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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+"""BRK02 — Donchian55 + Chandelier ATR trailing stop.
+
+IDEA:
+  - Enter LONG when close[i] breaks above the 55-bar Donchian high (prior bars, causal).
+  - Exit (go flat) when close[i] falls below the Chandelier trailing stop:
+      chandelier_stop = highest_close(22 bars, ending at i) - 3 * ATR(22, ending at i).
+  - Position is 0 (flat) or 1 (long). No short. Vol-targeted to 20% with 2x cap.
+
+Implementation (weights style, continuous position):
+  - Donchian high computed on PRIOR bars (shift(1) already done by al.donchian).
+  - Chandelier stop computed causally on current+prior bars:
+      hc[i] = max(close[i-21..i])   -> rolling max of close, window=22
+      atr22[i] = ATR(22 bars) at i
+      stop[i] = hc[i] - 3 * atr22[i]
+  - State machine:
+      if flat and close[i] > donchian_high[i]: go long
+      if long and close[i] < stop[i]: go flat
+
+Params tried: (don_win=55, atr_win=22, atr_mult=3.0) — canonical
+              (don_win=40, atr_win=22, atr_mult=2.5)  — tighter
+Best picked by min_asset_holdout_sharpe.
+"""
+
+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 chandelier_signal(df: pd.DataFrame, don_win: int = 55,
+                      atr_win: int = 22, atr_mult: float = 3.0) -> np.ndarray:
+    """Return a direction array in {0, 1} (0=flat, 1=long) using Donchian+Chandelier.
+    Causal: decision at i uses only data <= close[i]."""
+    close = df["close"].values.astype(float)
+    n = len(close)
+
+    # Donchian upper channel: highest HIGH over prior don_win bars (shift(1) in al.donchian)
+    don_high, _ = al.donchian(df, don_win)   # don_high[i] = max(high[i-don_win..i-1])
+
+    # ATR(atr_win) — causal, uses bars up to and including i
+    atr22 = al.atr(df, atr_win)
+
+    # Highest CLOSE over trailing atr_win bars (inclusive of i) — causal
+    highest_close = pd.Series(close).rolling(atr_win, min_periods=atr_win).max().values
+
+    # Chandelier stop at i
+    chandelier_stop = highest_close - atr_mult * atr22
+
+    # State machine: flat=0, long=1
+    pos = np.zeros(n, dtype=float)
+    state = 0  # start flat
+    for i in range(n):
+        c = close[i]
+        dh = don_high[i]
+        cs = chandelier_stop[i]
+
+        if state == 0:
+            # Enter long if close breaks above prior Donchian high (valid only if dh is defined)
+            if np.isfinite(dh) and c > dh:
+                state = 1
+        else:  # state == 1
+            # Exit long if close drops below chandelier stop (and stop is defined)
+            if np.isfinite(cs) and c < cs:
+                state = 0
+
+        pos[i] = float(state)
+
+    return pos
+
+
+def make_target(don_win: int = 55, atr_win: int = 22, atr_mult: float = 3.0):
+    """Factory returning a vol-targeted weight function for a given param set."""
+    def target_fn(df: pd.DataFrame) -> np.ndarray:
+        direction = chandelier_signal(df, don_win=don_win, atr_win=atr_win, atr_mult=atr_mult)
+        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return target_fn
+
+
+# --- Small param grid (4 configurations, TFs: 1d and 12h -> 8 backtest cells total)
+CONFIGS = [
+    dict(don_win=55, atr_win=22, atr_mult=3.0, label="D55-A22-M3.0"),
+    dict(don_win=40, atr_win=22, atr_mult=2.5, label="D40-A22-M2.5"),
+    dict(don_win=55, atr_win=14, atr_mult=3.0, label="D55-A14-M3.0"),
+    dict(don_win=70, atr_win=22, atr_mult=3.5, label="D70-A22-M3.5"),
+]
+
+TFS = ("1d", "12h")
+
+best_rep = None
+best_score = -999.0
+
+for cfg in CONFIGS:
+    lbl = cfg["label"]
+    fn = make_target(don_win=cfg["don_win"], atr_win=cfg["atr_win"], atr_mult=cfg["atr_mult"])
+    rep = al.study_weights(f"BRK02[{lbl}]", fn, tfs=TFS)
+    score = rep["verdict"].get("best_holdout_sharpe", -9)
+    print(f"Config {lbl}: grade={rep['verdict']['grade']} score(hold)={score:.3f}")
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+
+# Rename best result to canonical BRK02
+best_rep["name"] = "BRK02"
+print()
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))