research: porta artefatti da strategy-research-calendar (tracks F-I + eval crypto_backtest + lead OPZIONI/VRP)
Dal branch parallelo strategy-research-calendar (continuazione della linea TP01). Porta su main il record di ricerca + la fondazione del lead opzioni (NIENTE blob dati, niente codice in conflitto): - Tracks F/G/H/I (seasonality/calendar, prior-levels, volume-vol, momentum-reversal): tutti NEGATIVI/spurii -> confermano il soffitto Sharpe ~1.3 su BTC/ETH direzionale (calendar = buy&hold travestito; mean-reversion morta anche a fee 0). Diari + script. - trackD_lookahead_audit.py: audit anti-look-ahead (stesso esito del nostro fix >=12h). - eval-crypto-backtest-options.md: valutazione strategia esterna crypto_backtest. Cross-valida TP01 (il loro sleeve spot 12h ~ TP01: due ricerche indipendenti, stessa conclusione). Identifica il LEAD: sleeve income OPZIONI (vendita put settimanali delta-0.28, VRP IV>RV), scorrelato ~0.22 al trend -> via per superare il soffitto ~1.3. - options_real_quote_check.py + cerbero-bite-mainnet-verified.md: VERIFICATO su QUOTE REALI Deribit mainnet (cerbero-bite/MCP = mainnet, bit-identico a ccxt.deribit). Premio reale (BID, con skew) = 1.29x il modellato -> il backtest SOTTOSTIMA il premio; il rischio vero e' la CODA (short-vol) + liquidita' di roll in stress, non la magnitudine. NB: lo sleeve opzioni e' un LEAD, NON deployato: prezzato da modello (BS su DVOL) + 1 snapshot in regime calmo. Serve validazione real-chain multi-regime + stress crash + paper su testnet prima di aggiungerlo al portafoglio. Portafoglio attivo invariato: TP01 70% + XS01 30%. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""ADVERSARIAL LOOK-AHEAD / EXECUTION-LAG AUDIT of the trend portfolio across timeframes.
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Motivation (2026-06-19): a look-ahead bug (ffill mixed-timeframe on open-labeled bars) can
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inflate sub-daily Sharpe (e.g. 4h to ~1.6 vs a real ~1.1). This audit stress-tests OUR pipeline:
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1. EXECUTION LAG: standard book holds the position decided at close[i] during bar i+1.
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We re-run with an EXTRA bar of delay (held during i+2) — i.e. you cannot trade exactly at
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the close; there is one bar of slippage/latency. A genuine slow-trend edge barely moves; a
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timing artifact collapses. We sweep lag = 1 (standard) and 2 (conservative).
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2. RELABEL TEST: resample with label='left' (open-labeled, our default) vs label='right'
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(close-labeled). The realized Sharpe must be (near) identical; a large gap => the labeling
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leaks information.
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Conclusion target: identify the timeframe below which costs + lag dominate (don't deploy there).
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Run: uv run python scripts/research/trackD_lookahead_audit.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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from src.backtest.harness import load
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from src.strategies.trend_portfolio import simple_returns, realized_vol, tsmom_blend
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ASSETS = ["BTC", "ETH"]
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FEE_SIDE = 0.0005
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TARGET_VOL = 0.20
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LEVERAGE = 2.0
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LONG_ONLY = True
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TFS = {"4h": ("4h", 6), "6h": ("6h", 4), "8h": ("8h", 3), "12h": ("12h", 2), "1d": ("1D", 1)}
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def resample(df1h: pd.DataFrame, rule: str, label: str) -> pd.DataFrame:
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g = df1h.copy()
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idx = pd.to_datetime(g["timestamp"], unit="ms", utc=True)
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idx.name = "dt"
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g.index = idx
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out = g.resample(rule, label=label, closed="left").agg(
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{"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"})
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out = out.dropna(subset=["open"])
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out["datetime"] = out.index
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return out.reset_index(drop=True)
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def target_series(c, bpd):
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bpy = bpd * 365.25
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r = simple_returns(c)
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vol = realized_vol(r, 30 * bpd, bpy)
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direction = np.clip(tsmom_blend(c, (30 * bpd, 90 * bpd, 180 * bpd)), 0, None) if LONG_ONLY \
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else tsmom_blend(c, (30 * bpd, 90 * bpd, 180 * bpd))
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scal = np.where((vol > 0) & np.isfinite(vol), TARGET_VOL / vol, 0.0)
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tgt = np.clip(direction * scal, -LEVERAGE, LEVERAGE)
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tgt[~np.isfinite(tgt)] = 0.0
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return tgt, r
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def sleeve_net(df, bpd, lag):
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"""net[t] uses position decided at close[t-lag] (lag>=1). lag=1 = standard, lag=2 = +1 delay."""
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c = df["close"].values.astype(float)
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tgt, r = target_series(c, bpd)
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pos = np.zeros(len(tgt))
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pos[lag:] = tgt[:-lag]
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gross = pos * r
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turn = np.abs(np.diff(pos, prepend=0.0))
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net = gross - FEE_SIDE * turn
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net[:lag] = 0.0
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return np.clip(net, -0.99, None), pd.to_datetime(df["datetime"])
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def portfolio_metrics(dfs, bpd, lag):
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series = {}
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for a in ASSETS:
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net, ts = sleeve_net(dfs[a], bpd, lag)
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series[a] = pd.Series(net, index=pd.to_datetime(ts.values))
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J = pd.concat(series, axis=1, join="inner").dropna()
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combo = 0.5 * J["BTC"].values + 0.5 * J["ETH"].values
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bpy = bpd * 365.25
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sh = float(np.mean(combo) / np.std(combo) * np.sqrt(bpy)) if np.std(combo) > 0 else 0.0
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eq = np.cumprod(1.0 + np.clip(combo, -0.99, None))
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dd = float(np.max((np.maximum.accumulate(eq) - eq) / np.maximum.accumulate(eq)))
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yrs = (J.index[-1] - J.index[0]).days / 365.25
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cagr = eq[-1] ** (1 / yrs) - 1
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return sh, dd, cagr
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def main():
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raw = {a: load(a, "1h") for a in ASSETS}
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print("=" * 96)
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print("# LOOK-AHEAD / EXECUTION-LAG AUDIT — trend portfolio (long-flat, tvol20, lev2), per timeframe")
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print("# lag1 = standard (decision held next bar). lag2 = +1 bar execution delay (conservative).")
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print("# left/right = resample label (open vs close). Big gap => labeling leak.")
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print("=" * 96)
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print(f" {'TF':<5s}{'Sh lag1(L)':>12s}{'Sh lag2(L)':>12s}{'Sh lag1(R)':>12s}"
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f"{'CAGR l1':>10s}{'CAGR l2':>10s}{'DD l1':>8s}{'lag-decay':>11s}")
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for tf, (rule, bpd) in TFS.items():
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dfsL = {a: resample(raw[a], rule, "left") for a in ASSETS}
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dfsR = {a: resample(raw[a], rule, "right") for a in ASSETS}
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sh1L, dd1, cagr1 = portfolio_metrics(dfsL, bpd, 1)
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sh2L, _, cagr2 = portfolio_metrics(dfsL, bpd, 2)
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sh1R, _, _ = portfolio_metrics(dfsR, bpd, 1)
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decay = (sh1L - sh2L) / sh1L * 100 if sh1L else 0.0
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flag = " <-- robust" if sh2L >= 0.9 * sh1L and abs(sh1L - sh1R) < 0.1 else ""
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print(f" {tf:<5s}{sh1L:>12.2f}{sh2L:>12.2f}{sh1R:>12.2f}"
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f"{cagr1*100:>+9.1f}%{cagr2*100:>+9.1f}%{dd1*100:>7.1f}%{decay:>+10.0f}%{flag}")
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print("\n Interpretation:")
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print(" - If Sh lag2 << Sh lag1 (big lag-decay), the edge needs to trade AT the close -> sub-TF")
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print(" timing artifact / cost-fragile. Robust slow-trend should barely move with +1 bar.")
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print(" - If Sh lag1(left) != Sh lag1(right), the bar LABELING leaks -> look-ahead. Should match.")
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if __name__ == "__main__":
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main()
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