"""Phase 4 — 매수/매도 신호 생성.

순수 함수 generate_signals(state, dedup, settings). state 를 mutate.
"""
from __future__ import annotations
import logging
from datetime import datetime
from zoneinfo import ZoneInfo

logger = logging.getLogger(__name__)
KST = ZoneInfo("Asia/Seoul")

MOMENTUM_SCORES = {
    "strong_up": 1.0,
    "weak_up": 0.7,
    "neutral": 0.5,
    "weak_down": 0.3,
    "strong_down": 0.0,
}


def generate_signals(state, dedup, settings) -> None:
    """Phase 4 entry — state-mutating. Evaluation order: sell first (priority), then buy. A ticker receiving a sell signal in this cycle is excluded from buy evaluation to avoid silent overwrite."""
    _evaluate_sell_signals(state, dedup, settings)
    _evaluate_buy_signals(state, dedup, settings)


# ----- 매수 -----

def _evaluate_buy_signals(state, dedup, settings) -> None:
    candidates = _buy_candidates(state)
    for ticker, name, rank in candidates:
        existing = state.signals.get(ticker)
        if existing is not None and existing.get("action") == "sell":
            logger.debug("buy %s skipped: same-cycle sell precedence", ticker)
            continue
        if not _check_buy_hard_gate(state, ticker, settings):
            logger.debug("buy %s skipped: hard gate failed", ticker)
            continue
        confidence = _compute_buy_confidence(state, ticker, rank)
        if confidence <= settings.confidence_threshold:
            logger.debug("buy %s skipped: confidence %.3f <= %.3f",
                         ticker, confidence, settings.confidence_threshold)
            continue
        if dedup.is_recent(ticker, "buy", within_hours=24):
            logger.debug("buy %s skipped: dedup 24h", ticker)
            continue
        state.signals[ticker] = _build_buy_signal(state, ticker, name, rank, confidence)
        dedup.record(ticker, "buy", confidence=confidence)
        logger.info("signal emit %s buy conf=%.3f rank=%s", ticker, confidence, rank)


def _buy_candidates(state) -> list[tuple[str, str, int | None]]:
    """screener Top-N (rank 1..N) + portfolio (rank=None)."""
    candidates: list[tuple[str, str, int | None]] = []
    seen: set[str] = set()
    if state.screener_preview is not None:
        for i, item in enumerate(state.screener_preview.get("items", [])):
            ticker = item.get("ticker")
            if not ticker or ticker in seen:
                continue
            seen.add(ticker)
            name = item.get("name", ticker)
            candidates.append((ticker, name, i + 1))
    if state.portfolio is not None:
        for h in state.portfolio.get("holdings", []):
            ticker = h.get("ticker")
            if not ticker or ticker in seen:
                continue
            seen.add(ticker)
            candidates.append((ticker, h.get("name", ticker), None))
    return candidates


def _check_buy_hard_gate(state, ticker: str, settings) -> bool:
    pred = state.chronos_predictions.get(ticker)
    if pred is None or pred.get("median", 0) <= 0:
        return False
    spread = pred.get("q90", 0) - pred.get("q10", 0)
    if spread >= settings.chronos_spread_threshold:
        return False
    momentum = state.minute_momentum.get(ticker)
    if momentum != settings.min_momentum_for_buy:
        return False
    ap = state.asking_price.get(ticker)
    if ap is None or ap.get("bid_ratio", 0) < settings.asking_bid_ratio_threshold:
        return False
    return True


def _compute_buy_confidence(state, ticker: str, rank: int | None) -> float:
    pred = state.chronos_predictions[ticker]
    chronos_conf = pred["conf"]
    minute_score = MOMENTUM_SCORES.get(state.minute_momentum.get(ticker, "neutral"), 0.5)
    screener_norm = max(0.0, 1 - (rank - 1) / 20) if rank is not None else 0.0
    return chronos_conf * 0.5 + minute_score * 0.3 + screener_norm * 0.2


def _build_buy_signal(state, ticker: str, name: str, rank: int | None, confidence: float) -> dict:
    ap = state.asking_price[ticker]
    return {
        "ticker": ticker,
        "name": name,
        "action": "buy",
        "confidence_webai": confidence,
        "current_price": ap["current_price"],
        "avg_price": None,
        "pnl_pct": None,
        "context": _build_context(state, ticker, rank),
        "as_of": datetime.now(KST).isoformat(),
    }


# ----- 매도 -----

def _evaluate_sell_signals(state, dedup, settings) -> None:
    if state.portfolio is None:
        return
    for holding in state.portfolio.get("holdings", []):
        ticker = holding.get("ticker")
        if not ticker:
            continue
        sell = _try_stop_loss(state, holding, settings)
        if sell is None:
            sell = _try_anomaly(state, holding, settings)
        if sell is None:
            sell = _try_take_profit(state, holding, settings)
        if sell is None:
            continue
        if dedup.is_recent(ticker, "sell", within_hours=24):
            logger.debug("sell %s skipped: dedup 24h", ticker)
            continue
        state.signals[ticker] = sell
        dedup.record(ticker, "sell", confidence=sell["confidence_webai"])
        logger.info("signal emit %s sell conf=%.3f reason=%s",
                    ticker, sell["confidence_webai"],
                    sell.get("context", {}).get("sell_reason"))


def _try_stop_loss(state, holding: dict, settings) -> dict | None:
    pnl = holding.get("pnl_pct")
    if pnl is None or pnl >= settings.stop_loss_pct:
        return None
    return _build_sell_signal(state, holding, confidence=1.0, reason="stop_loss")


def _try_take_profit(state, holding: dict, settings) -> dict | None:
    pnl = holding.get("pnl_pct")
    if pnl is None or pnl <= settings.take_profit_pct:
        return None
    return _build_sell_signal(state, holding, confidence=0.6, reason="take_profit")


def _try_anomaly(state, holding: dict, settings) -> dict | None:
    ticker = holding["ticker"]
    pred = state.chronos_predictions.get(ticker)
    if pred is None or pred["median"] >= -0.01:
        return None
    momentum = state.minute_momentum.get(ticker)
    if momentum != "strong_down":
        return None
    ap = state.asking_price.get(ticker)
    if ap is None:
        return None
    if ap["bid_ratio"] > (1 - settings.asking_bid_ratio_threshold):
        return None
    minute_score = 1.0 - MOMENTUM_SCORES.get(momentum, 0.5)
    confidence = pred["conf"] * 0.5 + minute_score * 0.3 + 1.0 * 0.2
    if confidence <= settings.confidence_threshold:
        return None
    return _build_sell_signal(state, holding, confidence=confidence, reason="anomaly")


def _build_sell_signal(state, holding: dict, confidence: float, reason: str) -> dict:
    ticker = holding["ticker"]
    return {
        "ticker": ticker,
        "name": holding.get("name", ticker),
        "action": "sell",
        "confidence_webai": confidence,
        "current_price": holding.get("current_price"),
        "avg_price": holding.get("avg_price"),
        "pnl_pct": holding.get("pnl_pct"),
        "context": _build_context(state, ticker, rank=None, sell_reason=reason),
        "as_of": datetime.now(KST).isoformat(),
    }


# ----- Context -----

def _build_context(state, ticker: str, rank: int | None, sell_reason: str | None = None) -> dict:
    pred = state.chronos_predictions.get(ticker) or {}
    ap = state.asking_price.get(ticker) or {}
    news_item = _find_news_sentiment(state, ticker)
    screener_scores = _find_screener_scores(state, ticker)
    context: dict = {
        "chronos_pred_1d": pred.get("median"),
        "chronos_pred_conf": pred.get("conf"),
        "chronos_q10": pred.get("q10"),
        "chronos_q90": pred.get("q90"),
        "screener_rank": rank,
        "screener_scores": screener_scores,
        "minute_momentum": state.minute_momentum.get(ticker),
        "asking_bid_ratio": ap.get("bid_ratio"),
        "news_sentiment": news_item.get("score") if news_item else None,
        "news_reason": news_item.get("reason") if news_item else None,
    }
    if sell_reason is not None:
        context["sell_reason"] = sell_reason
    return context


def _find_news_sentiment(state, ticker: str) -> dict | None:
    if state.news_sentiment is None:
        return None
    for item in state.news_sentiment.get("items", []):
        if item.get("ticker") == ticker:
            return item
    return None


def _find_screener_scores(state, ticker: str) -> dict | None:
    if state.screener_preview is None:
        return None
    for item in state.screener_preview.get("items", []):
        if item.get("ticker") == ticker:
            return item.get("scores")
    return None