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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"""MRV05 — Williams %R Mean-Reversion
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HYPOTHESIS: Buy when %R(14) < -90 (oversold) with trend filter (close > SMA200);
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exit (go flat) when %R > -50 (momentum restored). Long-flat only.
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Williams %R = (Highest High(n) - Close) / (Highest High(n) - Lowest Low(n)) * -100
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Range: -100 (most oversold) to 0 (most overbought).
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%R < -80 = oversold zone; %R > -20 = overbought zone.
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The exit condition (%R > -50) is causal: we check %R[i] and decide position for bar i+1.
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This maps naturally to study_weights (continuous hold logic):
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- position[i] = 1 if %R[i] < -90 AND close[i] > SMA200[i] (buy signal)
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- position[i] = 0 if %R[i] > -50 (exit signal)
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- else hold previous position
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Variants (small grid, 4 configs):
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V1: %R entry -90, exit -50, SMA200 trend filter, long-flat
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V2: %R entry -85, exit -50, SMA200 trend filter, long-flat (slightly less oversold entry)
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V3: %R entry -90, exit -50, SMA50 trend filter, long-flat (shorter trend filter)
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V4: %R entry -90, exit -40, SMA200 trend filter, long-flat (later exit)
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Best variant selected by min-asset hold-out Sharpe.
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All positions are vol-targeted (20% annualized, 2x leverage cap).
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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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# ---------------------------------------------------------------------------
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# Williams %R calculation (causal: uses data <= bar i)
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# ---------------------------------------------------------------------------
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def williams_r(df: pd.DataFrame, win: int = 14) -> np.ndarray:
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"""Causal Williams %R. Value at i uses data[i-win+1 .. i].
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%R = (HH - Close) / (HH - LL) * -100
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Range: -100 (oversold) to 0 (overbought).
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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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wr = np.full(n, np.nan)
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# Vectorized rolling using pandas
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hh = pd.Series(h).rolling(win, min_periods=win).max().values
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ll = pd.Series(l).rolling(win, min_periods=win).min().values
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rng = hh - ll
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# Avoid division by zero
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valid = rng > 0
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wr[valid] = (hh[valid] - c[valid]) / rng[valid] * -100.0
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return wr
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# ---------------------------------------------------------------------------
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# Strategy factory
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# ---------------------------------------------------------------------------
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def make_wrpct_target(wr_entry: float = -90.0, wr_exit: float = -50.0,
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sma_win: int = 200, wr_win: int = 14):
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"""Williams %R long-flat mean-reversion with trend filter.
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Entry: %R[i] < wr_entry AND close[i] > SMA(sma_win)[i] -> go long
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Exit: %R[i] > wr_exit -> go flat
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Hold: otherwise, maintain current position
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Causal: position decided using data <= close[i], held during bar i+1.
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Vol-targeted: 20% annualized, 2x leverage cap.
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"""
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def target_fn(df):
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c = df["close"].values.astype(float)
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wr = williams_r(df, wr_win)
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sma_trend = al.sma(c, sma_win)
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# Vectorized state machine using ffill
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# Signal: 1 = enter long, 0 = exit to flat, NaN = hold
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# Priority: exit takes precedence over entry
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sig = np.where(
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wr > wr_exit, # exit condition
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0.0,
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np.where(
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(wr < wr_entry) & (c > sma_trend), # entry condition
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1.0,
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np.nan # hold
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)
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)
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# Start flat
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sig[0] = 0.0
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# Forward-fill NaN (hold previous position)
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pos = pd.Series(sig).ffill().fillna(0.0).values
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# Vol-target
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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 and pick best
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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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("MRV05-V1-WR90-exit50-SMA200", make_wrpct_target(-90.0, -50.0, 200, 14)),
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("MRV05-V2-WR85-exit50-SMA200", make_wrpct_target(-85.0, -50.0, 200, 14)),
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("MRV05-V3-WR90-exit50-SMA50", make_wrpct_target(-90.0, -50.0, 50, 14)),
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("MRV05-V4-WR90-exit40-SMA200", make_wrpct_target(-90.0, -40.0, 200, 14)),
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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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