"""metrics.py 纯函数单测。

H4: compute_metrics 从 daily_df["balance"] 自算 simple return（不再用 vnpy log return 列），
所以测试需构造含 "balance" 列的 daily_df。
"""
import sys, os
_VNPY_SRC = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0"))
sys.path.insert(0, _VNPY_SRC)

import pandas as pd
import numpy as np
import empyrical
from sanguo_backtest.metrics import compute_metrics, MetricsResult, BENCHMARK_SYMBOL


def _make_daily_balance(returns: np.ndarray) -> pd.DataFrame:
    """从收益率数组构造含 balance 列的 daily_df。

    compute_metrics 内部算 simple return = balance.pct_change().fillna(0)，
    所以实际传入 metrics 的 strat = [0, r0, r1, ...]（首项 NaN→0）。
    """
    idx = pd.date_range("2024-01-01", periods=len(returns), freq="B")
    balance = (1 + pd.Series(returns, index=idx)).cumprod()
    return pd.DataFrame({"balance": balance}, index=idx)


def _actual_strat(returns: np.ndarray) -> pd.Series:
    """compute_metrics 从 balance 推导出的实际 strat 序列（首项=0）。"""
    idx = pd.date_range("2024-01-01", periods=len(returns), freq="B")
    balance = (1 + pd.Series(returns, index=idx)).cumprod()
    return balance.pct_change().fillna(0)


def test_compute_metrics_scalars_match_empyrical():
    np.random.seed(42)
    raw_returns = np.random.normal(0.001, 0.02, 100)
    strat = _actual_strat(raw_returns)
    bench = pd.Series(np.random.normal(0.0005, 0.015, 100),
                      index=pd.date_range("2024-01-01", periods=100, freq="B"))
    daily_df = _make_daily_balance(raw_returns)

    res = compute_metrics(daily_df, bench)
    assert isinstance(res, MetricsResult)
    # 标量口径与 empyrical 直接计算一致（用推导出的 strat）
    assert abs(res.scalars["alpha"] - empyrical.alpha(strat, bench)) < 1e-9
    assert abs(res.scalars["beta"] - empyrical.beta(strat, bench)) < 1e-9
    assert abs(res.scalars["sharpe_ratio"] - empyrical.sharpe_ratio(strat)) < 1e-9
    assert abs(res.scalars["sortino_ratio"] - empyrical.sortino_ratio(strat)) < 1e-9
    assert abs(res.scalars["max_drawdown"] - empyrical.max_drawdown(strat)) < 1e-9
    assert abs(res.scalars["annual_volatility"] - empyrical.annual_volatility(strat)) < 1e-9


def test_compute_metrics_has_all_required_scalars():
    daily_df = _make_daily_balance([0.01, -0.005, 0.02, 0.0])
    bench = pd.Series([0.005, 0.001, 0.01, -0.002],
                      index=pd.date_range("2024-01-01", periods=4, freq="B"))
    res = compute_metrics(daily_df, bench)
    required = {"total_return", "annual_return", "alpha", "beta", "sharpe_ratio",
                "sortino_ratio", "information_ratio", "annual_volatility", "max_drawdown",
                "benchmark_return", "benchmark_volatility"}
    assert required.issubset(res.scalars.keys())


def test_compute_metrics_series_keys_and_length():
    raw_returns = np.random.normal(0, 0.01, 50)
    daily_df = _make_daily_balance(raw_returns)
    bench = pd.Series(np.random.normal(0, 0.01, 50),
                      index=pd.date_range("2024-01-01", periods=50, freq="B"))
    res = compute_metrics(daily_df, bench)
    for key in ["equity_curve", "benchmark_curve", "alpha", "beta", "drawdown"]:
        assert key in res.series
        assert len(res.series[key]) == 50
    assert res.series["drawdown"].max() <= 1e-9  # 回撤 <= 0


def test_benchmark_symbol_map():
    assert BENCHMARK_SYMBOL["hs300"] == "sh000300"
    assert BENCHMARK_SYMBOL["zz500"] == "sz000905"


def test_compute_metrics_ffill_aligns_benchmark():
    """H5: benchmark 有缺失日期时 ffill 对齐，不丢策略交易日。"""
    raw_returns = np.random.normal(0, 0.01, 10)
    daily_df = _make_daily_balance(raw_returns)
    # benchmark 只有部分日期（模拟停盘日缺失）
    partial_dates = daily_df.index[::2]  # 隔日取一个
    bench = pd.Series([0.001] * len(partial_dates), index=partial_dates)
    res = compute_metrics(daily_df, bench)
    # 不应崩溃，且 strat 长度 = daily_df 行数（没有被 dropna 削短）
    assert len(res.series["equity_curve"]) == 10


def test_compute_metrics_raises_without_balance():
    """缺少 balance 列时应抛 ValueError。"""
    idx = pd.date_range("2024-01-01", periods=5, freq="B")
    daily_df = pd.DataFrame({"net_pnl": [1, 2, 3, 4, 5]}, index=idx)
    bench = pd.Series([0.01] * 5, index=idx)
    try:
        compute_metrics(daily_df, bench)
        assert False, "应抛 ValueError"
    except ValueError as e:
        assert "balance" in str(e)