from pathlib import Path

import numpy as np
import pandas as pd
import pytest

from multi_swarm.genome.hypothesis import ModelTier
from multi_swarm.llm.client import CompletionResult
from multi_swarm.orchestrator.run import RunConfig, run_phase1
from multi_swarm.persistence.repository import Repository


@pytest.fixture
def synthetic_ohlcv():
    idx = pd.date_range("2024-01-01", periods=500, freq="1h", tz="UTC")
    close = 100 + np.cumsum(np.random.RandomState(0).normal(0.01, 1.0, 500))
    return pd.DataFrame(
        {
            "open": close,
            "high": close + 0.5,
            "low": close - 0.5,
            "close": close,
            "volume": 1.0,
        },
        index=idx,
    )


@pytest.fixture
def fake_llm(mocker):
    """LLM mock che ritorna sempre una strategia valida."""
    fake = mocker.MagicMock()
    fake.complete.return_value = CompletionResult(
        text=(
            "```lisp\n(strategy "
            "(when (gt (indicator rsi 14) 70.0) (entry-short)) "
            "(when (lt (indicator rsi 14) 30.0) (entry-long)))\n```"
        ),
        input_tokens=200,
        output_tokens=80,
        tier=ModelTier.C,
        model="qwen",
    )
    return fake


def test_e2e_minimal_run_completes(
    tmp_path: Path,
    synthetic_ohlcv,
    fake_llm,
    mocker,
):
    cfg = RunConfig(
        run_name="e2e-test",
        population_size=5,
        n_generations=2,
        elite_k=1,
        tournament_k=2,
        p_crossover=0.5,
        seed=42,
        model_tier=ModelTier.C,
        symbol="BTC/USDT",
        timeframe="1h",
        fees_bp=5.0,
        n_trials_dsr=10,
        db_path=tmp_path / "runs.db",
    )

    run_id = run_phase1(cfg, ohlcv=synthetic_ohlcv, llm=fake_llm)

    repo = Repository(db_path=tmp_path / "runs.db")
    run = repo.get_run(run_id)
    assert run["status"] == "completed"
    gens = repo.list_generations(run_id)
    assert len(gens) == 2
    evals = repo.list_evaluations(run_id)
    assert len(evals) >= 5  # almeno una popolazione