import asyncio
import logging
from dataclasses import dataclass, asdict
from datetime import datetime

from .binance import binance_client
from .config import settings
from .periods import to_ms

logger = logging.getLogger(__name__)


@dataclass
class SymbolStats:
    symbol: str
    quote_volume: float
    price_change_pct: float
    open_price: float
    last_price: float
    rank: int = 0
    is_high_volume: bool = False
    is_high_change: bool = False

    def to_dict(self) -> dict:
        d = asdict(self)
        d["quote_volume_fmt"] = format_volume(self.quote_volume)
        d["price_change_pct_fmt"] = f"{self.price_change_pct:+.2f}%"
        return d


def format_volume(vol: float) -> str:
    if vol >= 1e8:
        return f"{vol / 1e8:.2f}亿"
    if vol >= 1e4:
        return f"{vol / 1e4:.2f}万"
    return f"{vol:.0f}"


def _aggregate_klines(klines: list, start_ms: int, end_ms: int) -> tuple[float, float, float]:
    quote_vol = 0.0
    open_price = 0.0
    last_price = 0.0
    first = True
    for k in klines:
        open_time = int(k[0])
        if open_time < start_ms or open_time >= end_ms:
            continue
        if first:
            open_price = float(k[1])
            first = False
        last_price = float(k[4])
        quote_vol += float(k[7])
    return quote_vol, open_price, last_price


async def _fetch_symbol_stats(
    symbol: str,
    start_ms: int,
    end_ms: int,
    prices: dict[str, float],
    sem: asyncio.Semaphore,
) -> SymbolStats | None:
    async with sem:
        try:
            klines = await binance_client.get_klines(symbol, start_ms, end_ms)
            quote_vol, open_price, last_price = _aggregate_klines(klines, start_ms, end_ms)
            if open_price <= 0 and last_price <= 0:
                return None
            if open_price <= 0:
                open_price = last_price
            if last_price <= 0:
                last_price = prices.get(symbol, open_price)
            if last_price <= 0:
                return None
            pct = ((last_price - open_price) / open_price) * 100 if open_price > 0 else 0.0
            return SymbolStats(
                symbol=symbol,
                quote_volume=quote_vol,
                price_change_pct=pct,
                open_price=open_price,
                last_price=last_price,
            )
        except Exception as e:
            logger.warning("Failed %s: %s", symbol, e)
            return None


async def aggregate_period(
    start: datetime,
    end: datetime,
    use_live_prices: bool = False,
) -> list[dict]:
    symbols = await binance_client.get_usdt_perpetual_symbols()
    start_ms = to_ms(start)
    end_ms = to_ms(end)

    prices: dict[str, float] = {}
    if use_live_prices:
        try:
            prices = await binance_client.get_prices_batch(symbols)
        except Exception as e:
            logger.warning("Batch prices failed: %s", e)

    sem = asyncio.Semaphore(settings.max_concurrency)
    tasks = [
        _fetch_symbol_stats(s, start_ms, end_ms, prices, sem) for s in symbols
    ]
    results = await asyncio.gather(*tasks)
    stats = [r for r in results if r is not None and r.quote_volume > 0]
    stats.sort(key=lambda x: x.quote_volume, reverse=True)
    top = stats[: settings.top_n]

    for i, s in enumerate(top, 1):
        s.rank = i
        s.is_high_volume = s.quote_volume >= settings.volume_threshold
        s.is_high_change = abs(s.price_change_pct) >= settings.change_threshold

    return [s.to_dict() for s in top]


def enrich_snapshot_meta(
    items: list[dict],
    period_start: datetime,
    period_end: datetime,
) -> dict:
    return {
        "period_start": period_start.isoformat(),
        "period_end": period_end.isoformat(),
        "updated_at": datetime.now().isoformat(),
        "top_n": settings.top_n,
        "volume_threshold": settings.volume_threshold,
        "change_threshold": settings.change_threshold,
        "items": items,
    }