feat(analysis): tabella per-anno (PnL/DD) versioni migliorate + portafoglio

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-05-28 23:52:25 +02:00
parent 783fa5546f
commit 1b099bb47b
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+"""Tabella per-anno (PnL% e DD% intra-anno) delle versioni MIGLIORATE:
+ROT02 (dual-momentum), le 3 sleeve e il PORTAFOGLIO combinato.
+Tutto NETTO. Riusa gli engine di honest_improve / honest_improve2.
+"""
+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 scripts.analysis.honest_improve2 import (  # noqa: E402
+    dip_market_gated, _daily_equity, _norm, _tr_basket_daily, _rot_daily_equity,
+)
+
+
+def _year_dd(eq: pd.Series) -> dict[int, float]:
+    out = {}
+    for y, g in eq.groupby(eq.index.year):
+        peak = g.iloc[0]; dd = 0.0
+        for v in g:
+            peak = max(peak, v); dd = max(dd, (peak - v) / peak if peak > 0 else 0.0)
+        out[int(y)] = dd * 100
+    return out
+
+
+def _year_pnl(eq: pd.Series) -> dict[int, float]:
+    out = {}
+    for y, g in eq.groupby(eq.index.year):
+        out[int(y)] = (g.iloc[-1] / g.iloc[0] - 1) * 100
+    return out
+
+
+def table(name, eq):
+    eq = _norm(eq)
+    dd = _year_dd(eq); pnl = _year_pnl(eq)
+    print(f"\n  {name}")
+    print(f"  {'Anno':>6s}{'PnL%':>9s}{'DD%':>7s}")
+    print("  " + "-" * 22)
+    for y in sorted(pnl):
+        print(f"  {y:>6d}{pnl[y]:>+9.0f}{dd[y]:>7.0f}")
+    tot = (eq.iloc[-1] / eq.iloc[0] - 1) * 100
+    print(f"  {'TOT':>6s}{tot:>+9.0f}{_year_dd(eq) and max(_year_dd(eq).values()):>7.0f}(max anno)")
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("  RISULTATI PER ANNO — versioni migliorate (NETTO)")
+    print("=" * 60)
+
+    # ROT02 dal 2020 (dati paniere)
+    idx_rot = pd.date_range("2020-09-01", "2026-05-26", freq="1D", tz="UTC")
+    eq_rot = _rot_daily_equity(idx_rot)
+    table("ROT02 — dual-momentum rotation (1d)", eq_rot)
+
+    # sleeve + portafoglio dal 2021
+    idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
+    d = dip_market_gated("BTC", market_n=0, return_equity=True)
+    eq_dip = _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx))
+    eq_tr = _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx))
+    eq_r2 = _norm(_rot_daily_equity(idx))
+    table("Sleeve DIP01 — BTC (1h)", eq_dip)
+    table("Sleeve TR01 — basket (4h)", eq_tr)
+    table("Sleeve ROT02 (1d)", eq_r2)
+
+    drets = pd.DataFrame({"DIP": eq_dip.pct_change().fillna(0),
+                          "TR": eq_tr.pct_change().fillna(0),
+                          "ROT": eq_r2.pct_change().fillna(0)})
+    combo = (1 + drets.mean(axis=1)).cumprod()
+    table("PORTAFOGLIO equal-weight (daily rebal)", combo)