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>
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
"""BRK10 — Vol-contraction (squeeze) long
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HYPOTHESIS: When Bollinger bandwidth is in its low expanding-percentile (squeeze detected),
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go long-flat on subsequent upside close > midline. Honest entry at close[i].
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Strategy logic:
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- Compute Bollinger bandwidth = (upper - lower) / middle
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- Compute expanding percentile of bandwidth to define "squeeze" (low vol percentile)
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- Long signal: bandwidth in low percentile (squeeze) AND close > midline (momentum up)
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- Vol-targeted position, long-flat (no short)
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Internal grid (<=4 configs, total backtests <=6):
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- bb_win: Bollinger window [20, 30]
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- squeeze_pct: bandwidth percentile threshold [25, 20]
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Best config picked by min(BTC/ETH) hold-out 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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import pandas as pd
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def make_target(df: pd.DataFrame, bb_win: int = 20, k: float = 2.0,
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squeeze_pct: float = 25.0) -> np.ndarray:
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"""
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BRK10: vol-contraction squeeze long.
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- Compute BB bandwidth = (upper - lower) / mid (all causal via bbands)
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- Use expanding percentile of bandwidth to define squeeze
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- Long when: bandwidth <= squeeze_pct expanding percentile AND close > midline
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- Vol-targeted position, long-flat
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"""
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c = df["close"].values.astype(float)
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n = len(c)
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# Bollinger bands (causal: uses data <= i)
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upper, mid, lower = al.bbands(c, win=bb_win, k=k)
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# Bandwidth = (upper - lower) / mid; avoid div by zero
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bw = np.where(mid > 0, (upper - lower) / mid, np.nan)
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# Expanding percentile of bandwidth (causal: uses data <= i)
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# squeeze = bandwidth is in the lower squeeze_pct% of historical values
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squeeze_mask = np.zeros(n, dtype=bool)
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bw_series = pd.Series(bw)
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for i in range(bb_win, n):
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hist = bw_series.iloc[:i+1].dropna().values
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if len(hist) < bb_win:
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continue
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threshold = np.percentile(hist, squeeze_pct)
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if np.isfinite(bw[i]) and bw[i] <= threshold:
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squeeze_mask[i] = True
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# Direction: long when squeeze AND close > midline
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# NaN midline bars -> flat
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direction = np.where(
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squeeze_mask & np.isfinite(mid) & (c > mid),
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1.0,
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0.0
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)
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# Vol-targeted, long-flat
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target = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target
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# Grid: bb_win x squeeze_pct (4 configs, both tested on 1d only -> 4 total backtests <= 6)
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GRID = [
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dict(bb_win=20, squeeze_pct=25.0),
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dict(bb_win=20, squeeze_pct=20.0),
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dict(bb_win=30, squeeze_pct=25.0),
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dict(bb_win=30, squeeze_pct=20.0),
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]
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best_rep = None
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best_score = -9999.0
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best_cfg = None
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TFS = ("1d",)
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for cfg in GRID:
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print(f"\n--- Testing config: {cfg} ---")
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label = f"BRK10_bb{cfg['bb_win']}_sq{int(cfg['squeeze_pct'])}"
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fn = lambda df, c=cfg: make_target(df, bb_win=c["bb_win"], squeeze_pct=c["squeeze_pct"])
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rep = al.study_weights(label, fn, tfs=TFS)
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# Score = min holdout Sharpe across assets in best TF
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score = rep["verdict"].get("best_holdout_sharpe", -9999.0) or -9999.0
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print(al.fmt(rep))
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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_cfg = cfg
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print("\n" + "=" * 70)
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print(f"BEST CONFIG: {best_cfg}")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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