import numpy as np
import pandas as pd
import pytest

from multi_swarm.agents.falsification import FalsificationAgent, FalsificationReport
from multi_swarm.protocol.parser import parse_strategy


@pytest.fixture
def trending_ohlcv() -> pd.DataFrame:
    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,
    )


def test_falsification_returns_report(trending_ohlcv: pd.DataFrame) -> None:
    src = (
        "(strategy "
        "(when (gt (indicator rsi 14) 70.0) (entry-short)) "
        "(when (lt (indicator rsi 14) 30.0) (entry-long)))"
    )
    ast = parse_strategy(src)
    agent = FalsificationAgent(fees_bp=5.0, n_trials_dsr=20)
    report = agent.evaluate(ast, trending_ohlcv)
    assert isinstance(report, FalsificationReport)
    assert isinstance(report.sharpe, float)
    assert isinstance(report.dsr, float)
    assert 0.0 <= report.dsr <= 1.0
    assert isinstance(report.max_drawdown, float)
    assert isinstance(report.n_trades, int)


def test_falsification_zero_trades_returns_zero_metrics(trending_ohlcv: pd.DataFrame) -> None:
    src = "(strategy (when (gt (feature close) 1e9) (entry-long)))"
    ast = parse_strategy(src)
    agent = FalsificationAgent(fees_bp=5.0, n_trials_dsr=20)
    report = agent.evaluate(ast, trending_ohlcv)
    assert report.n_trades == 0
    assert report.sharpe == 0.0