research(intraday): de-bias del lead ERM (filone B) — falso positivo + gate selection-on-holdout

L'analisi di robustezza affonda lo "earns_slot=True" di ERM: era prodotto da
selezione-sull'hold-out + coda 2026 + multiple-testing non corretto.
  A) deflated-Sharpe FAIL: 0.00 (tutti 122 trial) / 0.16 (no-TOD) / 0.24 (solo-ERM) << 0.95
  B) selezione in-sample-only -> ALTRA cella (long-flat, corr->TP01 0.53) = NEUTRAL, no slot
  C) ensemble del plateau (no cherry-pick) -> ADDS ma robust_oos=False -> no slot
  D) uplift FULL solo +0.10, negativo 2021/2022; uplift HOLD +0.30 concentrato nel 2026
=> ERM SCARTATO come sleeve. Conferma ennesima del soffitto BTC/ETH-direzionale ~1.3.

Lezione CODIFICATA in altlib (LESSON 4, test in tests/test_harness_realism.py):
  - deflated_sharpe()       Bailey & Lopez de Prado, PASS >= 0.95
  - select_cell_insample()  scelta cella col solo Sharpe pre-HOLDOUT (no peeking)
  - study_family_honest()   gate combinato: earns_slot[cella in-sample] AND DSR>=0.95
Regola: una strategia direzionale grid-searched si giudica con study_family_honest,
non chiamando study_marginal sulla cella a max hold-out. Verificato end-to-end su ERM
(earns_slot_honest=False). Chiude il punto cieco gemello di CC01.

Diario aggiornato (verdetto downgrade), CLAUDE.md aggiornato. Test 119/119 verdi.
Nessun impatto live (branch separato).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/research/alt/altlib.py

@@ -29,12 +29,14 @@ from __future__ import annotations
 
 import inspect
 import json
+import math
 import sys
 from functools import lru_cache
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
+from scipy.stats import norm
 
 # --- make `from src...` work no matter where the agent's script lives -------
 _ROOT = Path(__file__).resolve().parents[3]
@@ -670,6 +672,94 @@ def causality_ok(target_fn, tf: str = "1h", assets=CERTIFIED,
                 reason=("length-mismatch on prefix" if bad else None))
 
 
+# ===========================================================================
+# SELECTION-ON-HOLDOUT GATE — codified 2026-06-29 from filone B (intraday ERM).
+#
+# LESSON 3: intraday_regime.py picked its "winner" cell by MAX hold-out Sharpe over a ~60-cell
+# grid, then ran study_marginal on THAT cell -> earns_slot=True. But the slot was an artifact of
+# SELECTING THE CELL ON THE HOLD-OUT: picking the cell IN-SAMPLE-ONLY (no peeking) lands on a
+# DIFFERENT, TP01-correlated cell that scores NEUTRAL, and the standalone Sharpe deflates to
+# DSR~0.0-0.24 over the trials searched. study_marginal alone can't catch this — it judges ONE
+# stream and never sees how the cell was chosen. The fix is two-fold and lives here:
+#   (1) choose the cell IN-SAMPLE-ONLY (or walk-forward) BEFORE scoring the marginal, and
+#   (2) DEFLATE the standalone Sharpe for the number of cells/families searched.
+# Twin of the CC01 ("implausible Sharpe -> hidden risk") and alt-sweep ("hold-out-fitting") blind
+# spots, in its "selection-on-holdout" form.
+# ===========================================================================
+def deflated_sharpe(sr_ann, all_sr_ann, daily_ret, dpy: float = 365.25):
+    """Deflated Sharpe Ratio (Bailey & Lopez de Prado): P(true Sharpe > the MAX Sharpe expected
+    under the null of N independent trials). Penalizes multiple-testing — a standalone Sharpe ~1
+    over a 100+ cell grid is routinely NOT significant once deflated. sr_ann = annualized Sharpe
+    of the CHOSEN config; all_sr_ann = the Sharpe of EVERY cell searched; daily_ret = the chosen
+    config's daily returns (for skew/kurt/T). Returns (DSR, expected_null_max_sharpe_ann);
+    PASS if DSR >= 0.95."""
+    r = np.asarray(pd.Series(daily_ret).dropna().values, float)
+    T = len(r)
+    if T < 30 or np.std(r) == 0:
+        return float("nan"), float("nan")
+    sr = sr_ann / math.sqrt(dpy)
+    trials = np.asarray([s / math.sqrt(dpy) for s in all_sr_ann if np.isfinite(s)], float)
+    N = max(len(trials), 2)
+    var_tr = float(np.var(trials, ddof=1)) if N > 1 else 0.0
+    emc = 0.5772156649
+    z1 = norm.ppf(1 - 1.0 / N)
+    z2 = norm.ppf(1 - 1.0 / (N * math.e))
+    sr0 = math.sqrt(var_tr) * ((1 - emc) * z1 + emc * z2)
+    sk = float(pd.Series(r).skew())
+    ku = float(pd.Series(r).kurt()) + 3.0                      # pandas kurt = excess
+    den = math.sqrt(max(1e-9, 1 - sk * sr + (ku - 1) / 4.0 * sr ** 2))
+    dsr = float(norm.cdf((sr - sr0) * math.sqrt(T - 1) / den))
+    return dsr, sr0 * math.sqrt(dpy)
+
+
+def select_cell_insample(factory, grid, tfs, fee_side: float = FEE_SIDE) -> dict:
+    """Pick a config WITHOUT looking at the hold-out: rank grid cells by IN-SAMPLE (pre-HOLDOUT)
+    standalone Sharpe of the 50/50 candidate. `factory(tf=..., **params)` -> target_fn; each grid
+    item is a dict of factory kwargs (besides tf). Returns the in-sample-best cell, all rows
+    (sorted), and EVERY cell's FULL Sharpe (for deflated_sharpe). This is the honest replacement
+    for picking the max-hold-out cell."""
+    rows = []
+    for tf in tfs:
+        for params in grid:
+            try:
+                daily = candidate_daily(factory(tf=tf, **params), tf=tf, fee_side=fee_side)
+            except Exception:
+                continue
+            ins = daily[daily.index < HOLDOUT]
+            is_sh = _sh(ins) if len(ins) > 60 else float("nan")
+            rows.append(dict(tf=tf, params=params, insample_sharpe=round(is_sh, 3),
+                             full_sharpe=round(_sh(daily), 3)))
+    valid = [r for r in rows if np.isfinite(r["insample_sharpe"])]
+    chosen = max(valid, key=lambda r: r["insample_sharpe"]) if valid else None
+    return dict(chosen=chosen,
+                rows=sorted(valid, key=lambda r: r["insample_sharpe"], reverse=True),
+                all_full_sharpe=[r["full_sharpe"] for r in rows])
+
+
+def study_family_honest(name: str, factory, grid, tfs, fee_side: float = FEE_SIDE,
+                        dsr_min: float = 0.95) -> dict:
+    """HARDENED family gate. A grid-searched directional candidate earns a slot ONLY if, picking
+    the cell IN-SAMPLE-ONLY (no hold-out peeking), it STILL earns_slot via study_marginal AND its
+    standalone Sharpe survives deflation for the WHOLE grid searched. Use this INSTEAD of
+    cherry-picking the max-hold cell and calling study_marginal on it."""
+    sel = select_cell_insample(factory, grid, tfs, fee_side=fee_side)
+    ch = sel["chosen"]
+    if ch is None:
+        return dict(name=name, chosen=None, earns_slot_honest=False,
+                    reason="no valid in-sample cell")
+    fn = factory(tf=ch["tf"], **ch["params"])
+    sm = study_marginal(f"{name} ISpick {ch['params']}", fn, tf=ch["tf"], fee_side=fee_side)
+    daily = candidate_daily(fn, tf=ch["tf"], fee_side=fee_side)
+    dsr, sr0 = deflated_sharpe(_sh(daily), sel["all_full_sharpe"], daily)
+    dsr_pass = bool(np.isfinite(dsr) and dsr >= dsr_min)
+    return dict(name=name, n_cells=len(sel["all_full_sharpe"]), chosen=ch, rows=sel["rows"],
+                marginal=sm, earns_slot_marginal=bool(sm["earns_slot"]),
+                deflated_sharpe=round(dsr, 3) if np.isfinite(dsr) else None,
+                expected_null_max=round(sr0, 3) if np.isfinite(sr0) else None,
+                dsr_pass=dsr_pass,
+                earns_slot_honest=bool(sm["earns_slot"] and dsr_pass))
+
+
 # ===========================================================================
 # DRIVERS — run a hypothesis across both assets, several TFs, with a fee sweep.
 # ===========================================================================