"""
Yahoo Finance client for live market data.
"""

import logging
from datetime import datetime, timedelta
from typing import Any

import pandas as pd
import yfinance as yf

from .base import BaseClient

logger = logging.getLogger(__name__)


class YFinanceClient(BaseClient):
    """Client for Yahoo Finance API using yfinance library."""

    def __init__(self, **kwargs):
        """
        Initialize Yahoo Finance client.

        Args:
            **kwargs: Configuration options
        """
        super().__init__(**kwargs)
        self.session = None

    def test_connection(self) -> bool:
        """Test Yahoo Finance connection by fetching a known ticker."""
        try:
            ticker = yf.Ticker("AAPL")
            info = ticker.info
            return bool(info and "symbol" in info)
        except Exception as e:
            logger.error(f"Yahoo Finance connection test failed: {e}")
            return False

    def get_data(
        self, symbol: str, start_date: str, end_date: str, **kwargs
    ) -> dict[str, Any]:
        """
        Get historical price data for a symbol.

        Args:
            symbol: Stock ticker symbol
            start_date: Start date in YYYY-MM-DD format
            end_date: End date in YYYY-MM-DD format
            **kwargs: Additional parameters

        Returns:
            Dict[str, Any]: Price data with metadata
        """
        if not self.validate_date_range(start_date, end_date):
            raise ValueError(f"Invalid date range: {start_date} to {end_date}")

        try:
            ticker = yf.Ticker(symbol.upper())

            # Add one day to end_date to make it inclusive
            end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
            end_date_adjusted = end_date_obj + timedelta(days=1)
            end_date_str = end_date_adjusted.strftime("%Y-%m-%d")

            data = ticker.history(start=start_date, end=end_date_str)

            if data.empty:
                logger.warning(
                    f"No data found for {symbol} between {start_date} and {end_date}"
                )
                return {
                    "symbol": symbol,
                    "data": [],
                    "metadata": {
                        "source": "yahoo_finance",
                        "empty": True,
                        "reason": "no_data_available",
                    },
                }

            # Remove timezone info and format data
            if isinstance(data.index, pd.DatetimeIndex) and data.index.tz is not None:
                data.index = data.index.tz_localize(None)

            # Reset index to make Date a column
            data = data.reset_index()
            data["Date"] = data["Date"].dt.strftime("%Y-%m-%d %H:%M:%S")

            # Round numerical values
            numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
            for col in numeric_columns:
                if col in data.columns:
                    data[col] = data[col].round(2)

            # Convert to list of dictionaries
            records = data.to_dict("records")

            return {
                "symbol": symbol,
                "period": {"start": start_date, "end": end_date},
                "data": records,
                "metadata": {
                    "source": "yahoo_finance",
                    "record_count": len(records),
                    "columns": list(data.columns),
                    "retrieved_at": datetime.utcnow().isoformat(),
                },
            }

        except Exception as e:
            logger.error(f"Error fetching Yahoo Finance data for {symbol}: {e}")
            raise

    def get_company_info(self, symbol: str) -> dict[str, Any]:
        """
        Get company information for a symbol.

        Args:
            symbol: Stock ticker symbol

        Returns:
            Dict[str, Any]: Company information
        """
        try:
            ticker = yf.Ticker(symbol.upper())
            info = ticker.info

            return {
                "symbol": symbol,
                "info": info,
                "metadata": {
                    "source": "yahoo_finance",
                    "retrieved_at": datetime.utcnow().isoformat(),
                },
            }

        except Exception as e:
            logger.error(f"Error fetching company info for {symbol}: {e}")
            return {
                "symbol": symbol,
                "info": {},
                "metadata": {
                    "source": "yahoo_finance",
                    "error": str(e),
                    "retrieved_at": datetime.utcnow().isoformat(),
                },
            }

    def get_financials(
        self, symbol: str, statement_type: str = "income"
    ) -> dict[str, Any]:
        """
        Get financial statements for a symbol.

        Args:
            symbol: Stock ticker symbol
            statement_type: Type of statement ("income", "balance", "cashflow")

        Returns:
            Dict[str, Any]: Financial statement data
        """
        try:
            ticker = yf.Ticker(symbol.upper())

            if statement_type == "income":
                annual = ticker.financials
                quarterly = ticker.quarterly_financials
            elif statement_type == "balance":
                annual = ticker.balance_sheet
                quarterly = ticker.quarterly_balance_sheet
            elif statement_type == "cashflow":
                annual = ticker.cashflow
                quarterly = ticker.quarterly_cashflow
            else:
                raise ValueError(f"Unknown statement type: {statement_type}")

            result = {
                "symbol": symbol,
                "statement_type": statement_type,
                "annual": {},
                "quarterly": {},
                "metadata": {
                    "source": "yahoo_finance",
                    "retrieved_at": datetime.utcnow().isoformat(),
                },
            }

            # Process annual data
            if not annual.empty:
                annual_data = annual.copy()
                if isinstance(annual_data.columns, pd.DatetimeIndex):
                    annual_data.columns = annual_data.columns.strftime("%Y-%m-%d")
                result["annual"] = annual_data.to_dict()

            # Process quarterly data
            if not quarterly.empty:
                quarterly_data = quarterly.copy()
                if isinstance(quarterly_data.columns, pd.DatetimeIndex):
                    quarterly_data.columns = quarterly_data.columns.strftime("%Y-%m-%d")
                result["quarterly"] = quarterly_data.to_dict()

            return result

        except Exception as e:
            logger.error(
                f"Error fetching {statement_type} financials for {symbol}: {e}"
            )
            return {
                "symbol": symbol,
                "statement_type": statement_type,
                "annual": {},
                "quarterly": {},
                "metadata": {
                    "source": "yahoo_finance",
                    "error": str(e),
                    "retrieved_at": datetime.utcnow().isoformat(),
                },
            }

    def get_available_symbols(self) -> list[str]:
        """
        Yahoo Finance doesn't provide a direct way to list all symbols.
        Return common major symbols as examples.
        """
        return [
            "AAPL",
            "MSFT",
            "GOOGL",
            "AMZN",
            "TSLA",
            "META",
            "NVDA",
            "AMD",
            "JPM",
            "JNJ",
            "V",
            "WMT",
            "PG",
            "UNH",
            "HD",
            "MA",
            "BAC",
            "DIS",
        ]