趋势跟踪

"""
海龟策略核心逻辑 - 简化版
"""
import backtrader as bt

class TurtleStrategy(bt.Strategy):
    params = (
        ('entry_period', 20),    # 入场通道周期
        ('exit_period', 10),     # 出场通道周期
        ('atr_period', 20),      # ATR计算周期
        ('risk_pct', 0.02),      # 每笔风险2%
        ('atr_multiplier', 2),   # 止损 = 入场价 - N*ATR
    )
    
    def __init__(self):
        # 唐奇安通道
        self.donchian_high = bt.indicators.Highest(
            self.data.high, period=self.p.entry_period
        )
        self.donchian_low = bt.indicators.Lowest(
            self.data.low, period=self.p.exit_period
        )
        
        # ATR
        self.atr = bt.indicators.ATR(period=self.p.atr_period)
        
        self.order = None
        self.entry_price = None
        self.position_size = 0
    
    def calculate_unit_size(self):
        """
        计算1个交易单位的股数
        公式: 单位股数 = (账户资金 * 风险比例) / (ATR * ATR倍数)
        """
        cash = self.broker.getvalue()
        risk_amount = cash * self.p.risk_pct
        stop_distance = self.atr[0] * self.p.atr_multiplier
        
        if stop_distance == 0:
            return 0
        
        unit_size = int(risk_amount / stop_distance)
        return unit_size
    
    def next(self):
        if self.order:
            return
        
        # 出场逻辑（优先）
        if self.position:
            if self.data.close[0] < self.donchian_low[0]:
                self.order = self.sell(size=self.position.size)
                return
        
        # 入场逻辑
        if not self.position:
            if self.data.close[0] > self.donchian_high[-1]:
                self.position_size = self.calculate_unit_size()
                if self.position_size > 0:
                    self.order = self.buy(size=self.position_size)
    
    def notify_order(self, order):
        if order.status in [order.Completed]:
            if order.isbuy():
                self.entry_price = order.executed.price
                print(f'海龟买入: {self.position_size}股 @ {order.executed.price:.2f}')
            else:
                pnl = (order.executed.price - self.entry_price) * order.executed.size
                print(f'海龟卖出: {order.executed.price:.2f}, 盈亏: {pnl:.2f}')
                self.entry_price = None
        self.order = None

请用Backtrader实现一个完整的海龟交易策略，要求：
1. 入场：价格突破20日高点时买入
2. 出场：价格跌破10日低点时卖出
3. 仓位：根据20日ATR计算，每笔交易风险不超过资金的2%
4. 止损：入场价 - 2倍ATR
5. 添加ADX过滤：ADX>25时才允许入场
6. 使用AKShare获取沪深300ETF(510300)数据回测
7. 输出夏普比率、最大回撤、胜率、盈亏比

请对比以下几组海龟策略参数在A股的表现：
- 短海龟：10日入场/5日出场
- 经典海龟：20日入场/10日出场
- 长海龟：55日入场/20日出场

要求用表格对比以下指标：
- 总收益率
- 最大回撤
- 胜率
- 盈亏比
- 交易次数
- 平均持仓天数

并分析：哪组参数最适合A股市场的特点？

请为海龟策略添加以下过滤条件，解释每个条件的作用：
1. 只在价格高于200日均线时做多（避免逆势交易）
2. 只在ADX>20时入场（避免震荡市）
3. 如果过去5日波动率超过过去20日波动率的2倍，暂停入场（避免极端波动）

请给出修改后的完整代码。

"""
海龟策略完整版 - 含ADX过滤和ATR仓位管理
"""
import backtrader as bt
import akshare as ak
import pandas as pd

class TurtleStrategy(bt.Strategy):
    params = (
        ('entry_period', 20),
        ('exit_period', 10),
        ('atr_period', 20),
        ('risk_pct', 0.02),
        ('atr_mult', 2),
        ('adx_period', 14),
        ('adx_filter', 25),
    )
    
    def __init__(self):
        self.donchian_high = bt.ind.Highest(self.data.high, period=self.p.entry_period)
        self.donchian_low = bt.ind.Lowest(self.data.low, period=self.p.exit_period)
        self.atr = bt.ind.ATR(period=self.p.atr_period)
        self.adx = bt.ind.AverageDirectionalMovementIndex(period=self.p.adx_period)
        self.order = None
    
    def calculate_size(self):
        cash = self.broker.getvalue()
        risk_amount = cash * self.p.risk_pct
        stop_dist = self.atr[0] * self.p.atr_mult
        return int(risk_amount / stop_dist) if stop_dist > 0 else 0
    
    def next(self):
        if self.order: return
        
        if self.position:
            if self.data.close[0] < self.donchian_low[0]:
                self.order = self.sell(size=self.position.size)
            return
        
        if (self.data.close[0] > self.donchian_high[-1] and 
            self.adx[0] > self.p.adx_filter):
            size = self.calculate_size()
            if size > 0:
                self.order = self.buy(size=size)
    
    def notify_order(self, order):
        if order.status in [order.Completed]:
            self.order = None

if __name__ == "__main__":
    cerebro = bt.Cerebro()
    cerebro.addstrategy(TurtleStrategy)
    cerebro.broker.setcash(100000.0)
    cerebro.broker.setcommission(commission=0.0003)
    
    # 请替换为实际数据加载代码
    # df = ak.stock_zh_a_hist(...)
    # data = bt.feeds.PandasData(dataname=df, ...)
    # cerebro.adddata(data)
    
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
    cerebro.addanalyzer(bt.analyzers.DrawDown, _name='dd')
    
    results = cerebro.run()
    strat = results[0]
    print(f'最终资金: {cerebro.broker.getvalue():.2f}')
    print(f"夏普: {strat.analyzers.sharpe.get_analysis().get('sharperatio','N/A')}")
    print(f"最大回撤: {strat.analyzers.dd.get_analysis().max.drawdown:.2f}%")