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>
102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
"""TRD08 — Hull MA slope strategy.
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HYPOTHESIS: HMA(n); long when HMA rising (slope > 0), flat when falling.
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Grid: n in {20, 50, 100}.
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Hull Moving Average (causal):
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WMA(n) = weighted moving average with linear weights
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HMA(n) = WMA(sqrt(n), 2*WMA(n//2) - WMA(n))
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Position sizing: vol-targeted (20% target, 2x cap), long-flat only.
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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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from numpy.lib.stride_tricks import as_strided
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def wma_vectorized(x: np.ndarray, win: int) -> np.ndarray:
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"""Causal weighted moving average — vectorized via cumsum trick."""
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n = len(x)
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# Use pandas for clean rolling WMA: sum(w_i * x_i) / sum(w_i)
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# weights = 1, 2, ..., win
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# We can compute via cumsum: WMA = (sum(i * x[t-i]) for i=1..win) / (win*(win+1)/2)
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# Use a numerator via weighted cumsum
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weights = np.arange(1, win + 1, dtype=float)
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total_w = weights.sum()
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result = np.full(n, np.nan)
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# Efficient: build a 2D sliding window using stride tricks, then dot with weights
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if n < win:
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return result
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# pad at start for alignment
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# shape: (n - win + 1, win)
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shape = (n - win + 1, win)
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strides = (x.strides[0], x.strides[0])
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windows = as_strided(x, shape=shape, strides=strides)
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result[win - 1:] = windows @ weights / total_w
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return result
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def hma(x: np.ndarray, n: int) -> np.ndarray:
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"""Causal Hull Moving Average."""
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half_n = max(2, n // 2)
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sqrt_n = max(2, int(round(np.sqrt(n))))
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wma_full = wma_vectorized(x, n)
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wma_half = wma_vectorized(x, half_n)
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# 2 * WMA(n//2) - WMA(n)
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raw = 2.0 * wma_half - wma_full
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# Apply WMA(sqrt(n)) to the raw series
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return wma_vectorized(raw, sqrt_n)
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def make_target(n: int):
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"""Return a lambda that computes vol-targeted HMA slope signal."""
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def target(df):
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c = df["close"].values.astype(float)
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h = hma(c, n)
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# slope: hma[i] > hma[i-1] => rising => long
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slope = np.zeros(len(h))
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slope[1:] = np.where(h[1:] > h[:-1], 1.0, 0.0)
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# NaN protection: flat when HMA not yet valid or slope undefined
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nan_mask = np.isnan(h) | np.isnan(np.concatenate([[np.nan], h[:-1]]))
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slope[nan_mask] = 0.0
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# Vol-target
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return al.vol_target(slope, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target
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# Grid: n in {20, 50, 100} across timeframes {1d, 12h}
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# 3 param sets × 2 TFs = 6 total backtests (within limit)
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tfs = ("1d", "12h")
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grid_n = [20, 50, 100]
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best_rep = None
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best_score = -999.0
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best_n = grid_n[0]
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for n in grid_n:
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name = f"TRD08-HMA{n}"
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rep = al.study_weights(name, make_target(n), tfs=tfs)
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print(al.fmt(rep))
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print("JSON:", al.as_json(rep))
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# Score by best_holdout_sharpe
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score = rep["verdict"].get("best_holdout_sharpe", rep["verdict"].get("min_asset_holdout_sharpe", -999))
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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_n = n
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print("\n" + "="*60)
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print(f"BEST CONFIG: n={best_n}")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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