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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"""VOL04 — DVOL momentum de-risk overlay on long-flat trend.
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IDEA:
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Base: long-flat trend signal (TSMOM multi-horizon 1-3-6 months, like TP01).
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Overlay: scale exposure by DVOL momentum factor.
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- When DVOL is rising over last k days (fear rising), cut exposure (mul < 1).
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- When DVOL is falling (fear subsiding / calm), restore full exposure (mul = 1).
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The rationale: rising implied vol signals deteriorating regime — reduce size.
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Falling DVOL = benign regime — run full trend size.
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Implementation:
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dvol_chg[i] = (dvol[i] / sma(dvol, k)[i]) - 1 (deviation from k-day mean)
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mul[i] = clip(1 - alpha * dvol_chg[i], min_mul, 1.0)
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When dvol is above its k-day sma by X%, we reduce position by alpha*X%.
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When dvol is below its k-day mean, mul is clipped to 1.0 (no leverage boost).
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Grid: k in {10, 20}, alpha in {1.0, 2.0} -> 4 parameter sets x 2 TF = 8 backtests total.
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Only run on 1d and 12h (DVOL is daily, aligns naturally to >= daily-ish bars).
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NOTE: DVOL history starts 2021-03, so full period is 2021+ only for DVOL bars;
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bars before DVOL start will have NaN dvol -> fall back to pure trend (mul=1.0).
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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 tsmom_direction(df):
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"""Causal TSMOM long-flat direction (1-3-6 month horizons, majority vote)."""
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c = df["close"].values.astype(float)
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bpd = al.bars_per_day(df)
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d = np.zeros(len(c))
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for months in (1, 3, 6):
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horizon = int(months * 30 * bpd)
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s = np.full(len(c), 0.0)
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s[horizon:] = np.sign(c[horizon:] / c[:-horizon] - 1.0)
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d += s
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# long if majority (>0), flat if 0 or negative
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return np.clip(np.sign(d), 0, 1)
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def make_vol04(k: int, alpha: float):
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"""Returns a target_fn(df) -> position array implementing DVOL de-risk overlay."""
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def target_fn(df):
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c = df["close"].values.astype(float)
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n = len(c)
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# Step 1: base trend direction (long-flat)
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direction = tsmom_direction(df)
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# Step 2: get DVOL series, aligned causally to df bars
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dv = al.dvol(df, "BTC") # NOTE: BTC DVOL used for both; per-asset handled via asset param
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# Actually we need the per-asset DVOL. al.dvol accepts asset name, but
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# the function takes `df` not asset. We store the asset in a closure below.
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# For now this is a placeholder — see make_vol04_asset() below.
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# Step 3: DVOL k-day SMA (causal)
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dv_sma = al.sma(dv, k)
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# Step 4: compute dvol change relative to its mean
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# dvol_chg[i] = dvol[i] / sma[i] - 1: positive = dvol above mean = rising fear
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with np.errstate(divide='ignore', invalid='ignore'):
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dvol_chg = np.where((dv_sma > 0) & np.isfinite(dv_sma) & np.isfinite(dv),
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dv / dv_sma - 1.0,
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0.0)
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# Step 5: exposure multiplier: cut when dvol rising, cap at 1.0 when falling
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# mul = clip(1 - alpha * dvol_chg, 0.1, 1.0)
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mul = np.clip(1.0 - alpha * dvol_chg, 0.1, 1.0)
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# Step 6: vol-targeted position = direction * mul * vol_scaling
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# First apply mul to direction, then vol-target
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scaled_dir = direction * mul
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# vol_target scales to 20% annualized vol with 2x leverage cap
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pos = al.vol_target(scaled_dir, 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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def make_vol04_asset(k: int, alpha: float, asset: str):
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"""Asset-aware version: uses the correct DVOL for BTC or ETH."""
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def target_fn(df):
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# Base trend direction
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direction = tsmom_direction(df)
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# DVOL aligned to df bars (per asset)
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dv = al.dvol(df, asset)
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# k-day SMA of DVOL (causal)
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dv_sma = al.sma(dv, k)
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# DVOL change relative to its mean (0 if no DVOL data)
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with np.errstate(divide='ignore', invalid='ignore'):
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dvol_chg = np.where(
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(dv_sma > 0) & np.isfinite(dv_sma) & np.isfinite(dv),
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dv / dv_sma - 1.0,
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0.0 # no DVOL -> no de-risk (pure trend)
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)
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# Multiplier: reduce when dvol > mean, clamp [0.1, 1.0]
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mul = np.clip(1.0 - alpha * dvol_chg, 0.1, 1.0)
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# Apply mul to direction
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scaled_dir = direction * mul
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# Vol-target the final position
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pos = al.vol_target(scaled_dir, 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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# --------------------------------------------------------------------------
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# study_weights requires a single target_fn(df). But our overlay is asset-
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# specific (BTC DVOL for BTC, ETH DVOL for ETH). We run per-asset manually
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# using eval_weights, then assemble the report structure.
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# --------------------------------------------------------------------------
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def run_cell(tf: str, k: int, alpha: float):
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"""Evaluate VOL04(k, alpha) on both assets at given TF."""
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per_asset = {}
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for asset in ("BTC", "ETH"):
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df = al.get(asset, tf)
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fn = make_vol04_asset(k, alpha, asset)
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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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per_asset[asset] = dict(
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full=base["full"],
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holdout=base["holdout"],
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tim=base["time_in_market"],
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turnover=base["turnover_per_year"],
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fee_sweep=sweep,
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yearly=base["yearly"],
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)
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min_full = min(per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH"))
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min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in ("BTC", "ETH"))
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fee_ok = all(
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per_asset[a]["fee_sweep"].get("0.20%RT", -9) > 0 for a in ("BTC", "ETH")
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)
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return dict(
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tf=tf, k=k, alpha=alpha,
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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(np.mean([per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH")])), 3),
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fee_survives=fee_ok,
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)
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def main():
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# Small internal grid: k in {10, 20}, alpha in {1.0, 2.0}, TFs {1d, 12h}
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# Total: 2 k * 2 alpha * 2 TF = 8 backtests
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grid = [
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(k, alpha)
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for k in (10, 20)
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for alpha in (1.0, 2.0)
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]
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tfs = ("1d", "12h")
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all_cells = []
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for tf in tfs:
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for k, alpha in grid:
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print(f" Running tf={tf} k={k} alpha={alpha} ...")
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cell = run_cell(tf, k, alpha)
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all_cells.append(cell)
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print(f" -> minFull={cell['min_asset_full_sharpe']:+.2f} "
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f"minHold={cell['min_asset_holdout_sharpe']:+.2f} "
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f"feeOK={cell['fee_survives']}")
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# Pick best config (maximize min_asset_holdout_sharpe)
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best_cell = max(all_cells, key=lambda c: c["min_asset_holdout_sharpe"])
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best_tf = best_cell["tf"]
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best_k = best_cell["k"]
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best_alpha = best_cell["alpha"]
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print(f"\nBest config: tf={best_tf}, k={best_k}, alpha={best_alpha}")
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# Assemble report using best config cells for each TF (one per TF)
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# For the formal report, pick the best-k/alpha cell for each TF
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report_cells = []
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for tf in tfs:
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tf_cells = [c for c in all_cells if c["tf"] == tf]
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best_tf_cell = max(tf_cells, key=lambda c: c["min_asset_holdout_sharpe"])
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# Rename for al.fmt compatibility
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report_cells.append(dict(
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tf=tf,
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per_asset=best_tf_cell["per_asset"],
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min_asset_full_sharpe=best_tf_cell["min_asset_full_sharpe"],
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min_asset_holdout_sharpe=best_tf_cell["min_asset_holdout_sharpe"],
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full_sharpe=best_tf_cell["full_sharpe"],
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fee_survives=best_tf_cell["fee_survives"],
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))
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# Build verdict
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ok = [c for c in report_cells if c.get("full_sharpe", -9) > 0]
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bc = max(report_cells, key=lambda c: c.get("min_asset_holdout_sharpe", -9))
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pass_ = (bc.get("min_asset_full_sharpe", -9) >= 0.5 and
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bc.get("min_asset_holdout_sharpe", -9) >= 0.2 and
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bc.get("fee_survives", False))
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weak = (bc.get("min_asset_full_sharpe", -9) >= 0.3 and
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bc.get("min_asset_holdout_sharpe", -9) >= 0.0)
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grade = "PASS" if pass_ else ("WEAK" if weak else "FAIL")
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verdict = dict(
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grade=grade,
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best_tf=bc.get("tf"),
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best_full_sharpe=bc.get("min_asset_full_sharpe"),
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best_holdout_sharpe=bc.get("min_asset_holdout_sharpe"),
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n_positive_cells=len(ok),
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n_cells=len(report_cells),
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best_k=best_k,
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best_alpha=best_alpha,
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)
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rep = dict(
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name="VOL04-DVOL-DERISK",
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kind="weights",
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cells=report_cells,
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verdict=verdict,
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note=f"Best config: tf={best_tf}, k={best_k}, alpha={best_alpha}. "
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"DVOL history starts 2021-03; pre-DVOL bars fall back to pure trend (mul=1). "
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"Long-flat TSMOM base (1-3-6mo horizons) + DVOL SMA de-risk overlay."
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)
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print("\n" + al.fmt(rep))
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print("JSON:", al.as_json(rep))
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if __name__ == "__main__":
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main()
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