feat(portfolio): Deribit-only executable book (TP01+SKH01) + periodic rebalancing

- deribit_book_sleeves(): TP01 75% + SKH01 25% — the two directional BTC/ETH legs on
  ONE venue (Deribit), both since 2019. Excludes XS01 (Hyperliquid/stat-mode) & VRP01
  (modeled options). FULL Sharpe 1.78 / HOLD 1.17 / DD 9.4% (research).
- rebalance_sim(): realistic PERIODIC rebalancing (drift between dates, turnover cost at
  Deribit-taker ~5bps/side) vs the idealized continuous rebalance of combined_daily.
  period=1 + cost=0 reduces to continuous (tested).
- run_deribit_book.py: report — continuous vs weekly/biweekly/monthly rebal, per-year,
  accumulation €2k & $600-real, min-order $5 note. Finding: turnover is LOW (0.2-0.4x/yr),
  so monthly rebal (€7,919) ~= continuous (€7,938) — cost is negligible; daily would be
  sub-min-order fiction at $600 -> use >= weekly.
- +2 tests (rebalance_sim continuity & cost). Full suite green.

TP01 is the only live-armed leg; SKH01 is the candidate 2nd leg (validate execution code first).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

tests/test_portfolio.py

@@ -7,7 +7,7 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from src.portfolio.portfolio import Sleeve, StrategyPortfolio, to_daily, metrics
+from src.portfolio.portfolio import Sleeve, StrategyPortfolio, to_daily, metrics, rebalance_sim
 
 
 def _const_sleeve(name, weight, val, n=400):
@@ -15,6 +15,37 @@ def _const_sleeve(name, weight, val, n=400):
     return Sleeve(name, weight, lambda: pd.Series(val, index=idx))
 
 
+def _ret_series(vals):
+    idx = pd.date_range("2020-01-01", periods=len(vals), freq="1D", tz="UTC")
+    return pd.Series(vals, index=idx)
+
+
+def test_rebalance_sim_no_cost_period1_matches_continuous():
+    """period=1 + cost=0 deve coincidere col rebalance-continuo (weighted-return giornaliero)."""
+    rng = np.random.default_rng(0)
+    A = _ret_series(rng.normal(0.001, 0.02, 300))
+    B = _ret_series(rng.normal(0.000, 0.03, 300))
+    w = {"A": 0.6, "B": 0.4}
+    sim = rebalance_sim({"A": A, "B": B}, w, period_days=1, cost_rate=0.0)
+    cont = 0.6 * A + 0.4 * B
+    assert np.allclose(sim["daily"].values, cont.values, atol=1e-12)
+    assert sim["n_rebalances"] == 300
+
+
+def test_rebalance_sim_cost_reduces_return_and_counts():
+    """Il costo del turnover abbassa il rendimento; ribilanci meno frequenti = meno costo."""
+    rng = np.random.default_rng(1)
+    A = _ret_series(rng.normal(0.001, 0.02, 360))
+    B = _ret_series(rng.normal(0.001, 0.04, 360))
+    w = {"A": 0.5, "B": 0.5}
+    free = rebalance_sim({"A": A, "B": B}, w, period_days=7, cost_rate=0.0)["daily"]
+    weekly = rebalance_sim({"A": A, "B": B}, w, period_days=7, cost_rate=0.001)
+    monthly = rebalance_sim({"A": A, "B": B}, w, period_days=30, cost_rate=0.001)
+    assert weekly["daily"].sum() < free.sum()                  # il costo morde
+    assert monthly["n_rebalances"] < weekly["n_rebalances"]    # mensile ribilancia meno
+    assert weekly["turnover_per_year"] > 0
+
+
 def test_single_sleeve_equals_itself():
     s = _const_sleeve("A", 1.0, 0.001)
     pf = StrategyPortfolio([s])