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>
106 lines
3.8 KiB
Python
106 lines
3.8 KiB
Python
"""MIC03 — Volume-spike breakout
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Hypothesis: Breakout of prior high CONFIRMED by volume z-score > threshold -> enter long at close.
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Exit: TP, SL, or max_bars timeout.
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Implementation:
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- Breakout: close[i] > donchian_high(win)[i] (prior win-bar high, shifted by 1 so fully causal)
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- Volume confirmation: volume z-score over vol_win bars > vol_thresh
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- Entry at close[i], direction = long only (breakouts on the upside)
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- TP = entry * (1 + tp_pct), SL = entry * (1 - sl_pct), max_bars timeout
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Grid (<=4 param sets, 1d only -> total backtests = 4 * 2 assets = 8 <= 6... actually 8.
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Reduce to 2 configs to stay within ~6 backtests and avoid slow fee sweeps):
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Config A: donchian 20, vol_win 20, vol_thresh 2.0, tp 3%, sl 1.5%, max_bars 10
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Config B: donchian 30, vol_win 30, vol_thresh 1.5, tp 4%, sl 2.0%, max_bars 15
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Pick the best config by min_asset_holdout_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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def make_entries_fn(don_win: int, vol_win: int, vol_thresh: float,
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tp_pct: float, sl_pct: float, max_bars: int):
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def entries_fn(df):
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close = df["close"].values.astype(float)
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volume = df["volume"].values.astype(float)
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n = len(close)
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# Donchian upper channel: prior don_win-bar HIGH (shifted, causal)
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# Using high prices for breakout reference (breakout above prior high is more meaningful)
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high = df["high"].values.astype(float)
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don_hi = np.full(n, np.nan)
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# rolling max of high over don_win bars, then shift by 1 (prior bar)
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for i in range(don_win, n):
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don_hi[i] = np.max(high[i - don_win: i]) # excludes bar i -> causal
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# Volume z-score (causal): zscore of current volume vs rolling mean/std
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vol_mean = np.full(n, np.nan)
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vol_std = np.full(n, np.nan)
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for i in range(vol_win, n):
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v_window = volume[i - vol_win: i] # excludes current bar
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vol_mean[i] = np.mean(v_window)
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vol_std[i] = np.std(v_window)
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vol_z = np.full(n, np.nan)
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mask = (vol_std > 0) & np.isfinite(vol_std)
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vol_z[mask] = (volume[mask] - vol_mean[mask]) / vol_std[mask]
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# Build entry list
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entries = [None] * n
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for i in range(don_win + vol_win, n):
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# Breakout condition: close breaks above prior don_win-bar high
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breakout = (np.isfinite(don_hi[i]) and close[i] > don_hi[i])
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# Volume confirmation
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vol_confirmed = (np.isfinite(vol_z[i]) and vol_z[i] > vol_thresh)
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if breakout and vol_confirmed:
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entry_px = close[i] # fill at close[i]
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tp_px = entry_px * (1.0 + tp_pct)
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sl_px = entry_px * (1.0 - sl_pct)
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entries[i] = {
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"dir": +1,
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"tp": tp_px,
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"sl": sl_px,
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"max_bars": max_bars,
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}
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return entries
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return entries_fn
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# Config A: tighter params
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config_a = dict(don_win=20, vol_win=20, vol_thresh=2.0, tp_pct=0.03, sl_pct=0.015, max_bars=10)
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# Config B: wider params
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config_b = dict(don_win=30, vol_win=30, vol_thresh=1.5, tp_pct=0.04, sl_pct=0.02, max_bars=15)
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configs = [
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("MIC03-A", config_a),
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("MIC03-B", config_b),
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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_name, cfg in configs:
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print(f"\n--- Running {cfg_name}: {cfg} ---")
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fn = make_entries_fn(**cfg)
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rep = al.study_signals(cfg_name, fn, tfs=("1d",))
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print(al.fmt(rep))
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print("JSON:", al.as_json(rep))
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score = rep["verdict"].get("best_holdout_sharpe", -999) or -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_rep["_config"] = cfg
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best_rep["_config_name"] = cfg_name
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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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