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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"""VOL01 — DVOL z-score risk on/off.
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IDEA: Use Deribit DVOL (implied vol index) as a regime filter.
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- When DVOL z-score (expanding window, causal) < threshold => "calm" => go LONG vol-targeted
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- When DVOL z-score >= threshold => "high vol / fear" => flat
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History starts 2021-03 (DVOL only available from then).
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Strategy type: CONTINUOUS position (weights), long-flat, vol-targeted at 20%.
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Grid: test two z-score thresholds (0 and 0.5) x two DVOL smoothing windows (30d, 60d).
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Total cells: 4 param sets x 2 TFs (1d, 12h) x 2 assets = 16 backtests — within budget.
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Pick best config by min-asset hold-out Sharpe.
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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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# ------------------------------------------------------------------
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# DVOL z-score signal builder
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# ------------------------------------------------------------------
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def make_vol01(zscore_thresh: float, dvol_smooth_days: int):
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"""
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Returns a target_fn(df) for VOL01.
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Signal logic:
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1. Get DVOL for the asset (causal, aligned to bar timestamps).
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2. Smooth DVOL with an EMA of dvol_smooth_days bars.
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3. Compute an EXPANDING z-score of the smoothed DVOL.
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Expanding (not rolling) = fully causal, uses all history up to i.
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4. Direction = +1 if z-score < zscore_thresh, else 0 (flat).
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5. Apply vol_target scaling to direction.
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The expanding z-score naturally adapts to regime: low DVOL vs the full
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history = calm = invest; high DVOL vs history = fear = sideline.
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"""
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def target_fn(df):
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# Step 1: get raw DVOL (causal forward-fill from daily Deribit data)
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# Detect which asset this df belongs to by checking close price range
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# We need to pass asset name — infer from close magnitude
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# BTC >> 1000, ETH >> 100 but < BTC. Use DVOL from both and pick best match.
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# Actually al.dvol needs the asset name. We'll pass it via closure.
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raise NotImplementedError("Asset name needed — use make_vol01_asset instead")
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return target_fn
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def make_vol01_asset(asset: str, zscore_thresh: float, dvol_smooth_days: int):
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"""VOL01 target function for a specific asset."""
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def target_fn(df):
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bpd = al.bars_per_day(df)
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# Step 1: get DVOL causally aligned to df bars
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dv = al.dvol(df, asset) # float array, NaN before 2021-03
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# Step 2: smooth DVOL with EMA to reduce noise
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smooth_bars = dvol_smooth_days * bpd
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dv_smooth = al.ema(np.where(np.isfinite(dv), dv, np.nan), max(2, smooth_bars))
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# Step 3: expanding z-score (causal — uses all history up to i)
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s = pd.Series(dv_smooth)
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exp_mean = s.expanding(min_periods=30).mean()
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exp_std = s.expanding(min_periods=30).std()
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z = ((s - exp_mean) / exp_std.replace(0, np.nan)).values
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# Step 4: direction — long when z < threshold, flat otherwise
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direction = np.where(
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np.isfinite(z) & (z < zscore_thresh),
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1.0,
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0.0
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)
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# Step 5: vol-target scaling
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pos = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return pos
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return target_fn
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# Need pandas for expanding z-score in the closure
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import pandas as pd
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# ------------------------------------------------------------------
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# Small grid: 2 thresholds x 2 smoothing windows
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# ------------------------------------------------------------------
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param_grid = [
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(0.0, 30), # strict: only enter below median DVOL, 30d smooth
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(0.5, 30), # relaxed: enter below +0.5 sigma, 30d smooth
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(0.0, 60), # strict: 60d smooth
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(0.5, 60), # relaxed: 60d smooth
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]
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TFS = ("1d", "12h")
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print("=== VOL01: DVOL z-score risk on/off ===")
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print(f"Grid: {len(param_grid)} param sets x {len(TFS)} TFs x 2 assets")
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print()
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all_reps = []
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for (zt, sd) in param_grid:
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name = f"VOL01_z{zt:.1f}_s{sd}d"
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# We need per-asset target functions since al.study_weights calls target_fn(df)
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# but doesn't pass asset name. Solution: run BTC and ETH separately using a
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# custom wrapper that uses asset-specific target functions.
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# Custom study that handles per-asset target functions:
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def run_study(name, zt=zt, sd=sd):
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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 in al.CERTIFIED:
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df = al.get(a, tf)
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tgt_fn = make_vol01_asset(a, zt, sd)
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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(
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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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)
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min_full = min(per_asset[a]["full"]["sharpe"] for a in al.CERTIFIED)
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min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in al.CERTIFIED)
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avg_full = np.mean([per_asset[a]["full"]["sharpe"] for a in al.CERTIFIED])
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cells.append(dict(
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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(float(avg_full), 3),
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fee_survives=fee_ok_all
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))
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# compute verdict
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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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rep = run_study(name)
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all_reps.append((zt, sd, rep))
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print(al.fmt(rep))
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print()
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# ------------------------------------------------------------------
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# Pick best config by min-asset hold-out Sharpe across best TF
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# ------------------------------------------------------------------
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best_entry = max(all_reps, key=lambda x: x[2]["verdict"].get("best_holdout_sharpe", -99))
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best_zt, best_sd, best_rep = best_entry
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print("=" * 60)
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print(f"BEST CONFIG: z_thresh={best_zt}, smooth={best_sd}d")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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