張郎生活的筆記

效果圖 : 

import datetime
import pandas as pd
import backtrader as bt

class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        
        self.dataclose = self.datas[0].close
        self.order = None
        self.buyprice = None
        self.buycomm = None
        self.kd = bt.indicators.StochasticSlow(self.datas[0], period = 9, period_dfast= 3, period_dslow = 3)

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        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:  # Sell
                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')

        # Write down: no pending order
        self.order = None

    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                 (trade.pnl, trade.pnlcomm))

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])

        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return

        # Check if we are in the market
        if not self.position:

            # Not yet ... we MIGHT BUY if ...
            if self.kd[-1] > 30 and self.kd[0] < 30 :

                # BUY, BUY, BUY!!! (with all possible default parameters)
                self.log('BUY CREATE, %.2f' % self.dataclose[0])

                # Keep track of the created order to avoid a 2nd order
                self.order = self.buy()

        else:

            if self.kd[-1] < 90 and self.kd[0] > 90:
                # SELL, SELL, SELL!!! (with all possible default parameters)
                self.log('SELL CREATE, %.2f' % self.dataclose[0])

                # Keep track of the created order to avoid a 2nd order
                self.order = self.sell()

if __name__ == '__main__':
        cerebro = bt.Cerebro()
        cerebro.addstrategy(TestStrategy)
        cerebro.broker.setcash(100000.0)
       
        dataframe = pd.read_csv('d://0050.tw.csv', index_col=0, parse_dates=True)
        dataframe['openinterest'] = 0
        data0 = bt.feeds.PandasData(dataname=dataframe,
                                   fromdate = datetime.datetime(2008, 1, 1),
                                   todate = datetime.datetime(2010, 1, 1)
                                    )

        cerebro.adddata(data0) 
        print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

        cerebro.run()

        print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
        cerebro.plot()

基本上都差不多, 而KD值全名是 stochastic oscillato 所以我們程式碼要打

self.kd = bt.indicators.StochasticSlow(self.datas[0], period = 9, period_dfast= 3, period_dslow = 3)

後面的參數大家可以自行設定

那因為我不知道怎麼買比較好, 於是乎亂寫一通

if self.kd[-1] > 30 and self.kd[0] < 30

昨天KD比30大, 今天KD比30小, 我就給他買買買

 if self.kd[-1] < 90 and self.kd[0] > 90:

這行大家就看懂了吧~