Adriano Dal Pastro
a85289d7c7
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>
106 lines
4.4 KiB
Python
106 lines
4.4 KiB
Python
"""XS01 — Cross-Sectional Reversion (market-neutral su 8 cripto). FAMIGLIA NUOVA.
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Distinta dai pairs PR01 (pairwise) e dai fade (single-asset): ogni HOLD ore classifica
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gli 8 asset per rendimento su LB ore e va LONG i perdenti relativi / SHORT i vincenti
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(peso ∝ -(ret - media_cross-section)), market-neutral gross 1. Cattura il FATTORE
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reversione cross-sezionale. Scorrelato (~0) da pairs e fade -> diversificatore.
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Engine ONESTO (no look-ahead, verificato): pesi a barra i da close[<=i]; ingresso a
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close[i], uscita a close[i+HOLD]; roll NON sovrapposto (riallinea ogni HOLD barre).
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Fee = 0.10% RT/book (turnover gross 1 -> 2*fee_rt). PnL su capitale composto (pos, lev).
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Validazione (sessione 2026-06-09, lb48 hold12, fee 0.10% RT, OOS ultimo 30%):
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FULL Sharpe ~3.3 / OOS ~3.4, plateau lb 12-72 x hold 6-24 (OOS 2-3.9), 4/5 anni+.
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Decorrelato (-0.006 da PR01 ETH/BTC). Cost-sensitive: muore ~0.35% RT/book.
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Gate PORT06: +XS01 -> OOS Sharpe 9.66->10.07, FULL DD 3.68->3.46 (OOS DD +0.06pp a mezza size).
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.data.downloader import load_data
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UNIVERSE = ["BTC", "ETH", "LTC", "ADA", "SOL", "BNB", "XRP", "DOGE"]
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FEE_RT, LEV, POS, OOS_FRAC = 0.0005, 3.0, 0.15, 0.30
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LB, HOLD = 48, 12
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def aligned_panel(assets=UNIVERSE, tf="1h"):
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dfs = {a: load_data(a, tf)[["timestamp", "close"]].rename(columns={"close": a}).set_index("timestamp")
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for a in assets}
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M = pd.concat(dfs.values(), axis=1, join="inner").sort_index()
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return M[assets]
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def xsec_sim(assets=UNIVERSE, tf="1h", lb=LB, hold=HOLD, fee_rt=FEE_RT, lev=LEV,
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pos=POS, split_frac=0.0):
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M = aligned_panel(assets, tf)
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C = M[assets].values
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ts = pd.to_datetime(M.index, unit="ms", utc=True)
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n = len(C); logC = np.log(C)
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split = int(n * split_frac)
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cap = peak = 1000.0; dd = 0.0
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trades = wins = 0; rets = []; yearly = {}; yearly_n = {}
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eq_ts, eq_v = [], []
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last = -1; i = max(lb, split)
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fee = 2 * fee_rt # gross 1 -> turnover 2 (entra+esce)
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while i < n - hold:
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if i <= last:
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i += 1; continue
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dm = (logC[i] - logC[i - lb]); dm = dm - dm.mean()
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w = -dm; gw = np.sum(np.abs(w))
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if gw < 1e-9:
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i += 1; continue
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w = w / gw # market-neutral, gross 1
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book = float(np.sum(w * (logC[i + hold] - logC[i])))
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net = book - fee
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cap = max(cap + cap * pos * lev * net, 10.0)
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peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
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trades += 1; wins += net > 0; rets.append(net * pos); last = i + hold
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eq_ts.append(ts[i + hold]); eq_v.append(cap)
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yearly[ts[i].year] = yearly.get(ts[i].year, 0.0) + net * 100
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yearly_n[ts[i].year] = yearly_n.get(ts[i].year, 0) + 1
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i += 1
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yrs_span = (ts[-1] - ts[max(split, 0)]).days / 365.25 or 1
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sharpe = 0.0
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if len(rets) > 1 and np.std(rets) > 0:
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sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades / yrs_span))
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ret_tot = (cap / 1000 - 1) * 100
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cagr = ((cap / 1000) ** (1 / yrs_span) - 1) * 100 if cap > 0 else -100
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return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot,
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cagr=cagr, dd=dd * 100, sharpe=sharpe, yearly=yearly, yearly_n=yearly_n,
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eq_ts=eq_ts, eq_v=eq_v)
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def check_no_lookahead():
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M = aligned_panel(); logC = np.log(M.values); i = 1000
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a = (logC[i] - logC[i - LB])
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Cp = logC.copy(); Cp[i + 1:] += 0.5
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b = (Cp[i] - Cp[i - LB])
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print(f" no-look-ahead: segnale invariato col futuro perturbato -> "
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f"{'OK' if np.allclose(a, b) else 'VIOLAZIONE'}")
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def run():
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print("=" * 84)
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print(" XS01 — Cross-Sectional Reversion (8 asset, market-neutral) | netto fee 0.10% RT/book")
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print("=" * 84)
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check_no_lookahead()
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f = xsec_sim()
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o = xsec_sim(split_frac=1 - OOS_FRAC)
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yrs = f["yearly"]; pos_y = sum(1 for v in yrs.values() if v > 0)
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print(f" trade {f['trades']} | win {f['win']:.1f}% | CAGR {f['cagr']:.0f}% | DD {f['dd']:.0f}% | "
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f"Sharpe FULL {f['sharpe']:.2f} / OOS {o['sharpe']:.2f} | anni+ {pos_y}/{len(yrs)}")
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print(" per anno:", " ".join(f"{y}:{v:+.0f}%" for y, v in sorted(yrs.items())))
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if __name__ == "__main__":
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run()
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