"""Trade setup outcome evaluation service.

Closes the feedback loop on R:R scanner setups: walks daily OHLCV bars
after detection and records whether the stop or the target was hit first.

Outcome semantics (entry is the close at detection time, i.e. market entry):
  - target_hit: target reached before the stop
  - stop_hit:   stop reached before the target
  - ambiguous:  stop AND target both within the same daily bar — with daily
                granularity the order is unknowable, counted as a loss in stats
  - expired:    neither level hit within ``max_bars`` trading days
  - (NULL):     not enough bars yet to decide — re-evaluated on the next run
"""

from __future__ import annotations

import logging
from dataclasses import dataclass
from datetime import date, datetime, timezone

from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from app.models.ohlcv import OHLCVRecord
from app.models.trade_setup import TradeSetup
from app.services.qualification import setup_qualifies

logger = logging.getLogger(__name__)

OUTCOME_TARGET_HIT = "target_hit"
OUTCOME_STOP_HIT = "stop_hit"
OUTCOME_AMBIGUOUS = "ambiguous"
OUTCOME_EXPIRED = "expired"

DEFAULT_MAX_BARS = 30

# Confidence buckets for the performance breakdown
_CONFIDENCE_BUCKETS = [
    ("<50%", 0.0, 50.0),
    ("50-70%", 50.0, 70.0),
    ("≥70%", 70.0, 100.01),
]


@dataclass(frozen=True)
class Bar:
    date: date
    high: float
    low: float


def evaluate_setup_against_bars(
    direction: str,
    stop_loss: float,
    target: float,
    bars: list[Bar],
    max_bars: int = DEFAULT_MAX_BARS,
) -> tuple[str | None, date | None]:
    """Determine a setup's outcome from daily bars strictly after detection.

    Returns (outcome, outcome_date); (None, None) while still undecided.
    """
    for i, bar in enumerate(bars):
        if i >= max_bars:
            break
        if direction == "long":
            stop_hit = bar.low <= stop_loss
            target_hit = bar.high >= target
        else:
            stop_hit = bar.high >= stop_loss
            target_hit = bar.low <= target

        if stop_hit and target_hit:
            return OUTCOME_AMBIGUOUS, bar.date
        if stop_hit:
            return OUTCOME_STOP_HIT, bar.date
        if target_hit:
            return OUTCOME_TARGET_HIT, bar.date

    if len(bars) >= max_bars:
        return OUTCOME_EXPIRED, bars[max_bars - 1].date

    return None, None


async def evaluate_pending_setups(
    db: AsyncSession,
    max_bars: int = DEFAULT_MAX_BARS,
) -> dict[str, int]:
    """Evaluate all unevaluated trade setups against stored OHLCV data.

    Bars are fetched once per ticker. Setups that cannot be decided yet
    remain NULL and are picked up on the next run.
    """
    result = await db.execute(
        select(TradeSetup).where(TradeSetup.actual_outcome.is_(None))
    )
    pending = list(result.scalars().all())

    summary = {"evaluated": 0, "still_pending": 0, "by_outcome": {}}
    if not pending:
        return summary

    by_ticker: dict[int, list[TradeSetup]] = {}
    for setup in pending:
        by_ticker.setdefault(setup.ticker_id, []).append(setup)

    now = datetime.now(timezone.utc)

    for ticker_id, setups in by_ticker.items():
        earliest = min(s.detected_at for s in setups).date()
        bars_result = await db.execute(
            select(OHLCVRecord)
            .where(
                OHLCVRecord.ticker_id == ticker_id,
                OHLCVRecord.date > earliest,
            )
            .order_by(OHLCVRecord.date.asc())
        )
        records = list(bars_result.scalars().all())
        all_bars = [Bar(date=r.date, high=r.high, low=r.low) for r in records]

        for setup in setups:
            detected_date = setup.detected_at.date()
            bars = [b for b in all_bars if b.date > detected_date]
            outcome, outcome_date = evaluate_setup_against_bars(
                setup.direction, setup.stop_loss, setup.target, bars, max_bars
            )
            if outcome is None:
                summary["still_pending"] += 1
                continue
            setup.actual_outcome = outcome
            setup.outcome_date = outcome_date
            setup.evaluated_at = now
            summary["evaluated"] += 1
            summary["by_outcome"][outcome] = summary["by_outcome"].get(outcome, 0) + 1

    await db.commit()
    return summary


def _realized_r(setup: TradeSetup) -> float | None:
    """Realized result in R-multiples: win = +rr_ratio, loss = -1R, expired = 0R."""
    if setup.actual_outcome == OUTCOME_TARGET_HIT:
        return setup.rr_ratio
    if setup.actual_outcome in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS):
        return -1.0
    if setup.actual_outcome == OUTCOME_EXPIRED:
        return 0.0
    return None


def _bucket_stats(setups: list[TradeSetup]) -> dict:
    wins = sum(1 for s in setups if s.actual_outcome == OUTCOME_TARGET_HIT)
    losses = sum(
        1 for s in setups if s.actual_outcome in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS)
    )
    expired = sum(1 for s in setups if s.actual_outcome == OUTCOME_EXPIRED)
    decided = wins + losses
    realized = [r for s in setups if (r := _realized_r(s)) is not None]

    return {
        "total": len(setups),
        "wins": wins,
        "losses": losses,
        "expired": expired,
        "hit_rate": round(wins / decided * 100, 1) if decided else None,
        "avg_r": round(sum(realized) / len(realized), 3) if realized else None,
        "total_r": round(sum(realized), 2) if realized else None,
    }


def _confidence_bucket(score: float | None) -> str | None:
    if score is None:
        return None
    for label, lo, hi in _CONFIDENCE_BUCKETS:
        if lo <= score < hi:
            return label
    return None


async def get_performance_stats(
    db: AsyncSession,
    config: dict | None = None,
) -> dict:
    """Aggregate outcome statistics over all evaluated trade setups.

    avg_r is the expectancy per trade in R-multiples (win = +rr_ratio,
    loss = -1R, expired = 0R). A positive avg_r means the signals have
    been profitable on a risk-adjusted basis.

    When ``config`` (an activation-gate dict) is supplied, the overall,
    direction and action breakdowns cover only qualified setups. The
    confidence breakdown deliberately stays unfiltered: it is the
    instrument for validating the gate itself.
    """
    result = await db.execute(
        select(TradeSetup).where(TradeSetup.actual_outcome.is_not(None))
    )
    evaluated = list(result.scalars().all())

    pending_result = await db.execute(
        select(TradeSetup.id).where(TradeSetup.actual_outcome.is_(None))
    )
    pending_count = len(pending_result.scalars().all())

    if config is not None:
        qualified = [s for s in evaluated if setup_qualifies(s, config)]
    else:
        qualified = evaluated

    by_direction: dict[str, list[TradeSetup]] = {}
    by_action: dict[str, list[TradeSetup]] = {}
    by_confidence: dict[str, list[TradeSetup]] = {}

    for setup in qualified:
        by_direction.setdefault(setup.direction, []).append(setup)
        action = setup.recommended_action or "NONE"
        by_action.setdefault(action, []).append(setup)

    # Confidence buckets always cover the full evaluated population
    for setup in evaluated:
        bucket = _confidence_bucket(setup.confidence_score)
        if bucket is not None:
            by_confidence.setdefault(bucket, []).append(setup)

    bucket_order = [label for label, _, _ in _CONFIDENCE_BUCKETS]

    return {
        "overall": _bucket_stats(qualified),
        "pending": pending_count,
        "by_direction": {k: _bucket_stats(v) for k, v in sorted(by_direction.items())},
        "by_action": {k: _bucket_stats(v) for k, v in sorted(by_action.items())},
        "by_confidence": {
            label: _bucket_stats(by_confidence[label])
            for label in bucket_order
            if label in by_confidence
        },
    }