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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+"""MRV06 — VWAP Deviation Reversion
+
+IDEA: On 1h bars, compute a rolling session VWAP (using typical price * volume).
+Fade deviations > k*sigma back to VWAP (mean-reversion).
+Regime gate: only trade in the direction of the daily trend (using a simple trend filter).
+
+Variants tested:
+  - k = 1.5 vs 2.0 (deviation threshold)
+  - sigma window = 24h vs 48h (rolling window for sigma)
+
+TF: 1h (VWAP is most meaningful at 1h granularity)
+Style: continuous weights (study_weights)
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+import pandas as pd
+
+
+def compute_vwap_deviation(df: pd.DataFrame, vwap_win: int, k: float,
+                           sigma_win: int) -> np.ndarray:
+    """
+    Compute VWAP deviation signal with regime gate.
+
+    VWAP: rolling typical_price * volume / rolling volume (causal window).
+    Signal: when price deviates > k*sigma above VWAP -> short (expect reversion)
+            when price deviates > k*sigma below VWAP -> long (expect reversion)
+    Regime gate: only long when daily trend (slow EMA > fast EMA at 1h scale).
+
+    All computations causal (value at i uses data <= i).
+    """
+    close = df["close"].values.astype(float)
+    high = df["high"].values.astype(float)
+    low = df["low"].values.astype(float)
+    volume = df["volume"].values.astype(float)
+
+    # Typical price (causal: same bar is fine, we're using it for VWAP at i)
+    typical = (high + low + close) / 3.0
+
+    # Rolling VWAP (causal window)
+    s = pd.Series
+    tp_vol = typical * np.where(volume > 0, volume, np.nan)
+
+    # Rolling VWAP over vwap_win bars
+    vwap_num = s(tp_vol).rolling(vwap_win, min_periods=vwap_win // 2).sum()
+    vwap_den = s(volume).rolling(vwap_win, min_periods=vwap_win // 2).sum()
+    vwap = (vwap_num / vwap_den.replace(0, np.nan)).values
+
+    # Deviation from VWAP
+    deviation = close - vwap
+
+    # Rolling sigma of deviation
+    sigma = s(deviation).rolling(sigma_win, min_periods=sigma_win // 2).std().values
+
+    # Normalized deviation (z-score wrt rolling sigma)
+    z = np.where(sigma > 0, deviation / sigma, 0.0)
+
+    # Mean-reversion signal:
+    # z > k => price is too high above VWAP => short (negative position)
+    # z < -k => price is too low below VWAP => long (positive position)
+    # Gradual: use -z/k clipped to [-1, 1] when |z| > k, else 0
+    signal = np.where(np.abs(z) > k, -np.sign(z), 0.0)
+
+    # Regime gate using daily trend: EMA(50d) vs EMA(10d) at 1h scale
+    # Only allow long when fast EMA > slow EMA (uptrend), allow short any time
+    # (crypto is fundamentally bullish-biased; mean-reversion shorts in downtrend risky)
+    ema_fast = al.ema(close, 10 * 24)  # 10-day EMA
+    ema_slow = al.ema(close, 50 * 24)  # 50-day EMA
+
+    # In uptrend (fast > slow): allow both long and short mean-reversion
+    # In downtrend (fast < slow): allow only short mean-reversion (with VWAP)
+    uptrend = ema_fast > ema_slow
+
+    # Filter: only take longs in uptrend regime
+    gated = np.where(signal > 0, signal * uptrend.astype(float), signal)
+
+    # Apply vol-targeting for position sizing
+    result = al.vol_target(gated, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    result = np.nan_to_num(result, nan=0.0)
+
+    return result
+
+
+def make_target(vwap_win: int, k: float, sigma_win: int):
+    """Factory: returns a target_fn(df) -> weights array."""
+    def target_fn(df: pd.DataFrame) -> np.ndarray:
+        return compute_vwap_deviation(df, vwap_win=vwap_win, k=k, sigma_win=sigma_win)
+    target_fn.__name__ = f"vwap_dev_w{vwap_win}_k{k}_s{sigma_win}"
+    return target_fn
+
+
+# Small internal grid (<=4 param sets)
+# VWAP window: 24h (1 session) vs 48h (2 sessions)
+# k threshold: 1.5 vs 2.0
+# sigma_win tied to vwap_win
+CONFIGS = [
+    # (vwap_win, k, sigma_win, label)
+    (24,  1.5, 48,  "vwap24h_k1.5_s48h"),
+    (24,  2.0, 48,  "vwap24h_k2.0_s48h"),
+    (48,  1.5, 96,  "vwap48h_k1.5_s96h"),
+    (48,  2.0, 96,  "vwap48h_k2.0_s96h"),
+]
+
+best_rep = None
+best_hold = -999.0
+
+print("=== MRV06 VWAP Deviation Reversion ===")
+print(f"Testing {len(CONFIGS)} configs on 1h bars (BTC + ETH)\n")
+
+for vwap_win, k, sigma_win, label in CONFIGS:
+    print(f"--- Config: {label} ---")
+    fn = make_target(vwap_win, k, sigma_win)
+    rep = al.study_weights(
+        f"MRV06-{label}",
+        fn,
+        tfs=("1h",)
+    )
+    print(al.fmt(rep))
+    hold_sharpe = rep["verdict"].get("best_holdout_sharpe", -999)
+    if hold_sharpe > best_hold:
+        best_hold = hold_sharpe
+        best_rep = rep
+    print()
+
+# Print best config
+print("\n\n=== BEST CONFIG ===")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))