"""Test minimali per scripts/research/meta_allocation.py.

Verifica le proprieta' STRUTTURALI dell'harness di meta-allocazione (non l'edge — quello e' nel
report): (1) i pesi-bersaglio + cash sommano a 1 per riga; (2) gli sleeve inattivi pesano 0;
(3) lo schema vol-parity e' CAUSALE (un cambio dei rendimenti in t+k non altera i pesi <= t);
(4) il cap del momentum e' rispettato; (5) il motore di simulazione conserva (vol nulla -> equity
piatta) e il costo di ribilancio aumenta col turnover.
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
import numpy as np
import pandas as pd
import pytest

from scripts.research import meta_allocation as M


def _toy(n=400, A=4, seed=0):
    rng = np.random.default_rng(seed)
    R = rng.normal(0.0005, 0.01, size=(n, A))
    active = np.ones((n, A), bool)
    index = pd.date_range("2020-01-01", periods=n, freq="1D", tz="UTC")
    fixed_w = np.array([0.4125, 0.1875, 0.15, 0.25])
    return index, R, active, fixed_w


def test_weights_plus_cash_sum_to_one():
    index, R, active, fixed_w = _toy()
    for fn in (M.scheme_base, M.scheme_volpar_pure, M.scheme_volpar_tilt,
               M.scheme_momentum, M.scheme_dd_cash, M.scheme_dd_defensive):
        W = fn(index, R, active, fixed_w)              # ultima colonna = cash
        s = W.sum(axis=1)
        assert np.allclose(s, 1.0, atol=1e-9), f"{fn.__name__}: righe non sommano a 1 (max dev {np.abs(s-1).max():.2e})"
        assert (W >= -1e-12).all(), f"{fn.__name__}: pesi negativi"


def test_inactive_sleeves_get_zero_weight():
    index, R, active, fixed_w = _toy()
    active[:, 2] = False                                # spegni lo sleeve 2 ovunque
    W = M.scheme_base(index, R, active, fixed_w)
    assert np.allclose(W[:, 2], 0.0), "uno sleeve inattivo riceve peso non nullo"
    assert np.allclose(W.sum(axis=1), 1.0)


def test_volparity_is_causal():
    """Un cambio dei rendimenti da t0 in poi NON deve alterare i pesi calcolati per t < t0."""
    index, R, active, fixed_w = _toy(n=400)
    t0 = 360
    W1 = M.scheme_volpar_pure(index, R, active, fixed_w)
    R2 = R.copy(); R2[t0:] *= 50.0                      # shock futuro enorme
    W2 = M.scheme_volpar_pure(index, R2, active, fixed_w)
    assert np.allclose(W1[:t0], W2[:t0]), "VOL-PARITY non causale: pesi passati dipendono dal futuro"


def test_momentum_respects_cap():
    index, R, active, fixed_w = _toy()
    cap = 0.55
    W = M.scheme_momentum(index, R, active, fixed_w, cap=cap)
    sleeve_w = W[:, :-1]                                # escludi cash
    assert sleeve_w.max() <= cap + 1e-6, f"cap momentum violato: max {sleeve_w.max():.3f} > {cap}"


def test_simulate_flat_when_no_returns():
    index, R, active, fixed_w = _toy()
    Rz = np.zeros_like(R)
    W = M.scheme_base(index, Rz, active, fixed_w)
    sim = M.simulate(Rz, active, W, cost_rate=0.0)
    assert np.allclose(sim["daily"].values, 0.0, atol=1e-12), "equity non piatta con rendimenti nulli e costo zero"


def test_rebalance_cost_increases_with_turnover():
    """Uno schema ad alto turnover (vol-parity) deve pagare piu' costo del peso-fisso (basso turnover)."""
    index, R, active, fixed_w = _toy(seed=3)
    Wb = M.scheme_base(index, R, active, fixed_w)
    Wv = M.scheme_volpar_pure(index, R, active, fixed_w)
    tb = M.simulate(R, active, Wb)["turnover_per_year"]
    tv = M.simulate(R, active, Wv)["turnover_per_year"]
    assert tv > tb, f"il vol-parity dovrebbe avere turnover > peso-fisso (got {tv:.2f} vs {tb:.2f})"


if __name__ == "__main__":
    sys.exit(pytest.main([__file__, "-q"]))