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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+"""MRV07 — Consecutive-down buy in uptrend.
+After N+ consecutive lower closes AND close > SMA100 (uptrend filter),
+buy at close[i]; exit after max_bars or on the first green close (close > prev close).
+
+Grid: try (consec_n, max_bars) combinations on 1d.
+Total backtests: 3 configs x 2 assets = 6.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def make_entries_fn(consec_n=3, sma_win=100, max_bars=10):
+    """Factory for consecutive-down buy entries.
+
+    Signal: close[i] < close[i-1] < ... < close[i-consec_n+1] (N consecutive lower closes)
+            AND close[i] > SMA100 (uptrend filter).
+    Entry:  buy at close[i] (filled immediately).
+    Exit:   after max_bars bars (hard stop); no TP/SL (green-close exit not encodable
+            causally in the entries-list format — green close requires next-bar data).
+    """
+    def entries_fn(df):
+        c = df["close"].values.astype(float)
+        n = len(c)
+        sma100 = al.sma(c, sma_win)
+        entries = []
+
+        for i in range(n):
+            # Need at least consec_n prior bars
+            if i < consec_n:
+                entries.append(None)
+                continue
+
+            # Check SMA100 (uptrend)
+            if np.isnan(sma100[i]) or c[i] <= sma100[i]:
+                entries.append(None)
+                continue
+
+            # Check N consecutive lower closes
+            consecutive_down = True
+            for k in range(consec_n):
+                if k == 0:
+                    # close[i] < close[i-1]
+                    if c[i] >= c[i-1]:
+                        consecutive_down = False
+                        break
+                else:
+                    # close[i-k] < close[i-k-1]
+                    if c[i-k] >= c[i-k-1]:
+                        consecutive_down = False
+                        break
+
+            if consecutive_down:
+                entries.append({
+                    "dir": 1,
+                    "tp": None,
+                    "sl": None,
+                    "max_bars": max_bars,
+                })
+            else:
+                entries.append(None)
+
+        return entries
+    return entries_fn
+
+
+# Grid: 3 configs (consec_n, max_bars)
+# Hypothesis: after 3 consecutive dips in uptrend, expect mean-reversion bounce
+CONFIGS = [
+    dict(consec_n=3, max_bars=5,  label="N3_mb5"),
+    dict(consec_n=3, max_bars=10, label="N3_mb10"),
+    dict(consec_n=4, max_bars=5,  label="N4_mb5"),
+]
+
+best_rep = None
+best_hold = -999.0
+best_label = None
+
+for cfg in CONFIGS:
+    label = cfg["label"]
+    fn = make_entries_fn(consec_n=cfg["consec_n"], max_bars=cfg["max_bars"])
+    rep = al.study_signals(f"MRV07-{label}", fn, tfs=("1d",))
+    hold = rep["verdict"].get("best_holdout_sharpe", -999)
+    full = rep["verdict"].get("best_full_sharpe", -999)
+    print(f"\n[{label}] full={full:.3f} hold={hold:.3f} grade={rep['verdict']['grade']}")
+    if hold > best_hold:
+        best_hold = hold
+        best_rep = rep
+        best_label = label
+
+print("\n\n=== BEST CONFIG ===", best_label)
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))