"""
성과 평가 엔진 - PerformanceEvaluator

기능:
  1. compute_metrics()   - 핵심 성과 지표 계산
  2. get_grade()         - 지표별 S/A/B/C/D/F 등급 산출
  3. generate_expert_panel() - Ollama LLM 5명 전문가 의견
  4. generate_weekly_report() - 텔레그램 HTML 주간 보고서
"""
import json
import math
from datetime import datetime, timedelta

from modules.utils.performance_db import PerformanceDB


class PerformanceEvaluator:
    def __init__(self):
        self.perf_db = PerformanceDB()

    # ─────────────────────────────────────────
    # 1. 핵심 지표 계산
    # ─────────────────────────────────────────

    def compute_metrics(self, snapshots, trades):
        """성과 지표를 딕셔너리로 반환.

        Args:
            snapshots (list): daily_snapshots 리스트
            trades (list): trade_records 리스트

        Returns:
            dict: 지표 딕셔너리 (또는 {"error": ...})
        """
        if not snapshots:
            return {"error": "스냅샷 데이터 없음 (운영 시작 후 첫 영업일까지 대기)"}

        metrics = {}

        # ── 수익률 ──────────────────────────────
        initial = snapshots[0].get("total_eval", 0)
        current = snapshots[-1].get("total_eval", 0)
        metrics["total_return_pct"] = round(
            (current - initial) / initial * 100, 2) if initial > 0 else 0.0

        cutoff_7 = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
        recent_snaps = [s for s in snapshots if s.get("date", "") >= cutoff_7]
        if len(recent_snaps) >= 2:
            w_init = recent_snaps[0].get("total_eval", 0)
            w_curr = recent_snaps[-1].get("total_eval", 0)
            metrics["weekly_return_pct"] = round(
                (w_curr - w_init) / w_init * 100, 2) if w_init > 0 else 0.0
        else:
            metrics["weekly_return_pct"] = 0.0

        # ── 리스크 지표 ──────────────────────────
        daily_returns = [s.get("daily_return_pct", 0.0) / 100.0 for s in snapshots]

        if len(daily_returns) >= 2:
            mean_daily = sum(daily_returns) / len(daily_returns)
            variance = sum((r - mean_daily) ** 2 for r in daily_returns) / len(daily_returns)
            std_daily = math.sqrt(variance) if variance > 0 else 0.0
            annual_return = mean_daily * 252

            # Sharpe Ratio
            if std_daily > 0:
                metrics["sharpe_ratio"] = round(
                    annual_return / (std_daily * math.sqrt(252)), 3)
            else:
                metrics["sharpe_ratio"] = 0.0

            # Sortino Ratio (하방 편차만 사용)
            downside = [r for r in daily_returns if r < 0]
            if downside:
                dv = sum(r ** 2 for r in downside) / len(downside)
                ds = math.sqrt(dv)
                metrics["sortino_ratio"] = round(
                    annual_return / (ds * math.sqrt(252)), 3) if ds > 0 else 0.0
            else:
                metrics["sortino_ratio"] = 10.0  # 손실 없음

            # Max Drawdown
            peak = snapshots[0].get("total_eval", 0)
            max_dd = 0.0
            for snap in snapshots:
                ev = snap.get("total_eval", 0)
                if ev > peak:
                    peak = ev
                if peak > 0:
                    dd = (peak - ev) / peak * 100
                    if dd > max_dd:
                        max_dd = dd
            metrics["max_drawdown_pct"] = round(max_dd, 2)

            # Calmar Ratio
            ann_pct = annual_return * 100
            metrics["calmar_ratio"] = round(
                ann_pct / max_dd, 3) if max_dd > 0 else 0.0
        else:
            metrics["sharpe_ratio"] = 0.0
            metrics["sortino_ratio"] = 0.0
            metrics["max_drawdown_pct"] = 0.0
            metrics["calmar_ratio"] = 0.0

        # ── 매매 지표 ─────────────────────────────
        closed = [t for t in trades
                  if t.get("action") == "BUY" and t.get("outcome_return_pct") is not None]

        if closed:
            wins = [t for t in closed if t.get("outcome_return_pct", 0) > 0]
            losses = [t for t in closed if t.get("outcome_return_pct", 0) <= 0]

            metrics["win_rate_pct"] = round(
                len(wins) / len(closed) * 100, 1)

            total_profit = sum(t["outcome_return_pct"] for t in wins)
            total_loss = abs(sum(t["outcome_return_pct"] for t in losses))
            metrics["profit_factor"] = round(
                total_profit / total_loss, 3) if total_loss > 0 else 10.0

            hd_list = [t["holding_days"] for t in closed if t.get("holding_days") is not None]
            metrics["avg_holding_days"] = round(
                sum(hd_list) / len(hd_list), 1) if hd_list else 0.0
        else:
            metrics["win_rate_pct"] = 0.0
            metrics["profit_factor"] = 0.0
            metrics["avg_holding_days"] = 0.0

        metrics["total_trades"] = len(closed)

        # ── 벤치마크 Alpha ────────────────────────
        kospi_vals = [s.get("benchmark_kospi_close") for s in snapshots]
        kospi_valid = [k for k in kospi_vals if k is not None]
        if len(kospi_valid) >= 2:
            kospi_ret = (kospi_valid[-1] - kospi_valid[0]) / kospi_valid[0] * 100
            metrics["alpha"] = round(metrics["total_return_pct"] - kospi_ret, 2)
            metrics["kospi_return_pct"] = round(kospi_ret, 2)
        else:
            metrics["alpha"] = 0.0
            metrics["kospi_return_pct"] = 0.0

        # ── AI 품질 지표 ──────────────────────────
        if closed:
            # LSTM 방향 정확도
            correct = 0
            direction_n = 0
            for t in closed:
                pred = t.get("ai_prediction_change")
                outcome = t.get("outcome_return_pct")
                if pred is not None and outcome is not None:
                    if (pred > 0) == (outcome > 0):
                        correct += 1
                    direction_n += 1
            metrics["lstm_direction_accuracy"] = round(
                correct / direction_n * 100, 1) if direction_n > 0 else 0.0

            # 신호별 수익 상관도
            outcomes = [t.get("outcome_return_pct", 0) for t in closed]

            def pearson(xs, ys):
                n = len(xs)
                if n < 2:
                    return 0.0
                mx = sum(xs) / n
                my = sum(ys) / n
                num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
                denom_x = sum((x - mx) ** 2 for x in xs)
                denom_y = sum((y - my) ** 2 for y in ys)
                denom = math.sqrt(denom_x * denom_y)
                return num / denom if denom > 0 else 0.0

            corr_tech = pearson([t.get("tech_score", 0) for t in closed], outcomes)
            corr_sent = pearson([t.get("sentiment_score", 0) for t in closed], outcomes)
            corr_lstm = pearson([t.get("lstm_score", 0) for t in closed], outcomes)

            metrics["signal_correlation"] = {
                "tech": round(corr_tech, 3),
                "sentiment": round(corr_sent, 3),
                "lstm": round(corr_lstm, 3)
            }
            metrics["best_signal_source"] = max(
                ["tech", "sentiment", "lstm"],
                key=lambda k: abs(metrics["signal_correlation"][k])
            )
        else:
            metrics["lstm_direction_accuracy"] = 0.0
            metrics["signal_correlation"] = {"tech": 0.0, "sentiment": 0.0, "lstm": 0.0}
            metrics["best_signal_source"] = "unknown"

        metrics["snapshot_count"] = len(snapshots)
        return metrics

    # ─────────────────────────────────────────
    # 2. 등급 산출
    # ─────────────────────────────────────────

    def get_grade(self, metric, value):
        """지표 이름과 값으로 S/A/B/C/D/F 등급 반환."""
        # MDD는 낮을수록 좋음
        if metric == "max_drawdown_pct":
            thresholds = [(5, "S"), (10, "A"), (15, "B"), (20, "C"), (30, "D")]
            for threshold, grade in thresholds:
                if value < threshold:
                    return grade
            return "F"

        grade_rules = {
            "sharpe_ratio":           [(2.0, "S"), (1.5, "A"), (1.0, "B"), (0.5, "C"), (0.0, "D")],
            "sortino_ratio":          [(3.0, "S"), (2.0, "A"), (1.5, "B"), (1.0, "C"), (0.0, "D")],
            "win_rate_pct":           [(70,  "S"), (60,  "A"), (50,  "B"), (40,  "C"), (30,  "D")],
            "profit_factor":          [(3.0, "S"), (2.0, "A"), (1.5, "B"), (1.0, "C"), (0.5, "D")],
            "alpha":                  [(15,  "S"), (10,  "A"), (5,   "B"), (0,   "C"), (-5,  "D")],
            "total_return_pct":       [(30,  "S"), (20,  "A"), (10,  "B"), (0,   "C"), (-10, "D")],
            "weekly_return_pct":      [(5,   "S"), (3,   "A"), (1,   "B"), (0,   "C"), (-1,  "D")],
            "lstm_direction_accuracy":[(70,  "S"), (60,  "A"), (55,  "B"), (50,  "C"), (40,  "D")],
            "calmar_ratio":           [(3.0, "S"), (2.0, "A"), (1.0, "B"), (0.5, "C"), (0.0, "D")],
        }

        thresholds = grade_rules.get(metric, [])
        for threshold, grade in thresholds:
            if value >= threshold:
                return grade
        return "F"

    # ─────────────────────────────────────────
    # 3. 전문가 패널 (Ollama LLM)
    # ─────────────────────────────────────────

    def generate_expert_panel(self, metrics):
        """5명의 전문가 역할로 Ollama에 평가를 요청.

        Returns:
            list[dict]: [{role, grade, comment, suggestion}, ...]
        """
        from modules.services.ollama import OllamaManager
        ollama = OllamaManager()

        sig_corr = metrics.get("signal_correlation", {})
        experts = [
            {
                "role": "Risk Manager",
                "focus": "risk level assessment and bankruptcy risk",
                "data": (
                    f"Sharpe={metrics.get('sharpe_ratio', 0):.2f}, "
                    f"Sortino={metrics.get('sortino_ratio', 0):.2f}, "
                    f"MDD={metrics.get('max_drawdown_pct', 0):.1f}%, "
                    f"Calmar={metrics.get('calmar_ratio', 0):.2f}"
                )
            },
            {
                "role": "Fund Manager",
                "focus": "alpha generation vs market benchmark",
                "data": (
                    f"TotalReturn={metrics.get('total_return_pct', 0):.2f}%, "
                    f"Alpha={metrics.get('alpha', 0):.2f}%, "
                    f"KOSPI={metrics.get('kospi_return_pct', 0):.2f}%, "
                    f"WeeklyReturn={metrics.get('weekly_return_pct', 0):.2f}%"
                )
            },
            {
                "role": "Quant Analyst",
                "focus": "AI model validity and signal quality",
                "data": (
                    f"LSTM_Accuracy={metrics.get('lstm_direction_accuracy', 0):.1f}%, "
                    f"TechCorr={sig_corr.get('tech', 0):.3f}, "
                    f"SentCorr={sig_corr.get('sentiment', 0):.3f}, "
                    f"LSTMCorr={sig_corr.get('lstm', 0):.3f}, "
                    f"BestSignal={metrics.get('best_signal_source', 'N/A')}"
                )
            },
            {
                "role": "Trader",
                "focus": "trading strategy effectiveness",
                "data": (
                    f"WinRate={metrics.get('win_rate_pct', 0):.1f}%, "
                    f"ProfitFactor={metrics.get('profit_factor', 0):.2f}, "
                    f"AvgHolding={metrics.get('avg_holding_days', 0):.1f}days, "
                    f"TotalTrades={metrics.get('total_trades', 0)}"
                )
            },
            {
                "role": "Portfolio PM",
                "focus": "overall strategy direction and sustainability",
                "data": (
                    f"WeeklyReturn={metrics.get('weekly_return_pct', 0):.2f}%, "
                    f"Sharpe={metrics.get('sharpe_ratio', 0):.2f}, "
                    f"WinRate={metrics.get('win_rate_pct', 0):.1f}%, "
                    f"Alpha={metrics.get('alpha', 0):.2f}%, "
                    f"MDD={metrics.get('max_drawdown_pct', 0):.1f}%"
                )
            }
        ]

        results = []
        for exp in experts:
            prompt = (
                f"You are a professional {exp['role']} evaluating an AI stock trading bot. "
                f"Your focus: {exp['focus']}. "
                f"Performance data: {exp['data']}. "
                f"Respond ONLY with valid JSON (no markdown, no extra text): "
                f"{{\"grade\":\"S|A|B|C|D|F\","
                f"\"comment\":\"1 sentence evaluation in Korean\","
                f"\"suggestion\":\"1 sentence improvement tip in Korean\"}}"
            )
            try:
                resp = ollama.request_inference(prompt)
                if not resp:
                    raise ValueError("Empty response from Ollama")
                data = json.loads(resp)
                results.append({
                    "role": exp["role"],
                    "grade": data.get("grade", "C"),
                    "comment": data.get("comment", "(응답 없음)"),
                    "suggestion": data.get("suggestion", "데이터 축적 필요")
                })
            except Exception as e:
                print(f"[Evaluator] Expert panel [{exp['role']}] error: {e}")
                results.append({
                    "role": exp["role"],
                    "grade": "C",
                    "comment": "평가 데이터가 부족합니다.",
                    "suggestion": "더 많은 거래 데이터 축적 후 재평가를 권장합니다."
                })

        return results

    # ─────────────────────────────────────────
    # 4. 주간 보고서 생성
    # ─────────────────────────────────────────

    def generate_weekly_report(self):
        """주간 성과 보고서 (텔레그램 HTML 형식) 반환."""
        snapshots = self.perf_db.load_snapshots(days=7)
        # 매매 완료 건은 30일치 사용 (주간 거래 수가 적을 수 있음)
        trades = self.perf_db.load_trades(days=30)

        metrics = self.compute_metrics(snapshots, trades)

        if "error" in metrics:
            return (
                f"<b>[주간 성과 평가 보고서]</b>\n"
                f"⚠️ {metrics['error']}\n"
                f"<i>매일 오전 09:05~09:15에 스냅샷이 저장됩니다.</i>"
            )

        # 등급 계산
        g_sharpe = self.get_grade("sharpe_ratio", metrics.get("sharpe_ratio", 0))
        g_win    = self.get_grade("win_rate_pct", metrics.get("win_rate_pct", 0))
        g_mdd    = self.get_grade("max_drawdown_pct", metrics.get("max_drawdown_pct", 0))
        g_alpha  = self.get_grade("alpha", metrics.get("alpha", 0))
        g_weekly = self.get_grade("weekly_return_pct", metrics.get("weekly_return_pct", 0))
        g_lstm   = self.get_grade("lstm_direction_accuracy",
                                  metrics.get("lstm_direction_accuracy", 0))

        # 종합 등급 (Sharpe/Win/MDD/Alpha 평균)
        grade_map = {"S": 5, "A": 4, "B": 3, "C": 2, "D": 1, "F": 0}
        grade_rev = {v: k for k, v in grade_map.items()}
        key_grades = [grade_map[g] for g in [g_sharpe, g_win, g_mdd, g_alpha]]
        overall_grade = grade_rev[round(sum(key_grades) / len(key_grades))]

        # 전문가 패널 (Ollama 호출)
        try:
            experts = self.generate_expert_panel(metrics)
        except Exception as e:
            print(f"[Evaluator] Expert panel skipped: {e}")
            experts = []

        now_str = datetime.now().strftime("%Y/%m/%d %H:%M")
        corr = metrics.get("signal_correlation", {})

        report = (
            f"📊 <b>[주간 성과 평가 보고서]</b>  <code>{now_str}</code>\n"
            f"━━━━━━━━━━━━━━━━━━━━━━\n"
            f"\n<b>■ 수익률</b>\n"
            f"  주간: <code>{metrics.get('weekly_return_pct', 0):+.2f}%</code> [{g_weekly}]"
            f"  누적: <code>{metrics.get('total_return_pct', 0):+.2f}%</code>\n"
            f"  Alpha: <code>{metrics.get('alpha', 0):+.2f}%</code> [{g_alpha}]"
            f"  vs KOSPI <code>{metrics.get('kospi_return_pct', 0):+.2f}%</code>\n"
            f"\n<b>■ 리스크</b>\n"
            f"  Sharpe: <code>{metrics.get('sharpe_ratio', 0):.2f}</code> [{g_sharpe}]"
            f"  Sortino: <code>{metrics.get('sortino_ratio', 0):.2f}</code>\n"
            f"  MDD: <code>{metrics.get('max_drawdown_pct', 0):.1f}%</code> [{g_mdd}]"
            f"  Calmar: <code>{metrics.get('calmar_ratio', 0):.2f}</code>\n"
            f"\n<b>■ 매매 통계</b>\n"
            f"  승률: <code>{metrics.get('win_rate_pct', 0):.1f}%</code> [{g_win}]"
            f"  PF: <code>{metrics.get('profit_factor', 0):.2f}</code>\n"
            f"  평균보유: <code>{metrics.get('avg_holding_days', 0):.1f}일</code>"
            f"  완료매매: <code>{metrics.get('total_trades', 0)}건</code>\n"
            f"\n<b>■ AI 품질</b>\n"
            f"  LSTM 방향정확도: <code>{metrics.get('lstm_direction_accuracy', 0):.1f}%</code>"
            f" [{g_lstm}]\n"
            f"  신호 상관도 — Tech: <code>{corr.get('tech', 0):.3f}</code>"
            f"  Sent: <code>{corr.get('sentiment', 0):.3f}</code>"
            f"  LSTM: <code>{corr.get('lstm', 0):.3f}</code>\n"
            f"  최고기여 신호: <code>{metrics.get('best_signal_source', 'N/A')}</code>\n"
        )

        if experts:
            role_icons = {
                "Risk Manager":  "🛡",
                "Fund Manager":  "💼",
                "Quant Analyst": "🧮",
                "Trader":        "📈",
                "Portfolio PM":  "🏦"
            }
            report += "\n<b>■ 전문가 패널 의견</b>\n"
            for exp in experts:
                icon = role_icons.get(exp["role"], "👤")
                report += (
                    f"{icon} <b>{exp['role']}</b> [{exp['grade']}]\n"
                    f"  {exp['comment']}\n"
                    f"  💡 {exp['suggestion']}\n"
                )

        report += (
            f"\n━━━━━━━━━━━━━━━━━━━━━━\n"
            f"🏆 <b>종합 등급: [{overall_grade}]</b>\n"
            f"<i>스냅샷 {metrics.get('snapshot_count', 0)}일 | 완료매매 {metrics.get('total_trades', 0)}건 기준</i>"
        )

        return report