"""
Insider Data Service for aggregating and analyzing insider trading data.
"""

import logging
from dataclasses import dataclass
from enum import Enum
from typing import Any

from tradingagents.config import TradingAgentsConfig

from .clients.finnhub_client import FinnhubClient

logger = logging.getLogger(__name__)


class InsiderDataRepository:
    """Simple repository for insider data - placeholder implementation."""

    def __init__(self, data_dir: str):
        self.data_dir = data_dir

    def get_data(self, symbol: str, start_date: str, end_date: str) -> dict:
        return {}

    def store_data(self, symbol: str, data: dict) -> bool:
        return True


class DataQuality(Enum):
    """Data quality levels for insider data."""

    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"


@dataclass
class InsiderTransaction:
    """Insider transaction data."""

    date: str  # YYYY-MM-DD format
    insider_name: str
    title: str
    transaction_type: str  # "Purchase", "Sale", "Exercise", etc.
    shares: int
    price_per_share: float
    total_value: float
    shares_owned_after: int
    filing_date: str


@dataclass
class InsiderSentimentContext:
    """Insider sentiment context for trading analysis."""

    symbol: str
    period: dict[str, str]  # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
    transactions: list[InsiderTransaction]
    sentiment_score: float  # -1.0 to 1.0 (-1: very bearish, 1: very bullish)
    net_buying_value: float  # Net buying (positive) or selling (negative) in USD
    insider_count: int  # Number of unique insiders in period
    transaction_count: int
    analysis_summary: str
    metadata: dict[str, Any]


@dataclass
class InsiderTransactionContext:
    """Insider transaction context for detailed transaction analysis."""

    symbol: str
    period: dict[str, str]  # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
    transactions: list[InsiderTransaction]
    total_transaction_value: float
    net_insider_activity: float  # Net buying/selling activity
    top_insiders: list[dict[str, Any]]  # Top insiders by transaction value
    transaction_summary: dict[str, Any]  # Summary stats by transaction type
    analysis_summary: str
    metadata: dict[str, Any]


class InsiderDataService:
    """Service for insider trading data aggregation and analysis."""

    def __init__(
        self,
        client: FinnhubClient,
        repository: InsiderDataRepository,
    ):
        """
        Initialize insider data service.

        Args:
            client: Client for insider data (e.g., FinnhubClient)
            repository: Repository for cached insider data
            online_mode: Whether to use live data
            **kwargs: Additional configuration
        """
        self.client = client
        self.repository = repository

    @staticmethod
    def build(_config: TradingAgentsConfig):
        client = FinnhubClient("")
        repo = InsiderDataRepository("")
        return InsiderDataService(client, repo)

    def get_insider_sentiment_context(
        self,
        symbol: str,
        start_date: str,
        end_date: str,
    ) -> InsiderSentimentContext:
        """
        Get insider sentiment context for a company.

        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:
            InsiderSentimentContext: Insider sentiment analysis
        """
        # TODO: Implement insider sentiment analysis
        transactions = []
        sentiment_score = 0.0
        net_buying_value = 0.0
        analysis_summary = f"Insider sentiment analysis for {symbol}"

        metadata = {
            "data_quality": DataQuality.HIGH if transactions else DataQuality.LOW,
            "service": "insider_data",
            "data_source": "placeholder",
            "analysis_method": "insider_sentiment",
        }

        return InsiderSentimentContext(
            symbol=symbol,
            period={"start": start_date, "end": end_date},
            transactions=transactions,
            sentiment_score=sentiment_score,
            net_buying_value=net_buying_value,
            insider_count=0,
            transaction_count=len(transactions),
            analysis_summary=analysis_summary,
            metadata=metadata,
        )

    def get_insider_transaction_context(
        self,
        symbol: str,
        start_date: str,
        end_date: str,
    ) -> InsiderTransactionContext:
        """
        Get insider transaction context for detailed analysis.

        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:
            InsiderTransactionContext: Detailed insider transaction analysis
        """
        # TODO: Implement insider transaction analysis
        transactions = []
        total_transaction_value = 0.0
        net_insider_activity = 0.0
        top_insiders = []
        transaction_summary = {}
        analysis_summary = f"Insider transaction analysis for {symbol}"

        metadata = {
            "data_quality": DataQuality.HIGH if transactions else DataQuality.LOW,
            "service": "insider_data",
            "data_source": "placeholder",
            "analysis_method": "insider_transactions",
        }

        return InsiderTransactionContext(
            symbol=symbol,
            period={"start": start_date, "end": end_date},
            transactions=transactions,
            total_transaction_value=total_transaction_value,
            net_insider_activity=net_insider_activity,
            top_insiders=top_insiders,
            transaction_summary=transaction_summary,
            analysis_summary=analysis_summary,
            metadata=metadata,
        )

    def update_insider_sentiment(
        self,
        symbol: str,
        start_date: str,
        end_date: str,
    ):
        pass  # TODO: fetch insider sentiment with finnhub client, save with repo

    def update_insider_transactions(
        self,
        symbol: str,
        start_date: str,
        end_date: str,
    ):
        pass  # TODO: fetch insider transactions with finnhub client, save with repo