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>
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
"""TRD12 — Triple-MA alignment (SMA10 > SMA50 > SMA200).
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Long only when all three SMAs are in full bullish alignment; flat otherwise.
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No look-ahead: SMA values at i use close[0..i], position held during bar i+1.
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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 triple_ma_weights(df, short=10, mid=50, long=200, use_vol_target=True):
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"""Return position array: +1 when SMA_short > SMA_mid > SMA_long, else 0."""
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c = df["close"].values
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s = al.sma(c, short)
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m = al.sma(c, mid)
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l = al.sma(c, long)
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# Bullish alignment: short > mid > long
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bullish = (s > m) & (m > l)
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# Direction: +1 or 0 (long-only)
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direction = np.where(bullish, 1.0, 0.0)
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# Replace NaN regions (first `long` bars) with 0
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direction = np.where(np.isnan(s) | np.isnan(m) | np.isnan(l), 0.0, direction)
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if use_vol_target:
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return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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else:
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return direction
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# Run study on 1d and 12h timeframes (Triple-MA needs long history, so >=12h)
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# We try two configurations: with and without vol-targeting
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# That's 2 configs x 2 TFs = 4 total backtests (within the <=6 limit)
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print("=" * 60)
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print("TRD12 — Triple-MA alignment (SMA10 > SMA50 > SMA200)")
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print("=" * 60)
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# Config 1: with vol-targeting
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rep_vt = al.study_weights(
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"TRD12-VT",
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lambda df: triple_ma_weights(df, use_vol_target=True),
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tfs=("1d", "12h"),
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)
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print("\n--- Vol-targeted ---")
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print(al.fmt(rep_vt))
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print("JSON:", al.as_json(rep_vt))
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# Config 2: raw (no vol-targeting, simple long/flat)
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rep_raw = al.study_weights(
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"TRD12-RAW",
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lambda df: triple_ma_weights(df, use_vol_target=False),
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tfs=("1d", "12h"),
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)
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print("\n--- Raw (no vol-target) ---")
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print(al.fmt(rep_raw))
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print("JSON:", al.as_json(rep_raw))
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