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>
105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
"""MRV08 — Daily gap-fill (adapted for 24/7 crypto)
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HYPOTHESIS: On 1d bars, if the day opens well BELOW the prior close (gap-down),
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go LONG expecting reversion toward prior close. SL below the day open.
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IMPORTANT: Crypto trades 24/7 — open[i] vs close[i-1] gaps are typically <0.1%
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on Deribit 1d resampled bars (max gap found = 0.089%). True overnight gaps don't exist.
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ADAPTED INTERPRETATION: "Gap" operationalized as a large down day:
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- Bar i closes gap_thresh% below prior close (big intraday decline)
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- Enter LONG at close[i], TP = close[i-1] (full reversion), SL below
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- This captures the "gap fill" spirit: buy after a large daily drop expecting recovery
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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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# Grid: (gap_thresh, sl_frac, max_bars, label)
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CONFIGS = [
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(0.015, 0.015, 3, "down1.5%_sl1.5%_3d"), # moderate down day, 3d hold
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(0.020, 0.020, 3, "down2%_sl2%_3d"), # bigger down day only
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(0.015, 0.020, 5, "down1.5%_sl2%_5d"), # more time to recover
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(0.020, 0.015, 5, "down2%_sl1.5%_5d"), # tighter SL, longer hold
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]
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def make_entries(df, gap_thresh=0.015, sl_frac=0.015, max_bars=3):
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"""
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Reversion after a large down day:
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- If close[i] < close[i-1] * (1 - gap_thresh): "gap" trigger
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- Entry: LONG at close[i]
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- TP: close[i-1] (prior close recovery)
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- SL: close[i] * (1 - sl_frac)
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- Hold up to max_bars days
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Causal: uses only close[i] and close[i-1].
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"""
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c = df["close"].values.astype(float)
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n = len(df)
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entries = [None] * n
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for i in range(1, n):
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prior_close = c[i - 1]
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cur_close = c[i]
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if prior_close <= 0:
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continue
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ret = (cur_close - prior_close) / prior_close
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if ret >= -gap_thresh:
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continue
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tp = prior_close
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sl = cur_close * (1.0 - sl_frac)
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if tp <= cur_close or sl >= cur_close:
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continue
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entries[i] = {"dir": +1, "tp": tp, "sl": sl, "max_bars": max_bars}
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return entries
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# Diagnostic: check trade counts per config
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print("=== MRV08 Daily Gap-Fill (Crypto Adapted) ===")
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print("NOTE: True overnight gaps don't exist in 24/7 crypto.")
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print("Using 'large down day' as gap proxy (close[i] < close[i-1] * (1-thresh))")
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print()
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for gt, sf, mb, label in CONFIGS:
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df_btc = al.get("BTC", "1d")
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ent_btc = make_entries(df_btc, gt, sf, mb)
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n_btc = sum(1 for e in ent_btc if e is not None)
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df_eth = al.get("ETH", "1d")
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ent_eth = make_entries(df_eth, gt, sf, mb)
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n_eth = sum(1 for e in ent_eth if e is not None)
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print(f" {label}: BTC trades={n_btc}, ETH trades={n_eth}")
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print()
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# Run all configs
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best_rep = None
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best_min_hold = -999.0
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for gap_thresh, sl_frac, max_bars, label in CONFIGS:
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name = f"MRV08-{label}"
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def make_fn(gt=gap_thresh, sf=sl_frac, mb=max_bars):
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return lambda df: make_entries(df, gap_thresh=gt, sl_frac=sf, max_bars=mb)
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rep = al.study_signals(name, make_fn(), tfs=("1d",))
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v = rep["verdict"]
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min_hold = v.get("best_holdout_sharpe", -999)
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print(f"\n--- Config: {label} ---")
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print(al.fmt(rep))
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print("JSON:", al.as_json(rep))
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if min_hold > best_min_hold:
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best_min_hold = min_hold
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best_rep = rep
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print("\n\n=== BEST CONFIG ===")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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