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>
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
"""BRK07 — N-day-high momentum (long-flat)
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IDEA: Long-flat: position 1 while close is within X% of its rolling 100-bar max, else 0.
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Trend-persistence proxy. Optionally vol-targeted.
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Grid: threshold X% in {2%, 5%} x vol_target in {False, True} -> 4 configs, 2 TFs = 8 total backtests.
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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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LOOKBACK = 100 # fixed as per hypothesis
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def make_target(df, threshold_pct: float = 5.0, use_vol_target: bool = True) -> np.ndarray:
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"""Long (1) when close is within threshold_pct% of its rolling 100-bar max, else 0."""
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c = df["close"].values.astype(float)
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n = len(c)
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# Rolling max of close over last LOOKBACK bars (causal: includes close[i])
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roll_max = (
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__import__("pandas").Series(c)
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.rolling(LOOKBACK, min_periods=LOOKBACK)
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.max()
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.values
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)
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# Position: 1 if close >= roll_max * (1 - threshold_pct/100), else 0
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threshold = threshold_pct / 100.0
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direction = np.where(
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(roll_max > 0) & np.isfinite(roll_max) & (c >= roll_max * (1.0 - threshold)),
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1.0,
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0.0
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)
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# Before we have enough bars, stay flat
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direction[:LOOKBACK - 1] = 0.0
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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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configs = [
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{"threshold_pct": 2.0, "use_vol_target": False, "label": "thr2pct_flat"},
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{"threshold_pct": 5.0, "use_vol_target": False, "label": "thr5pct_flat"},
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{"threshold_pct": 2.0, "use_vol_target": True, "label": "thr2pct_vt"},
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{"threshold_pct": 5.0, "use_vol_target": True, "label": "thr5pct_vt"},
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]
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best_rep = None
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best_score = -9999.0
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for cfg in configs:
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label = cfg["label"]
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threshold_pct = cfg["threshold_pct"]
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use_vol_target = cfg["use_vol_target"]
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print(f"\n=== BRK07 config: {label} (threshold={threshold_pct}%, vol_target={use_vol_target}) ===")
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fn = lambda df, t=threshold_pct, v=use_vol_target: make_target(df, t, v)
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rep = al.study_weights(
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f"BRK07-{label}",
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fn,
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tfs=("1d", "12h"),
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)
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print(al.fmt(rep))
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print("JSON:", al.as_json(rep))
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# Score = min holdout sharpe across both assets in best TF
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score = rep["verdict"].get("best_holdout_sharpe", -9999.0) or -9999.0
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if score > best_score:
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best_score = score
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best_rep = rep
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best_cfg = cfg
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print("\n\n========== BEST CONFIG ==========")
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print(f"Config: {best_cfg['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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