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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"""MRV04 — IBS (Internal Bar Strength) Mean-Reversion
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HYPOTHESIS: Internal Bar Strength = (close - low) / (high - low).
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Long when IBS < low_thresh (closed near low = oversold position within bar),
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flat (or short) when IBS > high_thresh (closed near high = overbought).
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Classic daily mean-reversion edge. Testing on certified BTC/ETH daily bars.
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Variants tested:
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V1: Long-flat thresholds 0.20/0.80 (classic textbook)
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V2: Long-flat thresholds 0.25/0.75 (slightly wider)
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V3: Long-short thresholds 0.20/0.80 (adds short leg)
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V4: Long-flat thresholds 0.15/0.85 (tighter = rarer signals)
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Best variant selected by min-asset hold-out Sharpe.
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All positions are vol-targeted (20% annualized, 2× leverage cap).
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Evaluated on 1d timeframe (IBS is a daily signal by design).
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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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# ---------------------------------------------------------------------------
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# IBS calculation (causal: uses close, high, low of the same bar i)
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# ---------------------------------------------------------------------------
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def ibs(df) -> np.ndarray:
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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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rng = h - l
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# Avoid division by zero (doji bars with zero range)
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result = np.where(rng > 0, (c - l) / rng, 0.5)
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return result
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# ---------------------------------------------------------------------------
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# Variant builders
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# ---------------------------------------------------------------------------
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def make_ibs_longflat(low_thresh: float, high_thresh: float):
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"""Long when IBS < low_thresh, flat when IBS > high_thresh, hold otherwise."""
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def target_fn(df):
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ibs_val = ibs(df)
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pos = np.full(len(df), np.nan)
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pos[0] = 0.0
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for i in range(1, len(df)):
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if ibs_val[i] < low_thresh:
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pos[i] = 1.0 # go long
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elif ibs_val[i] > high_thresh:
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pos[i] = 0.0 # go flat
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else:
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pos[i] = pos[i - 1] # hold
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pos = np.nan_to_num(pos, nan=0.0)
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return al.vol_target(pos, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target_fn
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def make_ibs_longshort(low_thresh: float, high_thresh: float):
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"""Long when IBS < low_thresh, short when IBS > high_thresh, hold otherwise."""
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def target_fn(df):
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ibs_val = ibs(df)
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pos = np.full(len(df), np.nan)
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pos[0] = 0.0
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for i in range(1, len(df)):
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if ibs_val[i] < low_thresh:
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pos[i] = 1.0 # go long
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elif ibs_val[i] > high_thresh:
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pos[i] = -1.0 # go short
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else:
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pos[i] = pos[i - 1] # hold
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pos = np.nan_to_num(pos, nan=0.0)
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return al.vol_target(pos, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target_fn
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# ---------------------------------------------------------------------------
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# Vectorized version (faster, equivalent logic using ffill)
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# ---------------------------------------------------------------------------
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def make_ibs_longflat_vec(low_thresh: float, high_thresh: float):
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"""Vectorized long-flat IBS strategy."""
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def target_fn(df):
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ibs_val = ibs(df)
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# Signal: 1=long, 0=flat, NaN=hold (ffill)
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sig = np.where(ibs_val < low_thresh, 1.0,
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np.where(ibs_val > high_thresh, 0.0, np.nan))
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sig[0] = 0.0 # start flat
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pos = sig.copy()
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# forward-fill NaN (hold previous)
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import pandas as pd
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pos = pd.Series(pos).ffill().fillna(0.0).values
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return al.vol_target(pos, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target_fn
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def make_ibs_longshort_vec(low_thresh: float, high_thresh: float):
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"""Vectorized long-short IBS strategy."""
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def target_fn(df):
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import pandas as pd
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ibs_val = ibs(df)
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sig = np.where(ibs_val < low_thresh, 1.0,
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np.where(ibs_val > high_thresh, -1.0, np.nan))
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sig[0] = 0.0
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pos = pd.Series(sig).ffill().fillna(0.0).values
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return al.vol_target(pos, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target_fn
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# ---------------------------------------------------------------------------
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# Run all variants
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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TFS = ("1d",)
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variants = [
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("MRV04-V1-LF-0.20/0.80", make_ibs_longflat_vec(0.20, 0.80)),
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("MRV04-V2-LF-0.25/0.75", make_ibs_longflat_vec(0.25, 0.75)),
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("MRV04-V3-LS-0.20/0.80", make_ibs_longshort_vec(0.20, 0.80)),
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("MRV04-V4-LF-0.15/0.85", make_ibs_longflat_vec(0.15, 0.85)),
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]
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results = []
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for name, fn in variants:
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print(f"\nRunning {name} ...")
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rep = al.study_weights(name, fn, tfs=TFS)
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print(al.fmt(rep))
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results.append(rep)
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# Pick best by min_asset_holdout_sharpe
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best = max(results, key=lambda r: r["verdict"].get("best_holdout_sharpe", -99))
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print("\n" + "=" * 60)
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print(f"BEST VARIANT: {best['name']}")
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print(al.fmt(best))
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print("JSON:", al.as_json(best))
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