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>
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
"""TRD11 — SMA50 slope momentum
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HYPOTHESIS: Position = sign of slope of SMA(50) over last k bars (long-flat variant).
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The slope of SMA(50) captures the direction of the medium-term trend.
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Long-flat: go long when slope > 0, flat otherwise.
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Grid: slope_window (k) in {3, 5, 10} bars.
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Vol-targeted position (target_vol=20%, leverage_cap=2x).
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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_target(sma_period: int = 50, slope_win: int = 5, long_flat: bool = True):
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"""Return a target function for study_weights.
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sma_period: period of the SMA
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slope_win: number of bars to measure the slope over (slope = sma[i] - sma[i-slope_win])
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long_flat: if True, only go long (flat when slope <= 0); if False, long/short
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"""
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def target(df):
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c = df["close"].values.astype(float)
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s = al.sma(c, sma_period)
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# Slope = change in SMA over slope_win bars (causal: uses s[i] vs s[i-slope_win])
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slope = np.full(len(s), np.nan)
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for i in range(slope_win, len(s)):
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if np.isfinite(s[i]) and np.isfinite(s[i - slope_win]):
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slope[i] = s[i] - s[i - slope_win]
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# Direction signal
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if long_flat:
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direction = np.where(slope > 0, 1.0, 0.0)
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else:
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direction = np.where(slope > 0, 1.0, np.where(slope < 0, -1.0, 0.0))
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# Mask NaN slope with flat
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direction = np.where(np.isfinite(slope), direction, 0.0)
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# Vol-target
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tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return tgt
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target.__name__ = f"sma{sma_period}_slope{slope_win}_{'lf' if long_flat else 'ls'}"
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return target
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# Small internal grid: slope windows [3, 5, 10] all long-flat, plus one L/S variant
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configs = [
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{"sma_period": 50, "slope_win": 3, "long_flat": True},
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{"sma_period": 50, "slope_win": 5, "long_flat": True},
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{"sma_period": 50, "slope_win": 10, "long_flat": True},
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{"sma_period": 50, "slope_win": 5, "long_flat": False}, # L/S variant
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]
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best_rep = None
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best_score = -999.0
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for cfg in configs:
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name = f"TRD11-sma{cfg['sma_period']}-k{cfg['slope_win']}-{'LF' if cfg['long_flat'] else 'LS'}"
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fn = make_target(**cfg)
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rep = al.study_weights(name, fn, tfs=("1d", "12h"))
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# Score = min of BTC/ETH full Sharpe (most conservative)
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cells = rep.get("cells", [])
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best_cell_score = -999.0
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for cell in cells:
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pa = cell.get("per_asset", {})
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btc_sh = pa.get("BTC", {}).get("full", {}).get("sharpe", -999)
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eth_sh = pa.get("ETH", {}).get("full", {}).get("sharpe", -999)
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min_sh = min(btc_sh, eth_sh)
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# Also require positive holdout on both
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btc_ho = pa.get("BTC", {}).get("holdout", {}).get("sharpe", -999)
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eth_ho = pa.get("ETH", {}).get("holdout", {}).get("sharpe", -999)
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if btc_ho > 0 and eth_ho > 0:
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min_sh += 0.5 # bonus for positive holdout
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if min_sh > best_cell_score:
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best_cell_score = min_sh
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if best_cell_score > best_score:
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best_score = best_cell_score
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best_rep = rep
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print(f"\n*** NEW BEST: {name} score={best_cell_score:.3f} ***")
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print(al.fmt(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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