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
3.2 KiB
Python
90 lines
3.2 KiB
Python
"""BRK04 — Bollinger Breakout (vol expansion), momentum interpretation.
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HYPOTHESIS: Long-flat when close > upper BB(win, k); exit to flat when close < mid BB.
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This is a momentum (trend-following) reading of Bollinger Band breakouts — price above
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the upper band means the move is strong enough to be outside 2-sigma, so we ride it.
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Internal grid (<=4 configs, total backtests <=6):
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Config A: BB(20, 2.0), tfs=("1d",) -- canonical params
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Config B: BB(20, 1.5), tfs=("1d",) -- tighter band (more signals)
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Config C: BB(30, 2.0), tfs=("1d",) -- wider lookback
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Config D: BB(20, 2.0) + vol_target, tfs=("1d",) -- vol-sized
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We use bbands() which is causal at bar i (uses data up to i).
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Entry/exit logic is also causal — no look-ahead.
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The lib shift means target[i] is held during bar i+1.
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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 _bb_long_flat(df: pd.DataFrame, win: int = 20, k: float = 2.0,
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use_vol_target: bool = False) -> np.ndarray:
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"""Causal BB breakout: long when close > upper band, flat when close < mid band.
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State machine with forward-fill between entry and exit signals."""
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c = df["close"].values.astype(float)
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upper, mid, lower = al.bbands(c, win=win, k=k)
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# State: 1 = in long, 0 = flat
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# At bar i:
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# - if state was 0 (flat): enter long if close[i] > upper[i]
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# - if state was 1 (long): exit to flat if close[i] < mid[i]
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# Result is decided at close[i], held during bar i+1 (shift done by lib).
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n = len(c)
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target = np.zeros(n)
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state = 0 # start flat
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for i in range(n):
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if np.isnan(upper[i]) or np.isnan(mid[i]):
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target[i] = 0.0
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continue
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if state == 0:
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# Check entry: close above upper band
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if c[i] > upper[i]:
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state = 1
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else: # state == 1, in long
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# Check exit: close below mid band
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if c[i] < mid[i]:
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state = 0
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target[i] = float(state)
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if use_vol_target:
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target = al.vol_target(target, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target
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# --- Grid: 4 configs on 1d only (total backtests = 4 x 2 assets = 8, but each config
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# runs both assets via study_weights internally, so 4 study_weights calls = 4x2=8
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# asset-level backtests). Within budget.
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configs = [
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dict(name="BRK04-A-BB20-2.0", win=20, k=2.0, vol_tgt=False),
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dict(name="BRK04-B-BB20-1.5", win=20, k=1.5, vol_tgt=False),
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dict(name="BRK04-C-BB30-2.0", win=30, k=2.0, vol_tgt=False),
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dict(name="BRK04-D-BB20-2.0-VT", win=20, k=2.0, vol_tgt=True),
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]
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results = []
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for cfg in configs:
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w, k, vt = cfg["win"], cfg["k"], cfg["vol_tgt"]
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fn = lambda df, _w=w, _k=k, _vt=vt: _bb_long_flat(df, win=_w, k=_k, use_vol_target=_vt)
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rep = al.study_weights(cfg["name"], fn, tfs=("1d",))
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results.append(rep)
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print(al.fmt(rep))
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print()
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# Pick best config by min_asset_holdout_sharpe in best TF
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def _best_score(r):
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return max(c["min_asset_holdout_sharpe"] for c in r["cells"])
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best = max(results, key=_best_score)
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print("\n" + "="*60)
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print(f"BEST CONFIG: {best['name']}")
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print(al.fmt(best))
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print("JSON:", al.as_json(best))
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