Adriano Dal Pastro
aad69f9790
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>
58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
"""Test del filone DVOL-DIREZIONALE (scripts/research/dvol_directional.py).
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Verifica le proprieta' che DEVONO valere per ogni segnale del progetto:
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* il percentile espandente del DVOL e' CAUSALE (rank su un prefisso == rank(full)[:cut]);
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* il leader (DVOL-fear long-flat) passa causality_ok di altlib (niente future-peeking);
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* a $600 (eval_weights_smallcap) l'haircut e' trascurabile (segnale eseguibile, low-turnover);
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* sign-falsification: la tesi (buy-the-fear) batte il suo flip (buy-the-calm) sull'era DVOL.
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"""
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import sys
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from pathlib import Path
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import numpy as np
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import pytest
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT))
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sys.path.insert(0, str(ROOT / "scripts" / "research" / "alt"))
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import altlib as al # noqa: E402
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from scripts.research import dvol_directional as dd # noqa: E402
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def test_expanding_rank_is_causal():
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"""rank calcolato su un prefisso deve coincidere con rank(full) ristretto al prefisso."""
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x = np.asarray(al.dvol(al.get("BTC", "1d"), "BTC"), float)
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full = dd._expanding_rank_arr(x)
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cut = int(len(x) * 0.7)
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pref = dd._expanding_rank_arr(x[:cut])
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a, b = full[:cut], pref
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m = np.isfinite(a) & np.isfinite(b)
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assert m.sum() > 100
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assert np.max(np.abs(a[m] - b[m])) < 1e-9
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def test_leader_causality_ok():
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"""Il leader (DVOL-fear q0.4 long-flat) non deve avere look-ahead (altlib causality_ok)."""
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fn = dd.make_dvol_level(0.4, "fear", True)
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co = al.causality_ok(fn, tf="1d")
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assert co["ok"], co
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assert co["max_tail_diff"] <= 1e-6
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@pytest.mark.parametrize("asset", ["BTC", "ETH"])
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def test_leader_executable_at_600(asset):
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"""A $600 il segnale e' a basso turnover: haircut Sharpe trascurabile, trade eseguiti reali."""
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fn = dd.make_dvol_level(0.4, "fear", True)
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df = al.get(asset, "1d")
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sc = al.eval_weights_smallcap(df, al._call_target(fn, df, asset), capital=600, min_order=5)
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assert sc["n_executed_trades"] > 10
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assert abs(sc["sharpe_haircut"]) < 0.10
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def test_sign_falsification():
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"""La tesi (buy-the-fear) deve battere il flip (buy-the-calm) sull'era DVOL."""
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thesis = dd.era_full_sharpe(dd.make_dvol_level(0.5, "fear", True))["sharpe"]
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flip = dd.era_full_sharpe(dd.make_dvol_level(0.5, "calm", True))["sharpe"]
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assert thesis > flip
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