"""
자산 배분(Asset Allocation) 전략 구현체

전술적 자산배분, 리스크 패리티 등 다양한 자산에 배분하는 전략들을 포함합니다.
"""

from typing import Dict, Any
import time
import random
from datetime import datetime, timedelta
import yfinance as yf
import pandas as pd
from ..base import BaseQuantStrategy, strategy


@strategy
class BoldAssetAllocation(BaseQuantStrategy):
    """Bold Asset Allocation (BAA) 전략

    상대 모멘텀과 절대 모멘텀을 결합한 공격적 전술적 자산배분 전략.
    느린 상대 모멘텀(SMA12)과 빠른 절대 모멘텀(13612W)을 조합하여
    카나리아 유니버스 기반의 크래시 보호 메커니즘을 구현.
    """

    @property
    def name(self) -> str:
        return "BoldAssetAllocation"

    @property
    def description(self) -> str:
        return "상대 모멘텀과 절대 모멘텀을 결합한 공격적 전술적 자산배분 전략. 카나리아 유니버스 기반 크래시 보호"

    @property
    def version(self) -> str:
        return "1.0.0"

    @property
    def default_parameters(self) -> Dict[str, Any]:
        return {
            "initial_capital": 100000,
            "variant": "BAA-G12",  # BAA-G12 (balanced), BAA-G4 (aggressive), BAA-SPY
            "offensive_top": 6,  # TO: 공격 유니버스에서 선택할 자산 수 (BAA-G12: 6, BAA-G4: 1)
            "defensive_top": 3,  # TD: 방어 유니버스에서 선택할 자산 수
            "breadth_param": 1,  # B: 카나리아 자산 중 몇 개가 bad일 때 방어로 전환
            "transaction_cost": 0.001,  # 0.1% 거래비용
            "as_of_date": None,  # 기준일 (None이면 현재 날짜)
            "use_real_data": False,  # 실제 데이터 사용 여부
        }

    def validate_parameters(self, parameters: Dict[str, Any]) -> bool:
        required_params = ["initial_capital", "variant"]
        for param in required_params:
            if param not in parameters:
                return False

        if parameters["initial_capital"] <= 0:
            return False

        valid_variants = ["BAA-G12", "BAA-G4", "BAA-SPY", "BAA-G12/T3", "BAA-G4/T2"]
        if parameters.get("variant") not in valid_variants:
            return False

        return True

    def _calculate_sma12_momentum(self, prices: pd.Series) -> float:
        """SMA(12) 모멘텀 계산: pt / SMA(13) - 1"""
        if len(prices) < 13:
            return 0.0

        # numpy scalar 또는 pandas Series를 float로 변환
        mean_val = prices.tail(13).mean()
        sma13 = float(mean_val.item()) if hasattr(mean_val, 'item') else float(mean_val)

        current_val = prices.iloc[-1]
        current_price = float(current_val.item()) if hasattr(current_val, 'item') else float(current_val)

        return (current_price / sma13) - 1

    def _calculate_13612w_momentum(self, prices: pd.Series) -> float:
        """13612W 모멘텀 계산: 가중평균 (1m:12, 3m:4, 6m:2, 12m:1)"""
        if len(prices) < 252:  # 최소 12개월 데이터 필요
            return 0.0

        current_val = prices.iloc[-1]
        current_price = float(current_val.item()) if hasattr(current_val, 'item') else float(current_val)

        # 각 기간별 수익률 계산
        def get_price(idx):
            val = prices.iloc[idx]
            return float(val.item()) if hasattr(val, 'item') else float(val)

        ret_1m = (current_price / get_price(-21) - 1) if len(prices) > 21 else 0.0
        ret_3m = (current_price / get_price(-63) - 1) if len(prices) > 63 else 0.0
        ret_6m = (current_price / get_price(-126) - 1) if len(prices) > 126 else 0.0
        ret_12m = (current_price / get_price(-252) - 1) if len(prices) > 252 else 0.0

        # 가중평균
        weighted_momentum = (12 * ret_1m + 4 * ret_3m + 2 * ret_6m + 1 * ret_12m) / 19
        return float(weighted_momentum)

    def _get_ticker_data(self, ticker: str, end_date: datetime) -> pd.Series:
        """특정 티커의 가격 데이터 가져오기"""
        start_date = end_date - timedelta(days=400)  # 13개월 + 여유

        try:
            data = yf.download(ticker, start=start_date, end=end_date, progress=False, auto_adjust=True)
            if data.empty:
                return pd.Series()

            # 데이터 구조에 따라 처리
            if 'Close' in data.columns:
                return data['Close']
            elif isinstance(data, pd.Series):
                return data
            else:
                return pd.Series()
        except Exception as e:
            print(f"Error downloading {ticker}: {e}")
            return pd.Series()

    def _calculate_portfolio_real_data(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """실제 데이터를 사용한 포트폴리오 계산"""
        variant = parameters.get("variant", "BAA-G12")
        as_of_date = parameters.get("as_of_date")
        initial_capital = parameters["initial_capital"]

        # 기준일 설정
        if as_of_date is None:
            as_of_date = datetime.now()
        elif isinstance(as_of_date, str):
            as_of_date = datetime.strptime(as_of_date, "%Y-%m-%d")

        # 자산 유니버스 정의
        universe_config = {
            "BAA-G12": {
                "offensive": ["SPY", "QQQ", "IWM", "VGK", "EWJ", "VWO", "VNQ", "DBC", "GLD", "TLT", "HYG", "LQD"],
                "defensive": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary": ["SPY", "VWO", "VEA", "BND"],
                "offensive_top": 6,
            },
            "BAA-G4": {
                "offensive": ["QQQ", "VWO", "VEA", "BND"],
                "defensive": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary": ["SPY", "VWO", "VEA", "BND"],
                "offensive_top": 1,
            },
            "BAA-G12/T3": {
                "offensive": ["SPY", "QQQ", "IWM", "VGK", "EWJ", "VWO", "VNQ", "DBC", "GLD", "TLT", "HYG", "LQD"],
                "defensive": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary": ["SPY", "VWO", "VEA", "BND"],
                "offensive_top": 3,
            },
            "BAA-G4/T2": {
                "offensive": ["QQQ", "VWO", "VEA", "BND"],
                "defensive": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary": ["SPY", "VWO", "VEA", "BND"],
                "offensive_top": 2,
            },
            "BAA-SPY": {
                "offensive": ["SPY"],
                "defensive": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary": ["SPY", "VWO", "VEA", "BND"],
                "offensive_top": 1,
            }
        }

        config = universe_config.get(variant, universe_config["BAA-G12"])
        offensive_top = parameters.get("offensive_top", config["offensive_top"])
        defensive_top = parameters.get("defensive_top", 3)
        breadth_param = parameters.get("breadth_param", 1)

        # 카나리아 유니버스 체크 (13612W 모멘텀 사용)
        canary_bad_count = 0
        canary_status = {}

        for ticker in config["canary"]:
            prices = self._get_ticker_data(ticker, as_of_date)
            if not prices.empty:
                momentum = self._calculate_13612w_momentum(prices)
                is_bad = bool(momentum < 0)
                canary_status[ticker] = {
                    "momentum": float(momentum),
                    "is_bad": is_bad
                }
                if is_bad:
                    canary_bad_count += 1

        # 방어 모드 여부 결정
        is_defensive = canary_bad_count >= breadth_param

        # 포트폴리오 구성
        portfolio = {}
        current_prices = {}

        if is_defensive:
            # 방어 유니버스에서 선택
            defensive_scores = {}
            for ticker in config["defensive"]:
                prices = self._get_ticker_data(ticker, as_of_date)
                if not prices.empty:
                    momentum = self._calculate_sma12_momentum(prices)
                    defensive_scores[ticker] = momentum
                    price_val = prices.iloc[-1]
                    current_prices[ticker] = float(price_val.item()) if hasattr(price_val, 'item') else float(price_val)

            # 상위 defensive_top개 선택
            sorted_defensive = sorted(defensive_scores.items(), key=lambda x: x[1], reverse=True)
            selected_assets = []

            for ticker, momentum in sorted_defensive[:defensive_top]:
                # BIL보다 나쁜 자산은 BIL로 교체
                bil_prices = self._get_ticker_data("BIL", as_of_date)
                bil_momentum = self._calculate_sma12_momentum(bil_prices) if not bil_prices.empty else 0

                if momentum < bil_momentum:
                    selected_assets.append("BIL")
                    if "BIL" not in current_prices and not bil_prices.empty:
                        price_val = bil_prices.iloc[-1]
                        current_prices["BIL"] = float(price_val.item()) if hasattr(price_val, 'item') else float(price_val)
                else:
                    selected_assets.append(ticker)

            # 선택된 자산이 없는 경우 BIL로 대체
            if not selected_assets:
                selected_assets = ["BIL"]
                bil_prices = self._get_ticker_data("BIL", as_of_date)
                if not bil_prices.empty:
                    price_val = bil_prices.iloc[-1]
                    current_prices["BIL"] = float(price_val.item()) if hasattr(price_val, 'item') else float(price_val)

            # 동일 가중
            weight_per_asset = 1.0 / len(selected_assets)
            for ticker in selected_assets:
                if ticker in portfolio:
                    portfolio[ticker] += weight_per_asset
                else:
                    portfolio[ticker] = weight_per_asset

        else:
            # 공격 유니버스에서 선택 (SMA12 상대 모멘텀)
            offensive_scores = {}
            for ticker in config["offensive"]:
                prices = self._get_ticker_data(ticker, as_of_date)
                if not prices.empty:
                    momentum = self._calculate_sma12_momentum(prices)
                    offensive_scores[ticker] = momentum
                    price_val = prices.iloc[-1]
                    current_prices[ticker] = float(price_val.item()) if hasattr(price_val, 'item') else float(price_val)

            # 상위 offensive_top개 선택
            sorted_offensive = sorted(offensive_scores.items(), key=lambda x: x[1], reverse=True)
            selected_assets = [ticker for ticker, _ in sorted_offensive[:offensive_top]]

            # 선택된 자산이 없는 경우 BIL로 대체
            if not selected_assets:
                selected_assets = ["BIL"]
                bil_prices = self._get_ticker_data("BIL", as_of_date)
                if not bil_prices.empty:
                    price_val = bil_prices.iloc[-1]
                    current_prices["BIL"] = float(price_val.item()) if hasattr(price_val, 'item') else float(price_val)

            # 동일 가중
            weight_per_asset = 1.0 / len(selected_assets)
            for ticker in selected_assets:
                portfolio[ticker] = weight_per_asset

        # 실제 매수 수량 계산
        allocation_details = []
        total_allocated = 0

        for ticker, weight in portfolio.items():
            if ticker in current_prices:
                allocation_amount = float(initial_capital * weight)
                price = float(current_prices[ticker])
                shares = int(allocation_amount / price)  # 정수 주식만 매수
                actual_amount = float(shares * price)

                allocation_details.append({
                    "ticker": ticker,
                    "weight": round(weight * 100, 2),
                    "target_amount": round(allocation_amount, 2),
                    "current_price": round(price, 2),
                    "shares": shares,
                    "actual_amount": round(actual_amount, 2)
                })
                total_allocated += actual_amount

        cash_remaining = initial_capital - total_allocated

        return {
            "as_of_date": as_of_date.strftime("%Y-%m-%d"),
            "mode": "defensive" if is_defensive else "offensive",
            "canary_status": canary_status,
            "canary_bad_count": canary_bad_count,
            "breadth_threshold": breadth_param,
            "portfolio": allocation_details,
            "total_allocated": round(total_allocated, 2),
            "cash_remaining": round(cash_remaining, 2),
            "initial_capital": initial_capital,
            "portfolio_summary": {
                ticker: weight for ticker, weight in portfolio.items()
            }
        }

    def execute(self, parameters: Dict[str, Any] = None) -> Dict[str, Any]:
        if parameters is None:
            parameters = self.default_parameters

        if not self.validate_parameters(parameters):
            raise ValueError("Invalid parameters")

        # 실제 데이터를 사용하는 경우
        if parameters.get("use_real_data", False):
            real_portfolio = self._calculate_portfolio_real_data(parameters)
            return {
                "strategy": self.name,
                "version": self.version,
                "variant": parameters.get("variant", "BAA-G12"),
                "mode": "real_data_portfolio",
                "execution_time": "variable",
                "parameters_used": parameters,
                **real_portfolio
            }

        variant = parameters.get("variant", "BAA-G12")

        # 시뮬레이션 실행
        time.sleep(1.8)  # 실행 시간 시뮬레이션

        # 전략 변형에 따른 결과 시뮬레이션 (Full Sample 기준)
        variant_stats = {
            "BAA-G12": {
                "cagr": 0.146,
                "max_dd": 0.087,
                "volatility": 0.085,
                "sharpe": 1.19,
                "upi": 4.81,
                "defensive_fraction": 0.572,
                "turnover": 4.72,
                "offensive_assets": ["SPY", "QQQ", "IWM", "VGK", "EWJ", "VWO", "VNQ", "DBC", "GLD", "TLT", "HYG", "LQD"],
                "defensive_assets": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary_assets": ["SPY", "VWO", "VEA", "BND"]
            },
            "BAA-G4": {
                "cagr": 0.210,
                "max_dd": 0.146,
                "volatility": 0.136,
                "sharpe": 1.21,
                "upi": 5.20,
                "defensive_fraction": 0.572,
                "turnover": 5.23,
                "offensive_assets": ["QQQ", "VWO", "VEA", "BND"],
                "defensive_assets": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary_assets": ["SPY", "VWO", "VEA", "BND"]
            },
            "BAA-G12/T3": {
                "cagr": 0.164,
                "max_dd": 0.114,
                "volatility": 0.104,
                "sharpe": 1.13,
                "upi": 4.23,
                "defensive_fraction": 0.572,
                "turnover": 5.13,
                "offensive_assets": ["SPY", "QQQ", "IWM", "VGK", "EWJ", "VWO", "VNQ", "DBC", "GLD", "TLT", "HYG", "LQD"],
                "defensive_assets": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary_assets": ["SPY", "VWO", "VEA", "BND"]
            },
            "BAA-G4/T2": {
                "cagr": 0.177,
                "max_dd": 0.127,
                "volatility": 0.106,
                "sharpe": 1.25,
                "upi": 5.08,
                "defensive_fraction": 0.572,
                "turnover": 5.02,
                "offensive_assets": ["QQQ", "VWO", "VEA", "BND"],
                "defensive_assets": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary_assets": ["SPY", "VWO", "VEA", "BND"]
            },
            "BAA-SPY": {
                "cagr": 0.114,
                "max_dd": 0.162,
                "volatility": 0.095,
                "sharpe": 0.72,
                "upi": 1.88,
                "defensive_fraction": 0.572,
                "turnover": 4.73,
                "offensive_assets": ["SPY"],
                "defensive_assets": ["TIP", "DBC", "BIL", "IEF", "TLT", "LQD", "BND"],
                "canary_assets": ["SPY", "VWO", "VEA", "BND"]
            }
        }

        stats = variant_stats.get(variant, variant_stats["BAA-G12"])

        # 약간의 랜덤성 추가 (±5%)
        cagr = stats["cagr"] * random.uniform(0.95, 1.05)
        max_dd = stats["max_dd"] * random.uniform(0.95, 1.05)
        volatility = stats["volatility"] * random.uniform(0.95, 1.05)

        # 최종 자본 계산 (CAGR 기반 - 간단히 1년 성과로 시뮬레이션)
        final_capital = parameters["initial_capital"] * (1 + cagr)
        profit_loss = final_capital - parameters["initial_capital"]
        profit_rate = (profit_loss / parameters["initial_capital"]) * 100

        # 거래 횟수 (Turnover 기반)
        trades_count = int(stats["turnover"] * 12 * random.uniform(0.9, 1.1))  # 연간 turnover

        # Keller Ratio 계산: K = R(1-2D/(1-2D)) when D<25%
        if max_dd < 0.25:
            keller_ratio = cagr * (1 - 2 * max_dd) / (1 - 2 * max_dd)
        else:
            keller_ratio = 0.0

        return {
            "strategy": self.name,
            "version": self.version,
            "variant": variant,
            "profit_loss": round(profit_loss, 2),
            "profit_rate": round(profit_rate, 2),
            "final_capital": round(final_capital, 2),
            "cagr": round(cagr * 100, 2),
            "max_drawdown": round(max_dd * 100, 2),
            "volatility": round(volatility * 100, 2),
            "sharpe_ratio": round(stats["sharpe"], 2),
            "upi": round(stats["upi"], 2),
            "keller_ratio": round(keller_ratio * 100, 2),
            "defensive_fraction": round(stats["defensive_fraction"] * 100, 2),
            "trades_executed": trades_count,
            "annual_turnover": round(stats["turnover"] * 100, 2),
            "execution_time": "1.8s",
            "parameters_used": parameters,
            "portfolio_details": {
                "offensive_universe": stats["offensive_assets"],
                "defensive_universe": stats["defensive_assets"],
                "canary_universe": stats["canary_assets"],
                "offensive_selection": parameters.get("offensive_top", 6),
                "defensive_selection": parameters.get("defensive_top", 3),
                "relative_momentum": "SMA(12)",
                "absolute_momentum": "13612W",
                "breadth_parameter": parameters.get("breadth_param", 1)
            },
            "methodology": {
                "relative_momentum_filter": "SMA(12) - slow momentum for offensive/defensive selection",
                "absolute_momentum_filter": "13612W - fast momentum for canary universe",
                "crash_protection": "Switch to defensive when any canary asset shows negative momentum (B=1)",
                "rebalancing": "Monthly, last trading day"
            }
        }