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>
98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
"""CMB04 — Momentum + Low-Vol Filter
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HYPOTHESIS: TSMOM long-flat taken only when realized vol is below its rolling median
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(avoid high-vol whipsaw). Vol-target the rest.
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Grid: 2 vol-filter windows (30d vs 60d rolling median lookback) x 2 TFs (1d, 12h) = 4 cells total.
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Best config chosen by min(BTC,ETH) holdout Sharpe.
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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 altlib as al
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import numpy as np
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def cmb04_target(df, vol_filter_days: int = 30):
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"""
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TSMOM multi-horizon (1m/3m/6m) long-flat, gated by a low-vol filter:
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- Compute realized vol (30d) at each bar.
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- Compute rolling median of that vol over vol_filter_days.
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- Only take the TSMOM signal when realized_vol < rolling_median (low-vol regime).
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- In high-vol regime: go flat (0).
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- Vol-target the resulting direction.
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"""
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c = df["close"].values.astype(float)
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bpd = al.bars_per_day(df)
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bpy = bpd * 365.25
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# --- TSMOM multi-horizon direction (1m, 3m, 6m) ---
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horizons = (30 * bpd, 90 * bpd, 180 * bpd)
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direction = np.zeros(len(c))
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for h in horizons:
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h = int(h)
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sig = np.full(len(c), np.nan)
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if h < len(c):
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sig[h:] = np.sign(c[h:] / c[:-h] - 1.0)
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direction += np.nan_to_num(sig, nan=0.0)
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# Majority vote -> long or flat
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direction = np.clip(np.sign(direction), 0.0, 1.0) # long-flat only
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# --- Realized vol (30d causal) ---
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rv_win = max(2, 30 * bpd)
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r = al.simple_returns(c)
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rv = al.realized_vol(r, rv_win, bpy)
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# --- Rolling median of realized vol over vol_filter_days ---
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med_win = max(2, vol_filter_days * bpd)
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rv_median = (
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al._series_if_array(rv).rolling(med_win, min_periods=max(2, med_win // 2)).median().values
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if hasattr(al, "_series_if_array")
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else __import__("pandas").Series(rv).rolling(med_win, min_periods=max(2, med_win // 2)).median().values
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)
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# --- Gate: only enter when rv < median (low-vol regime) ---
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low_vol_gate = np.where(
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np.isfinite(rv) & np.isfinite(rv_median) & (rv < rv_median),
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1.0,
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0.0
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)
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gated_direction = direction * low_vol_gate
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# --- Vol-target the gated direction ---
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pos = al.vol_target(gated_direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return pos
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def make_target_fn(vol_filter_days: int):
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def fn(df):
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return cmb04_target(df, vol_filter_days=vol_filter_days)
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return fn
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if __name__ == "__main__":
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import pandas as pd
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best_rep = None
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best_hold = -9.0
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best_label = ""
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configs = [
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("CMB04-vf30", 30),
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("CMB04-vf60", 60),
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]
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for label, vfd in configs:
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fn = make_target_fn(vfd)
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rep = al.study_weights(label, fn, tfs=("1d", "12h"))
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v = rep["verdict"]
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h = v.get("best_holdout_sharpe", -9)
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print(al.fmt(rep))
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print(f" [grid] {label}: holdout={h:.3f}")
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if h > best_hold:
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best_hold = h
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best_rep = rep
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best_label = label
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print("\n=== BEST CONFIG ===", best_label)
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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