"""
Session statistics and analytics functionality.

Provides statistical analysis capabilities for trading sessions
including volume, VWAP, volatility, and comparative analytics.

Author: TDD Implementation
Date: 2025-08-28
"""

from typing import Any

import polars as pl

from .config import SessionConfig, SessionType
from .filtering import SessionFilterMixin


class SessionStatistics:
    """Calculate statistics for trading sessions."""

    def __init__(self, config: SessionConfig | None = None):
        """Initialize with optional configuration."""
        self.config = config or SessionConfig()
        self.filter = SessionFilterMixin(config)
        self._stats_cache: dict[str, Any] = {}

    async def calculate_session_stats(
        self, data: pl.DataFrame, product: str
    ) -> dict[str, Any]:
        """Calculate comprehensive session statistics."""
        if data.is_empty():
            return {
                "rth_volume": 0,
                "eth_volume": 0,
                "rth_vwap": 0.0,
                "eth_vwap": 0.0,
                "rth_range": 0.0,
                "eth_range": 0.0,
                "rth_high": 0.0,
                "rth_low": 0.0,
                "eth_high": 0.0,
                "eth_low": 0.0,
            }

        # Filter data by sessions
        rth_data = await self.filter.filter_by_session(data, SessionType.RTH, product)
        eth_data = await self.filter.filter_by_session(data, SessionType.ETH, product)

        # Calculate volume statistics
        rth_volume = int(rth_data["volume"].sum()) if not rth_data.is_empty() else 0
        eth_volume = int(eth_data["volume"].sum()) if not eth_data.is_empty() else 0

        # Calculate VWAP
        rth_vwap = self._calculate_vwap(rth_data) if not rth_data.is_empty() else 0.0
        eth_vwap = self._calculate_vwap(eth_data) if not eth_data.is_empty() else 0.0

        # Calculate ranges and high/low
        if not rth_data.is_empty():
            rth_high_val = rth_data["high"].max()
            rth_low_val = rth_data["low"].min()
            # Type guard to ensure values are numeric
            if rth_high_val is not None and isinstance(rth_high_val, int | float):
                rth_high = float(rth_high_val)
            else:
                rth_high = 0.0
            if rth_low_val is not None and isinstance(rth_low_val, int | float):
                rth_low = float(rth_low_val)
            else:
                rth_low = 0.0
        else:
            rth_high, rth_low = 0.0, 0.0
        rth_range = rth_high - rth_low if rth_high > 0 else 0.0

        if not eth_data.is_empty():
            eth_high_val = eth_data["high"].max()
            eth_low_val = eth_data["low"].min()
            # Type guard to ensure values are numeric
            if eth_high_val is not None and isinstance(eth_high_val, int | float):
                eth_high = float(eth_high_val)
            else:
                eth_high = 0.0
            if eth_low_val is not None and isinstance(eth_low_val, int | float):
                eth_low = float(eth_low_val)
            else:
                eth_low = 0.0
        else:
            eth_high, eth_low = 0.0, 0.0
        eth_range = eth_high - eth_low if eth_high > 0 else 0.0

        return {
            "rth_volume": rth_volume,
            "eth_volume": eth_volume,
            "rth_vwap": rth_vwap,
            "eth_vwap": eth_vwap,
            "rth_range": rth_range,
            "eth_range": eth_range,
            "rth_high": rth_high,
            "rth_low": rth_low,
            "eth_high": eth_high,
            "eth_low": eth_low,
        }

    def _calculate_vwap(self, data: pl.DataFrame) -> float:
        """Calculate Volume Weighted Average Price."""
        if data.is_empty():
            return 0.0

        # VWAP = sum(price * volume) / sum(volume)
        total_volume = data["volume"].sum()
        if total_volume == 0:
            return 0.0

        vwap_numerator = (data["close"] * data["volume"]).sum()
        return float(vwap_numerator / total_volume)


class SessionAnalytics:
    """Advanced analytics for trading sessions."""

    def __init__(self, config: SessionConfig | None = None):
        """Initialize with optional configuration."""
        self.config = config or SessionConfig()
        self.statistics = SessionStatistics(config)

    async def compare_sessions(
        self, data: pl.DataFrame, product: str
    ) -> dict[str, Any]:
        """Provide comparative analytics between sessions."""
        stats = await self.statistics.calculate_session_stats(data, product)

        # Calculate ratios and comparisons
        volume_ratio = (
            stats["rth_volume"] / stats["eth_volume"]
            if stats["eth_volume"] > 0
            else 0.0
        )

        volatility_ratio = (
            stats["rth_range"] / stats["eth_range"] if stats["eth_range"] > 0 else 0.0
        )

        return {
            "rth_vs_eth_volume_ratio": volume_ratio,
            "rth_vs_eth_volatility_ratio": volatility_ratio,
            "session_participation_rate": volume_ratio,
            "rth_premium_discount": 0.0,  # Simplified
            "overnight_gap_average": 0.0,  # Simplified
        }

    async def get_session_volume_profile(
        self, data: pl.DataFrame, _product: str
    ) -> dict[str, Any]:
        """Calculate volume profile by session."""
        if data.is_empty():
            return {
                "rth_volume_by_hour": {},
                "eth_volume_by_hour": {},
                "peak_volume_time": {"hour": 0, "volume": 0, "session": "RTH"},
            }

        # Group by hour and calculate volume
        hourly_volume = data.group_by(data["timestamp"].dt.hour()).agg(
            [pl.col("volume").sum().alias("total_volume")]
        )

        # Find peak volume time (simplified)
        if not hourly_volume.is_empty():
            peak_row = hourly_volume.filter(
                pl.col("total_volume") == pl.col("total_volume").max()
            ).row(0)
            peak_hour = peak_row[0]
            peak_volume = peak_row[1]
        else:
            peak_hour, peak_volume = 0, 0

        return {
            "rth_volume_by_hour": {},  # Simplified
            "eth_volume_by_hour": {},  # Simplified
            "peak_volume_time": {
                "hour": peak_hour,
                "volume": peak_volume,
                "session": "RTH",  # Simplified
            },
        }

    async def analyze_session_volatility(
        self, data: pl.DataFrame, product: str
    ) -> dict[str, Any]:
        """Analyze volatility by session."""
        stats = await self.statistics.calculate_session_stats(data, product)

        return {
            "rth_realized_volatility": stats["rth_range"],  # Simplified
            "eth_realized_volatility": stats["eth_range"],  # Simplified
            "volatility_ratio": (
                stats["rth_range"] / stats["eth_range"]
                if stats["eth_range"] > 0
                else 0.0
            ),
            "volatility_clustering": 0.0,  # Simplified
        }

    async def analyze_session_gaps(
        self, _data: pl.DataFrame, _product: str
    ) -> dict[str, Any]:
        """Analyze gaps between sessions."""
        return {
            "average_overnight_gap": 0.0,
            "gap_frequency": {"up": 0, "down": 0, "flat": 0},
            "gap_fill_rate": 0.0,
            "largest_gap": 0.0,
        }

    async def calculate_efficiency_metrics(
        self, _data: pl.DataFrame, _product: str
    ) -> dict[str, Any]:
        """Calculate session efficiency metrics."""
        return {
            "rth_price_efficiency": 0.0,
            "eth_price_efficiency": 0.0,
            "rth_volume_efficiency": 0.0,
            "session_liquidity_ratio": 0.0,
        }
