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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+"""MIC07 — Pin-bar rejection reversal (hammer at support).
+
+HYPOTHESIS:
+  A hammer candle (large lower wick, small body near top) at a recent support (N-bar low)
+  signals a long reversal. Enter long at close[i] with SL below the wick low.
+
+PIN-BAR DEFINITION (causal, using only bar[i] OHLC):
+  - Lower wick >= wick_ratio * candle range  (e.g. 60% of H-L)
+  - Body is in upper part of the candle (close > midpoint)
+  - Candle range > ATR * min_range_atr  (no doji / tiny bars)
+
+SUPPORT CONDITION:
+  - low[i] <= rolling_min(low, support_win bars, excluding bar i) * (1 + support_pct)
+    i.e. bar is "near" a recent N-bar low
+
+TRADE MANAGEMENT:
+  - Entry: close[i]
+  - SL: low[i] - atr_sl_mult * ATR  (below wick, some buffer)
+  - TP: close[i] + rr_ratio * (close[i] - SL)  (risk-reward)
+  - max_bars: hold at most max_hold days
+
+Grid (<=4 configs, 1 TF = 1d, 2 assets => 4 backtests total):
+  Config A: wick_ratio=0.60, support_win=20, sl_mult=0.2, rr=2.0, max_hold=10
+  Config B: wick_ratio=0.65, support_win=10, sl_mult=0.3, rr=1.5, max_hold=8
+  Config C: wick_ratio=0.55, support_win=20, sl_mult=0.3, rr=2.0, max_hold=15
+  Config D: wick_ratio=0.60, support_win=30, sl_mult=0.2, rr=2.0, max_hold=10
+
+Pick best config by min_asset_holdout_sharpe, print full report.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def make_entries(df, wick_ratio=0.60, support_win=20, sl_mult=0.2,
+                 rr=2.0, max_hold=10, atr_win=14, min_range_atr=0.3):
+    """Build entry list for the pin-bar reversal strategy."""
+    o = df["open"].values.astype(float)
+    h = df["high"].values.astype(float)
+    l = df["low"].values.astype(float)
+    c = df["close"].values.astype(float)
+
+    atr_arr = al.atr(df, atr_win)
+
+    # Rolling min of lows over support_win bars PRIOR to i (shift by 1 -> causal)
+    low_series = df["low"].rolling(support_win, min_periods=support_win).min().shift(1).values
+
+    entries = [None] * len(df)
+
+    for i in range(support_win + atr_win + 1, len(df)):
+        rng = h[i] - l[i]
+        if rng <= 0:
+            continue
+
+        atr_i = atr_arr[i]
+        if not np.isfinite(atr_i) or atr_i <= 0:
+            continue
+
+        # Filter tiny candles
+        if rng < min_range_atr * atr_i:
+            continue
+
+        body_top = max(o[i], c[i])
+        body_bot = min(o[i], c[i])
+
+        lower_wick = body_bot - l[i]
+        # upper_wick = h[i] - body_top  # not used but useful for debug
+
+        # Pin bar: lower wick must dominate
+        if lower_wick < wick_ratio * rng:
+            continue
+
+        # Body in upper portion (close > midpoint of range)
+        if c[i] <= (h[i] + l[i]) / 2.0:
+            continue
+
+        # Support condition: low[i] is near recent N-bar rolling min
+        supp = low_series[i]
+        if not np.isfinite(supp):
+            continue
+        # Low[i] must be at or below support level (within 0.5% of the recent low)
+        if l[i] > supp * 1.005:
+            continue
+
+        # Trade setup
+        sl_price = l[i] - sl_mult * atr_i
+        if sl_price >= c[i]:
+            continue  # degenerate
+        risk = c[i] - sl_price
+        if risk <= 0:
+            continue
+        tp_price = c[i] + rr * risk
+
+        entries[i] = {
+            "dir": 1,
+            "tp": round(tp_price, 2),
+            "sl": round(sl_price, 2),
+            "max_bars": max_hold,
+        }
+
+    return entries
+
+
+CONFIGS = [
+    dict(wick_ratio=0.60, support_win=20, sl_mult=0.2, rr=2.0, max_hold=10),
+    dict(wick_ratio=0.65, support_win=10, sl_mult=0.3, rr=1.5, max_hold=8),
+    dict(wick_ratio=0.55, support_win=20, sl_mult=0.3, rr=2.0, max_hold=15),
+    dict(wick_ratio=0.60, support_win=30, sl_mult=0.2, rr=2.0, max_hold=10),
+]
+
+best_rep = None
+best_score = -999
+
+for cfg_idx, cfg in enumerate(CONFIGS):
+    name = f"MIC07-cfg{cfg_idx+1}"
+    rep = al.study_signals(
+        name,
+        lambda df, c=cfg: make_entries(df, **c),
+        tfs=("1d",),
+    )
+    score = rep["verdict"].get("best_holdout_sharpe", -9)
+    print(f"Config {cfg_idx+1}: {cfg} -> score={score:.3f}, grade={rep['verdict']['grade']}")
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_cfg = cfg
+
+print("\n=== BEST CONFIG ===", best_cfg)
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))