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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+"""MRV03 — Z-score reversion trend-gated (discrete signals, 1d).
+
+HYPOTHESIS: Fade |zscore(close,20)| > 2 toward mean ONLY when the long-horizon
+trend (SMA200 slope) is flat. Skip entries in strong trends.
+
+Logic:
+- z = zscore(close, 20): deviation from 20-bar mean
+- slope = (SMA200[i] - SMA200[i-slope_win]) / SMA200[i-slope_win]: recent slope of SMA200
+- Gate: |slope| < flat_thresh  → trend is flat → allow mean-reversion
+- Entry: if z > +2 → SHORT (price too high, expect reversion to mean)
+         if z < -2 → LONG (price too low, expect reversion to mean)
+- Exit: TP at SMA20 (mean reversion target), SL at 3*ATR14, max_bars=10
+
+Grid: 2 param sets (zscore_win x flat_thresh):
+  A: zscore_win=20, flat_thresh=0.005
+  B: zscore_win=20, flat_thresh=0.010
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+# ── CONFIG GRID (keep total backtests ≤ 6: 2 params × 1 TF × 2 assets = 4 per config) ──
+CONFIGS = [
+    dict(label="A", zscore_win=20, slope_win=5,  flat_thresh=0.005, z_thresh=2.0, max_bars=10),
+    dict(label="B", zscore_win=20, slope_win=5,  flat_thresh=0.010, z_thresh=2.0, max_bars=10),
+]
+
+
+def make_entries_fn(zscore_win: int, slope_win: int, flat_thresh: float,
+                    z_thresh: float, max_bars: int):
+    """Return an entries_fn(df) for study_signals."""
+    sma200_win = 200
+
+    def entries_fn(df):
+        c = df["close"].values.astype(float)
+        n = len(c)
+
+        # Indicators (all causal: value at i uses data <=i)
+        z = al.zscore(c, zscore_win)
+        sma20 = al.sma(c, zscore_win)          # mean-reversion target = rolling mean
+        sma200 = al.sma(c, sma200_win)
+        atr14 = al.atr(df, 14)
+
+        # SMA200 slope: fractional change over last slope_win bars
+        sma200_prev = np.full(n, np.nan)
+        sma200_prev[slope_win:] = sma200[:-slope_win]
+        slope = np.where(
+            (sma200_prev > 0) & np.isfinite(sma200_prev),
+            (sma200 - sma200_prev) / sma200_prev,
+            np.nan,
+        )
+
+        entries = [None] * n
+        for i in range(sma200_win + slope_win, n):
+            zi = z[i]
+            si = slope[i]
+            ci = c[i]
+            atr_i = atr14[i]
+            m20_i = sma20[i]
+
+            # NaN guard
+            if not (np.isfinite(zi) and np.isfinite(si) and np.isfinite(ci)
+                    and np.isfinite(atr_i) and np.isfinite(m20_i)):
+                continue
+
+            # Gate: trend must be flat
+            if abs(si) >= flat_thresh:
+                continue
+
+            # Signal
+            if zi > z_thresh:
+                # Price is stretched UP → SHORT toward mean
+                entries[i] = {
+                    "dir": -1,
+                    "tp": m20_i,                    # mean reversion target
+                    "sl": ci + 3.0 * atr_i,        # stop above
+                    "max_bars": max_bars,
+                }
+            elif zi < -z_thresh:
+                # Price is stretched DOWN → LONG toward mean
+                entries[i] = {
+                    "dir": +1,
+                    "tp": m20_i,                    # mean reversion target
+                    "sl": ci - 3.0 * atr_i,        # stop below
+                    "max_bars": max_bars,
+                }
+
+        return entries
+
+    return entries_fn
+
+
+def run():
+    results = []
+    for cfg in CONFIGS:
+        print(f"\n--- Config {cfg['label']}: zscore_win={cfg['zscore_win']}, "
+              f"slope_win={cfg['slope_win']}, flat_thresh={cfg['flat_thresh']}, "
+              f"z_thresh={cfg['z_thresh']}, max_bars={cfg['max_bars']} ---")
+        entries_fn = make_entries_fn(
+            zscore_win=cfg["zscore_win"],
+            slope_win=cfg["slope_win"],
+            flat_thresh=cfg["flat_thresh"],
+            z_thresh=cfg["z_thresh"],
+            max_bars=cfg["max_bars"],
+        )
+        rep = al.study_signals(
+            f"MRV03-{cfg['label']}",
+            entries_fn,
+            tfs=("1d",),
+        )
+        print(al.fmt(rep))
+        print("JSON:", al.as_json(rep))
+        results.append((cfg, rep))
+
+    # Pick best config by min_asset_holdout_sharpe
+    best_cfg, best_rep = max(
+        results,
+        key=lambda x: x[1]["verdict"].get("best_holdout_sharpe", -99),
+    )
+    print(f"\n=== BEST CONFIG: {best_cfg['label']} ===")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))
+    return best_rep
+
+
+if __name__ == "__main__":
+    run()