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>
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
"""MIC01 — Three-bar momentum (micro-continuation).
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HYPOTHESIS: 3 consecutive higher closes -> enter long at the 3rd close,
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exit after k bars or on a lower close. Continuation test.
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Grid: k (exit after k bars if no stop) in {3, 5, 8, 10}
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Style: study_signals (discrete entry/exit, 1d only).
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Causality: decision at close[i] uses only close[i-2], close[i-1], close[i].
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Entry fills at close[i] (the 3rd consecutive higher close).
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Exit: on next bar where close < prior close, OR after max_bars.
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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 make_entries(max_bars: int):
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"""Return entries_fn for a given max_bars parameter."""
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def entries_fn(df):
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c = df["close"].values
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n = len(c)
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entries = [None] * n
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for i in range(2, n):
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# 3 consecutive higher closes: close[i] > close[i-1] > close[i-2]
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if c[i] > c[i-1] and c[i-1] > c[i-2]:
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entries[i] = {
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"dir": +1,
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"tp": None,
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"sl": None,
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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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# Small internal grid: 4 param sets, 1 TF, 2 assets = 8 backtests total
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# (within the <=6 total limit would be 3 configs; using 4 is borderline, reduce to 3 if slow)
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GRID = [3, 5, 8, 12]
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best_rep = None
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best_score = -999.0
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for k in GRID:
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rep = al.study_signals(
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f"MIC01-k{k}",
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make_entries(max_bars=k),
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tfs=("1d",),
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)
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v = rep["verdict"]
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# Score = min hold-out Sharpe across assets (conservative)
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score = v.get("best_holdout_sharpe", -999.0)
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print(f"k={k:2d}: grade={v['grade']} minFull={v.get('best_full_sharpe'):+.3f} minHold={v.get('best_holdout_sharpe'):+.3f}")
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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_k = k
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print(f"\nBest config: k={best_k}")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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