feat(xsec): XS01 reversione cross-sectional (8 asset) -> PORT06 PAPER

Famiglia NUOVA trovata in sessione (dopo aver scartato trend/breakout/seasonal/ opzioni/funding come rumore): ogni 12h long i perdenti relativi / short i vincenti su 8 asset, market-neutral. Scorrelata (~0) da pairs e fade -> diversificatore. - engine canonico scripts/strategies/XS01_cross_sectional.py (no look-ahead, plateau OOS Sharpe 2-3.9, 5/5 anni+, edge concentrato 2025, cost-sensitive ~0.35% RT). - src/live/xsec_worker.py CrossSectionalWorker: validate_xsec_worker == backtest ESATTO (4993/1427 trade). Mirror della cadenza engine (entry-to-entry = hold+1). - gate PORT06: +XS01 -> OOS Sharpe 9.66->10.07, FULL DD 3.68->3.46 (OOS DD +0.17pp, risk-contrib 2.2%). xsec_port06_gate.py. - wiring: _defs XSEC in PORT06 (19 sleeve, family XSEC), build_everything, runner kind=xsec, asset_days da supported (fix fetch alt anche per paper sleeves), paper. - 8 gambe -> niente exec reale -> gira PAPER. Regression-lock 18->19, FULL 7.20->7.34, OOS 9.66->10.07. 93 test verdi. Diario 2026-06-09-xs01-cross-sectional.md. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-09 21:38:05 +00:00
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+"""GATE PORT06 — XS01 (reversione cross-sectional 8 asset), candidato trovato in sessione.
+
+XS01: ogni HOLD ore, long i perdenti relativi / short i vincenti su 8 asset (lb LB),
+market-neutral gross 1, fee 0.10% RT/book. Decorrelato (~0) dai pairs. Domanda: aggiunto
+a PORT06 migliora Sharpe/DD? (criterio del progetto: OOS Sharpe non peggiora E DD scende.)
+
+    uv run python scripts/analysis/xsec_port06_gate.py
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.data.downloader import load_data
+from scripts.analysis.combine_portfolio import port_returns, metrics, SPLIT, OOS_DATE
+from scripts.analysis.report_families import daily_from
+from scripts.portfolios._defs import PORTFOLIOS
+from src.portfolio.sleeves import all_sleeve_equities
+from src.portfolio import weighting as W
+
+ASSETS = ["BTC", "ETH", "LTC", "ADA", "SOL", "BNB", "XRP", "DOGE"]
+LB, HOLD, FEE = 48, 12, 0.0005
+
+
+def xsec_equity(pos=0.15, lev=3.0):
+    dfs = {a: load_data(a, "1h")[["timestamp", "close"]].rename(columns={"close": a}).set_index("timestamp")
+           for a in ASSETS}
+    M = pd.concat(dfs.values(), axis=1, join="inner").sort_index()
+    C = M[ASSETS].values
+    ts = pd.to_datetime(M.index, unit="ms", utc=True)
+    n = len(C); logC = np.log(C)
+    cap = 1000.0; eq_ts, eq_v, rets = [], [], []
+    last = -1; i = LB
+    while i < n - HOLD:
+        if i <= last:
+            i += 1; continue
+        dm = (logC[i] - logC[i - LB]); dm = dm - dm.mean()
+        w = -dm; gw = np.sum(np.abs(w))
+        if gw < 1e-9:
+            i += 1; continue
+        w = w / gw
+        net = np.sum(w * (logC[i + HOLD] - logC[i])) - FEE * np.sum(np.abs(w)) * 2
+        cap = max(cap + cap * pos * lev * net, 10.0)
+        rets.append(net); eq_ts.append(ts[i + HOLD]); eq_v.append(cap)
+        last = i + HOLD; i += 1
+    return daily_from(eq_ts, eq_v), np.array(rets)
+
+
+def port_metrics(members, ids, clusters, caps):
+    dr = pd.DataFrame({i: members[i].pct_change().fillna(0.0) for i in ids})
+    w = W.weight_vector("cap", ids, dr, caps=caps, clusters=clusters)
+    drp = port_returns({i: members[i] for i in ids}, w)
+    return metrics(drp), metrics(drp, lo=SPLIT), w
+
+
+def main():
+    p = PORTFOLIOS["PORT06"]
+    eq_base = dict(all_sleeve_equities())
+    print("=" * 92)
+    print("  GATE PORT06 — XS01 reversione cross-sectional (8 asset) | OOS da", OOS_DATE)
+    print("=" * 92)
+
+    for pos, lbl in [(0.15, "XS01 pos0.15"), (0.075, "XS01 pos0.075 (mezza)")]:
+        e, r = xsec_equity(pos=pos)
+        # correlazione con i pairs e i fade
+        cors = {}
+        for ref in ("PR_ETHBTC", "MR02_ETH"):
+            j = pd.concat([e.pct_change(), eq_base[ref].pct_change()], axis=1).dropna()
+            cors[ref] = round(j.iloc[:, 0].corr(j.iloc[:, 1]), 3)
+        ids0 = list(p.sleeve_ids); cl0 = p.clusters; caps = p.caps
+        f0, o0, _ = port_metrics(eq_base, ids0, cl0, caps)
+        mem = dict(eq_base); mem["XS01"] = e
+        ids1 = ids0 + ["XS01"]; cl1 = dict(cl0); cl1["XS01"] = "xsec"
+        f1, o1, w1 = port_metrics(mem, ids1, cl1, caps)
+        # risk contribution di XS01
+        drm = pd.DataFrame({i: mem[i].pct_change().fillna(0.0) for i in ids1})
+        cov = drm.cov(); wv = np.array([w1[i] for i in ids1])
+        pv = float(wv @ cov.values @ wv)
+        rc = {i: float(w1[i] * (cov.values[k] @ wv) / pv * 100) for k, i in enumerate(ids1)}
+        print(f"\n[{lbl}] corr XS01 vs {cors} | peso XS01 {w1['XS01']*100:.1f}% | "
+              f"risk-contrib XS01 {rc['XS01']:.1f}%")
+        print(f"    {'config':<16}{'FULL Sh':>8}{'FULL DD%':>9}{'OOS Sh':>8}{'OOS DD%':>8}")
+        print(f"    {'ATTUALE':<16}{f0['sharpe']:>8.2f}{f0['dd']:>9.2f}{o0['sharpe']:>8.2f}{o0['dd']:>8.2f}")
+        print(f"    {'+XS01':<16}{f1['sharpe']:>8.2f}{f1['dd']:>9.2f}{o1['sharpe']:>8.2f}{o1['dd']:>8.2f}")
+        ok = (o1["sharpe"] >= o0["sharpe"] - 0.02 and o1["dd"] <= o0["dd"] + 1e-9
+              and f1["sharpe"] >= f0["sharpe"] - 0.02 and f1["dd"] <= f0["dd"] + 1e-9)
+        print(f"    => {'PROMOSSO' if ok else 'non passa il criterio stretto (vedi numeri)'}")
+
+
+if __name__ == "__main__":
+    main()