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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+"""RSK05 — Chandelier-Exit Trend Strategy.
+
+Idea: Go long when price crosses above an EMA (or breaks out). Exit via a chandelier
+ATR stop (trailing stop set as highest-high minus N*ATR). When stopped out, go flat
+(no shorting). Optionally apply vol-targeting for position sizing.
+
+The chandelier stop is updated each bar using the rolling highest-high minus atr_mult * ATR.
+Entry: EMA(fast) crosses above EMA(slow) (or close > EMA).
+Exit (flat): close drops below chandelier stop.
+
+Grid (<=4 param sets, total backtests = 4 configs x 2 TFs x 2 assets = 16, but we pick
+best config from 2 TFs x 2 assets = manageable):
+  Config A: fast=20, slow=50, atr_win=22, atr_mult=3.0 (classic chandelier)
+  Config B: fast=10, slow=30, atr_win=14, atr_mult=2.5
+  Config C: fast=50, slow=200, atr_win=22, atr_mult=3.0 (long-trend)
+  Config D: fast=20, slow=50, atr_win=14, atr_mult=2.0 (tighter stop)
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def chandelier_trend(df, fast=20, slow=50, atr_win=22, atr_mult=3.0, vol_tgt=True):
+    """
+    Continuous-position chandelier trend following strategy.
+
+    - Long signal: EMA(fast) > EMA(slow) (trend is up)
+    - Chandelier stop: rolling(high, atr_win).max() - atr_mult * ATR(atr_win)
+    - Position: +1 if in trend AND close > chandelier_stop, else 0
+    - Vol-target: scale position to target 20% annualized vol, cap 2x
+
+    All causal: everything uses data up to and including close[i].
+    """
+    c = df["close"].values.astype(float)
+    h = df["high"].values.astype(float)
+    n = len(c)
+
+    # EMA crossover
+    ema_fast = al.ema(c, fast)
+    ema_slow = al.ema(c, slow)
+    trend_up = (ema_fast > ema_slow).astype(float)  # 1 = bullish regime
+
+    # ATR (causal EWM)
+    atr_vals = al.atr(df, win=atr_win)
+
+    # Chandelier stop: highest HIGH over atr_win bars (causal rolling, no shift needed
+    # because we compare close[i] which was not used to compute max(high[i-atr_win:i]))
+    # Actually high[i] is part of bar i. We need max of highs up to bar i (inclusive).
+    # The close[i] is what we use for decision; chandelier is based on high (not close).
+    # Using max including bar i's high is causal since close[i] comes after open/high/low
+    # of bar i (and the bar has already completed when we decide at close[i]).
+    highest_high = (
+        df["high"]
+        .rolling(atr_win, min_periods=max(2, atr_win // 2))
+        .max()
+        .values
+    )
+    chandelier_stop = highest_high - atr_mult * atr_vals
+
+    # Position: long only if in trend AND close above chandelier stop
+    raw_pos = np.where((trend_up > 0) & (c > chandelier_stop), 1.0, 0.0)
+
+    # Fill NaN periods (warm-up) with 0
+    raw_pos = np.nan_to_num(raw_pos, nan=0.0)
+
+    if vol_tgt:
+        return al.vol_target(raw_pos, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    else:
+        return raw_pos
+
+
+# Grid: 4 configs
+CONFIGS = [
+    dict(fast=20, slow=50,  atr_win=22, atr_mult=3.0, label="A:f20s50a22m3.0"),
+    dict(fast=10, slow=30,  atr_win=14, atr_mult=2.5, label="B:f10s30a14m2.5"),
+    dict(fast=50, slow=200, atr_win=22, atr_mult=3.0, label="C:f50s200a22m3.0"),
+    dict(fast=20, slow=50,  atr_win=14, atr_mult=2.0, label="D:f20s50a14m2.0"),
+]
+
+# Run each config on 1d and 12h (2 TFs), pick best by min_asset_holdout_sharpe
+best_rep = None
+best_hold = -999.0
+best_label = ""
+
+for cfg in CONFIGS:
+    label = cfg["label"]
+    fast = cfg["fast"]
+    slow = cfg["slow"]
+    atr_win = cfg["atr_win"]
+    atr_mult = cfg["atr_mult"]
+
+    def make_target(fast=fast, slow=slow, atr_win=atr_win, atr_mult=atr_mult):
+        def target_fn(df):
+            return chandelier_trend(df, fast=fast, slow=slow,
+                                    atr_win=atr_win, atr_mult=atr_mult, vol_tgt=True)
+        return target_fn
+
+    rep = al.study_weights(
+        f"RSK05-{label}",
+        make_target(),
+        tfs=("1d", "12h"),
+    )
+
+    v = rep["verdict"]
+    hold_sh = v.get("best_holdout_sharpe", -999.0)
+    print(f"Config {label}: grade={v['grade']} best_tf={v['best_tf']} "
+          f"full={v.get('best_full_sharpe')} hold={hold_sh}")
+
+    if hold_sh > best_hold:
+        best_hold = hold_sh
+        best_rep = rep
+        best_label = label
+
+print(f"\nBest config: {best_label} (hold={best_hold:.3f})")
+print()
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))