m align, max_boost={MTF_MAX_BOOST:.2f})", mtf_acb, pq_data_5s, date_strings, vol_p60 ) # ── DELTA ANALYSIS ───────────────────────────────────────────────────────────── print(f"\n{'═'*65}") print(f" MTF vs BASELINE DELTA") print(f"{'═'*65}") delta_roi = mtf_result['roi'] - base_result['roi'] delta_pf = mtf_result['pf'] - base_result['pf'] delta_dd = mtf_result['dd'] - base_result['dd'] delta_sharpe = mtf_result['sharpe'] - base_result['sharpe'] delta_wr = mtf_result['wr'] - base_result['wr'] delta_n = mtf_result['n_trades'] - base_result['n_trades'] delta_h2h1 = mtf_result['h2h1'] - base_result['h2h1'] print(f" ΔROI: {delta_roi:+.2f}% pp ({base_result['roi']:+.2f}% → {mtf_result['roi']:+.2f}%)") print(f" ΔPF: {delta_pf:+.4f} ({base_result['pf']:.4f} → {mtf_result['pf']:.4f})") print(f" ΔDD: {delta_dd:+.2f}% pp ({base_result['dd']:.2f}% → {mtf_result['dd']:.2f}%)") print(f" ΔSharpe: {delta_sharpe:+.3f} ({base_result['sharpe']:.3f} → {mtf_result['sharpe']:.3f})") print(f" ΔWR: {delta_wr:+.1f}% pp ({base_result['wr']:.1f}% → {mtf_result['wr']:.1f}%)") print(f" ΔTrades: {delta_n:+d} ({base_result['n_trades']} → {mtf_result['n_trades']})") print(f" ΔH2/H1: {delta_h2h1:+.3f} ({base_result['h2h1']:.3f} → {mtf_result['h2h1']:.3f})") efficiency_gain = delta_roi / base_result['roi'] * 100 if base_result['roi'] != 0 else 0 dd_cost = delta_dd / base_result['dd'] * 100 if base_result['dd'] != 0 else 0 print(f"\n Efficiency (ΔROI as % of baseline): {efficiency_gain:+.1f}%") print(f" DD cost (ΔDD as % of baseline): {dd_cost:+.1f}%") roi_per_dd = delta_roi / delta_dd if delta_dd != 0 else float('inf') print(f" ROI/DD ratio (Δ): {roi_per_dd:.2f}") # MTF boost log summary print(f"\n 1m Alignment → MTF multiplier summary:") log_items = sorted(mtf_acb.mtf_log.items()) boost_by_date = {ds: info for ds, info in zip(date_strings, [mtf_acb.mtf_log.get(ds, (0, 1.0, 1.0)) for ds in date_strings])} high_mult_days = [(ds, a, m, b) for ds, (a, m, b) in mtf_acb.mtf_log.items() if m > 1.05] high_mult_days.sort(key=lambda x: -x[2]) print(f" High MTF multiplier days (>1.05x): {len(high_mult_days)}") for ds, a, m, b in high_mult_days[:8]: print(f" {ds}: align={a:.3f} → mtf_mult={m:.3f}x final_boost={b:.3f}x") # ── Per-day delta table (top winners and losers) ──────────────────────────────── base_by_date = {s['date']: s for s in base_dstats} mtf_by_date = {s['date']: s for s in mtf_dstats} day_deltas = [] for ds in date_strings: if ds in base_by_date and ds in mtf_by_date: dp = mtf_by_date[ds]['pnl'] - base_by_date[ds]['pnl'] day_deltas.append((ds, dp, mtf_acb.mtf_log.get(ds, (0.0, 1.0, 1.0)))) day_deltas.sort(key=lambda x: -abs(x[1])) print(f"\n Largest per-day P&L differences (MTF - Baseline):") for ds, dp, (a, m, b) in day_deltas[:10]: print(f" {ds}: ΔPNL={dp:+.0f} align={a:.3f} mtf_mult={m:.3f}x") # ── Sub-period breakdown ──────────────────────────────────────────────────────── def sub_period_roi(dstats, start, end, label, cap_start): sub = [s for s in dstats if start <= s['date'] <= end] if not sub: return roi_s = (sub[-1]['capital'] - cap_start) / cap_start * 100 t_s = sum(s['trades'] for s in sub) print(f" {label:30s}: ROI={roi_s:+.1f}% T={t_s}") print(f"\n Sub-period comparison:") print(f" {'Period':<30} Baseline MTF") periods = [ ('Jan 2026', '2026-01-01', '2026-01-31'), ('Feb 2026', '2026-02-01', '2026-02-25'), ] for label, s, e in periods: b_sub = [x for x in base_dstats if s <= x['date'] <= e] m_sub = [x for x in mtf_dstats if s <= x['date'] <= e] if b_sub and m_sub: # Find cap at start of period all_b = [x for x in base_dstats if x['date'] < s] b_cap0 = all_b[-1]['capital'] if all_b else ENGINE_KWARGS['initial_capital'] all_m = [x for x in mtf_dstats if x['date'] < s] m_cap0 = all_m[-1]['capital'] if all_m else ENGINE_KWARGS['initial_capital'] b_roi = (b_sub[-1]['capital'] - b_cap0) / b_cap0 * 100 m_roi = (m_sub[-1]['capital'] - m_cap0) / m_cap0 * 100 delta = m_roi - b_roi b_t = sum(x['trades'] for x in b_sub) m_t = sum(x['trades'] for x in m_sub) print(f" {label:30s}: {b_roi:+5.1f}% (T={b_t}) {m_roi:+5.1f}% (T={m_t}) Δ={delta:+.1f}%") # ── Statistical note ──────────────────────────────────────────────────────────── print(f"\n Statistical note:") print(f" Trade count: baseline={base_result['n_trades']} MTF={mtf_result['n_trades']}") print(f" (Same entries — MTF only changes sizing, not entry decisions.)") print(f" Iron Rule preserved: 1m signal NEVER gated entries.") print(f" 1m vel_div cross-corr with 5s: max |r|<0.013 at all lags (orthogonal)") # ── Save results ─────────────────────────────────────────────────────────────── ts = datetime.now().strftime('%Y%m%d_%H%M%S') run_dir = Path(__file__).parent / 'run_logs' run_dir.mkdir(exist_ok=True) summary = { 'experiment': 'mtf_5s_1m_combined', 'date_range': f'{DATE_START}_to_{DATE_END}', 'mtf_params': { 'vd_1m_threshold': VD_1M_THRESHOLD, 'mtf_max_boost': MTF_MAX_BOOST, 'align_norm': ALIGN_NORM, }, 'baseline': base_result, 'mtf': mtf_result, 'delta': { 'roi_pp': delta_roi, 'pf': delta_pf, 'dd_pp': delta_dd, 'sharpe': delta_sharpe, 'wr_pp': delta_wr, 'n_trades': delta_n, 'h2h1': delta_h2h1, 'efficiency_pct': efficiency_gain, }, 'alignment_stats': { 'n_dates': len(date_strings), 'n_missing_1m': len(missing_1m), 'align_mean': float(np.mean(aligns)), 'align_std': float(np.std(aligns)), 'align_min': float(np.min(aligns)), 'align_max': float(np.max(aligns)), 'mtf_mult_mean': float(np.mean(mtf_mults)), 'mtf_mult_max': float(np.max(mtf_mults)), 'n_high_align': len(high_mult_days), }, 'engine_kwargs': ENGINE_KWARGS, 'run_ts': ts, } with open(run_dir / f'mtf_5s_1m_{ts}.json', 'w') as f: json.dump(summary, f, indent=2, default=str) # Save alignment CSV with open(run_dir / f'mtf_alignment_{ts}.csv', 'w', newline='') as f: w = csv.writer(f) w.writerow(['date', 'align_frac', 'n_bearish', 'n_bars', 'mtf_mult', 'vd_median', 'vd_p5', 'vd_p95']) for ds in date_strings: st = klines_stats.get(ds, {}) a = klines_align.get(ds, 0.0) m = 1.0 + MTF_MAX_BOOST * min(a / ALIGN_NORM, 1.0) if ALIGN_NORM > 0 else 1.0 w.writerow([ ds, f'{a:.4f}', st.get('n_bearish', 0), st.get('n_bars', 0), f'{m:.4f}', f'{st.get("vd_median", 0):.4f}', f'{st.get("vd_p5", 0):.4f}', f'{st.get("vd_p95", 0):.4f}', ]) # Save daily CSVs for label, dstats in [('baseline', base_dstats), ('mtf', mtf_dstats)]: with open(run_dir / f'mtf_daily_{label}_{ts}.csv', 'w', newline='') as f: w = csv.writer(f) w.writerow(['date', 'pnl', 'capital', 'trades', 'boost', 'beta', 'mc_status']) for s in dstats: w.writerow([s['date'], f'{s["pnl"]:.4f}', f'{s["capital"]:.4f}', s['trades'], f'{s["boost"]:.4f}', f'{s["beta"]:.4f}', s['mc_status']]) print(f"\nResults saved:") print(f" {run_dir}/mtf_5s_1m_{ts}.json") print(f" {run_dir}/mtf_daily_baseline_{ts}.csv") print(f" {run_dir}/mtf_daily_mtf_{ts}.csv") print(f" {run_dir}/mtf_alignment_{ts}.csv") print(f"\n{'='*65}") print(f" MTF EXPERIMENT COMPLETE") print(f"{'='*65}") print(f" Hypothesis: 1m orthogonal conditioning improves 5s alpha") if delta_roi > 0 and delta_dd <= 2.0: verdict = "CONFIRMED — positive ROI with controlled DD" elif delta_roi > 0 and delta_dd > 2.0: verdict = "PARTIAL — positive ROI but elevated DD; needs calibration" elif delta_roi <= 0: verdict = "REJECTED — 1m conditioning does not improve 5s ROI" else: verdict = "INCONCLUSIVE" print(f" Verdict: {verdict}") print(f" ΔROI: {delta_roi:+.2f}% ΔDD: {delta_dd:+.2f}% ΔSharpe: {delta_sharpe:+.3f}") print(f"{'='*65}")