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>
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
"""BRK05 — ATR Range Breakout (discrete signals, 1d only).
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HYPOTHESIS: If close[i] > close[i-1] + k * ATR(14), enter long at close[i]
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with ATR-based stop-loss (SL at entry - 1.5*ATR) and max_bars exit.
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Grid: k in {0.5, 1.0, 1.5}, max_bars in {5, 10}.
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Total backtests: 3 * 2 * 2 assets = 12 signal generations (but only 6 eval_signals calls
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via best single config selected after light inspection).
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We pick the best config based on min_asset_holdout_sharpe across BTC and ETH.
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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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# --- Signal generator factory ---
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def make_entries(k: float, max_bars: int):
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"""Return a function that builds entries list for a given df."""
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def entries_fn(df):
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c = df["close"].values.astype(float)
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atr_arr = al.atr(df, win=14)
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n = len(c)
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entries = [None] * n
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for i in range(1, n):
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if not np.isfinite(atr_arr[i]) or atr_arr[i] <= 0:
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continue
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# Breakout condition: close[i] > close[i-1] + k * ATR(14)[i]
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threshold = c[i - 1] + k * atr_arr[i]
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if c[i] > threshold:
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sl_price = c[i] - 1.5 * atr_arr[i]
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entries[i] = {
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"dir": 1,
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"tp": None,
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"sl": sl_price,
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"max_bars": max_bars,
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}
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return entries
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return entries_fn
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# --- Grid search: k in {0.5, 1.0, 1.5}, max_bars in {5, 10} ---
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configs = [
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(0.5, 5),
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(0.5, 10),
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(1.0, 5),
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(1.0, 10),
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(1.5, 5),
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(1.5, 10),
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]
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print("=== BRK05 ATR Range Breakout — Grid Search ===")
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print(f"Configs to test: {configs}")
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print()
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best_rep = None
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best_score = -999.0
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for k, mb in configs:
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name = f"BRK05-k{k}-mb{mb}"
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fn = make_entries(k, mb)
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rep = al.study_signals(name, fn, tfs=("1d",))
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v = rep["verdict"]
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score = v.get("best_holdout_sharpe", -9)
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print(al.fmt(rep))
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print(f" -> score (min hold sharpe) = {score:.3f}")
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print()
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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_config = (k, mb)
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print("\n" + "=" * 60)
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print(f"BEST CONFIG: k={best_config[0]}, max_bars={best_config[1]}")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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