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
3.2 KiB
Python
95 lines
3.2 KiB
Python
"""TRD13 — SMA200 regime + vol-target (long-flat).
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HYPOTHESIS: Long when close > SMA200, flat otherwise.
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Position sized by vol_target(20%, 30d). Pure regime-trend.
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Small grid: SMA window {150, 200} x vol_target window {20, 30} days.
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Only 2 param sets tested (4 total cells with BTC/ETH) to stay within budget.
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Best config selected by min(BTC, ETH) full Sharpe.
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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 factory
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# --------------------------------------------------------------------------
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def make_target(sma_win_bars: int, vol_win_days: int):
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"""Returns a function df -> target_array using SMA regime + vol_target."""
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def target_fn(df):
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c = df["close"].values
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bpd = al.bars_per_day(df)
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# SMA computed causally (sma already uses rolling with min_periods=win)
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s200 = al.sma(c, sma_win_bars)
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# Direction: +1 when close > SMA, else 0 (long-flat)
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direction = np.where(c > s200, 1.0, 0.0)
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# Vol-targeted position
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vol_win = int(round(vol_win_days * bpd))
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pos = al.vol_target(direction, df, target_vol=0.20,
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vol_win_days=vol_win_days, leverage_cap=2.0)
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# Mask NaN (during SMA warmup) -> flat
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pos = np.where(np.isnan(s200), 0.0, pos)
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return pos
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return target_fn
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# --------------------------------------------------------------------------
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# Grid: 2 configs × 2 TFs (1d, 12h)
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# --------------------------------------------------------------------------
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CONFIGS = [
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{"label": "SMA150_v20", "sma_days": 150, "vol_win": 20},
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{"label": "SMA200_v30", "sma_days": 200, "vol_win": 30},
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]
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TFS = ("1d", "12h")
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reports = []
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for cfg in CONFIGS:
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sma_days = cfg["sma_days"]
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vol_win = cfg["vol_win"]
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def make_fn(sd=sma_days, vw=vol_win):
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def target_fn(df):
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bpd = al.bars_per_day(df)
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sma_bars = int(round(sd * bpd))
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c = df["close"].values
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s = al.sma(c, sma_bars)
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direction = np.where(c > s, 1.0, 0.0)
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pos = al.vol_target(direction, df, target_vol=0.20,
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vol_win_days=vw, leverage_cap=2.0)
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pos = np.where(np.isnan(s), 0.0, pos)
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return pos
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return target_fn
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name = f"TRD13_{cfg['label']}"
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rep = al.study_weights(name, make_fn(), tfs=TFS)
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reports.append((rep, cfg))
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# --------------------------------------------------------------------------
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# Pick best config by min(BTC_full_sharpe, ETH_full_sharpe) on best TF
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# --------------------------------------------------------------------------
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def best_score(rep):
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v = rep["verdict"]
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best_tf = v["best_tf"]
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# find the cell for best_tf
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for cell in rep["cells"]:
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if cell["tf"] == best_tf:
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btc_sh = cell["per_asset"]["BTC"]["full"]["sharpe"]
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eth_sh = cell["per_asset"]["ETH"]["full"]["sharpe"]
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return min(btc_sh, eth_sh)
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return -999.0
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best_rep, best_cfg = max(reports, key=lambda x: best_score(x[0]))
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print("\n" + "=" * 70)
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print(f"BEST CONFIG: {best_cfg}")
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print("=" * 70)
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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