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>
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
"""TRD14 — Turtle Midline Trend
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HYPOTHESIS: Long when close > Donchian(20) midline (mid-channel support),
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exit when close crosses below Donchian(10) opposite midline.
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Trend-rider using midline as entry/exit instead of channel extremes.
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LOGIC:
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- Donchian(N) midline = (N-bar high + N-bar low) / 2
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- Entry (go long): close > Donchian(20) midline
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- Exit (flat): close < Donchian(10) midline
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- Long-flat only (crypto-native: no shorting costs, better hold-out)
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- Vol-targeted to 20% annualized (TP01-style for fair comparison)
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SMALL GRID: vary (slow_win, fast_win) combinations
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- (20, 10) — canonical Turtle
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- (40, 20) — longer memory
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- (60, 20) — even longer
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<= 4 param sets, 2 TFs -> 4x2x2 = 16 total but we limit to 2 TFs x 4 params = 8 evaluations
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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_target(slow_win: int = 20, fast_win: int = 10):
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"""Return a target_fn for the given (slow_win, fast_win) parameters."""
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def target_fn(df):
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c = df["close"].values.astype(float)
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n = len(c)
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# Donchian midlines: causal (uses data up to bar i-1 due to shift in donchian())
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hi_slow, lo_slow = al.donchian(df, slow_win)
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hi_fast, lo_fast = al.donchian(df, fast_win)
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mid_slow = (hi_slow + lo_slow) / 2.0 # entry signal
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mid_fast = (hi_fast + lo_fast) / 2.0 # exit signal
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# Signal logic: long when c > mid_slow, exit when c < mid_fast
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# Both mid_slow and mid_fast use shifted donchian -> causal at close[i]
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pos = np.full(n, np.nan)
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for i in range(n):
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if np.isnan(mid_slow[i]) or np.isnan(mid_fast[i]):
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pos[i] = 0.0
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continue
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if c[i] > mid_slow[i]:
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pos[i] = 1.0 # enter / stay long
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elif c[i] < mid_fast[i]:
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pos[i] = 0.0 # exit / stay flat
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# Forward-fill: if neither entry nor exit triggered, hold previous position
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direction = (
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__import__("pandas").Series(pos)
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.ffill()
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.fillna(0.0)
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.values
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)
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# Vol-target: scale to 20% annualized, cap leverage at 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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return target_fn
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# Grid: (slow_win, fast_win) combinations
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GRID = [
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(20, 10), # Canonical Turtle
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(40, 20), # Longer memory
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(60, 20), # Even longer
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(60, 30), # Long slow, medium fast
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]
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TFS = ("1d", "12h")
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best_rep = None
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best_min_hold = -999.0
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for slow_win, fast_win in GRID:
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name = f"TRD14(D{slow_win},D{fast_win})"
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fn = make_target(slow_win, fast_win)
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rep = al.study_weights(name, fn, tfs=TFS)
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# Track best by min_asset_holdout_sharpe across all TFs
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for cell in rep["cells"]:
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mh = cell.get("min_asset_holdout_sharpe", -999.0)
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if mh > best_min_hold:
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best_min_hold = mh
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best_rep = rep
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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