from __future__ import annotations

from typing import Any

from database import ReviewDatabase


class StrategyTrackingService:
    def __init__(self, database: ReviewDatabase) -> None:
        self.database = database

    def record_run(
        self,
        user_id: int,
        run_id: int,
        selection_date: str,
        strategy_name: str,
        candidates: list[dict[str, Any]],
    ) -> int:
        return self.database.save_strategy_tracks(
            user_id, run_id, selection_date, strategy_name, candidates
        )

    def list_tracking(self, user_id: int, limit_batches: int = 12) -> dict[str, Any]:
        tracks = self.database.list_strategy_tracks(user_id, limit_batches)
        if not tracks:
            return {"batches": [], "summary": self._summary([])}

        bars = self.database.load_tracking_bars(
            [(item["ts_code"], item["selection_date"]) for item in tracks], 5
        )
        batches: dict[int, dict[str, Any]] = {}
        all_items: list[dict[str, Any]] = []
        for track in tracks:
            key = (track["ts_code"], track["selection_date"])
            metrics = self.calculate_metrics(float(track["entry_price"]), bars.get(key, []))
            item = {
                "id": track["id"],
                "code": track["code"],
                "name": track["name"],
                "sector": track["sector"],
                "entry_price": round(float(track["entry_price"]), 2),
                **metrics,
            }
            all_items.append(item)
            batch = batches.setdefault(
                int(track["run_id"]),
                {
                    "run_id": int(track["run_id"]),
                    "selection_date": track["selection_date"],
                    "strategy_name": track["strategy_name"],
                    "items": [],
                },
            )
            batch["items"].append(item)

        ordered = list(batches.values())
        for batch in ordered:
            batch["summary"] = self._summary(batch["items"])
        return {"batches": ordered, "summary": self._summary(all_items)}

    @staticmethod
    def calculate_metrics(entry_price: float, bars: list[dict[str, Any]]) -> dict[str, Any]:
        valid = [row for row in bars[:5] if float(row.get("close") or 0) > 0]
        if entry_price <= 0 or not valid:
            return {
                "observed_days": 0,
                "status": "等待 T+1",
                "t1_open": None,
                "t1_close": None,
                "t3_close": None,
                "t5_close": None,
                "max_gain": None,
                "max_drawdown": None,
            }

        def change(price: Any) -> float:
            return round((float(price or 0) / entry_price - 1) * 100, 2)

        observed = len(valid)
        return {
            "observed_days": observed,
            "status": "已完成" if observed >= 5 else f"跟踪中 {observed}/5",
            "t1_open": change(valid[0]["open"]),
            "t1_close": change(valid[0]["close"]),
            "t3_close": change(valid[2]["close"]) if observed >= 3 else None,
            "t5_close": change(valid[4]["close"]) if observed >= 5 else None,
            "max_gain": max(change(row["high"]) for row in valid),
            "max_drawdown": min(change(row["low"]) for row in valid),
        }

    @staticmethod
    def _summary(items: list[dict[str, Any]]) -> dict[str, Any]:
        completed = [item for item in items if item.get("t5_close") is not None]
        t1 = [float(item["t1_close"]) for item in items if item.get("t1_close") is not None]
        t5 = [float(item["t5_close"]) for item in completed]
        return {
            "total": len(items),
            "observed": len(t1),
            "completed": len(completed),
            "t1_win_rate": round(sum(value > 0 for value in t1) / len(t1) * 100, 1) if t1 else None,
            "t5_win_rate": round(sum(value > 0 for value in t5) / len(t5) * 100, 1) if t5 else None,
            "average_t5": round(sum(t5) / len(t5), 2) if t5 else None,
        }