research(crypto): 4 filoni 2026-06-29 — ERM lead sub-daily (forward), 3 scartati/deboli

Ricerca onesta su BTC/ETH + universo HL, branch separato (nessun impatto live).
Harness condiviso altlib (causale, fee 0.10% RT, marginal vs TP01, day-boundary,
haircut $600). Test 19/19 verdi.

- A DVOL direzionale  -> LEAD hedge/DD-dampener, NON sleeve (buy-the-fear; is_hedge).
- B Intraday ERM 8h   -> LEAD forte / forward-monitor: earns_slot=True, ADDS oltre
                         SKH01 (TP01+SKH+ERM 60/25/15 FULL 1.88/HOLD 1.46/DD 8.9%).
                         Caveat: plateau hold-out single-row, multiple-testing non
                         deflazionato, exec 8h. Controllo TOD = FAIL atteso.
- C Cross-sectional non-mom (low-vol HL) -> DEBOLE/forward-monitor (deflated-Sh 0.13,
                         storia 2.5a, non eseguibile $600) STAT-MODE.
- D Macro regime-gate -> RIDONDANTE col trend (corr->TP01 0.989), SCARTATO.

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

tests/test_dvol_directional.py

@@ -0,0 +1,57 @@
+"""Test del filone DVOL-DIREZIONALE (scripts/research/dvol_directional.py).
+
+Verifica le proprieta' che DEVONO valere per ogni segnale del progetto:
+  * il percentile espandente del DVOL e' CAUSALE (rank su un prefisso == rank(full)[:cut]);
+  * il leader (DVOL-fear long-flat) passa causality_ok di altlib (niente future-peeking);
+  * a $600 (eval_weights_smallcap) l'haircut e' trascurabile (segnale eseguibile, low-turnover);
+  * sign-falsification: la tesi (buy-the-fear) batte il suo flip (buy-the-calm) sull'era DVOL.
+"""
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+sys.path.insert(0, str(ROOT / "scripts" / "research" / "alt"))
+
+import altlib as al  # noqa: E402
+from scripts.research import dvol_directional as dd  # noqa: E402
+
+
+def test_expanding_rank_is_causal():
+    """rank calcolato su un prefisso deve coincidere con rank(full) ristretto al prefisso."""
+    x = np.asarray(al.dvol(al.get("BTC", "1d"), "BTC"), float)
+    full = dd._expanding_rank_arr(x)
+    cut = int(len(x) * 0.7)
+    pref = dd._expanding_rank_arr(x[:cut])
+    a, b = full[:cut], pref
+    m = np.isfinite(a) & np.isfinite(b)
+    assert m.sum() > 100
+    assert np.max(np.abs(a[m] - b[m])) < 1e-9
+
+
+def test_leader_causality_ok():
+    """Il leader (DVOL-fear q0.4 long-flat) non deve avere look-ahead (altlib causality_ok)."""
+    fn = dd.make_dvol_level(0.4, "fear", True)
+    co = al.causality_ok(fn, tf="1d")
+    assert co["ok"], co
+    assert co["max_tail_diff"] <= 1e-6
+
+
+@pytest.mark.parametrize("asset", ["BTC", "ETH"])
+def test_leader_executable_at_600(asset):
+    """A $600 il segnale e' a basso turnover: haircut Sharpe trascurabile, trade eseguiti reali."""
+    fn = dd.make_dvol_level(0.4, "fear", True)
+    df = al.get(asset, "1d")
+    sc = al.eval_weights_smallcap(df, al._call_target(fn, df, asset), capital=600, min_order=5)
+    assert sc["n_executed_trades"] > 10
+    assert abs(sc["sharpe_haircut"]) < 0.10
+
+
+def test_sign_falsification():
+    """La tesi (buy-the-fear) deve battere il flip (buy-the-calm) sull'era DVOL."""
+    thesis = dd.era_full_sharpe(dd.make_dvol_level(0.5, "fear", True))["sharpe"]
+    flip = dd.era_full_sharpe(dd.make_dvol_level(0.5, "calm", True))["sharpe"]
+    assert thesis > flip