#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
CYB50 最优参数完整回测 + 数据核对
参数: min_trend_prob=0.3, require_daily_uptrend=True
"""

import csv
import json
from datetime import datetime, timedelta
from collections import deque
import math
import os

class TechnicalIndicators:
    @staticmethod
    def sma(data, period):
        if len(data) < period:
            return None
        return sum(data[-period:]) / period

    @staticmethod
    def rsi(prices, period=14):
        if len(prices) < period + 1:
            return None
        gains, losses = [], []
        for i in range(1, len(prices)):
            change = prices[i] - prices[i-1]
            gains.append(change if change > 0 else 0)
            losses.append(abs(change) if change < 0 else 0)
        avg_gain = sum(gains[-period:]) / period
        avg_loss = sum(losses[-period:]) / period
        if avg_loss == 0:
            return 100
        return 100 - (100 / (1 + avg_gain / avg_loss))

    @staticmethod
    def bollinger_bands(prices, period=20, std_dev=2):
        if len(prices) < period:
            return None, None, None
        middle = sum(prices[-period:]) / period
        variance = sum((p - middle) ** 2 for p in prices[-period:]) / period
        std = math.sqrt(variance)
        return middle + std*std_dev, middle, middle - std*std_dev

    @staticmethod
    def macd(prices, fast=12, slow=26, signal=9):
        if len(prices) < slow:
            return None, None, None
        def calc_ema(data, period):
            mult = 2 / (period + 1)
            ema = data[0]
            for p in data[1:]:
                ema = (p - ema) * mult + ema
            return ema
        macd_vals = []
        for i in range(slow, len(prices)+1):
            f = calc_ema(prices[i-fast:i], fast)
            s = calc_ema(prices[i-slow:i], slow)
            macd_vals.append(f - s)
        sig = calc_ema(macd_vals[-signal:], signal) if len(macd_vals) >= signal else None
        return macd_vals[-1], sig, macd_vals[-1] - sig if sig else None


class DailyTrendManager:
    def __init__(self, daily_file):
        self.daily_data = {}
        self.daily_trend = {}
        self.load_daily_data(daily_file)
        self.calculate_daily_trend()

    def load_daily_data(self, filepath):
        with open(filepath, 'r', encoding='utf-8-sig') as f:
            reader = csv.DictReader(f)
            for row in reader:
                try:
                    dt = datetime.strptime(row['datetime'], '%Y-%m-%d %H:%M:%S')
                    self.daily_data[dt.strftime('%Y-%m-%d')] = {
                        'open': float(row['open']), 'high': float(row['high']),
                        'low': float(row['low']), 'close': float(row['close'])
                    }
                except:
                    continue

    def calculate_daily_trend(self, ma_period=20):
        dates = sorted(self.daily_data.keys())
        closes = [self.daily_data[d]['close'] for d in dates]
        for i, date in enumerate(dates):
            if i < ma_period - 1:
                self.daily_trend[date] = {'trend': 0, 'ma20': None, 'trend_strength': 0}
                continue
            ma20 = sum(closes[i-ma_period+1:i+1]) / ma_period
            close = closes[i]
            trend = 1 if close > ma20 * 1.02 else (-1 if close < ma20 * 0.98 else 0)
            self.daily_trend[date] = {
                'trend': trend, 'ma20': ma20,
                'trend_strength': (close - ma20) / ma20 * 100
            }

    def get_daily_trend(self, date_str):
        return self.daily_trend.get(date_str, {'trend': 0, 'ma20': None, 'trend_strength': 0})


class MarketRegimeManager:
    def __init__(self, regime_file):
        self.regime_data = {}
        self.load_regime_data(regime_file)

    def load_regime_data(self, filepath):
        with open(filepath, 'r', encoding='utf-8-sig') as f:
            reader = csv.DictReader(f)
            for row in reader:
                self.regime_data[row['datetime']] = {
                    'state': int(row['state']),
                    'prob_trend': float(row['prob_trend'])
                }

    def get_regime(self, dt_str):
        return self.regime_data.get(dt_str, {'state': 0, 'prob_trend': 0.0})


class BacktestEngine:
    def __init__(self):
        self.initial_capital = 1000000
        self.position_size = 0.5
        self.capital = self.initial_capital
        self.position = 0
        self.entry_price = 0
        self.holding_periods = 0
        self.max_holding_periods = 16

        self.equity_curve = []
        self.trades = []
        self.prices = deque(maxlen=100)

    def calculate_signals(self):
        if len(self.prices) < 50:
            return None
        pl = list(self.prices)
        return {
            'rsi': TechnicalIndicators.rsi(pl),
            'bb_middle': TechnicalIndicators.bollinger_bands(pl)[1],
            'ma5': TechnicalIndicators.sma(pl, 5),
            'ma10': TechnicalIndicators.sma(pl, 10),
            'macd': TechnicalIndicators.macd(pl)[0],
            'macd_signal': TechnicalIndicators.macd(pl)[1],
            'price': pl[-1]
        }

    def check_long_signal(self, s):
        if not s:
            return False, ""
        c = []
        if s['rsi'] and s['rsi'] < 65: c.append('RSI<65')
        if s['ma5'] and s['ma10'] and s['ma5'] > s['ma10']: c.append('MA5>MA10')
        if s['macd'] and s['macd_signal'] and s['macd'] > s['macd_signal']: c.append('MACD金叉')
        if s['bb_middle'] and s['price'] > s['bb_middle']: c.append('价格>中轨')
        return (True, '+'.join(c)) if len(c) >= 3 else (False, f"{len(c)}/3")

    def check_exit(self, s, price):
        if not s or self.position == 0:
            return False, ""
        if price <= self.entry_price * 0.975: return True, f"止损({price:.2f})"
        if price >= self.entry_price * 1.04: return True, f"止盈({price:.2f})"
        if self.holding_periods >= self.max_holding_periods: return True, "时间平仓"
        if s['rsi'] and s['rsi'] > 75: return True, f"RSI超买({s['rsi']:.1f})"
        return False, ""

    def open(self, price, time_str, reason):
        val = self.capital * self.position_size
        self.position = val / price
        self.entry_price = price
        self.holding_periods = 0
        self.trades.append({'action': 'OPEN', 'time': time_str, 'price': price,
                           'shares': self.position, 'value': val, 'reason': reason})

    def close(self, price, time_str, reason):
        if self.position == 0: return
        pnl = (price - self.entry_price) * self.position
        pnl_pct = (price / self.entry_price - 1) * 100
        self.capital += pnl
        self.trades.append({'action': 'CLOSE', 'time': time_str, 'price': price,
                           'shares': self.position, 'pnl': pnl, 'pnl_pct': pnl_pct, 'reason': reason})
        self.position = 0

    def update(self, ts, o, h, l, c, dm, rm):
        self.prices.append(c)
        dt_str = ts.strftime('%Y-%m-%d %H:%M:%S')
        date_str = ts.strftime('%Y-%m-%d')

        daily = dm.get_daily_trend(date_str)
        regime = rm.get_regime(dt_str)

        equity = self.capital + (self.position * c if self.position > 0 else 0)
        self.equity_curve.append({'time': dt_str, 'equity': equity, 'close': c, 'position': 1 if self.position else 0,
                                  'daily_trend': daily['trend'], 'daily_strength': daily['trend_strength'],
                                  'regime_state': regime['state'], 'regime_prob': regime['prob_trend']})

        if self.position > 0:
            self.holding_periods += 1
            s = self.calculate_signals()
            ex, reason = self.check_exit(s, c)
            if ex: self.close(c, dt_str, reason)
        else:
            s = self.calculate_signals()
            ok, tech_reason = self.check_long_signal(s)
            if ok and daily['trend'] == 1 and regime['state'] == 1 and regime['prob_trend'] >= 0.3:
                self.open(c, dt_str, f"{tech_reason}|日线向上|30分钟趋势{regime['prob_trend']:.2f}")
        return equity


def load_data(fp):
    data = []
    with open(fp, 'r', encoding='utf-8-sig') as f:
        for row in csv.DictReader(f):
            try:
                data.append({
                    'datetime': datetime.strptime(row['DateTime'], '%Y-%m-%d %H:%M:%S'),
                    'open': float(row['Open']), 'high': float(row['High']),
                    'low': float(row['Low']), 'close': float(row['Close'])
                })
            except:
                continue
    return data


def verify_data_integrity(data, dm, rm):
    """核对数据完整性"""
    print("\n" + "="*70)
    print("数据准确性核对报告")
    print("="*70)

    issues = []
    checked = 0

    for row in data:
        dt_str = row['datetime'].strftime('%Y-%m-%d %H:%M:%S')
        date_str = row['datetime'].strftime('%Y-%m-%d')

        # 检查日线数据
        if date_str not in dm.daily_data:
            issues.append(f"缺少日线数据: {date_str}")

        # 检查30分钟状态
        if dt_str not in rm.regime_data:
            issues.append(f"缺少30分钟状态: {dt_str}")

        checked += 1
        if checked % 1000 == 0:
            print(f"  已核对 {checked}/{len(data)} 条数据...")

    print(f"\n数据核对完成:")
    print(f"  总数据条数: {len(data)}")
    print(f"  日线数据: {len(dm.daily_data)}条")
    print(f"  30分钟状态: {len(rm.regime_data)}条")
    print(f"  发现问题: {len(issues)}个")

    if issues:
        print(f"\n前10个问题:")
        for i in issues[:10]:
            print(f"  - {i}")

    return len(issues) == 0


def run_backtest(data_file, daily_file, regime_file, output_dir='final_backtest'):
    os.makedirs(output_dir, exist_ok=True)

    print("加载数据...")
    data = load_data(data_file)
    dm = DailyTrendManager(daily_file)
    rm = MarketRegimeManager(regime_file)

    # 核对数据
    data_ok = verify_data_integrity(data, dm, rm)
    if not data_ok:
        print("\n[警告] 数据存在问题，但继续回测...")

    print("\n运行最优参数回测...")
    engine = BacktestEngine()

    for row in data:
        engine.update(row['datetime'], row['open'], row['high'], row['low'], row['close'], dm, rm)

    # 统计
    initial = engine.initial_capital
    final = engine.equity_curve[-1]['equity']
    total_ret = (final / initial - 1) * 100

    closed = [t for t in engine.trades if t['action'] == 'CLOSE']
    wins = [t for t in closed if t['pnl'] > 0]
    losses = [t for t in closed if t['pnl'] <= 0]

    win_rate = len(wins) / len(closed) * 100 if closed else 0
    total_profit = sum(t['pnl'] for t in wins) if wins else 0
    total_loss = sum(t['pnl'] for t in losses) if losses else 0
    profit_factor = abs(total_profit / total_loss) if total_loss else 0

    # 计算最大回撤
    peak = initial
    max_dd = 0
    for e in engine.equity_curve:
        if e['equity'] > peak:
            peak = e['equity']
        dd = (peak - e['equity']) / peak * 100
        if dd > max_dd:
            max_dd = dd

    # 保存权益曲线
    with open(f"{output_dir}/equity_final.csv", 'w', newline='') as f:
        w = csv.DictWriter(f, fieldnames=['time', 'equity', 'close', 'position', 'daily_trend', 'daily_strength', 'regime_state', 'regime_prob'])
        w.writeheader()
        w.writerows(engine.equity_curve)

    # 保存交易记录
    with open(f"{output_dir}/trades_final.csv", 'w', newline='') as f:
        if engine.trades:
            # 获取所有可能的字段
            all_fields = set()
            for t in engine.trades:
                all_fields.update(t.keys())
            fieldnames = sorted(all_fields)
            w = csv.DictWriter(f, fieldnames=fieldnames)
            w.writeheader()
            w.writerows(engine.trades)

    # 生成详细报告
    report = f"""
================================================================================
CYB50 最优参数回测报告 - 详细版
================================================================================
回测参数:
  - 初始资金: 1,000,000 元
  - 持仓上限: 50%
  - 30分钟趋势概率阈值: 0.3 (最优)
  - 日线要求: 必须向上 (MA20之上)
  - 止损: -2.5% | 止盈: +4% | 最大持仓: 16周期(8小时)

================================================================================
整体表现
================================================================================
  初始资金:        {initial:>15,.2f} 元
  最终资金:        {final:>15,.2f} 元
  净盈亏:          {final-initial:>15,.2f} 元
  总收益率:        {total_ret:>15.2f} %
  最大回撤:        {max_dd:>15.2f} %

================================================================================
交易统计
================================================================================
  总交易次数:      {len(closed):>15} 笔
  盈利次数:        {len(wins):>15} 笔
  亏损次数:        {len(losses):>15} 笔
  胜率:            {win_rate:>15.2f} %
  盈亏比:          {profit_factor:>15.2f}
  总盈利:          {total_profit:>15,.2f} 元
  总亏损:          {total_loss:>15,.2f} 元
  平均每笔盈利:    {total_profit/len(wins) if wins else 0:>15,.2f} 元
  平均每笔亏损:    {total_loss/len(losses) if losses else 0:>15,.2f} 元

================================================================================
最近10笔交易明细
================================================================================
"""
    for t in closed[-10:]:
        report += f"  {t['time']} | 平仓价: {t['price']:.2f} | 盈亏: {t['pnl']:>+10,.2f} ({t['pnl_pct']:+.2f}%) | {t['reason']}\n"

    report += f"""
================================================================================
数据核对结果
================================================================================
  30分钟数据条数:  {len(data)} 条
  日线数据条数:    {len(dm.daily_data)} 条
  30分钟状态条数:  {len(rm.regime_data)} 条
  数据完整性:      {'通过 ✓' if data_ok else '存在问题 ✗'}

================================================================================
文件输出
================================================================================
  - {output_dir}/equity_final.csv (权益曲线)
  - {output_dir}/trades_final.csv (交易明细)
  - {output_dir}/report_final.txt (本报告)
================================================================================
"""

    with open(f"{output_dir}/report_final.txt", 'w') as f:
        f.write(report)

    print(report)
    print(f"\n所有文件已保存到: {output_dir}/")

    return engine


if __name__ == '__main__':
    run_backtest(
        'cyb50_30min_2023_to_20260325.csv',
        '../data-fetch/data/399673_SZ_day_20150101_20260325.csv',
        '../../market-regime-identifier-30/cyb50_30min_regime_result.csv'
    )