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>
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
"""MIC08 — OBV Trend
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Hypothesis: On-balance-volume trend: long when OBV above its EMA (volume confirms price).
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Long-flat. Continuous weights via al.study_weights.
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Grid: obv_ema_period in (20, 50) x tfs (1d, 12h) = 4 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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def compute_obv(df) -> np.ndarray:
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"""Compute On-Balance-Volume causally."""
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close = df["close"].values
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volume = df["volume"].values
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n = len(close)
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obv = np.zeros(n)
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for i in range(1, n):
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if close[i] > close[i - 1]:
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obv[i] = obv[i - 1] + volume[i]
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elif close[i] < close[i - 1]:
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obv[i] = obv[i - 1] - volume[i]
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else:
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obv[i] = obv[i - 1]
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return obv
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def make_target(ema_period: int):
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def target(df) -> np.ndarray:
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obv = compute_obv(df)
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obv_ema = al.ema(obv, ema_period)
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# Long when OBV > its EMA, flat otherwise
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signal = np.where(obv > obv_ema, 1.0, 0.0)
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# Use vol-targeting to size the position
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sized = al.vol_target(signal, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return sized
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return target
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# Grid search: 2 EMA periods x 2 timeframes = 4 total backtests
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results = []
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for ema_p in (20, 50):
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rep = al.study_weights(
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f"MIC08-OBV-EMA{ema_p}",
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make_target(ema_p),
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tfs=("1d", "12h"),
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)
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results.append((rep["verdict"].get("best_holdout_sharpe", -9), ema_p, rep))
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# Pick best by hold-out Sharpe
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results.sort(key=lambda x: x[0], reverse=True)
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best_holdout, best_ema, best_rep = results[0]
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print(f"\n=== Best config: EMA period={best_ema} ===")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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