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>
2026-06-20 19:50:39 +00:00
parent bf84bc91e2
commit 5ac4e16af8
111 changed files with 16924 additions and 0 deletions
@@ -0,0 +1,111 @@
+"""SEA03 — Weekend Effect
+HYPOTHESIS: Crypto prices behave differently on weekend bars vs weekday bars.
+We test long/flat (and long/short) positions on weekend bars only,
+with the direction chosen by expanding in-sample sign of weekend vs weekday returns.
+
+VARIANTS tested (<=4 param sets, <=6 total backtests with 2 TFs):
+  V1: Fixed long on weekends (Sat/Sun bars), flat on weekdays
+  V2: Expanding-sign direction on weekends (long or short), flat on weekdays
+  V3: V2 + vol-targeting
+  Best config selected by min_asset_holdout_sharpe.
+
+We use 1d bars (weekend = day_of_week in {5,6}: Saturday, Sunday).
+On hourly bars there may not be a clean weekend partition, so we use 1d only.
+"""
+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 _is_weekend(df: pd.DataFrame) -> np.ndarray:
+    """Return boolean array: True if this bar is a weekend bar (Sat or Sun)."""
+    dt = pd.to_datetime(df["datetime"], utc=True)
+    return (dt.dt.dayofweek >= 5).values  # 5=Sat, 6=Sun
+
+
+def _expanding_weekend_sign(df: pd.DataFrame) -> np.ndarray:
+    """For each bar, compute expanding-mean return on weekend bars vs weekday bars.
+    Return +1 if weekend historically outperforms weekday, else -1.
+    This is causal: at bar i we use only returns from bars 0..i-1.
+    Returns array of +1/-1 (same sign for all bars on the same day as rolling expands).
+    """
+    c = df["close"].values.astype(float)
+    r = al.simple_returns(c)
+    is_wk = _is_weekend(df)
+
+    # Expanding cumulative mean of weekend returns and weekday returns up to bar i-1
+    # We look at sign(mean_wkend - mean_wkday) to decide direction for bar i
+    sign_arr = np.ones(len(r))  # default +1 (long)
+
+    cum_wkend_sum = 0.0
+    cum_wkend_n = 0
+    cum_wkday_sum = 0.0
+    cum_wkday_n = 0
+
+    for i in range(1, len(r)):
+        # Use return of bar i-1
+        if is_wk[i - 1]:
+            cum_wkend_sum += r[i - 1]
+            cum_wkend_n += 1
+        else:
+            cum_wkday_sum += r[i - 1]
+            cum_wkday_n += 1
+
+        if cum_wkend_n >= 5 and cum_wkday_n >= 5:
+            mean_wk = cum_wkend_sum / cum_wkend_n
+            mean_wd = cum_wkday_sum / cum_wkday_n
+            sign_arr[i] = 1.0 if mean_wk >= mean_wd else -1.0
+        # else: not enough history, default +1
+
+    return sign_arr
+
+
+# ---- Variant 1: Fixed long on weekends, flat on weekdays ----
+def v1_fixed_long(df: pd.DataFrame) -> np.ndarray:
+    is_wk = _is_weekend(df)
+    # position: +1 on weekend bars, 0 on weekday bars
+    return is_wk.astype(float)
+
+
+# ---- Variant 2: Expanding-sign direction on weekends, flat on weekdays ----
+def v2_expanding_sign(df: pd.DataFrame) -> np.ndarray:
+    is_wk = _is_weekend(df)
+    sign = _expanding_weekend_sign(df)
+    # Long or short on weekend depending on expanding sign, flat on weekdays
+    return np.where(is_wk, sign, 0.0)
+
+
+# ---- Variant 3: V2 + vol targeting ----
+def v3_voltarget(df: pd.DataFrame) -> np.ndarray:
+    direction = v2_expanding_sign(df)
+    return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+
+# ---- Variant 4: Long weekdays (inverse hypothesis) ----
+def v4_fixed_long_weekday(df: pd.DataFrame) -> np.ndarray:
+    is_wk = _is_weekend(df)
+    return (~is_wk).astype(float)
+
+
+if __name__ == "__main__":
+    variants = [
+        ("SEA03-V1-weekend-long", v1_fixed_long),
+        ("SEA03-V2-expanding-sign", v2_expanding_sign),
+        ("SEA03-V3-voltarget", v3_voltarget),
+        ("SEA03-V4-weekday-long", v4_fixed_long_weekday),
+    ]
+
+    results = []
+    for name, fn in variants:
+        print(f"\nRunning {name}...")
+        rep = al.study_weights(name, fn, tfs=("1d",))
+        print(al.fmt(rep))
+        results.append(rep)
+
+    # Pick best config by min_asset_holdout_sharpe across all cells
+    best_rep = max(results, key=lambda r: r["verdict"].get("best_holdout_sharpe", -99))
+    print("\n\n=== BEST CONFIG ===")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))