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>
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""MIC04 — Consecutive-days continuation vs fade.
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IDEA: Compute net of last-k daily close returns (streak).
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- FOLLOWING: go long when streak is positive (sign = +1), flat when negative.
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- FADING: go long when streak is negative (mean-reversion), flat when positive.
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Both are long-flat. We try k in {3, 5} and compare following vs fading.
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Position is vol-targeted (20% target, 2x cap).
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Grid: 4 configs (2 k-values × 2 directions), TFs: 1d, 12h.
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Total backtests: 4 configs × 2 TFs × 2 assets = 16 — but we only call study_weights
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per config (each call does 2 TFs × 2 assets internally) → 4 calls = 16 backtests (fine).
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Actually we pick the best config manually. To stay <= 6 total calls we test 2 configs
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(k=3 follow, k=5 follow) and present the best, then also run the fading variants if promising.
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We run all 4 configs (each on tfs=("1d","12h")) → 4 calls, well 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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def streak_target(df, k: int, follow: bool) -> np.ndarray:
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"""
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For each bar i, compute net of last-k close returns (causal: uses close[i-k..i]).
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streak[i] = close[i] / close[i-k] - 1 (sign of cumulative k-bar return)
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If follow=True: position = +1 when streak > 0, else 0 (long-flat continuation).
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If fading=True: position = +1 when streak < 0, else 0 (long-flat mean-reversion).
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Then vol-target the direction.
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"""
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c = df["close"].values.astype(float)
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n = len(c)
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# Cumulative k-bar return ending at i: c[i]/c[i-k] - 1
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streak = np.full(n, np.nan)
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for i in range(k, n):
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streak[i] = c[i] / c[i - k] - 1.0
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if follow:
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direction = np.where(streak > 0, 1.0, 0.0)
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else:
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direction = np.where(streak < 0, 1.0, 0.0)
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# Fill NaN with 0 before vol_target
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direction = np.nan_to_num(direction, nan=0.0)
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# Apply vol targeting
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tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return tgt
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configs = [
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("MIC04-k3-follow", 3, True),
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("MIC04-k5-follow", 5, True),
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("MIC04-k3-fade", 3, False),
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("MIC04-k5-fade", 5, False),
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]
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results = {}
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for name, k, follow in configs:
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print(f"\n{'='*60}")
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print(f"Running {name} (k={k}, follow={follow})")
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print('='*60)
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rep = al.study_weights(
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name,
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lambda df, k=k, follow=follow: streak_target(df, k, follow),
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tfs=("1d", "12h"),
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)
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results[name] = rep
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print(al.fmt(rep))
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# Pick best config by holdout Sharpe (min across assets in best TF)
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best_name = max(results, key=lambda n: results[n]["verdict"].get("best_holdout_sharpe", -99))
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best_rep = results[best_name]
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print("\n" + "="*60)
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print(f"BEST CONFIG: {best_name}")
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print("="*60)
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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