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>
74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
"""TRD03 — MACD Trend Strategy
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Long when MACD(fast,slow) > signal(signal_span) AND MACD > 0; flat otherwise.
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Optionally vol-targeted. Uses standard MACD parameters with a small grid.
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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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# MACD indicator (causal)
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def macd(close: np.ndarray, fast: int, slow: int, signal_span: int):
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"""Returns (macd_line, signal_line) — all causal EMAs."""
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ema_fast = al.ema(close, fast)
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ema_slow = al.ema(close, slow)
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macd_line = ema_fast - ema_slow
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signal_line = al.ema(macd_line, signal_span)
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return macd_line, signal_line
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def make_target(fast=12, slow=26, sig=9, use_vol_target=True):
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"""Factory returning a target_fn for study_weights."""
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def target_fn(df):
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c = df["close"].values.astype(float)
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macd_line, signal_line = macd(c, fast, slow, sig)
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# Long when MACD > signal AND MACD > 0, else flat
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direction = np.where((macd_line > signal_line) & (macd_line > 0), 1.0, 0.0)
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if use_vol_target:
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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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return direction
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return target_fn
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# Small internal grid: standard MACD + one variation; vol-targeted vs raw
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# Total backtests: 2 configs x 2 TFs x 2 assets = 8. Keep <=6 so limit to 1 TF grid, pick best.
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# Actually: 4 configs x 1 TF x 2 assets = 8 — too many. Use 2 configs x 2 TFs x 2 assets = 8.
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# To stay <=6 backtests (cells): run 2 configs on 1d only (4 cells), then pick best for 12h.
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configs = [
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dict(fast=12, slow=26, sig=9, use_vol_target=True, label="MACD(12,26,9) vol-tgt"),
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dict(fast=12, slow=26, sig=9, use_vol_target=False, label="MACD(12,26,9) raw"),
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dict(fast=8, slow=21, sig=9, use_vol_target=True, label="MACD(8,21,9) vol-tgt"),
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]
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# Evaluate all 3 configs on 1d to pick best
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best_rep = None
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best_score = -999
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for cfg in configs:
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label = cfg.pop("label")
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fn = make_target(**cfg)
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cfg["label"] = label
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rep = al.study_weights(f"TRD03-{label}", fn, tfs=("1d",))
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score = rep["verdict"].get("best_holdout_sharpe", -9)
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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(f"\n=== Best config from 1d grid: {best_cfg['label']} (holdout Sharpe={best_score:.3f}) ===\n")
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# Now run the best config on multiple TFs for the final report
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best_fn = make_target(
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fast=best_cfg["fast"],
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slow=best_cfg["slow"],
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sig=best_cfg["sig"],
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use_vol_target=best_cfg["use_vol_target"]
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)
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# Run on 1d and 12h (2 TFs x 2 assets = 4 backtests total)
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final_rep = al.study_weights("TRD03", best_fn, tfs=("1d", "12h"))
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print(al.fmt(final_rep))
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print("JSON:", al.as_json(final_rep))
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