"""QlibEngine —— 基于 Qlib 数据管线的研究引擎（QuantEngine 实现）。

v1 能力（本阶段已打通并测试）：
  1. provider.build_qlib_dataset：把本地行情按 qlib 二进制格式落盘（data/qlib）
  2. dataset.ensure_qlib_init + D.features：从 QlibDataset 读取行情（真实 qlib 通路）
  3. 在 Qlib 读取的行情面板上执行 TopK 因子回测 → 标准 BacktestResult
     （与 LocalEngine 记账规则一致：无未来函数、成本/涨跌停/停牌近似 + unimplemented 标注）

说明：
- 因子分（score）在源行情上按 app.quant.factors 计算（注册表口径一致）；
  qlib 侧负责数据读取供给（未来可由 Qlib Dataset 直接产出特征）。
- Alpha158 特征集 + LightGBM 预测信号的模型增强（walk-forward 训练/预测）为下一步
  TODO，详见 docs/ROADMAP.md §2 与 docs/QLIB_VERIFICATION.md。
- 默认研究引擎仍是 LocalEngine（app/quant/engine.py）；切换只需注入本类。
"""

from __future__ import annotations

from pathlib import Path

import pandas as pd

from app.domain.entities.research import BacktestResult, FactorTestReport, ResearchSpec
from app.quant.composite import build_factor_panels_full
from app.quant.engine import QuantEngine
from app.quant.local_engine import (
    TopKBacktestRunner,
    composite_score,
    run_spec_factor_test,
)
from app.quant.qlib_adapter.dataset import ensure_qlib_init, load_close_panel
from app.quant.qlib_adapter.provider import build_qlib_dataset

_ENGINE_NOTE = "QlibEngine v1：本地行情→QlibDataset(bin)→D.features 读取→TopK 因子回测"


class QlibEngine(QuantEngine):
    """基于 Qlib 数据管线的引擎；因子评估复用共享实现，回测从 QlibDataset 读取行情。"""

    name = "qlib"

    # qlib 落盘需要 OHLCV（vwap/factor 由本地合成，不来自行情表）
    _DUMP_COLUMNS = {"open", "high", "low", "close", "volume", "amount"}

    def __init__(self, qlib_dir: Path | None = None) -> None:
        # 默认落盘到 data/qlib（与 storage.qlib_dir 一致）；可注入临时目录便于测试
        self.qlib_dir = qlib_dir or _default_qlib_dir()

    def required_columns(self, spec: ResearchSpec) -> set[str]:
        # 回测需把全字段落盘成 qlib 数据集；因子测试走共享实现，只需 close+因子字段
        if spec.type == "backtest":
            return set(self._DUMP_COLUMNS)
        from app.quant.engine import factor_required_columns

        return factor_required_columns(spec)

    def run_factor_test(
        self, daily: pd.DataFrame, spec: ResearchSpec, horizon_days: int = 21
    ) -> FactorTestReport:
        # 因子评估与数据无关，直接复用共享实现（与 LocalEngine 同口径）
        report, _panels = run_spec_factor_test(daily, spec, horizon_days)
        return report

    def run_backtest(
        self, daily: pd.DataFrame, spec: ResearchSpec, eligibility_fn=None
    ) -> BacktestResult:
        """回测（与 LocalEngine 共用 TopKBacktestRunner → 口径一致）。

        `eligibility_fn`（选股条件/时点 ST 过滤）必须透传，否则条件与
        `exclude_st` 在 Qlib 引擎下会被**静默忽略**（AGENT.md §24 禁止假装支持）。
        """
        # 与 LocalEngine 同口径：复合分与买卖理由/因子曲线用**同一张**原始因子面板
        full = build_factor_panels_full(daily, spec.factors)
        score = composite_score([(d.name, p, w, d.direction) for d, p, w in full])
        factor_panels = {d.name: (d, p) for d, p, _w in full}

        self.qlib_dir.mkdir(parents=True, exist_ok=True)
        uri = build_qlib_dataset(daily, self.qlib_dir)
        ensure_qlib_init(uri)

        symbols = [str(s) for s in daily["symbol"].unique()]
        start, end = spec.period
        close = load_close_panel(uri, symbols, start, end)
        if close.empty:
            raise RuntimeError(
                "QlibDataset 读取为空：请检查 build_qlib_dataset 落盘与 provider_uri"
            )
        close = close.sort_index()

        result = TopKBacktestRunner(
            spec, score, close, eligibility_fn=eligibility_fn, factor_panels=factor_panels
        ).run()
        result.config_snapshot = spec.model_dump(mode="json")
        note = _ENGINE_NOTE
        result.unimplemented = [note, *result.unimplemented]
        return result


def _default_qlib_dir() -> Path:
    from app.core.config import PROJECT_ROOT

    return PROJECT_ROOT / "data" / "qlib"