chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita

Reset del progetto su fondamenta verificate dopo la scoperta che l'intera libreria "validata OOS" era artefatto di feed contaminato (print fantasma del feed Cerbero TESTNET + storico Binance/USDT). - Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE 50-82% barre flat; XRP/BNB non certificabili). - Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST con segnale residuo, da ri-validare in isolamento. - Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio, runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/ portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/ (preservati, non cancellati). Diario consolidato in un unico documento. - Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal + src/backtest/engine + load_data; tool dati certificati (rebuild_history, certify_feed, audit_feed, multi_source_check). - Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-19 15:16:03 +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()