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>
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
"""RSK06 — Time-stop momentum
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HYPOTHESIS: Enter long on a breakout of the N-bar Donchian high, then EXIT
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after exactly M bars (hard time-stop), no trailing. Tests whether momentum
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has a fixed horizon with a clean carry/decay structure.
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Signal style: al.study_signals (discrete entry/exit, 1d only).
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Grid (<=4 param sets, total backtests = 4 * 2 assets = 8 <= 12 max):
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We test (breakout_window, hold_bars) pairs:
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A: (20, 10) — mid-term breakout, short hold
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B: (20, 20) — mid-term breakout, mid hold
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C: (40, 10) — longer breakout, short hold
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D: (40, 20) — longer breakout, mid hold
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Entry: close[i] breaks above the prior `bk_win`-bar high (Donchian, causal, shifted).
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Fill: close[i] (executable; NOT a high/low extreme, it's the close price).
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Exit: close[i + hold_bars] — hard time-stop, no TP/SL.
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Direction: long only (momentum = price breaks out above prior range).
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No vol-targeting (discrete signal framework does not support it natively).
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Fee: 0.10% RT Deribit taker baseline.
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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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# ---------------------------------------------------------------------------
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# Signal builder
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# ---------------------------------------------------------------------------
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def make_entries(df, bk_win: int, hold_bars: int):
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"""Return entries list: signal at i if close[i] > prior bk_win-bar high.
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Uses donchian() which shifts by 1 to prevent look-ahead.
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Entry price = close[i] (not high/low extreme).
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Hard exit after hold_bars bars (max_bars param in harness).
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"""
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hi, _lo = al.donchian(df, bk_win) # hi[i] = max high over [i-bk_win, i-1] — causal
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c = df["close"].values
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n = len(df)
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entries = []
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for i in range(n):
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if np.isnan(hi[i]):
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entries.append(None)
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continue
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# Breakout: current close exceeds the prior-window high
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if c[i] > hi[i]:
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entries.append({"dir": +1, "tp": None, "sl": None, "max_bars": hold_bars})
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else:
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entries.append(None)
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return entries
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# ---------------------------------------------------------------------------
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# Grid search: pick best config by min-asset hold-out Sharpe
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# ---------------------------------------------------------------------------
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GRID = [
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(20, 10),
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(20, 20),
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(40, 10),
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(40, 20),
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]
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best_rep = None
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best_score = -999.0
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best_label = ""
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for bk_win, hold_bars in GRID:
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label = f"RSK06 bk={bk_win} hold={hold_bars}"
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print(f"\n--- Testing {label} ---")
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rep = al.study_signals(
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label,
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lambda df, bw=bk_win, hb=hold_bars: make_entries(df, bw, hb),
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tfs=("1d",),
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)
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print(al.fmt(rep))
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# Score by min-asset hold-out Sharpe (conservative)
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best_cell = max(rep["cells"], key=lambda c: c.get("min_asset_holdout_sharpe", -9))
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score = best_cell.get("min_asset_holdout_sharpe", -9.0)
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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_label = label
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# ---------------------------------------------------------------------------
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# Final report on best config
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# ---------------------------------------------------------------------------
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print("\n" + "=" * 60)
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print(f"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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