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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+"""RSK02 — TSMOM long-flat with fast kill-switch on sharp short-horizon drawdown.
+
+IDEA:
+  Base signal = TSMOM (multi-horizon momentum: 1m, 3m, 6m) long-flat, vol-targeted (TP01-style).
+  Kill-switch: if the position is long AND price has dropped >= `dd_thresh` (e.g. -10%) in the
+  last `dd_bars` bars, go flat immediately (hold 0) until momentum re-triggers.
+
+  The kill-switch aims to avoid the worst tail events that TSMOM rides through (sharp crashes).
+  It should not improve Sharpe much but should cut max drawdown meaningfully.
+
+Small grid: 2 param sets × 2 TFs = 4 total backtests.
+  Config A: dd_thresh=-0.10, dd_bars=5  (10% in 5 bars)
+  Config B: dd_thresh=-0.08, dd_bars=3  (8% in 3 bars — tighter)
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def tsmom_direction(df) -> np.ndarray:
+    """Multi-horizon TSMOM: long if majority of 1m/3m/6m momentum is positive, else flat.
+    Causal: uses close[i] returns through i."""
+    c = df["close"].values.astype(float)
+    bpd = al.bars_per_day(df)
+
+    horizons_days = [21, 63, 126]  # ~1m, 3m, 6m
+    signals = []
+    for h in horizons_days:
+        win = max(2, int(h * bpd))
+        # Return over last `win` bars ending at i (causal)
+        ret = np.full(len(c), np.nan)
+        ret[win:] = c[win:] / c[:-win] - 1.0
+        signals.append(np.sign(ret))
+
+    # Vote: positive direction if at least 2 of 3 horizons are positive
+    votes = np.nansum(np.stack(signals, axis=0), axis=0)
+    direction = np.where(votes > 0, 1.0, 0.0)  # long-flat only
+    # Need all 3 to be non-nan (warmup)
+    nan_mask = np.any(np.isnan(np.stack(signals, axis=0)), axis=0)
+    direction[nan_mask] = 0.0
+    return direction
+
+
+def rolling_drawdown(c: np.ndarray, win: int) -> np.ndarray:
+    """Rolling drawdown from the high of the last `win` bars (including current bar i).
+    Value at i = (c[i] - max(c[i-win+1:i+1])) / max(...), causal.
+    """
+    c = c.astype(float)
+    n = len(c)
+    dd = np.zeros(n)
+    # use pandas rolling max (includes current bar)
+    import pandas as pd
+    rolling_max = pd.Series(c).rolling(win, min_periods=1).max().values
+    dd = c / rolling_max - 1.0
+    return dd
+
+
+def make_target(dd_thresh: float, dd_bars: int):
+    """Returns a target_fn(df) -> position array."""
+    def target_fn(df):
+        c = df["close"].values.astype(float)
+
+        # 1. Base TSMOM direction (long or flat)
+        direction = tsmom_direction(df)
+
+        # 2. Kill-switch: compute rolling drawdown over dd_bars bars
+        rd = rolling_drawdown(c, dd_bars)
+
+        # 3. Kill: if drawdown within last dd_bars is below threshold, go flat
+        # We check the minimum drawdown in the last dd_bars window (most severe recent drop)
+        import pandas as pd
+        # min of rd over last dd_bars: how far price fell from any peak in window
+        # Using rolling min of dd to capture worst recent drawdown
+        recent_worst_dd = pd.Series(rd).rolling(dd_bars, min_periods=1).min().values
+        kill = recent_worst_dd <= dd_thresh  # True = kill signal active
+
+        # Apply kill: override direction to 0 when kill is active
+        direction_with_kill = np.where(kill, 0.0, direction)
+
+        # 4. Vol-target the final direction
+        tgt = al.vol_target(direction_with_kill, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+        return tgt
+
+    return target_fn
+
+
+if __name__ == "__main__":
+    configs = [
+        {"dd_thresh": -0.10, "dd_bars": 5,  "label": "kill10pct-5bar"},
+        {"dd_thresh": -0.08, "dd_bars": 3,  "label": "kill08pct-3bar"},
+    ]
+
+    best_rep = None
+    best_holdout = -999.0
+
+    for cfg in configs:
+        name = f"RSK02-{cfg['label']}"
+        target_fn = make_target(cfg["dd_thresh"], cfg["dd_bars"])
+        rep = al.study_weights(
+            name,
+            target_fn,
+            tfs=("1d", "12h"),
+        )
+        print(al.fmt(rep))
+        print("JSON:", al.as_json(rep))
+        print()
+
+        # Track best by holdout sharpe (min across assets)
+        ho = rep["verdict"].get("best_holdout_sharpe", -999.0)
+        if ho is not None and ho > best_holdout:
+            best_holdout = ho
+            best_rep = rep
+
+    print("=== BEST CONFIG ===")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))