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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+"""SEA02 — Day-of-week effect on 1d bars.
+
+HYPOTHESIS: Some weekdays have systematically positive (or negative) next-bar returns.
+We use an EXPANDING per-weekday expectancy (causal): at each bar i, we compute the
+average return for bars that share the same day-of-week, using only data up to and
+including bar i. If the expanding mean is positive -> long (+1). We vol-target the
+position (TP01-style) to 20% annualized.
+
+Variations tried (small grid, <=4 configs, <=6 total backtests):
+  A) raw day-of-week: long if expanding mean > 0, else flat (no short)
+  B) long-short: long if expanding mean > 0, short if < 0 (full L/S)
+
+Both run on 1d only (the only sensible TF for a day-of-week effect). Two configs -> 2
+study_weights calls x 2 assets each = 4 backtests total. Well within the 6-call limit.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+import pandas as pd
+
+
+def _dow_expectancy(df: pd.DataFrame, long_only: bool = True) -> np.ndarray:
+    """Compute expanding per-weekday expectancy and return a vol-targeted position array.
+
+    For each bar i:
+    1. Determine the day-of-week of bar i.
+    2. Use the EXPANDING mean of returns of all PRIOR bars (j < i) with the SAME weekday.
+       (We use j < i, not j <= i, to avoid any look-ahead — the return of bar i is not
+       yet realized when we decide at close[i].)
+    3. If expanding_mean[dow] > 0 -> direction = +1 (long)
+       If expanding_mean[dow] < 0 -> direction = -1 (short) if not long_only, else 0
+       If no prior same-weekday bar -> direction = 0 (flat, wait for history)
+    4. Vol-target the direction to 20% ann vol, cap 2x.
+    """
+    c = df["close"].values.astype(float)
+    r = al.simple_returns(c)
+    dt = pd.to_datetime(df["datetime"], utc=True)
+    dow = dt.dt.dayofweek.values  # Monday=0, Sunday=6
+
+    direction = np.zeros(len(c), dtype=float)
+    # Accumulate sum and count per weekday causally
+    dow_sum = np.zeros(7, dtype=float)
+    dow_cnt = np.zeros(7, dtype=int)
+
+    for i in range(len(c)):
+        d = dow[i]
+        # Decide with history up to bar i-1 (returns of bar i not yet known)
+        if dow_cnt[d] > 0:
+            mean_ret = dow_sum[d] / dow_cnt[d]
+            if mean_ret > 0:
+                direction[i] = 1.0
+            elif not long_only:
+                direction[i] = -1.0
+            # else: 0 (flat)
+        # else: flat (no history for this weekday yet)
+
+        # Now "observe" bar i's return for future decisions
+        dow_sum[d] += r[i]
+        dow_cnt[d] += 1
+
+    return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+
+def target_long_only(df: pd.DataFrame) -> np.ndarray:
+    return _dow_expectancy(df, long_only=True)
+
+
+def target_long_short(df: pd.DataFrame) -> np.ndarray:
+    return _dow_expectancy(df, long_only=False)
+
+
+if __name__ == "__main__":
+    print("=== SEA02: Day-of-week effect ===\n")
+
+    # Config A: long-only (long on positive-expectancy weekdays, flat otherwise)
+    rep_a = al.study_weights(
+        "SEA02-A-LongOnly",
+        target_long_only,
+        tfs=("1d",),
+    )
+    print(al.fmt(rep_a))
+    print("JSON:", al.as_json(rep_a))
+    print()
+
+    # Config B: long-short (long on positive weekdays, short on negative weekdays)
+    rep_b = al.study_weights(
+        "SEA02-B-LongShort",
+        target_long_short,
+        tfs=("1d",),
+    )
+    print(al.fmt(rep_b))
+    print("JSON:", al.as_json(rep_b))
+    print()
+
+    # Report best config
+    best_a = rep_a["verdict"]["best_holdout_sharpe"] or -999
+    best_b = rep_b["verdict"]["best_holdout_sharpe"] or -999
+    if best_a >= best_b:
+        best_rep = rep_a
+        best_name = "A-LongOnly"
+    else:
+        best_rep = rep_b
+        best_name = "B-LongShort"
+
+    print(f"\n>>> BEST CONFIG: {best_name}")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))