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
parent bf84bc91e2
commit 5ac4e16af8
111 changed files with 16924 additions and 0 deletions
@@ -0,0 +1,88 @@
+"""TRD14 — Turtle Midline Trend
+
+HYPOTHESIS: Long when close > Donchian(20) midline (mid-channel support),
+exit when close crosses below Donchian(10) opposite midline.
+Trend-rider using midline as entry/exit instead of channel extremes.
+
+LOGIC:
+- Donchian(N) midline = (N-bar high + N-bar low) / 2
+- Entry (go long): close > Donchian(20) midline
+- Exit (flat): close < Donchian(10) midline
+- Long-flat only (crypto-native: no shorting costs, better hold-out)
+- Vol-targeted to 20% annualized (TP01-style for fair comparison)
+
+SMALL GRID: vary (slow_win, fast_win) combinations
+  - (20, 10) — canonical Turtle
+  - (40, 20) — longer memory
+  - (60, 20) — even longer
+  <= 4 param sets, 2 TFs -> 4x2x2 = 16 total but we limit to 2 TFs x 4 params = 8 evaluations
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def make_target(slow_win: int = 20, fast_win: int = 10):
+    """Return a target_fn for the given (slow_win, fast_win) parameters."""
+    def target_fn(df):
+        c = df["close"].values.astype(float)
+        n = len(c)
+
+        # Donchian midlines: causal (uses data up to bar i-1 due to shift in donchian())
+        hi_slow, lo_slow = al.donchian(df, slow_win)
+        hi_fast, lo_fast = al.donchian(df, fast_win)
+        mid_slow = (hi_slow + lo_slow) / 2.0  # entry signal
+        mid_fast = (hi_fast + lo_fast) / 2.0  # exit signal
+
+        # Signal logic: long when c > mid_slow, exit when c < mid_fast
+        # Both mid_slow and mid_fast use shifted donchian -> causal at close[i]
+        pos = np.full(n, np.nan)
+        for i in range(n):
+            if np.isnan(mid_slow[i]) or np.isnan(mid_fast[i]):
+                pos[i] = 0.0
+                continue
+            if c[i] > mid_slow[i]:
+                pos[i] = 1.0   # enter / stay long
+            elif c[i] < mid_fast[i]:
+                pos[i] = 0.0   # exit / stay flat
+
+        # Forward-fill: if neither entry nor exit triggered, hold previous position
+        direction = (
+            __import__("pandas").Series(pos)
+            .ffill()
+            .fillna(0.0)
+            .values
+        )
+        # Vol-target: scale to 20% annualized, cap leverage at 2x
+        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+    return target_fn
+
+
+# Grid: (slow_win, fast_win) combinations
+GRID = [
+    (20, 10),   # Canonical Turtle
+    (40, 20),   # Longer memory
+    (60, 20),   # Even longer
+    (60, 30),   # Long slow, medium fast
+]
+
+TFS = ("1d", "12h")
+
+best_rep = None
+best_min_hold = -999.0
+
+for slow_win, fast_win in GRID:
+    name = f"TRD14(D{slow_win},D{fast_win})"
+    fn = make_target(slow_win, fast_win)
+    rep = al.study_weights(name, fn, tfs=TFS)
+    # Track best by min_asset_holdout_sharpe across all TFs
+    for cell in rep["cells"]:
+        mh = cell.get("min_asset_holdout_sharpe", -999.0)
+        if mh > best_min_hold:
+            best_min_hold = mh
+            best_rep = rep
+
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))