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>
109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
"""CMB05 — BB Squeeze -> Breakout (honest, leak-free).
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HYPOTHESIS: Bollinger Bandwidth at a multi-bar low (squeeze) then close > upper BB
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-> enter long at that close (entry at close[i], direction decided with data<=close[i]).
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Exit when close drops back below middle band, or max_bars reached, or SL hit.
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Tested on 1d only (study_signals, discrete). Small grid on:
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- BB window: 20 vs 30
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- Squeeze lookback: 50 vs 100
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Total configs: 4 — two assets each => 8 backtests. Within budget.
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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_entries(bb_win: int = 20, squeeze_lb: int = 50, squeeze_pct: float = 0.20, sl_mult: float = 2.0, max_bars: int = 30):
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"""
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Returns entries_fn(df) -> list[dict|None] for study_signals.
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Squeeze = BB bandwidth (upper-lower)/middle in lowest squeeze_pct quantile over squeeze_lb bars.
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Breakout = close[i] > upper[i] AND bandwidth is in compressed regime.
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Entry: long at close[i], honest (direction decided with close[i]).
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Exit: close < middle band (continuous) OR max_bars OR SL at entry - sl_mult*ATR.
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"""
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def entries_fn(df):
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c = df["close"].values.astype(float)
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n = len(c)
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# BB bands - causal (uses data up to i)
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upper, mid, lower = al.bbands(c, win=bb_win, k=2.0)
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# Bandwidth
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bw = np.where(mid != 0, (upper - lower) / mid, np.nan)
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# Squeeze: bandwidth in lowest squeeze_pct percentile over squeeze_lb bars (causal)
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# Use rolling quantile to flag "compressed" bandwidth
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bw_series = pd.Series(bw)
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bw_lo = bw_series.rolling(squeeze_lb, min_periods=squeeze_lb).quantile(squeeze_pct).values
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# ATR for SL
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atr_arr = al.atr(df, win=14)
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entries = [None] * n
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in_trade = False
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for i in range(squeeze_lb + bb_win, n):
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if np.isnan(upper[i]) or np.isnan(bw_lo[i]) or np.isnan(atr_arr[i]):
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continue
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if not np.isfinite(bw[i]):
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continue
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# Squeeze: bandwidth <= its rolling low-percentile threshold
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is_squeeze = bw[i] <= bw_lo[i]
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# Breakout: close[i] > upper[i] (decided at close[i], honest)
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breakout = c[i] > upper[i]
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if (not in_trade) and is_squeeze and breakout:
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sl_px = c[i] - sl_mult * atr_arr[i]
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entries[i] = {
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"dir": +1,
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"tp": None,
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"sl": sl_px,
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"max_bars": max_bars,
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}
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in_trade = True
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elif in_trade:
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# Exit signal: close falls below middle band -> reset flag
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if c[i] < mid[i]:
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in_trade = False
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return entries
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return entries_fn
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# Grid: 4 configs (2 bb_win x 2 squeeze_pct) with fixed squeeze_lb=100
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configs = [
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dict(bb_win=20, squeeze_lb=100, squeeze_pct=0.20),
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dict(bb_win=20, squeeze_lb=100, squeeze_pct=0.30),
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dict(bb_win=30, squeeze_lb=100, squeeze_pct=0.20),
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dict(bb_win=30, squeeze_lb=100, squeeze_pct=0.30),
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]
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best_rep = None
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best_score = -999.0
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print("=== CMB05: BB Squeeze -> Breakout ===")
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print(f"Grid: {len(configs)} configs x 2 assets x fee_sweep = honest eval\n")
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for cfg in configs:
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name = f"CMB05-BB{cfg['bb_win']}-SQ{cfg['squeeze_lb']}-P{int(cfg['squeeze_pct']*100)}"
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fn = make_entries(bb_win=cfg["bb_win"], squeeze_lb=cfg["squeeze_lb"], squeeze_pct=cfg["squeeze_pct"])
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rep = al.study_signals(name, fn, tfs=("1d",))
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v = rep["verdict"]
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score = v.get("best_holdout_sharpe", -9)
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print(f" {name}: grade={v['grade']} fullSh={v.get('best_full_sharpe'):.3f} holdSh={v.get('best_holdout_sharpe'):.3f}")
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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_rep["_cfg"] = cfg
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print("\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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