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>
95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
"""MRV07 — Consecutive-down buy in uptrend.
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After N+ consecutive lower closes AND close > SMA100 (uptrend filter),
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buy at close[i]; exit after max_bars or on the first green close (close > prev close).
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Grid: try (consec_n, max_bars) combinations on 1d.
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Total backtests: 3 configs x 2 assets = 6.
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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_fn(consec_n=3, sma_win=100, max_bars=10):
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"""Factory for consecutive-down buy entries.
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Signal: close[i] < close[i-1] < ... < close[i-consec_n+1] (N consecutive lower closes)
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AND close[i] > SMA100 (uptrend filter).
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Entry: buy at close[i] (filled immediately).
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Exit: after max_bars bars (hard stop); no TP/SL (green-close exit not encodable
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causally in the entries-list format — green close requires next-bar data).
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"""
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def entries_fn(df):
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c = df["close"].values.astype(float)
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n = len(c)
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sma100 = al.sma(c, sma_win)
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entries = []
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for i in range(n):
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# Need at least consec_n prior bars
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if i < consec_n:
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entries.append(None)
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continue
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# Check SMA100 (uptrend)
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if np.isnan(sma100[i]) or c[i] <= sma100[i]:
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entries.append(None)
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continue
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# Check N consecutive lower closes
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consecutive_down = True
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for k in range(consec_n):
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if k == 0:
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# close[i] < close[i-1]
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if c[i] >= c[i-1]:
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consecutive_down = False
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break
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else:
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# close[i-k] < close[i-k-1]
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if c[i-k] >= c[i-k-1]:
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consecutive_down = False
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break
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if consecutive_down:
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entries.append({
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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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else:
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entries.append(None)
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return entries
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return entries_fn
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# Grid: 3 configs (consec_n, max_bars)
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# Hypothesis: after 3 consecutive dips in uptrend, expect mean-reversion bounce
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CONFIGS = [
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dict(consec_n=3, max_bars=5, label="N3_mb5"),
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dict(consec_n=3, max_bars=10, label="N3_mb10"),
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dict(consec_n=4, max_bars=5, label="N4_mb5"),
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]
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best_rep = None
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best_hold = -999.0
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best_label = None
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for cfg in CONFIGS:
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label = cfg["label"]
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fn = make_entries_fn(consec_n=cfg["consec_n"], max_bars=cfg["max_bars"])
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rep = al.study_signals(f"MRV07-{label}", fn, tfs=("1d",))
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hold = rep["verdict"].get("best_holdout_sharpe", -999)
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full = rep["verdict"].get("best_full_sharpe", -999)
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print(f"\n[{label}] full={full:.3f} hold={hold:.3f} grade={rep['verdict']['grade']}")
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if hold > best_hold:
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best_hold = hold
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best_rep = rep
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best_label = label
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print("\n\n=== 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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