Adriano Dal Pastro
5ac4e16af8
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>
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
"""Demo / validation of the MARGINAL-vs-TP01 scorer (2026-06-20).
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Shows the lesson of the 104-hypothesis sweep operationalized: strategies that scored
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an absolute PASS but are just TP01/TSMOM with an overlay collapse to REDUNDANT/NEUTRAL/
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DILUTES under marginal scoring, while the one genuine diversifier (STA05 long-short)
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earns ADDS. Run: uv run python scripts/research/alt/marginal_demo.py
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import numpy as np
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import pandas as pd
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import altlib as al
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from src.strategies.trend_portfolio import CANONICAL, TrendPortfolio
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def tsmom_dir(df):
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"""Long-flat multi-horizon TSMOM direction in {0,1} (the bare TP01 trend signal)."""
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c = df["close"].values.astype(float)
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bpd = al.bars_per_day(df)
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d = np.zeros(len(c))
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for h in (30 * bpd, 90 * bpd, 180 * bpd):
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s = np.full(len(c), np.nan)
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s[h:] = np.sign(c[h:] / c[:-h] - 1.0)
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d += np.nan_to_num(s)
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return np.clip(np.sign(d), 0, None)
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def tp01_target(df):
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return TrendPortfolio(**CANONICAL).target_series(df)
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FAST, SLOW = [5, 10, 20, 40], [40, 80, 120, 200]
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PAIRS = [(f, s) for f in FAST for s in SLOW if f < s]
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def sta05(df, long_only):
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c = df["close"].values.astype(float)
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v = np.zeros(len(c))
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for f, s in PAIRS:
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v += np.sign(al.ema(c, f) - al.ema(c, s))
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d = v / len(PAIRS)
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if long_only:
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d = np.clip(d, 0.0, 1.0)
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return al.vol_target(d, df, 0.20, 30, 2.0)
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def vol03(df, asset):
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"""DVOL-gated TSMOM (active only when DVOL below its expanding median)."""
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d = tsmom_dir(df)
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dv = pd.Series(al.dvol(df, asset))
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thr = dv.expanding(min_periods=30).quantile(0.5)
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gate = dv.isna() | thr.isna() | (dv < thr)
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d = np.where(gate.values, d, 0.0)
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return al.vol_target(d, df, 0.20, 30, 2.0)
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def cmb04(df):
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"""Momentum + low-vol filter (TSMOM taken only when realized vol below expanding median)."""
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d = tsmom_dir(df)
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bpd = al.bars_per_day(df)
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rv = al.realized_vol(al.simple_returns(df["close"].values.astype(float)), 30 * bpd, bpd * 365.25)
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med = pd.Series(rv).expanding(min_periods=60).median().values
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d = np.where((rv < med) | np.isnan(med), d, 0.0)
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return al.vol_target(d, df, 0.20, 30, 2.0)
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CANDIDATES = [
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("TP01-itself (sanity)", tp01_target),
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("STA05 long-short (the lead)", lambda df: sta05(df, False)),
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("STA05 long-only", lambda df: sta05(df, True)),
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("VOL03 DVOL-gated TSMOM (overlay)", vol03),
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("CMB04 momentum+low-vol (overlay)", cmb04),
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]
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print("=" * 78)
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print("MARGINAL SCORING vs TP01 baseline — absolute Sharpe is NOT enough for a slot")
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print("=" * 78)
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rows = []
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for name, fn in CANDIDATES:
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rep = al.study_marginal(name, fn, tf="1d")
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print()
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print(al.fmt_marginal(rep))
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rows.append((name, rep["abs_grade"], rep["marginal_verdict"], rep["earns_slot"]))
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print("\n" + "=" * 78)
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print(f"{'candidate':<36s} {'absolute':>9s} {'marginal':>10s} {'earns_slot':>11s}")
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for n, ag, mv, es in rows:
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print(f"{n:<36s} {ag:>9s} {mv:>10s} {str(es):>11s}")
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# sanity asserts: baseline reproduces, and an overlay-on-TSMOM does NOT earn a slot
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sanity = al.marginal_vs_tp01(al.candidate_daily(tp01_target))
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assert sanity["corr_full"] > 0.95, f"TP01-vs-itself corr should be ~1, got {sanity['corr_full']}"
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assert abs(sanity["blends"]["w25"]["uplift_full"]) < 0.05, "TP01-vs-itself uplift should be ~0"
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print("\nSANITY OK: TP01-vs-itself corr", sanity["corr_full"],
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"uplift_full", sanity["blends"]["w25"]["uplift_full"], "->", sanity["marginal_verdict"])
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