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>
2026-06-20 19:50:39 +00:00
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+"""TRD09 — Aroon Trend Strategy
+Aroon(period): long when AroonUp > AroonDown AND AroonUp > 70.
+Uses vol-targeting (TP01-style) for position sizing.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def aroon(df, period: int = 25):
+    """Compute Aroon Up and Aroon Down (causal).
+    AroonUp[i]   = 100 * (bars since highest high in [i-period..i]) / period
+    AroonDown[i] = 100 * (bars since lowest  low  in [i-period..i]) / period
+    Both in [0, 100].
+    """
+    high = df["high"].values.astype(float)
+    low  = df["low"].values.astype(float)
+    n = len(high)
+    aroon_up   = np.full(n, np.nan)
+    aroon_down = np.full(n, np.nan)
+
+    # Vectorized using pandas rolling argmax/argmin
+    import pandas as pd
+    h_series = pd.Series(high)
+    l_series = pd.Series(low)
+
+    for i in range(period, n):
+        window_h = high[i - period: i + 1]
+        window_l = low[i - period: i + 1]
+        # position of max/min within window (0=oldest, period=current)
+        idx_max = np.argmax(window_h)  # periods ago = period - idx_max
+        idx_min = np.argmin(window_l)
+        aroon_up[i]   = 100.0 * idx_max / period
+        aroon_down[i] = 100.0 * idx_min / period
+
+    return aroon_up, aroon_down
+
+
+def make_target(period: int = 25, threshold: float = 70.0, use_vol_target: bool = True):
+    """Return a target function for al.study_weights."""
+    def target_fn(df):
+        up, dn = aroon(df, period)
+        # Long signal: AroonUp > AroonDown AND AroonUp > threshold
+        direction = np.where(
+            (up > dn) & (up > threshold),
+            1.0,
+            0.0  # flat otherwise (long-flat, no short)
+        )
+        direction[~np.isfinite(up)] = 0.0
+
+        if use_vol_target:
+            return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+        else:
+            return direction
+
+    return target_fn
+
+
+if __name__ == "__main__":
+    # Small grid: period x threshold (4 combos max)
+    configs = [
+        {"period": 25, "threshold": 70.0},
+        {"period": 14, "threshold": 70.0},
+        {"period": 25, "threshold": 60.0},
+        {"period": 40, "threshold": 70.0},
+    ]
+
+    best_rep = None
+    best_score = -999.0
+
+    for cfg in configs:
+        name = f"TRD09_p{cfg['period']}_t{int(cfg['threshold'])}"
+        print(f"\n=== Running {name} ===")
+        fn = make_target(period=cfg["period"], threshold=cfg["threshold"])
+        rep = al.study_weights(name, fn, tfs=("1d",))
+        print(al.fmt(rep))
+
+        # Score = min of BTC/ETH hold-out sharpe
+        cells = rep.get("cells", [])
+        if cells:
+            cell = cells[0]  # 1d
+            pa = cell.get("per_asset", {})
+            btc_ho = pa.get("BTC", {}).get("holdout", {}).get("sharpe", -999)
+            eth_ho = pa.get("ETH", {}).get("holdout", {}).get("sharpe", -999)
+            score = min(btc_ho, eth_ho)
+            if score > best_score:
+                best_score = score
+                best_rep = rep
+                best_cfg = cfg
+
+    print("\n\n=== BEST CONFIG ===")
+    print(f"Config: {best_cfg}")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))