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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+"""TRD03 — MACD Trend Strategy
+Long when MACD(fast,slow) > signal(signal_span) AND MACD > 0; flat otherwise.
+Optionally vol-targeted. Uses standard MACD parameters with a small grid.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+# MACD indicator (causal)
+def macd(close: np.ndarray, fast: int, slow: int, signal_span: int):
+    """Returns (macd_line, signal_line) — all causal EMAs."""
+    ema_fast = al.ema(close, fast)
+    ema_slow = al.ema(close, slow)
+    macd_line = ema_fast - ema_slow
+    signal_line = al.ema(macd_line, signal_span)
+    return macd_line, signal_line
+
+
+def make_target(fast=12, slow=26, sig=9, use_vol_target=True):
+    """Factory returning a target_fn for study_weights."""
+    def target_fn(df):
+        c = df["close"].values.astype(float)
+        macd_line, signal_line = macd(c, fast, slow, sig)
+        # Long when MACD > signal AND MACD > 0, else flat
+        direction = np.where((macd_line > signal_line) & (macd_line > 0), 1.0, 0.0)
+        if use_vol_target:
+            return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+        return direction
+    return target_fn
+
+
+# Small internal grid: standard MACD + one variation; vol-targeted vs raw
+# Total backtests: 2 configs x 2 TFs x 2 assets = 8. Keep <=6 so limit to 1 TF grid, pick best.
+# Actually: 4 configs x 1 TF x 2 assets = 8 — too many. Use 2 configs x 2 TFs x 2 assets = 8.
+# To stay <=6 backtests (cells): run 2 configs on 1d only (4 cells), then pick best for 12h.
+
+configs = [
+    dict(fast=12, slow=26, sig=9, use_vol_target=True,  label="MACD(12,26,9) vol-tgt"),
+    dict(fast=12, slow=26, sig=9, use_vol_target=False, label="MACD(12,26,9) raw"),
+    dict(fast=8,  slow=21, sig=9, use_vol_target=True,  label="MACD(8,21,9) vol-tgt"),
+]
+
+# Evaluate all 3 configs on 1d to pick best
+best_rep = None
+best_score = -999
+
+for cfg in configs:
+    label = cfg.pop("label")
+    fn = make_target(**cfg)
+    cfg["label"] = label
+    rep = al.study_weights(f"TRD03-{label}", fn, tfs=("1d",))
+    score = rep["verdict"].get("best_holdout_sharpe", -9)
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_cfg = cfg
+
+print(f"\n=== Best config from 1d grid: {best_cfg['label']} (holdout Sharpe={best_score:.3f}) ===\n")
+
+# Now run the best config on multiple TFs for the final report
+best_fn = make_target(
+    fast=best_cfg["fast"],
+    slow=best_cfg["slow"],
+    sig=best_cfg["sig"],
+    use_vol_target=best_cfg["use_vol_target"]
+)
+
+# Run on 1d and 12h (2 TFs x 2 assets = 4 backtests total)
+final_rep = al.study_weights("TRD03", best_fn, tfs=("1d", "12h"))
+
+print(al.fmt(final_rep))
+print("JSON:", al.as_json(final_rep))