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>
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
"""TRD04 — Supertrend(period, multiplier)
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Classic ATR-band trend flip: long when price above supertrend line, short/flat below.
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Grid: (period, mult) in [(10,3),(14,3),(10,2),(14,2)] — 4 configs x 2 TFs x 2 assets = 16 backtests.
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Style: continuous weights (vol-targeted, long-flat).
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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 supertrend_direction(df: pd.DataFrame, period: int = 10, mult: float = 3.0) -> np.ndarray:
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"""Compute Supertrend and return causal direction in {0, 1}.
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Long (1) when close > supertrend, flat (0) otherwise.
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The Supertrend uses ATR-based bands and flips only when price crosses the band.
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Causal: at bar i we use data up to and including close[i].
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"""
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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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n = len(c)
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# ATR via EWM (causal, same as al.atr)
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a = al.atr(df, period)
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hl2 = (h + l) / 2.0
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upper = hl2 + mult * a
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lower = hl2 - mult * a
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# Final upper/lower bands (adjusted to not widen against trend)
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final_upper = upper.copy()
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final_lower = lower.copy()
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direction = np.zeros(n, dtype=float) # 1 = uptrend (long), 0 = downtrend (flat)
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# Warm-up: first bar
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final_upper[0] = upper[0]
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final_lower[0] = lower[0]
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direction[0] = 1.0 if c[0] > hl2[0] else 0.0
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for i in range(1, n):
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# Tighten upper: new upper only replaces if lower than previous (or if prev close was above)
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if upper[i] < final_upper[i-1] or c[i-1] > final_upper[i-1]:
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final_upper[i] = upper[i]
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else:
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final_upper[i] = final_upper[i-1]
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# Tighten lower: new lower only replaces if higher than previous (or if prev close was below)
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if lower[i] > final_lower[i-1] or c[i-1] < final_lower[i-1]:
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final_lower[i] = lower[i]
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else:
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final_lower[i] = final_lower[i-1]
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# Determine direction (trend)
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prev_dir = direction[i-1]
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if prev_dir == 0.0: # was downtrend (flat)
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if c[i] > final_upper[i]:
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direction[i] = 1.0 # flip to uptrend
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else:
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direction[i] = 0.0 # stay flat
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else: # was uptrend
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if c[i] < final_lower[i]:
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direction[i] = 0.0 # flip to downtrend (flat)
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else:
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direction[i] = 1.0 # stay in uptrend
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return direction
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def make_target(period: int, mult: float):
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"""Returns a target_fn(df) that computes vol-targeted Supertrend weights."""
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def target_fn(df: pd.DataFrame) -> np.ndarray:
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direction = supertrend_direction(df, period=period, mult=mult)
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# vol-targeted: scale by realized vol, cap at 2x leverage, long-flat only
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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 target_fn
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# Small internal grid: 4 param sets
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GRID = [
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(10, 3.0),
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(14, 3.0),
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(10, 2.0),
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(14, 2.0),
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]
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TFS = ("1d", "12h")
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# Run each config on both TFs
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best_rep = None
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best_score = -999.0
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print("=== TRD04: Supertrend Grid Search ===")
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for period, mult in GRID:
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label = f"TRD04-ST({period},{mult})"
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fn = make_target(period, mult)
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rep = al.study_weights(label, fn, tfs=TFS)
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v = rep["verdict"]
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score = v.get("best_holdout_sharpe", -9)
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print(al.fmt(rep))
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print()
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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_period = period
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best_mult = mult
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print("\n" + "="*60)
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print(f"BEST CONFIG: period={best_period}, mult={best_mult}")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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