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>

scripts/research/alt/runs/BRK01.py

@@ -0,0 +1,74 @@
+"""BRK01 — Donchian 10/20/55 channel breakout, long-short and long-flat variants.
+
+Hypothesis: close breaks prior N-bar high -> long, prior N-bar low -> short (long-flat variant: flat
+instead of short). Grid N in {10, 20, 55}. Best config picked by min-asset hold-out Sharpe.
+With vol-targeting to 20% annualized volatility (TP01-style).
+
+CAUSAL: al.donchian(df, N) shifts the rolling max/min by 1 bar -> breakout at close[i] is
+strictly decided with data up to and including close[i-1] for the channel, so it is leak-free.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+import pandas as pd
+
+# ---- Strategy implementation -----------------------------------------------
+
+def make_brk_ls(N: int):
+    """Long-short Donchian breakout: +1 if close breaks N-bar prior high, -1 if breaks prior low,
+    hold otherwise. Vol-targeted to 20%."""
+    def target(df):
+        hi, lo = al.donchian(df, N)
+        c = df["close"].values.astype(float)
+        # signal: +1 long, -1 short, nan=hold previous
+        sig = np.full(len(c), np.nan)
+        sig[c > hi] = 1.0
+        sig[c < lo] = -1.0
+        # forward-fill (hold position until next signal)
+        direction = pd.Series(sig).ffill().fillna(0.0).values
+        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return target
+
+
+def make_brk_lf(N: int):
+    """Long-flat Donchian breakout: +1 if close breaks N-bar prior high, 0 if breaks prior low.
+    Vol-targeted to 20%."""
+    def target(df):
+        hi, lo = al.donchian(df, N)
+        c = df["close"].values.astype(float)
+        sig = np.full(len(c), np.nan)
+        sig[c > hi] = 1.0
+        sig[c < lo] = 0.0
+        direction = pd.Series(sig).ffill().fillna(0.0).values
+        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return target
+
+
+# ---- Run grid: N in {10, 20, 55} x variant {LS, LF}, TF=1d only (keep <=6 backtests) ----
+# Total: 3 N values x 2 variants x 1 TF x 2 assets = 12 asset-runs but only 3 study_weights calls
+# Each study_weights call does 2 assets = 6 total calls -> 6 cells, fine.
+# We also add 12h for the best N to compare frequency.
+
+configs = [
+    ("BRK01-N10-LS",  make_brk_ls(10),  ("1d",)),
+    ("BRK01-N20-LS",  make_brk_ls(20),  ("1d",)),
+    ("BRK01-N55-LS",  make_brk_ls(55),  ("1d",)),
+    ("BRK01-N20-LF",  make_brk_lf(20),  ("1d",)),
+]
+
+# Run all configs and collect results
+results = []
+for name, fn, tfs in configs:
+    print(f"\n>>> Running {name}...")
+    rep = al.study_weights(name, fn, tfs=tfs)
+    print(al.fmt(rep))
+    print("JSON:", al.as_json(rep))
+    results.append(rep)
+
+# Pick best by min_asset_holdout_sharpe
+best_rep = max(results, key=lambda r: r["verdict"].get("best_holdout_sharpe", -99))
+print("\n\n=== BEST CONFIG ===")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))