import logging
import pandas as pd
import yfinance as yf
from stockstats import wrap
from typing import Annotated
import os
from .config import get_config

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Public exception — lets callers catch stockstats/yfinance failures by type
# ---------------------------------------------------------------------------


class YFinanceError(Exception):
    """Raised when yfinance or stockstats data fetching/processing fails."""
    pass


# ---------------------------------------------------------------------------
# Internal helpers
# ---------------------------------------------------------------------------


def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
    """Normalize a stock DataFrame for stockstats: parse dates, drop invalid rows, fill price gaps.
    Ensure DataFrame has lowercase columns for stockstats."""
    df = data.copy()
    df.columns = [str(c).lower() for c in df.columns]

    if "date" in df.columns:
        df["date"] = pd.to_datetime(df["date"], errors="coerce")
        df = df.dropna(subset=["date"])

    price_cols = [c for c in ["open", "high", "low", "close", "volume"] if c in df.columns]
    if price_cols:
        df[price_cols] = df[price_cols].apply(pd.to_numeric, errors="coerce")

    if "close" in df.columns:
        df = df.dropna(subset=["close"])

    if price_cols:
        df[price_cols] = df[price_cols].ffill().bfill()

    return df


def _load_or_fetch_ohlcv(symbol: str) -> pd.DataFrame:
    """Single authority for loading OHLCV data: cache → yfinance download → normalize.

    Cache filename is always derived from today's date (15-year window) so the
    cache key never goes stale.  If a cached file exists but is corrupt (too few
    rows to be useful), it is deleted and re-fetched rather than silently
    returning bad data.

    Raises:
        YFinanceError: if the download returns an empty DataFrame or fails.
    """
    config = get_config()

    today_date = pd.Timestamp.today()
    start_date = today_date - pd.DateOffset(years=15)
    start_date_str = start_date.strftime("%Y-%m-%d")
    end_date_str = today_date.strftime("%Y-%m-%d")

    os.makedirs(config["data_cache_dir"], exist_ok=True)

    data_file = os.path.join(
        config["data_cache_dir"],
        f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
    )

    # ── Try to load from cache ────────────────────────────────────────────────
    if os.path.exists(data_file):
        try:
            data = pd.read_csv(data_file)  # no on_bad_lines="skip" — we want to know about corruption
        except Exception as exc:
            logger.warning(
                "Corrupt cache file for %s (%s) — deleting and re-fetching.", symbol, exc
            )
            os.remove(data_file)
            data = None
        else:
            # Validate: a 15-year daily file should have well over 100 rows
            if len(data) < 50:
                logger.warning(
                    "Cache file for %s has only %d rows — likely truncated, re-fetching.",
                    symbol, len(data),
                )
                os.remove(data_file)
                data = None
    else:
        data = None

    # ── Download from yfinance if cache miss / corrupt ────────────────────────
    if data is None:
        raw = yf.download(
            symbol,
            start=start_date_str,
            end=end_date_str,
            multi_level_index=False,
            progress=False,
            auto_adjust=True,
        )
        if raw.empty:
            raise YFinanceError(
                f"yfinance returned no data for symbol '{symbol}' "
                f"({start_date_str} → {end_date_str})"
            )
        data = raw.reset_index()
        data.to_csv(data_file, index=False)
        logger.debug("Downloaded and cached OHLCV for %s → %s", symbol, data_file)

    return data


# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------


class StockstatsUtils:
    @staticmethod
    def get_stock_stats(
        symbol: Annotated[str, "ticker symbol for the company"],
        indicator: Annotated[
            str, "quantitative indicators based off of the stock data for the company"
        ],
        curr_date: Annotated[
            str, "curr date for retrieving stock price data, YYYY-mm-dd"
        ],
    ):
        curr_date_dt = pd.to_datetime(curr_date)
        curr_date_str = curr_date_dt.strftime("%Y-%m-%d")

        data = _load_or_fetch_ohlcv(symbol)
        data = _clean_dataframe(data)
        df = wrap(data)
        # After wrap(), the date column becomes the datetime index (named 'date').
        # Access via df.index, not df["Date"] which stockstats would try to parse as an indicator.

        df[indicator]  # trigger stockstats to calculate the indicator
        date_index_strs = df.index.strftime("%Y-%m-%d")
        matching_rows = df[date_index_strs == curr_date_str]

        if not matching_rows.empty:
            return matching_rows[indicator].values[0]
        else:
            return "N/A: Not a trading day (weekend or holiday)"