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

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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>
This commit is contained in:
Adriano Dal Pastro
2026-06-09 21:38:05 +00:00
parent d3dab57532
commit a85289d7c7
12 changed files with 508 additions and 13 deletions
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scripts/analysis/report_families.py
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@@ -59,6 +59,11 @@ def build_everything():
t = tsmom_sim()
tsm = {"TSM01": daily_from(t["eq_ts"], t["eq_v"])}
shape = {f"SH_{a}": _norm(shape_daily_equity(a, IDX)) for a in ("BTC", "ETH")}
# XS01 — reversione cross-sectional (8 asset, market-neutral). Engine canonico
# scripts.strategies.XS01_cross_sectional (worker validato == backtest).
from scripts.strategies.XS01_cross_sectional import xsec_sim
x = xsec_sim()
tsm["XS01"] = daily_from(x["eq_ts"], x["eq_v"])
return S, pairs, tsm, shape
scripts/analysis/validate_xsec_worker.py
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@@ -0,0 +1,54 @@
"""Valida il CrossSectionalWorker: replay bar-per-bar == backtest XS01.xsec_sim?
Come validate_worker_pairs: alimenta il worker con finestre trailing crescenti del
pannello 8-asset e confronta capitale finale e n.trade col backtest di riferimento
scripts.strategies.XS01_cross_sectional.xsec_sim. Se combaciano, la semantica live e' fedele.
"""
from __future__ import annotations
import shutil
import sys
import tempfile
from pathlib import Path
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(PROJECT_ROOT))
from src.live.xsec_worker import CrossSectionalWorker
from scripts.strategies.XS01_cross_sectional import aligned_panel, xsec_sim, UNIVERSE, LB, HOLD
def main():
print("=" * 88)
print(" VALIDAZIONE CrossSectionalWorker — replay live vs backtest xsec_sim (fee 0.10% RT/book)")
print("=" * 88)
M = aligned_panel(UNIVERSE)
dfs = {a: pd.DataFrame({"timestamp": M.index.values, "close": M[a].values}) for a in UNIVERSE}
n = len(M)
tmp = Path(tempfile.mkdtemp(prefix="xsec_val_"))
try:
w = CrossSectionalWorker(UNIVERSE, tf="1h", params={"lb": LB, "hold": HOLD},
fee_rt=0.0005, data_dir=tmp)
w._save = lambda: None; w._log = lambda *a, **k: None; w._notify = lambda *a, **k: None
window = LB + 6
for k in range(LB + 1, n + 1): # prima finestra = lb+1 barre -> ingresso al bar lb
lo = max(0, k - window)
w.tick({a: dfs[a].iloc[lo:k] for a in UNIVERSE})
bt = xsec_sim(UNIVERSE)
bt_cap = 1000.0 * (1 + bt["ret"] / 100)
cap_ok = abs(w.capital - bt_cap) / bt_cap < 0.02 if bt_cap else False
trd_ok = abs(w.total_trades - bt["trades"]) <= max(2, bt["trades"] * 0.02)
ww = w.total_wins / w.total_trades * 100 if w.total_trades else 0
print(f"\n {'':<6}{'cap':>14}{'trades':>8}{'win%':>7}")
print(f" WORKER{w.capital:>14.0f}{w.total_trades:>8d}{ww:>7.1f}")
print(f" BCKTST{bt_cap:>14.0f}{bt['trades']:>8d}{bt['win']:>7.1f}")
print(f"\n ESITO: {'OK (replay == backtest)' if (cap_ok and trd_ok) else 'DIFF -> INDAGARE'}")
print(" (diff minime attese da bar finale aperta / troncamento)")
finally:
shutil.rmtree(tmp, ignore_errors=True)
if __name__ == "__main__":
main()
scripts/analysis/xsec_port06_gate.py
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@@ -0,0 +1,97 @@
"""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()