331 lines
12 KiB
Python
331 lines
12 KiB
Python
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"""MacroPostureSwitcher Backtest — 5s Gold Standard (56 days)
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=============================================================
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Same posture logic, applied to 5s data (vbt_cache/).
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At 5s resolution, 1 bar = 5s:
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MAX_HOLD = 240 bars = 20 min (matches 1m MAX_HOLD=20 bars)
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ExF from NPZ (same eigenvalues path, same dates).
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prev-day rvol computed from previous day's 5s prices.
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Outputs: ROI / WR / PF / Max-DD / Sharpe
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"""
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import sys, time, csv, gc, json
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sys.path.insert(0, str(__import__('pathlib').Path(__file__).parent.parent))
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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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from nautilus_dolphin.nautilus.macro_posture_switcher import MacroPostureSwitcher, Posture
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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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EIGEN_PATH = Path(r"C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues")
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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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ENTRY_T = 0.020
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MAX_HOLD = 240 # bars (240 × 5s = 20 min)
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EXF_KEYS = ['dvol_btc', 'fng', 'funding_btc', 'taker']
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def load_exf(date_str: str) -> dict:
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defaults = {'dvol_btc': 50.0, 'fng': 50.0, 'funding_btc': 0.0, 'taker': 1.0}
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dp = EIGEN_PATH / date_str
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if not dp.exists():
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return defaults
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files = sorted(dp.glob('scan_*__Indicators.npz'))[:5]
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if not files:
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return defaults
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buckets = defaultdict(list)
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for f in files:
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try:
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d = np.load(f, allow_pickle=True)
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if 'api_names' not in d:
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continue
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names = list(d['api_names'])
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vals = d['api_indicators']
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for k in EXF_KEYS:
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if k in names:
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v = float(vals[names.index(k)])
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if np.isfinite(v):
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buckets[k].append(v)
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except Exception:
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pass
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out = dict(defaults)
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for k, vs in buckets.items():
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if vs:
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out[k] = float(np.median(vs))
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return out
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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) | Entry T: ±{ENTRY_T} MaxHold: {MAX_HOLD}b={MAX_HOLD*5}s")
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# Pass 1: prev-day rvol / btc_ret
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print("Pass 1: lag-1 rvol...")
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t0 = time.time()
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day_rvol = {}
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day_btcret = {}
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for pf in parquet_files:
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ds = pf.stem
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try:
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df = pd.read_parquet(pf, columns=['BTCUSDT'])
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except Exception:
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continue
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btc = df['BTCUSDT'].values.astype(np.float64)
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btc = btc[np.isfinite(btc) & (btc > 0)]
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if len(btc) < 2:
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continue
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log_r = np.diff(np.log(btc))
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day_rvol[ds] = float(np.std(log_r))
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day_btcret[ds] = float((btc[-1] - btc[0]) / btc[0])
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dates_sorted = sorted(day_rvol.keys())
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prev_rvol = {d: day_rvol.get(dates_sorted[i-1]) if i > 0 else None for i, d in enumerate(dates_sorted)}
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prev_btcret = {d: day_btcret.get(dates_sorted[i-1]) if i > 0 else None for i, d in enumerate(dates_sorted)}
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print(f" Pass 1 done: {time.time()-t0:.1f}s")
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# Pass 2: trade simulation
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print("Pass 2: posture + crossover trades...")
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switcher = MacroPostureSwitcher(
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enable_long_posture=True,
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# 5s-calibrated rvol thresholds (1m values don't transfer — 5s std is ~7x smaller):
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# 5y klines 1m Q1 = 0.000455; 5s equivalent p25 = 0.000203, p75 = 0.000337
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rvol_pause_thresh=0.000203, # 5s p25 — was 0.000455 (1m Q1 = p90 of 5s → too restrictive)
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rvol_strong_thresh=0.000337, # 5s p75
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# dvol Q1 gate: validated — removes 19 noisy days, PF 1.036→1.116, Sharpe 2.7→5.8
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dvol_none_below=47.5,
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)
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equity = 1.0
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equity_curve = [equity]
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day_log = []
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trade_log = []
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stats_yr = defaultdict(lambda: {'wins':0,'losses':0,'gw':0.0,'gl':0.0,'n':0,'paused':0})
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stats_pos = defaultdict(lambda: {'wins':0,'losses':0,'gw':0.0,'gl':0.0,'n':0})
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for i, pf in enumerate(parquet_files):
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ds = pf.stem
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year = ds[:4]
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exf = load_exf(ds)
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pr = prev_rvol.get(ds)
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pb = prev_btcret.get(ds)
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decision = switcher.decide(
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dvol_btc=exf['dvol_btc'], fng=exf['fng'],
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funding_btc=exf['funding_btc'],
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realized_vol=pr, btc_day_return=pb,
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)
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if decision.posture == Posture.NONE:
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stats_yr[year]['paused'] += 1
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day_log.append({'date': ds, 'year': year, 'posture': 'NONE',
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'fear': round(decision.fear_score, 3), 'n_trades': 0,
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'day_ret': 0.0, 'equity': round(equity, 6)})
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continue
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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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if n < MAX_HOLD + 5:
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del vd, btc
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continue
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pos = decision.posture
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smult = decision.size_mult
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if pos == Posture.SHORT:
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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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sign = -1
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else:
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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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sign = +1
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day_rets_sized = []
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for t in range(n - MAX_HOLD):
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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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exit_bar = MAX_HOLD
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for k in range(1, MAX_HOLD + 1):
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tb = t + k
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if tb >= n:
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exit_bar = k; break
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if cross_back[tb]:
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exit_bar = k; break
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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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raw_ret = sign * (xp - ep) / ep
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sized_ret = raw_ret * smult
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day_rets_sized.append((raw_ret, sized_ret, exit_bar))
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# Per-trade log (first 50K trades)
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if len(trade_log) < 50_000:
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trade_log.append({'date': ds, 'year': year, 'posture': pos.value,
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't': t, 'hold': exit_bar,
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'raw_ret': round(raw_ret, 6),
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'sized_ret': round(sized_ret, 6),
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'size_mult': round(smult, 3)})
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del vd, btc, entry_mask, cross_back
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n_t = len(day_rets_sized)
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if n_t == 0:
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day_log.append({'date': ds, 'year': year, 'posture': pos.value,
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'fear': round(decision.fear_score, 3), 'n_trades': 0,
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'day_ret': 0.0, 'equity': round(equity, 6)})
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continue
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wins = sum(1 for r, _, _ in day_rets_sized if r >= 0)
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losses = n_t - wins
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gw = sum(r for r, _, _ in day_rets_sized if r >= 0)
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gl = sum(abs(r) for r, _, _ in day_rets_sized if r < 0)
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pf_d = gw / gl if gl > 0 else 999.0
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day_ret = sum(s for _, s, _ in day_rets_sized)
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day_ret_clamped = max(-0.5, min(day_ret, 2.0))
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equity *= (1 + day_ret_clamped)
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equity_curve.append(equity)
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s = stats_yr[year]
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s['wins'] += wins; s['losses'] += losses
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s['gw'] += gw; s['gl'] += gl; s['n'] += n_t
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sp = stats_pos[pos.value]
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sp['wins'] += wins; sp['losses'] += losses
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sp['gw'] += gw; sp['gl'] += gl; sp['n'] += n_t
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day_log.append({
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'date': ds, 'year': year, 'posture': pos.value,
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'fear': round(decision.fear_score, 3),
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'dvol': round(exf['dvol_btc'], 1), 'fng': round(exf['fng'], 1),
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'prev_rvol': round(pr, 7) if pr else None,
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'n_trades': n_t, 'wins': wins, 'losses': losses,
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'pf_day': round(pf_d, 4), 'day_ret': round(day_ret, 6),
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'equity': round(equity, 6),
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})
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elapsed = time.time() - t0
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print(f"Pass 2 done: {elapsed:.1f}s")
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# ── Metrics ────────────────────────────────────────────────────────────────────
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ec = np.array(equity_curve)
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roi = (ec[-1] - 1.0) * 100
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running_max = np.maximum.accumulate(ec)
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dd_arr = (running_max - ec) / running_max
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max_dd = float(np.max(dd_arr)) * 100
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daily_rets = np.array([d['day_ret'] for d in day_log if d['n_trades'] > 0])
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sharpe = float(np.mean(daily_rets) / np.std(daily_rets) * np.sqrt(252)) if len(daily_rets) > 1 and np.std(daily_rets) > 0 else 0.0
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tot_w = sum(s['wins'] for s in stats_yr.values())
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tot_l = sum(s['losses'] for s in stats_yr.values())
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tot_gw = sum(s['gw'] for s in stats_yr.values())
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tot_gl = sum(s['gl'] for s in stats_yr.values())
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tot_n = tot_w + tot_l
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pf = tot_gw / tot_gl if tot_gl > 0 else 999.0
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wr = tot_w / tot_n * 100 if tot_n > 0 else 0.0
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paused_days = sum(s['paused'] for s in stats_yr.values())
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active_days = sum(1 for d in day_log if d['posture'] != 'NONE')
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# ── Console ────────────────────────────────────────────────────────────────────
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print(f"\n{'='*80}")
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print(f" MacroPostureSwitcher — 5s Gold Standard Backtest")
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print(f" Entry: ±{ENTRY_T} MaxHold: {MAX_HOLD}b={MAX_HOLD*5}s Runtime: {elapsed:.0f}s")
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print(f"{'='*80}")
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print(f" ROI: {roi:>+8.2f}%")
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print(f" Max DD: {max_dd:>8.2f}%")
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print(f" Sharpe: {sharpe:>8.3f} (annualized, daily)")
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print(f" PF: {pf:>8.4f}")
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print(f" WR: {wr:>8.2f}%")
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print(f" N trades: {tot_n:>8,}")
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print(f" Active: {active_days} Paused: {paused_days}")
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print(f" Equity final: {ec[-1]:.4f}x")
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print(f"\n Per-year:")
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print(f" {'Year':<6} {'N':>7} {'WR%':>6} {'PF':>7} {'Paused':>6}")
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print(f" {'-'*40}")
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for yr in sorted(stats_yr.keys()):
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s = stats_yr[yr]
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n = s['wins'] + s['losses']
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if n == 0:
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print(f" {yr:<6} {'—':>7} {'—':>6} {'—':>7} {s['paused']:>6}")
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continue
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print(f" {yr:<6} {n:>7,} {s['wins']/n*100:>6.2f}% {s['gw']/max(1e-9,s['gl']):>7.4f} {s['paused']:>6}")
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print(f"\n Per-posture:")
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for pos, s in sorted(stats_pos.items()):
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n = s['wins'] + s['losses']
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if n == 0: continue
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print(f" {pos}: N={n:,} WR={s['wins']/n*100:.2f}% PF={s['gw']/max(1e-9,s['gl']):.4f}")
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# ── Save ───────────────────────────────────────────────────────────────────────
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LOG_DIR.mkdir(exist_ok=True)
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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day_csv = LOG_DIR / f"posture_5s_daily_{ts}.csv"
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if day_log:
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with open(day_csv, 'w', newline='') as f:
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w = csv.DictWriter(f, fieldnames=day_log[0].keys())
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w.writeheader(); w.writerows(day_log)
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print(f"\n → {day_csv}")
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eq_csv = LOG_DIR / f"posture_5s_equity_{ts}.csv"
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with open(eq_csv, 'w', newline='') as f:
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w = csv.writer(f)
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w.writerow(['idx','equity']); w.writerows(enumerate(equity_curve))
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print(f" → {eq_csv}")
|
|||
|
|
|
|||
|
|
if trade_log:
|
|||
|
|
tr_csv = LOG_DIR / f"posture_5s_trades_{ts}.csv"
|
|||
|
|
with open(tr_csv, 'w', newline='') as f:
|
|||
|
|
w = csv.DictWriter(f, fieldnames=trade_log[0].keys())
|
|||
|
|
w.writeheader(); w.writerows(trade_log)
|
|||
|
|
print(f" → {tr_csv}")
|
|||
|
|
|
|||
|
|
summary = {
|
|||
|
|
'mode': '5s_posture_backtest', 'ts': ts, 'runtime_s': round(elapsed, 1),
|
|||
|
|
'entry_t': ENTRY_T, 'max_hold_bars': MAX_HOLD, 'max_hold_sec': MAX_HOLD * 5,
|
|||
|
|
'roi_pct': round(roi, 4), 'max_dd_pct': round(max_dd, 4),
|
|||
|
|
'sharpe': round(sharpe, 4), 'pf': round(pf, 4),
|
|||
|
|
'wr_pct': round(wr, 3), 'n_trades': int(tot_n),
|
|||
|
|
'active_days': active_days, 'paused_days': int(paused_days),
|
|||
|
|
'equity_final': round(float(ec[-1]), 6),
|
|||
|
|
'per_year': {yr: {
|
|||
|
|
'n': int(stats_yr[yr]['n']), 'paused': int(stats_yr[yr]['paused']),
|
|||
|
|
'wr': round(stats_yr[yr]['wins']/max(1,stats_yr[yr]['n'])*100, 3),
|
|||
|
|
'pf': round(stats_yr[yr]['gw']/max(1e-9,stats_yr[yr]['gl']), 4),
|
|||
|
|
} for yr in sorted(stats_yr.keys())},
|
|||
|
|
}
|
|||
|
|
sum_json = LOG_DIR / f"posture_5s_summary_{ts}.json"
|
|||
|
|
with open(sum_json, 'w') as f:
|
|||
|
|
json.dump(summary, f, indent=2)
|
|||
|
|
print(f" → {sum_json}")
|
|||
|
|
print(f"\n Runtime: {elapsed:.0f}s")
|