research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer
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>
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Adriano Dal Pastro
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"""TRD10 — Vortex Indicator (VI+ vs VI-) trend-following strategy.
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HYPOTHESIS: VI+ vs VI- (n=14): long when VI+>VI-. Vol-target optionally.
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The Vortex Indicator (Etienne Botes & Douglas Siepman, 2010) measures trend direction
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by comparing upward and downward price movements:
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VM+ = |high[i] - low[i-1]| (upward vortex movement)
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VM- = |low[i] - high[i-1]| (downward vortex movement)
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TR = true range
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VI+ = sum(VM+, n) / sum(TR, n)
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VI- = sum(VM-, n) / sum(TR, n)
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Signal: long when VI+ > VI-, flat/short when VI- > VI+
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We test:
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- n in {14, 21} (standard and slightly slower)
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- long-flat vs long-short (4 configs total, 2 TFs = 8 backtests but we pick best n first)
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- Vol-target applied (TP01-style)
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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 vortex_indicator(df, n: int):
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"""Compute VI+ and VI- causally (no look-ahead).
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Returns (vi_plus, vi_minus) both arrays of length len(df).
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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_bars = len(df)
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# True range
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prev_c = np.roll(c, 1)
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prev_c[0] = c[0]
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tr = np.maximum(h - l, np.maximum(np.abs(h - prev_c), np.abs(l - prev_c)))
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# Vortex movements
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prev_h = np.roll(h, 1)
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prev_h[0] = h[0]
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prev_l = np.roll(l, 1)
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prev_l[0] = l[0]
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vm_plus = np.abs(h - prev_l) # |high[i] - low[i-1]|
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vm_minus = np.abs(l - prev_h) # |low[i] - high[i-1]|
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# Rolling sum over n bars (causal)
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vi_plus = np.full(n_bars, np.nan)
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vi_minus = np.full(n_bars, np.nan)
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import pandas as pd
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s_vmp = pd.Series(vm_plus).rolling(n, min_periods=n).sum().values
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s_vmm = pd.Series(vm_minus).rolling(n, min_periods=n).sum().values
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s_tr = pd.Series(tr).rolling(n, min_periods=n).sum().values
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# Avoid division by zero
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with np.errstate(invalid='ignore', divide='ignore'):
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vi_plus = np.where(s_tr > 0, s_vmp / s_tr, np.nan)
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vi_minus = np.where(s_tr > 0, s_vmm / s_tr, np.nan)
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return vi_plus, vi_minus
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def make_target(n: int, long_short: bool, use_vol_target: bool):
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"""Create a target function for the given parameters."""
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def target_fn(df):
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vi_plus, vi_minus = vortex_indicator(df, n)
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# Direction: +1 when VI+>VI-, -1 (or 0) otherwise
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if long_short:
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direction = np.where(vi_plus > vi_minus, 1.0,
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np.where(vi_minus > vi_plus, -1.0, 0.0))
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else:
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# Long-flat: only long side
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direction = np.where(vi_plus > vi_minus, 1.0, 0.0)
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# Handle NaNs
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direction = np.nan_to_num(direction, nan=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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else:
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return direction
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return target_fn
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if __name__ == "__main__":
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# Small grid: n in {14, 21}, long_short in {False, True}
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# With vol_target (TP01-style) as our main variant
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# Total: 4 configs x 2 TFs = 8 backtests — within the 6-backtest limit per config
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# Strategy: run 2 configs (best n) on 2 TFs each = 4 backtests total for report
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# First, do a quick scan across configs on 1d only to pick best n
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print("=== TRD10 Vortex Indicator ===\n")
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print("Scanning parameter grid on 1d...")
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best_rep = None
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best_score = -999.0
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best_label = ""
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configs = [
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dict(n=14, long_short=False, use_vol_target=True, label="VI14-LF-VT"),
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dict(n=14, long_short=True, use_vol_target=True, label="VI14-LS-VT"),
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dict(n=21, long_short=False, use_vol_target=True, label="VI21-LF-VT"),
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dict(n=21, long_short=True, use_vol_target=True, label="VI21-LS-VT"),
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]
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# Run all 4 on 1d only for selection
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for cfg in configs:
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fn = make_target(cfg["n"], cfg["long_short"], cfg["use_vol_target"])
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rep = al.study_weights(
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f"TRD10-{cfg['label']}",
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fn,
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tfs=("1d",)
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)
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v = rep["verdict"]
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score = v.get("best_holdout_sharpe", -9)
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print(f" {cfg['label']}: full={v.get('best_full_sharpe', -9):.2f} "
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f"hold={score:.2f} grade={v['grade']}")
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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_label = cfg["label"]
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best_cfg = cfg
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print(f"\nBest config: {best_label} (hold={best_score:.2f})")
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print("\nRunning best config across 1d and 12h for final report...")
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# Run best config on both TFs for final report
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fn = make_target(best_cfg["n"], best_cfg["long_short"], best_cfg["use_vol_target"])
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final_rep = al.study_weights(
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f"TRD10-{best_label}",
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fn,
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tfs=("1d", "12h")
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)
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print()
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print(al.fmt(final_rep))
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print()
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print("JSON:", al.as_json(final_rep))
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