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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+"""STA06 — Kalman Local Level+Slope Trend
+Hypothesis: Run a causal Kalman filter on log price with local level + slope states.
+The slope state gives a smooth, causal estimate of local trend direction.
+Long when filtered slope > 0, flat otherwise (long-only, crypto-style).
+Vol-targeted position like TP01.
+
+Grid: 2 observation-noise / process-noise ratio settings × 2 TFs = 4 total cells.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def kalman_slope(log_price: np.ndarray, q_level: float = 1e-4, q_slope: float = 1e-6,
+                 r_obs: float = 1e-2) -> np.ndarray:
+    """
+    Causal Kalman local-level + slope filter on log_price.
+
+    State: x = [level, slope]
+    Transition: level_{t+1} = level_t + slope_t
+                slope_{t+1} = slope_t
+    Observation: y_t = level_t + noise
+
+    Parameters:
+        q_level: process noise variance for the level
+        q_slope: process noise variance for the slope
+        r_obs:   observation noise variance
+
+    Returns slope array (same length as log_price), causal at each i.
+    """
+    n = len(log_price)
+    slope_out = np.zeros(n)
+
+    # State transition matrix F
+    F = np.array([[1.0, 1.0],
+                  [0.0, 1.0]])
+
+    # Process noise covariance Q
+    Q = np.array([[q_level, 0.0],
+                  [0.0, q_slope]])
+
+    # Observation matrix H (we observe only the level)
+    H = np.array([[1.0, 0.0]])
+
+    # Observation noise variance R
+    R = np.array([[r_obs]])
+
+    # Initialize state and covariance
+    x = np.array([[log_price[0]], [0.0]])   # [level, slope]
+    P = np.eye(2) * 1.0
+
+    for i in range(n):
+        # --- Predict ---
+        x_pred = F @ x
+        P_pred = F @ P @ F.T + Q
+
+        # --- Update with observation y[i] ---
+        y = np.array([[log_price[i]]])
+        S = H @ P_pred @ H.T + R
+        K = P_pred @ H.T @ np.linalg.inv(S)
+        x = x_pred + K @ (y - H @ x_pred)
+        P = (np.eye(2) - K @ H) @ P_pred
+
+        # Record slope (state[1]) at this bar — causal (uses data up to i)
+        slope_out[i] = x[1, 0]
+
+    return slope_out
+
+
+def make_target(q_slope: float):
+    """Factory: return a target_fn for a given Kalman noise configuration."""
+    def target_fn(df):
+        c = df["close"].values.astype(float)
+        lp = np.log(c)
+
+        # Kalman filter slope — fully causal recursive
+        # q_level scales with q_slope for coherence
+        q_level = q_slope * 100.0   # level noise 100x slope noise
+        r_obs = 1e-2                # observation noise fixed
+
+        slope = kalman_slope(lp, q_level=q_level, q_slope=q_slope, r_obs=r_obs)
+
+        # Direction: long when slope > 0, flat otherwise
+        direction = np.where(slope > 0, 1.0, 0.0)
+
+        # Vol-target the position (TP01 style)
+        pos = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+        return pos
+
+    return target_fn
+
+
+if __name__ == "__main__":
+    # Small grid: 2 q_slope values (controls filter responsiveness)
+    # Low q_slope = smoother/slower filter; high q_slope = more responsive
+    configs = [
+        ("q_slope=1e-6", 1e-6),   # slow, smooth
+        ("q_slope=1e-5", 1e-5),   # medium
+    ]
+
+    results = []
+    for label, q_slope in configs:
+        print(f"\n--- Running STA06 config: {label} ---")
+        rep = al.study_weights(
+            f"STA06-Kalman-{label}",
+            make_target(q_slope),
+            tfs=("1d", "12h"),
+        )
+        print(al.fmt(rep))
+        print("JSON:", al.as_json(rep))
+        results.append((label, q_slope, rep))
+
+    # Pick best config by min_asset_holdout_sharpe across all cells
+    best_label, best_q, best_rep = max(
+        results,
+        key=lambda x: x[2]["verdict"].get("best_holdout_sharpe", -99)
+    )
+    print(f"\n=== BEST CONFIG: {best_label} ===")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))