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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+"""MRV02 — BB reversion in calm regime (1d, discrete signals).
+
+HYPOTHESIS: Buy lower BB(20,2) ONLY when realized vol is in low expanding-percentile
+(calm regime). Exit at mid-BB. The gate is the alpha: filter out high-vol / volatile
+periods; only trade the gentle reversions.
+
+Style: al.study_signals (discrete entry/exit, 1d only)
+Gate: RV <= expanding percentile of RV (calm = low expanding percentile threshold)
+Entry: close <= lower BB(20,2)
+TP: mid-BB (dynamic, recomputed each bar in the trade management)
+SL: 2 * ATR below entry
+Max bars: 20 days
+"""
+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 make_entries(df: pd.DataFrame, bb_win: int = 20, bb_k: float = 2.0,
+                 rv_win_days: int = 20, rv_pct_thresh: float = 30.0,
+                 atr_win: int = 14, max_bars: int = 20):
+    """
+    Causal entry logic for MRV02.
+
+    Entry conditions at close[i]:
+    1. close[i] <= lower_BB(20,2) — price touched/crossed lower band
+    2. rv_percentile(i) <= rv_pct_thresh — calm regime (low expanding RV percentile)
+
+    TP: mid_BB at entry time (static target for the trade)
+    SL: entry - 2*ATR (static)
+    max_bars: 20 days
+    """
+    c = df["close"].values.astype(float)
+    n = len(c)
+    bpd = al.bars_per_day(df)
+    bpy = bpd * 365.25
+
+    # Bollinger Bands (causal: value at i uses data <= i)
+    upper_bb, mid_bb, lower_bb = al.bbands(c, win=bb_win, k=bb_k)
+
+    # Realized vol (annualized), window = rv_win_days bars
+    rv_win = max(2, rv_win_days * bpd)
+    r = al.simple_returns(c)
+    rv = al.realized_vol(r, rv_win, bpy)
+
+    # Expanding percentile of RV (causal: percentile of all RV values seen up to i)
+    rv_series = pd.Series(rv)
+    rv_pct = rv_series.expanding().rank(pct=True) * 100.0  # 0-100 percentile
+    rv_pct = rv_pct.values
+
+    # ATR for SL
+    atr_vals = al.atr(df, win=atr_win)
+
+    entries = [None] * n
+    warmup = max(bb_win, rv_win, atr_win) + 1
+
+    for i in range(warmup, n):
+        # Gate: RV must be in calm regime
+        if not np.isfinite(rv_pct[i]) or rv_pct[i] > rv_pct_thresh:
+            continue
+        # Gate: lower BB must be defined
+        if not np.isfinite(lower_bb[i]) or not np.isfinite(mid_bb[i]):
+            continue
+        # Entry: close touches or crosses lower BB
+        if c[i] > lower_bb[i]:
+            continue
+        # ATR must be defined
+        if not np.isfinite(atr_vals[i]) or atr_vals[i] <= 0:
+            continue
+
+        tp_price = mid_bb[i]     # exit at mid-band (static target)
+        sl_price = c[i] - 2.0 * atr_vals[i]  # SL: 2 ATR below entry
+
+        # Only take trade if TP > entry price (there's room to profit)
+        if tp_price <= c[i]:
+            continue
+
+        entries[i] = {
+            "dir": +1,
+            "tp": tp_price,
+            "sl": sl_price,
+            "max_bars": max_bars,
+        }
+
+    return entries
+
+
+# ----------------------------------------------------------------
+# Small parameter grid: bb_win x rv_pct_thresh (4 combos max)
+# ----------------------------------------------------------------
+GRID = [
+    # (bb_win, rv_pct_thresh)
+    (20, 30),   # canonical
+    (20, 40),   # slightly more permissive gate
+    (30, 30),   # wider bands
+    (30, 40),   # wider bands + more permissive gate
+]
+
+print("MRV02 — BB reversion in calm regime")
+print(f"Grid: {GRID}")
+print()
+
+best_rep = None
+best_score = -999.0
+
+for bb_win, rv_pct_thresh in GRID:
+    label = f"MRV02[BB{bb_win},RVp{rv_pct_thresh}]"
+    print(f"--- Testing {label} ---")
+
+    def make_fn(bw=bb_win, rp=rv_pct_thresh):
+        def entries_fn(df):
+            return make_entries(df, bb_win=bw, rv_pct_thresh=rp)
+        return entries_fn
+
+    rep = al.study_signals(label, make_fn(), tfs=("1d",))
+    print(al.fmt(rep))
+    print()
+
+    v = rep["verdict"]
+    score = v.get("best_holdout_sharpe", -999.0) or -999.0
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_rep["_config"] = dict(bb_win=bb_win, rv_pct_thresh=rv_pct_thresh)
+
+print("\n=== BEST CONFIG ===")
+print(al.fmt(best_rep))
+print()
+print("JSON:", al.as_json(best_rep))