81 lines
3.3 KiB
Python
81 lines
3.3 KiB
Python
"""Tabella unica consolidata: PnL% NETTO per anno, tutte le strategie a confronto.
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Colonne: DIP01(BTC) · TR01(basket) · ROT01(base) · ROT02(dual-mom) · PORTAFOGLIO.
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Ultima riga: TOT e DD full-period.
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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 scripts.analysis.honest_lab import available_assets, FEE_RT
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from scripts.analysis.honest_improve import _dd
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from scripts.analysis.honest_improve2 import (
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dip_market_gated, _daily_equity, _norm, _tr_basket_daily,
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)
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from scripts.analysis.honest_rotation import build_panel
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LEV, POS = 3.0, 0.15
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def rot_daily(idx, regime_n=0, lookback=60, top_k=2, gross=0.45):
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"""equity giornaliera della rotazione, con/senza overlay dual-momentum."""
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panel = build_panel(available_assets(), "1d")
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cols = list(panel.columns); P = panel.values; T, N = P.shape
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rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
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btc = P[:, cols.index("BTC")]
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bma = pd.Series(btc).rolling(max(regime_n, 2)).mean().values
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use_reg = regime_n and regime_n > 1
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cap = 1000.0; w = np.zeros(N); tl, cl = [], []
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start = max(lookback + 1, regime_n + 1 if use_reg else 0)
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for i in range(start, T - 1):
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risk_on = (btc[i] > bma[i]) if (use_reg and not np.isnan(bma[i])) else True
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mom = P[i] / P[i - lookback] - 1; order = np.argsort(mom)[::-1]
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chosen = [j for j in order if mom[j] > 0][:top_k] if risk_on else []
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nw = np.zeros(N)
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for j in chosen:
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nw[j] = gross / len(chosen)
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cap -= cap * np.abs(nw - w).sum() * (FEE_RT / 2); w = nw
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cap = max(cap * (1 + float(np.dot(w, rets[i + 1]))), 10.0)
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tl.append(panel.index[i]); cl.append(cap)
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return _norm(_daily_equity(tl, cl, idx))
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def year_pnl(eq):
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return {int(y): (g.iloc[-1] / g.iloc[0] - 1) * 100 for y, g in _norm(eq).groupby(eq.index.year)}
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if __name__ == "__main__":
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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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cols = {
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"DIP01(BTC)": _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx)),
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"TR01(bskt)": _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx)),
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"ROT01": rot_daily(idx, regime_n=0),
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"ROT02": rot_daily(idx, regime_n=100),
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}
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drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in {
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"DIP01(BTC)": cols["DIP01(BTC)"], "TR01(bskt)": cols["TR01(bskt)"], "ROT02": cols["ROT02"]
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}.items()})
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cols["PORTAF."] = (1 + drets.mean(axis=1)).cumprod()
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names = list(cols)
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py = {n: year_pnl(cols[n]) for n in names}
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years = sorted({y for n in names for y in py[n]})
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print("=" * 78)
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print(" PnL% NETTO PER ANNO — confronto strategie (leva 3x, fee 0.10% RT)")
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print("=" * 78)
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print(f" {'Anno':>6s}" + "".join(f"{n:>12s}" for n in names))
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print(" " + "-" * 72)
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for y in years:
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print(f" {y:>6d}" + "".join(f"{py[n].get(y, float('nan')):>+12.0f}" if y in py[n] else f"{'-':>12s}" for n in names))
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print(" " + "-" * 72)
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print(f" {'TOT%':>6s}" + "".join(f"{(cols[n].iloc[-1]/cols[n].iloc[0]-1)*100:>+12.0f}" for n in names))
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print(f" {'DDfull':>6s}" + "".join(f"{_dd(cols[n].values):>12.0f}" for n in names))
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