Python代码实现的完整策略

好的，下面是Python代码实现的完整策略：

import backtrader as bt
import tushare as ts

# 获取数据
data = ts.get_k_data('000001', start='2010-01-01', end='2021-07-01')
data = data.set_index('date')
data = data[['open', 'high', 'low', 'close', 'volume']]
data.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
data = bt.feeds.PandasData(dataname=data)

# 定义策略
class MyStrategy(bt.Strategy):
    params = (
        ('macd_fast', 12),
        ('macd_slow', 26),
        ('macd_signal', 9),
        ('bb_period', 20),
        ('bb_dev', 2),
        ('printlog', False),
    )

    def __init__(self):
        self.macd = bt.indicators.MACD(
            self.data,
            period_me1=self.params.macd_fast,
            period_me2=self.params.macd_slow,
            period_signal=self.params.macd_signal,
        )
        self.bb = bt.indicators.BollingerBands(
            self.data,
            period=self.params.bb_period,
            devfactor=self.params.bb_dev,
        )
        self.order = None
        self.buyprice = None
        self.buycomm = None

    def log(self, txt, dt=None, doprint=False):
        if self.params.printlog or doprint:
            dt = dt or self.data.datetime[0]
            print('%s, %s' % (dt.date(), txt))

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            return
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log('BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm: %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))
                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
            else:
                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm: %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))
                self.bar_executed = len(self)
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        self.order = None

    def next(self):
        if self.order:
            return

        if not self.position:
            if self.macd.macd[0] > self.macd.signal[0] and self.data.close[0] > self.bb.lines.top[0]:
                self.log('BUY CREATE, %.2f' % self.data.close[0])
                self.order = self.buy()
        else:
            if self.data.close[0] < self.bb.lines.mid[0]:
                self.log('SELL CREATE, %.2f' % self.data.close[0])
                self.order = self.sell()

    def stop(self):
        self.log('(Fast %d, Slow %d, Signal %d, BB %d, Dev %d) Ending Value %.2f' %
                 (self.params.macd_fast, self.params.macd_slow, self.params.macd_signal, self.params.bb_period, self.params.bb_dev, self.broker.getvalue()), doprint=True)

# 运行策略
cerebro = bt.Cerebro()
cerebro.addstrategy(MyStrategy)
cerebro.adddata(data)
cerebro.broker.setcash(1000000)
cerebro.broker.setcommission(commission=0.001)
cerebro.run()
cerebro.plot()

在这个策略中，我们使用了tushare库来获取数据，并将其导入到backtrader中。然后，我们定义了一个名为MyStrategy的策略，并使用MACD和Bollinger Bands指标来构建策略。在next()方法中，我们根据指标的信号来判断是否开仓或平仓。最后通过cerebro来运行策略并输出回测结果。

需要注意的是，这只是一个简单的示例策略，实际应用中需要根据具体情况进行修改和优化。