Adriano
9ebdfc7a7a
ROT02 concentrava il book su 2 soli asset (DD 40%). top_k=3 dimezza quasi il DD (40% -> 26%) e ALZA il ritorno full (+1095 -> +1303%, ret/DD da 27 a 50). Il vol-target abbassa il DD ma sacrifica ritorno (de-leverage) -> tenuto top_k=3 senza VT. Caveat: OOS di ROT02 cala (+98 -> +68%, DD 12 -> 14%), ma il portafoglio MASTER migliora lo Sharpe full (3.95 -> 4.23). Applicato a ROT02_dual_momentum.py e a _rot_daily_equity (usata da PORT01/PORT03). Docstring/CLAUDE/diario aggiornati. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
66 lines
2.8 KiB
Python
66 lines
2.8 KiB
Python
"""PORT01 — Portafoglio combinato delle 3 strategie oneste (equal-weight, daily rebal).
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Sleeve (meccanismi anti-correlati):
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DIP01 dip-buy reversion su BTC (1h) regime: reversione
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TR01 EMA 20/100 trend su paniere (4h) regime: momentum singolo
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ROT02 dual-momentum rotation (1d) regime: forza relativa + risk-off
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La diversificazione e' il vero motore di risk-reduction: il DD del portafoglio
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scende SOTTO quello della sleeve meno rischiosa, mantenendo una CAGR alta e
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azzerando quasi gli anni negativi (il 2022 bear passa da -30% di ROT a -1%).
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Risultato (netto, 2021-2026, leva 3x pos 15% per sleeve; ROT02 ora top_k=3):
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DIP01_BTC +322% DD 15% CAGR 31%
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TR01_basket +591% DD 27% CAGR 43%
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ROT02_dualmom +984% DD 26% CAGR 56% (top_k=3: DD 40->26, PnL su)
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PORTAFOGLIO +676% DD 13% CAGR 46% <-- DD piu' basso di ogni sleeve
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Per-anno: 2021 +224 · 2022 +1 · 2023 +48 · 2024 +48 · 2025 +10 · 2026 -2
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Logica e ricostruzione: scripts/analysis/honest_improve2.py.
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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 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 scripts.analysis.honest_improve import _dd # noqa: E402
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from scripts.analysis.honest_improve2 import ( # noqa: E402
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dip_market_gated, _daily_equity, _norm, _tr_basket_daily, _rot_daily_equity,
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)
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def run():
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idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
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d = dip_market_gated("BTC", market_n=0, return_equity=True)
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members = {
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"DIP01_BTC": _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx)),
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"TR01_basket": _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx)),
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"ROT02_dualmom": _norm(_rot_daily_equity(idx)),
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}
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drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in members.items()})
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port_ret = drets.mean(axis=1)
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combo = (1 + port_ret).cumprod()
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yrs = (idx[-1] - idx[0]).days / 365.25
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print("=" * 80)
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print(f" PORT01 — portafoglio equal-weight (daily rebal) | {idx[0].date()} -> {idx[-1].date()}")
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print("=" * 80)
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print(f" {'sleeve':<16s}{'ret%':>9s}{'DD%':>7s}{'CAGR%':>8s}")
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for name, s in members.items():
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r = (s.iloc[-1] / s.iloc[0] - 1) * 100
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cagr = ((s.iloc[-1] / s.iloc[0]) ** (1 / yrs) - 1) * 100
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print(f" {name:<16s}{r:>+9.0f}{_dd(s.values):>7.0f}{cagr:>8.0f}")
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r = (combo.iloc[-1] / combo.iloc[0] - 1) * 100
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cagr = ((combo.iloc[-1] / combo.iloc[0]) ** (1 / yrs) - 1) * 100
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print(f" {'PORTAFOGLIO':<16s}{r:>+9.0f}{_dd(combo.values):>7.0f}{cagr:>8.0f}")
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pa = port_ret.groupby(port_ret.index.year).apply(lambda x: ((1 + x).prod() - 1) * 100)
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print(" Per-anno: " + " ".join(f"{y}:{v:+.0f}%" for y, v in pa.items()))
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if __name__ == "__main__":
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run()
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