import json

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 = json.dumps(
        {
            "rules": [
                {
                    "condition": {
                        "op": "gt",
                        "args": [
                            {"kind": "indicator", "name": "rsi", "params": [14]},
                            {"kind": "literal", "value": 70.0},
                        ],
                    },
                    "action": "entry-short",
                },
                {
                    "condition": {
                        "op": "lt",
                        "args": [
                            {"kind": "indicator", "name": "rsi", "params": [14]},
                            {"kind": "literal", "value": 30.0},
                        ],
                    },
                    "action": "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 = json.dumps(
        {
            "rules": [
                {
                    "condition": {
                        "op": "gt",
                        "args": [
                            {"kind": "feature", "name": "close"},
                            {"kind": "literal", "value": 1e9},
                        ],
                    },
                    "action": "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