# tests/factor/test_batch_eval.py
"""批量引擎端到端:合成小库 → 2因子计算 → 指标落盘可查."""
import sqlite3
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0")))

import pytest

from sanguo_factor import alpha_datasets  # 挂载
from sanguo_factor.batch_eval import run_batch_eval
from sanguo_factor import eval_store

_DDL = """
CREATE TABLE dbbardata(
  symbol TEXT, exchange TEXT, datetime TEXT, interval TEXT,
  volume REAL, turnover REAL, open_interest REAL,
  open_price REAL, high_price REAL, low_price REAL, close_price REAL)
"""


@pytest.fixture(scope="module")
def db(tmp_path_factory):
    """3 只 × ~400 交易日(2017-06~2018-12)合成库:一只单调涨/一只震荡/一只反着走."""
    # Mount alpha datasets first to ensure factors are available
    alpha_datasets.mount_all()

    p = tmp_path_factory.mktemp("b")
    db = str(p / "qt.db")
    conn = sqlite3.connect(db)
    conn.execute(_DDL)
    import pandas as pd
    days = pd.bdate_range("2017-06-01", "2018-12-28")
    for i, day in enumerate(days):
        d = day.strftime("%Y-%m-%d")
        rows = [
            ("600000", "SSE", f"{d} 00:00:00", "d", 100.0, 1_000_000.0, 0,
             10 + i * 0.01, 10 + i * 0.01, 10 + i * 0.01, 10 + i * 0.01),      # 单调涨
            ("000001", "SZSE", f"{d} 00:00:00", "d", 100.0, 500_000.0, 0,
             5, 5, 5, 5),                                                      # 平盘(截面另一端)
            ("300001", "SZSE", f"{d} 00:00:00", "d", 200.0, 900_000.0, 0,
             20 - i * 0.005, 20 - i * 0.005, 20 - i * 0.005, 20 - i * 0.005),  # 缓跌
        ]
        conn.executemany("INSERT INTO dbbardata VALUES(?,?,?,?,?,?,?,?,?,?,?)", rows)
    conn.commit(); conn.close()
    return db


def test_run_batch_eval_end_to_end(db, tmp_path, monkeypatch):
    eval_db = str(tmp_path / "factor_eval.db")
    out = run_batch_eval(
        factor_names=["ma_20", "roc_5"], start="2018-01-01", end="2018-06-30",
        eval_db=eval_db, label="t", symbols=None, cfg=None, vnpy_db_override=db,
    )
    assert out["factors_done"] == 2
    assert out["errors"] == []
    rows = eval_store.get_rows(eval_db, out["run_id"])
    assert {r["factor"] for r in rows} == {"ma_20", "roc_5"}
    # ma_20 = ts_mean(close,20)/close:涨股该值持续低(均价低于现价) 跌股高 → 与次日收益负相关为主
    m = eval_store.get_detail(eval_db, out["run_id"], "ma_20")["metrics"]
    assert "1" in m and "5" in m and "10" in m and "turnover" in m
    assert isinstance(m["1"]["ic_mean"], float)


def test_bad_factor_recorded_not_fatal(db, tmp_path):
    eval_db = str(tmp_path / "factor_eval.db")
    out = run_batch_eval(
        factor_names=["不存在的因子", "kmid"], start="2018-01-01", end="2018-03-31",
        eval_db=eval_db, label="t2", cfg=None, vnpy_db_override=db,
    )
    assert out["factors_done"] == 2
    assert len(out["errors"]) == 1
    detail = eval_store.get_detail(eval_db, out["run_id"], "不存在的因子")
    assert "error" in detail["metrics"]