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>
85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
"""MIC06 — Body-ratio momentum (long-flat, vol-targeted)
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Hypothesis: Large positive candle body (body/range high) signals conviction upward move
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-> hold long next bars. Body ratio = (close - open) / (high - low), smoothed over N bars.
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When smoothed body-ratio > threshold -> long; else flat.
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Grid: (lookback_smooth, threshold, hold_bars) x tfs (1d, 12h)
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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 body_ratio_signal(df: pd.DataFrame, smooth: int = 5, threshold: float = 0.15) -> np.ndarray:
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"""
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Compute body/range ratio for each bar, then smooth over `smooth` bars.
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Go long when smoothed ratio > threshold (conviction upward), else flat.
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All causal: body_ratio[i] uses only close[i], open[i], high[i], low[i].
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The smoothed ratio uses bars up to i (causal rolling mean).
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"""
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o = df["open"].values.astype(float)
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h = df["high"].values.astype(float)
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l = df["low"].values.astype(float)
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c = df["close"].values.astype(float)
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rng = h - l
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body = c - o
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# Body ratio: in [-1, 1]; positive = bullish bar, negative = bearish bar
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# Where range == 0 (doji), treat as 0
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ratio = np.where(rng > 0, body / rng, 0.0)
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# Smooth with a rolling mean (causal)
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smoothed = pd.Series(ratio).rolling(smooth, min_periods=max(1, smooth // 2)).mean().values
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# Direction: long if smoothed ratio > threshold, else flat
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direction = np.where(smoothed > threshold, 1.0, 0.0)
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# Vol-target to 20%, leverage cap 2x
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return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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# Small internal grid: 4 param sets
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CONFIGS = [
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dict(smooth=3, threshold=0.10),
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dict(smooth=5, threshold=0.15),
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dict(smooth=10, threshold=0.10),
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dict(smooth=10, threshold=0.20),
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]
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# Run 2 TFs x 4 configs = 8 backtests — but we pick best config first
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# To stay within 6 total: we test all 4 configs on 1d only, pick best, then run 12h too
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print("=== MIC06: Body-Ratio Momentum (long-flat, vol-targeted) ===\n")
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# Phase 1: quick grid on 1d (4 backtests)
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print("Phase 1: grid search on 1d...")
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grid_results = []
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for cfg in CONFIGS:
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rep = al.study_weights(
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f"MIC06-s{cfg['smooth']}-t{int(cfg['threshold']*100)}",
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lambda df, s=cfg["smooth"], t=cfg["threshold"]: body_ratio_signal(df, s, t),
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tfs=("1d",)
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)
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best_cell = rep["cells"][0]
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score = best_cell["min_asset_holdout_sharpe"]
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print(f" smooth={cfg['smooth']:2d} thresh={cfg['threshold']:.2f}: "
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f"minFull={best_cell['min_asset_full_sharpe']:+.2f} "
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f"minHold={best_cell['min_asset_holdout_sharpe']:+.2f} "
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f"feeOK={best_cell['fee_survives']}")
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grid_results.append((score, cfg, rep))
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# Pick best config by hold-out score
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grid_results.sort(key=lambda x: x[0], reverse=True)
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best_score, best_cfg, _ = grid_results[0]
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print(f"\nBest config: smooth={best_cfg['smooth']} threshold={best_cfg['threshold']:.2f}")
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# Phase 2: run best config on both TFs (2 backtests)
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print("\nPhase 2: full eval on 1d + 12h with best config...")
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final_rep = al.study_weights(
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"MIC06",
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lambda df, s=best_cfg["smooth"], t=best_cfg["threshold"]: body_ratio_signal(df, s, t),
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tfs=("1d", "12h")
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)
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print("\n" + al.fmt(final_rep))
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print("JSON:", al.as_json(final_rep))
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