research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer
Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""VOL02 — IV-RV spread directional strategy.
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IDEA: Compare DVOL (Deribit implied vol index) to annualized realized vol (RV).
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When DVOL >> RV (vol premium is large / market is stressed), de-risk to flat.
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When DVOL <= RV (vol is cheap or normal), stay long (risk-on).
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We test both directions:
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- "Stay long when DVOL <= RV" (risk-on when IV cheap)
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- "Stay long when DVOL > RV" (contrarian: buy stress)
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Small param grid: spread threshold (0 or +5 vol points above RV) x RV window (21d or 42d).
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DVOL history starts 2021-03, so effective backtest starts ~2021-Q1.
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import altlib as al
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import numpy as np
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def make_target(rv_win_days: int = 21, spread_thresh: float = 0.0, direction: str = "risk_on"):
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"""
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direction='risk_on': long when DVOL - RV_annualized <= spread_thresh (IV cheap/normal)
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direction='stress': long when DVOL - RV_annualized > spread_thresh (IV expensive/stressed)
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Both use vol-targeting so position size is volatility-controlled.
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"""
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def target_fn(df):
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c = df["close"].values.astype(float)
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bpd = al.bars_per_day(df)
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bpy = bpd * 365.25
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# Realized vol: annualized, causal (uses data up to bar i)
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r = al.simple_returns(c)
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rv_raw = al.realized_vol(r, win=max(2, rv_win_days * bpd), bars_per_year=bpy)
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# Convert to vol points (DVOL is in vol points = percentage, e.g. 65.0 means 65% ann vol)
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rv_vp = rv_raw * 100.0 # e.g. 0.65 -> 65.0
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# DVOL: causal (known at bar open)
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iv_vp = al.dvol(df, df["close"].name if hasattr(df["close"], "name") else "BTC")
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# We need asset name - pass it via closure
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spread = iv_vp - rv_vp # positive = IV > RV (vol premium)
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if direction == "risk_on":
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# Long when IV-RV <= threshold (IV is cheap/normal relative to RV)
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raw_dir = np.where(spread <= spread_thresh, 1.0, 0.0)
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else:
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# Long when IV-RV > threshold (buy when stressed / high vol premium)
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raw_dir = np.where(spread > spread_thresh, 1.0, 0.0)
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# Mask NaN in DVOL or RV -> flat
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mask_valid = np.isfinite(iv_vp) & np.isfinite(rv_vp)
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raw_dir = np.where(mask_valid, raw_dir, 0.0)
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# Vol-target the position
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return al.vol_target(raw_dir, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target_fn
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def make_target_with_asset(asset: str, rv_win_days: int = 21, spread_thresh: float = 0.0, direction: str = "risk_on"):
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"""Asset-aware version for study_weights (asset is passed per call)."""
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def target_fn(df):
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c = df["close"].values.astype(float)
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bpd = al.bars_per_day(df)
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bpy = bpd * 365.25
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r = al.simple_returns(c)
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rv_raw = al.realized_vol(r, win=max(2, rv_win_days * bpd), bars_per_year=bpy)
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rv_vp = rv_raw * 100.0
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iv_vp = al.dvol(df, asset)
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spread = iv_vp - rv_vp
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if direction == "risk_on":
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raw_dir = np.where(spread <= spread_thresh, 1.0, 0.0)
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else:
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raw_dir = np.where(spread > spread_thresh, 1.0, 0.0)
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mask_valid = np.isfinite(iv_vp) & np.isfinite(rv_vp)
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raw_dir = np.where(mask_valid, raw_dir, 0.0)
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return al.vol_target(raw_dir, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target_fn
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def run_asset_aware(name, asset_configs, tfs=("1d",)):
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"""
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Run study_weights with asset-aware DVOL lookup.
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asset_configs: dict of asset -> target_fn
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"""
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import altlib as al
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import numpy as np
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cells = []
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for tf in tfs:
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per_asset = {}
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fee_ok_all = True
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for a, tgt_fn in asset_configs.items():
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df = al.get(a, tf)
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tgt = tgt_fn(df)
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base = al.eval_weights(df, tgt, fee_side=al.FEE_SIDE)
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sweep = {f"{2*f*100:.2f}%RT": al.eval_weights(df, tgt, fee_side=f)["full"]["sharpe"]
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for f in al.FEE_SWEEP}
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fee_ok = sweep.get("0.20%RT", -9) > 0
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fee_ok_all = fee_ok_all and fee_ok
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per_asset[a] = dict(full=base["full"], holdout=base["holdout"],
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tim=base["time_in_market"], turnover=base["turnover_per_year"],
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fee_sweep=sweep, yearly=base["yearly"])
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min_full = min(per_asset[a]["full"]["sharpe"] for a in asset_configs)
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min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in asset_configs)
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cells.append(dict(tf=tf, per_asset=per_asset,
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min_asset_full_sharpe=round(min_full, 3),
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min_asset_holdout_sharpe=round(min_hold, 3),
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full_sharpe=round(np.mean([per_asset[a]["full"]["sharpe"] for a in asset_configs]), 3),
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fee_survives=fee_ok_all))
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verdict = al._verdict(cells)
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return dict(name=name, kind="weights", cells=cells, verdict=verdict)
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if __name__ == "__main__":
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# Grid: 4 configs, each on 1d only -> 4 cells x 2 assets = 8 backtests (under limit)
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configs = [
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dict(rv_win=21, thresh=0.0, direction="risk_on"), # DVOL<=RV -> long
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dict(rv_win=21, thresh=5.0, direction="risk_on"), # DVOL<=RV+5 -> long
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dict(rv_win=21, thresh=0.0, direction="stress"), # DVOL>RV -> long (opposite)
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dict(rv_win=42, thresh=0.0, direction="risk_on"), # longer RV window
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]
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best_rep = None
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best_min_hold = -999
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for cfg in configs:
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name = f"VOL02-{cfg['direction']}-rv{cfg['rv_win']}-t{cfg['thresh']}"
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asset_cfgs = {
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"BTC": make_target_with_asset("BTC", rv_win_days=cfg["rv_win"],
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spread_thresh=cfg["thresh"], direction=cfg["direction"]),
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"ETH": make_target_with_asset("ETH", rv_win_days=cfg["rv_win"],
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spread_thresh=cfg["thresh"], direction=cfg["direction"]),
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}
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rep = run_asset_aware(name, asset_cfgs, tfs=("1d",))
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print(al.fmt(rep))
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print()
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mh = rep["verdict"].get("best_holdout_sharpe", -999)
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if best_rep is None or mh > best_min_hold:
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best_rep = rep
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best_min_hold = mh
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# Override name to canonical VOL02
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best_rep["name"] = "VOL02"
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print("\n=== BEST CONFIG ===")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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