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>
105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
"""RSK04 — Momentum-of-Momentum Sizing
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HYPOTHESIS: Size the TSMOM (long-flat) position by the STABILITY/AGREEMENT of
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multi-horizon momentum signals. When all horizons agree (strong consensus), take
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a larger position. When signals disagree, reduce exposure.
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MECHANISM:
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- Compute TSMOM signals for 3 horizons: 1M, 3M, 6M (same as TP01 canonical)
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- Direction = go long only if net signal > 0 (majority bullish), else flat
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- SIZE = fraction of horizons that agree with the majority direction
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e.g. all 3 agree -> size=1.0, 2/3 agree -> size=0.667, 1/3 -> flat
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- Apply vol-targeting on top of the sized position
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INTERNAL GRID (<=4 configs x 2 assets x 2 TFs = <=16 backtests):
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A: horizons=(1M,3M,6M), size by fraction-agreement
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B: horizons=(1M,3M,6M,12M), size by fraction-agreement (4 horizons)
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Two TFs: 1d, 12h -> 2 configs x 2 tfs x 2 assets = 8 backtests total
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CAUSAL: all signals use close[i] for the past horizon -> no leakage.
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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 make_target(horizons_months, tf):
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"""Return a target_fn(df) that implements momentum-of-momentum sizing."""
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def target_fn(df):
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c = df["close"].values.astype(float)
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n = len(c)
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bpd = al.bars_per_day(df)
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# Compute per-horizon signals: +1 (bullish) or 0 (bearish/flat)
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# Signal at bar i: sign of return over last `h` bars
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signals = []
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for months in horizons_months:
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h = int(round(months * 30.44 * bpd))
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h = max(h, 2)
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sig = np.zeros(n)
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# causal: sig[i] uses close[i] vs close[i-h]
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sig[h:] = np.where(c[h:] / c[:n-h] > 1.0, 1.0, 0.0)
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# NaN guard: first h bars stay 0
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signals.append(sig)
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signals = np.stack(signals, axis=1) # shape (n, num_horizons)
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num_horizons = len(horizons_months)
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# Net bullish count at each bar
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bullish_count = signals.sum(axis=1) # in [0, num_horizons]
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bearish_count = num_horizons - bullish_count
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# Direction: go long only if strict majority bullish
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direction = np.where(bullish_count > num_horizons / 2, 1.0, 0.0)
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# Size = fraction of horizons agreeing with the direction taken
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# If long: fraction_agree = bullish_count / num_horizons
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# If flat (direction=0): size = 0
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fraction_agree = np.where(
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direction > 0,
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bullish_count / num_horizons,
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0.0
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)
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# Apply vol-targeting with the agreement-sized direction
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# We pass the sized direction (0..1) into vol_target as if it were direction
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target = al.vol_target(fraction_agree, df, target_vol=0.20,
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vol_win_days=30, leverage_cap=2.0)
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return target
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return target_fn
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# Config A: 3 horizons (1M, 3M, 6M)
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horizons_A = [1, 3, 6]
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# Config B: 4 horizons (1M, 3M, 6M, 12M)
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horizons_B = [1, 3, 6, 12]
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# Run on 1d and 12h timeframes
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rep_A = al.study_weights(
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"RSK04-A(1M3M6M)",
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make_target(horizons_A, "1d"),
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tfs=("1d", "12h")
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)
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rep_B = al.study_weights(
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"RSK04-B(1M3M6M12M)",
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make_target(horizons_B, "1d"),
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tfs=("1d", "12h")
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)
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print("=== RSK04: Momentum-of-Momentum Sizing ===\n")
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print(al.fmt(rep_A))
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print()
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print(al.fmt(rep_B))
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print()
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print("JSON:", al.as_json(rep_A))
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print("JSON:", al.as_json(rep_B))
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# Determine best config by holdout sharpe
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best_rep = max([rep_A, rep_B],
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key=lambda r: r["verdict"].get("best_holdout_sharpe", -99))
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print("\n=== BEST CONFIG ===")
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print(al.fmt(best_rep))
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print("JSON_BEST:", al.as_json(best_rep))
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