"""Section builders for trading performance reports.

Each builder takes a CollectedData bundle and returns a typed Pydantic
section model.  All builders handle zero-activity gracefully by returning
zero values and empty lists when no data is available.
"""

from __future__ import annotations

import logging
from datetime import datetime, timezone

from services.reporting.collector import CollectedData
from services.reporting.models import (
    ModelQualitySection,
    ModelQualityWindow,
    PLSection,
    PositionDetail,
    PositionPerformanceSection,
    RecommendationAccuracySection,
    RiskMetricsSection,
)

logger = logging.getLogger(__name__)


def build_pnl_section(data: CollectedData) -> PLSection:
    """Build P&L section from collected data.

    Computes realized/unrealized P&L, daily return, cumulative return,
    win/loss counts, win rate, profit factor, and Sharpe ratio from
    portfolio_snapshot and closed positions.
    """
    snap = data.portfolio_snapshot

    if snap is None:
        return PLSection(
            realized_pnl=0.0,
            unrealized_pnl=0.0,
            daily_return=0.0,
            cumulative_return=0.0,
            win_count=0,
            loss_count=0,
            win_rate=0.0,
            profit_factor=0.0,
            sharpe_ratio=0.0,
        )

    # Compute profit factor from closed positions:
    # sum of gains / abs(sum of losses)
    gains = 0.0
    losses = 0.0
    for pos in data.closed_positions:
        rpnl = float(pos.get("realized_pnl", 0) or 0)
        if rpnl > 0:
            gains += rpnl
        elif rpnl < 0:
            losses += abs(rpnl)

    profit_factor = (gains / losses) if losses > 0 else 0.0

    return PLSection(
        realized_pnl=float(snap.get("realized_pnl", 0) or 0),
        unrealized_pnl=float(snap.get("unrealized_pnl", 0) or 0),
        daily_return=float(snap.get("daily_return", 0) or 0),
        cumulative_return=float(snap.get("cumulative_return", 0) or 0),
        win_count=int(snap.get("win_count", 0) or 0),
        loss_count=int(snap.get("loss_count", 0) or 0),
        win_rate=float(snap.get("win_rate", 0) or 0),
        profit_factor=profit_factor,
        sharpe_ratio=float(snap.get("sharpe_ratio", 0) or 0),
    )


def build_recommendation_accuracy_section(
    data: CollectedData,
) -> RecommendationAccuracySection:
    """Build recommendation accuracy section.

    Joins trading_decisions with prediction_outcomes to compute
    act/skip breakdown, win rate of acted recommendations, and
    average confidence of acted vs skipped.
    """
    if not data.trading_decisions:
        return RecommendationAccuracySection(
            total_evaluated=0,
            act_count=0,
            skip_count=0,
            acted_win_rate=0.0,
            avg_confidence_acted=0.0,
            avg_confidence_skipped=0.0,
        )

    # Build lookup: recommendation_id -> prediction_outcome
    # prediction_outcomes are joined with prediction_snapshots in the collector,
    # so they carry ticker, direction, action, confidence from the snapshot.
    # trading_decisions reference recommendations via recommendation_id.
    # We need to match trading_decisions -> recommendations -> prediction_outcomes.
    #
    # The collector fetches prediction_outcomes joined with prediction_snapshots
    # (po.prediction_id = ps.id).  Trading decisions reference recommendation_id.
    # Recommendations and prediction_snapshots share the same ticker, so we
    # match by recommendation_id on the trading_decision side.

    # Build recommendation_id -> recommendation dict for confidence lookup
    rec_by_id: dict[str, dict] = {}
    for rec in data.recommendations:
        rec_id = str(rec.get("id", ""))
        if rec_id:
            rec_by_id[rec_id] = rec

    # Build prediction_id -> prediction_outcome for profitability lookup
    # We also need to map recommendation_id -> prediction_outcome.
    # The link is: trading_decision.recommendation_id -> recommendation.id
    # and prediction_outcome has ticker from prediction_snapshots.
    # We match by ticker between recommendation and prediction_outcome.
    outcome_by_ticker: dict[str, list[dict]] = {}
    for po in data.prediction_outcomes:
        ticker = po.get("ticker", "")
        if ticker:
            outcome_by_ticker.setdefault(ticker, []).append(po)

    act_count = 0
    skip_count = 0
    acted_wins = 0
    acted_total_with_outcome = 0
    confidence_acted: list[float] = []
    confidence_skipped: list[float] = []

    for td in data.trading_decisions:
        decision = str(td.get("decision", "")).lower()
        rec_id = str(td.get("recommendation_id", ""))
        rec = rec_by_id.get(rec_id, {})
        conf = rec.get("confidence")
        ticker = td.get("ticker", "")

        if decision == "act":
            act_count += 1
            if conf is not None:
                confidence_acted.append(float(conf))

            # Check profitability from prediction_outcomes for this ticker
            ticker_outcomes = outcome_by_ticker.get(ticker, [])
            if ticker_outcomes:
                # Use the most recent outcome for this ticker
                latest = ticker_outcomes[-1]
                acted_total_with_outcome += 1
                if latest.get("profitable"):
                    acted_wins += 1
        else:
            skip_count += 1
            if conf is not None:
                confidence_skipped.append(float(conf))

    total_evaluated = act_count + skip_count
    acted_win_rate = (
        (acted_wins / acted_total_with_outcome)
        if acted_total_with_outcome > 0
        else 0.0
    )
    avg_confidence_acted = (
        (sum(confidence_acted) / len(confidence_acted))
        if confidence_acted
        else 0.0
    )
    avg_confidence_skipped = (
        (sum(confidence_skipped) / len(confidence_skipped))
        if confidence_skipped
        else 0.0
    )

    return RecommendationAccuracySection(
        total_evaluated=total_evaluated,
        act_count=act_count,
        skip_count=skip_count,
        acted_win_rate=acted_win_rate,
        avg_confidence_acted=avg_confidence_acted,
        avg_confidence_skipped=avg_confidence_skipped,
    )


def build_position_performance_section(
    data: CollectedData,
) -> PositionPerformanceSection:
    """Build position performance section.

    Lists each position (open and closed) with entry price,
    current/exit price, P&L, P&L%, and hold duration.
    """
    positions: list[PositionDetail] = []
    now = datetime.now(timezone.utc)

    # Open positions
    for pos in data.open_positions:
        entry_price = float(pos.get("avg_entry_price", 0) or 0)
        current_price = float(pos.get("current_price", 0) or 0)
        quantity = float(pos.get("quantity", 0) or 0)

        pnl = (current_price - entry_price) * quantity
        cost_basis = entry_price * quantity
        pnl_pct = (pnl / cost_basis * 100) if cost_basis > 0 else 0.0

        # Hold duration from updated_at to now
        updated_at = pos.get("updated_at")
        hold_hours = _compute_hold_hours(updated_at, now)

        positions.append(
            PositionDetail(
                ticker=pos.get("ticker", ""),
                entry_price=entry_price,
                current_or_exit_price=current_price,
                pnl=pnl,
                pnl_pct=pnl_pct,
                hold_duration_hours=hold_hours,
                status="open",
            )
        )

    # Closed positions
    for pos in data.closed_positions:
        entry_price = float(pos.get("avg_entry_price", 0) or 0)
        current_price = float(pos.get("current_price", 0) or 0)
        realized_pnl = float(pos.get("realized_pnl", 0) or 0)

        cost_basis = entry_price * float(pos.get("quantity", 0) or 0)
        # For closed positions, quantity is 0 in the DB, so use realized_pnl
        # directly. P&L% is based on the original cost basis which we can
        # approximate from entry_price and the realized_pnl.
        # If entry_price is available, compute pnl_pct from realized_pnl / cost.
        # Since quantity=0 for closed, we estimate original quantity from
        # realized_pnl and price difference, or just use realized_pnl directly.
        if entry_price > 0 and current_price != entry_price:
            # Estimate original quantity from realized_pnl / (exit - entry)
            price_diff = current_price - entry_price
            if price_diff != 0:
                est_quantity = abs(realized_pnl / price_diff)
                est_cost = entry_price * est_quantity
                pnl_pct = (realized_pnl / est_cost * 100) if est_cost > 0 else 0.0
            else:
                pnl_pct = 0.0
        else:
            pnl_pct = 0.0

        updated_at = pos.get("updated_at")
        hold_hours = _compute_hold_hours(updated_at, now)

        positions.append(
            PositionDetail(
                ticker=pos.get("ticker", ""),
                entry_price=entry_price,
                current_or_exit_price=current_price,
                pnl=realized_pnl,
                pnl_pct=pnl_pct,
                hold_duration_hours=hold_hours,
                status="closed",
            )
        )

    return PositionPerformanceSection(positions=positions)


def _compute_hold_hours(updated_at: datetime | str | None, now: datetime) -> float:
    """Compute hold duration in hours from updated_at to now."""
    if updated_at is None:
        return 0.0
    if isinstance(updated_at, str):
        try:
            updated_at = datetime.fromisoformat(updated_at)
        except (ValueError, TypeError):
            return 0.0
    if not isinstance(updated_at, datetime):
        return 0.0
    # Ensure timezone-aware comparison
    if updated_at.tzinfo is None:
        updated_at = updated_at.replace(tzinfo=timezone.utc)
    delta = now - updated_at
    return max(delta.total_seconds() / 3600.0, 0.0)


def build_risk_metrics_section(data: CollectedData) -> RiskMetricsSection:
    """Build risk metrics section.

    Extracts current risk tier, portfolio heat, max drawdown,
    current drawdown %, reserve pool balance, and circuit breaker
    event count from collected data.
    """
    snap = data.portfolio_snapshot

    if snap is None:
        return RiskMetricsSection(
            current_risk_tier="unknown",
            portfolio_heat=0.0,
            max_drawdown=0.0,
            current_drawdown_pct=0.0,
            reserve_pool_balance=data.reserve_pool_balance,
            circuit_breaker_event_count=len(data.circuit_breaker_events),
        )

    return RiskMetricsSection(
        current_risk_tier=str(snap.get("risk_tier", "unknown") or "unknown"),
        portfolio_heat=float(snap.get("portfolio_heat", 0) or 0),
        max_drawdown=float(snap.get("max_drawdown", 0) or 0),
        current_drawdown_pct=float(snap.get("current_drawdown_pct", 0) or 0),
        reserve_pool_balance=data.reserve_pool_balance,
        circuit_breaker_event_count=len(data.circuit_breaker_events),
    )


def build_model_quality_section(data: CollectedData) -> ModelQualitySection:
    """Build model quality section.

    Extracts latest model_metric_snapshot values for 7d, 30d, 90d
    lookback windows.
    """
    if not data.model_metric_snapshots:
        return ModelQualitySection(windows=[])

    # Group by lookback_window, take the latest (first in list since
    # collector orders by generated_at DESC)
    target_windows = {"7d", "30d", "90d"}
    latest_by_window: dict[str, dict] = {}

    for snap in data.model_metric_snapshots:
        window = snap.get("lookback_window", "")
        if window in target_windows and window not in latest_by_window:
            latest_by_window[window] = snap

    windows: list[ModelQualityWindow] = []
    for w in ("7d", "30d", "90d"):
        snap = latest_by_window.get(w)
        if snap is None:
            windows.append(
                ModelQualityWindow(
                    lookback=w,
                    win_rate=None,
                    directional_accuracy=None,
                    information_coefficient=None,
                    calibration_error=None,
                    brier_score=None,
                )
            )
        else:
            windows.append(
                ModelQualityWindow(
                    lookback=w,
                    win_rate=_safe_float(snap.get("win_rate")),
                    directional_accuracy=_safe_float(snap.get("directional_accuracy")),
                    information_coefficient=_safe_float(
                        snap.get("information_coefficient")
                    ),
                    calibration_error=_safe_float(snap.get("calibration_error")),
                    brier_score=_safe_float(snap.get("brier_score")),
                )
            )

    return ModelQualitySection(windows=windows)


def _safe_float(value: object) -> float | None:
    """Convert a value to float, returning None for None/invalid values."""
    if value is None:
        return None
    try:
        f = float(value)  # type: ignore[arg-type]
        # Replace NaN/inf with None
        if f != f or f == float("inf") or f == float("-inf"):
            return None
        return f
    except (ValueError, TypeError):
        return None