Adriano Dal Pastro
c8a390d6b7
scripts/portfolio/forecast_deribit_book.py — projects the Deribit-only book (TP01+SKH01) forward by pure compounding (reinvest winnings), monthly-aligned, NO external contributions (not a PAC). Deterministic @historical & @conservative CAGR + Monte-Carlo block-bootstrap (median/p10/p90), plus €/day run-rate @conservative. Parametric (--capital/--years/--cons-frac). Honest caveats baked in (bull-crypto sample, no leverage, SKH01 not live, conditional projection). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
107 lines
5.4 KiB
Python
107 lines
5.4 KiB
Python
"""PROIEZIONE ACCUMULO del book DERIBIT-ONLY (TP01 + SKH01) — compounding puro (reinvesti le
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vincite), allineamento MENSILE, NESSUN versamento esterno (non e' un PAC).
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Base: rendimenti mensili del book Deribit-only (rebal mensile, netto costo turnover). Proietta in
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avanti l'equity da un capitale iniziale:
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- DETERMINISTICO @CAGR storico e @CAGR conservativo (= storico × cons_frac, default metà);
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- MONTE CARLO block-bootstrap dei rendimenti mensili storici (mediana + banda p10/p90);
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- €/giorno run-rate (cresce col capitale, perche' si rigiocano le vincite).
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⚠️ ONESTA': lo storico e' un BULL crypto ~2019-26 -> il futuro sara' quasi certamente piu' magro.
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Pianificare sulla colonna conservativa; il MC non contiene un vero bear pluriennale (anche il p10
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e' ottimista). Nessuna leva. SKH01 e' research/forward-monitor (solo TP01 e' armato live). Non e'
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una garanzia: e' una proiezione condizionata "se il futuro somigliasse al passato".
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uv run python scripts/portfolio/forecast_deribit_book.py
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uv run python scripts/portfolio/forecast_deribit_book.py --capital 5000 --years 1,3,5,10 --cons-frac 0.5
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"""
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from __future__ import annotations
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import argparse
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import sys
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from pathlib import Path
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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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import numpy as np
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import pandas as pd
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from src.portfolio.portfolio import StrategyPortfolio, rebalance_sim
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from src.portfolio.sleeves import deribit_book_sleeves
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def book_monthly_returns(rebal_days: int, cost_rate: float) -> pd.Series:
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"""Rendimenti MENSILI del book Deribit-only (rebal periodico, netto costo)."""
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sl = deribit_book_sleeves()
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w = StrategyPortfolio(sl).weights()
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cols = {s.name: s.daily() for s in sl}
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r = rebalance_sim(cols, w, period_days=rebal_days, cost_rate=cost_rate)["daily"]
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return ((1.0 + r).resample("ME").prod() - 1.0).dropna()
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def main():
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ap = argparse.ArgumentParser(description="Proiezione accumulo book Deribit-only (compounding, allineamento mensile)")
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ap.add_argument("--capital", type=float, default=5000.0)
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ap.add_argument("--years", type=str, default="1,2,3,5,8,10")
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ap.add_argument("--cons-frac", type=float, default=0.5, help="CAGR conservativo = storico × questo (default 0.5)")
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ap.add_argument("--sims", type=int, default=4000)
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ap.add_argument("--block-months", type=int, default=3, help="blocco del bootstrap (mesi)")
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ap.add_argument("--rebal-days", type=int, default=30)
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ap.add_argument("--cost-rate", type=float, default=0.0005, help="fee per-lato sul turnover (Deribit taker)")
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ap.add_argument("--seed", type=int, default=7)
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a = ap.parse_args()
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cap = a.capital
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HY = [int(x) for x in a.years.split(",") if x.strip()]
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m = book_monthly_returns(a.rebal_days, a.cost_rate)
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mv = m.values
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nm = len(mv)
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g_month = float(np.prod(1.0 + mv) ** (1.0 / nm) - 1.0)
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cagr = (1.0 + g_month) ** 12 - 1.0
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vol_ann = float(mv.std() * np.sqrt(12))
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cons_cagr = cagr * a.cons_frac
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print("=" * 90)
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print(f" PROIEZIONE ACCUMULO — book Deribit-only (TP01+SKH01) | compounding, allineamento mensile, no versamenti")
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print(f" storico: {nm} mesi · CAGR {cagr*100:.1f}% · vol annua {vol_ann*100:.0f}% (bull crypto, no leva) | capitale €{cap:,.0f}")
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print("=" * 90)
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# Monte Carlo: block-bootstrap dei rendimenti mensili
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rng = np.random.default_rng(a.seed)
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blk = a.block_months
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maxM = max(HY) * 12
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nb = maxM // blk + 1
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starts = rng.integers(0, nm - blk, size=(a.sims, nb))
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paths = np.concatenate([mv[starts[:, k][:, None] + np.arange(blk)[None, :]] for k in range(nb)], axis=1)[:, :maxM]
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eqMC = cap * np.cumprod(1.0 + paths, axis=1)
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cons_m = (1.0 + cons_cagr) ** (1.0 / 12) - 1.0
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print(f"\n ACCUMULO (reinvesti le vincite):")
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print(f" {'oriz.':>6} | {'det @storico':>13} | {'det @conserv.':>14} | {'MC mediana':>11} | {'MC p10':>9} | {'MC p90':>10}")
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print(f" {'':>6} | {'('+format(cagr*100,'.0f')+'%)':>13} | {'('+format(cons_cagr*100,'.0f')+'%)':>14} |")
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print(" " + "-" * 80)
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for y in HY:
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mo = y * 12
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det = cap * (1.0 + g_month) ** mo
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detc = cap * (1.0 + cons_m) ** mo
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c = eqMC[:, mo - 1]
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print(f" {y:>4}a | €{det:>11,.0f} | €{detc:>12,.0f} | €{np.median(c):>9,.0f} | €{np.percentile(c,10):>7,.0f} | €{np.percentile(c,90):>8,.0f}")
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# €/giorno run-rate @conservativo (cresce col capitale)
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rd = (1.0 + cons_cagr) ** (1.0 / 365.0) - 1.0
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print(f"\n €/GIORNO run-rate @conservativo ({cons_cagr*100:.1f}% CAGR) — cresce col capitale (rigiochi le vincite):")
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print(f" {'oriz.':>6} | {'equity':>9} | {'€/giorno':>10} | {'€/mese':>8}")
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print(" " + "-" * 42)
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for y in [0] + HY:
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E = cap * (1.0 + cons_cagr) ** y
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print(f" {('oggi' if y==0 else str(y)+'a'):>6} | €{E:>7,.0f} | €{E*rd:>7,.2f} | €{E*rd*30:>6,.0f}")
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E_end = cap * (1.0 + cons_cagr) ** max(HY)
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print(f" media €/giorno su {max(HY)} anni: €{(E_end-cap)/(max(HY)*365):.2f}/g (profitto €{E_end-cap:,.0f})")
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need = 50 * 365 / cons_cagr if cons_cagr > 0 else float('inf')
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print(f" capitale per ~€50/giorno @{cons_cagr*100:.1f}%: ≈ €{need:,.0f}")
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print(f"\n ⚠️ Proiezione condizionata (storico = bull crypto); pianifica sul conservativo. Nessuna leva.")
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print(f" SKH01 = research/forward-monitor; solo TP01 e' armato live. Non e' una garanzia.")
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if __name__ == "__main__":
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main()
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