今天我们体测完1000米感觉人都要废了，目前在学习天勤量化，但是我还是打算仔细将这个量化框架介绍完，我们开始下面介绍来自官网，因为有时候官网打不开，我将仔细介绍，我们开始。
需要程序关注微信公众号，数据分析与运用，回复backtrader量化回测基础使用1就可以
因为文章比较长，我们自己看效果，我结合了金融数据库akshare,所以可以回测A股全部的股票，我们只需要输入股票代码，开始时间，开始资金就可以了。
下面为全部买卖点输出，收益率还是不错的
下面为绘制分析图，非常的好
下面我们介绍重要的概念线，可以同时开9条线，0代表目前的数据，-1代表昨天数据，-2代表前天数据。让我们来看看一系列的例子（从几乎空的到一个成熟的策略），但在粗略解释使用反向交易者时的2个基本概念之前，并非没有
线数据馈送、指标和策略都有线条。线是连接在一起形成此线的一系列点。在谈论市场时，数据馈送通常每天有以下几组点：打开”系列沿时间是一条线。因此，数据馈送通常有6行。如果我们还考虑“DateTime”（这是单个点的实际参考），我们可以数出7行。开盘， 高， 低， 收盘， 成交量， OpenInterest索引 0 方法访问行中的值时，将使用 index： 0 访问当前值并且使用-1访问“最后一个”输出值。这符合Python对可迭代的约定（一行可以被迭代，因此是可迭代的），其中索引-1用于访问迭代/数组的“最后一个”项。在我们的例子中，是最后一个被访问的输出值。因此，在 -1 之后是索引 0，它用于按行访问当前时刻。下面为一个简单的程序，获取账户值。
import backtrader as btif __name__ == '__main__':cerebro = bt.Cerebro()print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())cerebro.run()print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
执行后输出的结果
Starting Portfolio Value: 10000.00Final Portfolio Value: 10000.00
在此示例中：反向交易者被导入，Cerebro 引擎已实例化，生成的 cerebro 实例被告知运行（循环数据），结果被打印出来，虽然看起来不多，但让我们指出一些明确显示的内容：，Cerebro 引擎已在后台创建了一个代理实例，该实例已开始有一些现金，这种幕后经纪人实例化是平台中简化用户生活的一个不变特征。如果用户未设置任何代理，那么将放置一个缺省代理。10K货币单位是一些经纪人的常用值。
下面为生长现金代码，cerebro.set_cash(1000000)
import backtrader as btif __name__ == '__main__':cerebro = bt.Cerebro()cerebro.broker.setcash(100000.0)print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())cerebro.run()print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
下面为数据输入，官网的代码，也可以参考我的程序，数据怎么样输入以后会介绍，因为塔提供数据输入方式很多，代码如下
import datetime # For datetime objectsimport os.path # To manage pathsimport sys # To find out the script name (in argv[0])# Import the backtrader platformimport backtrader as btif __name__ == '__main__':# Create a cerebro entitycerebro = bt.Cerebro()# Datas are in a subfolder of the samples. Need to find where the script is# because it could have been called from anywheremodpath = os.path.dirname(os.path.abspath(sys.argv[0]))datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')# Create a Data Feeddata = bt.feeds.YahooFinanceCSVData(dataname=datapath,# Do not pass values before this datefromdate=datetime.datetime(2000, 1, 1),# Do not pass values after this datetodate=datetime.datetime(2000, 12, 31),reverse=False)# Add the Data Feed to Cerebrocerebro.adddata(data)# Set our desired cash startcerebro.broker.setcash(100000.0)# Print out the starting conditionsprint('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())# Run over everythingcerebro.run()# Print out the final resultprint('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
我们已经有数据输入了，但是没有数据输出，我们定义数据输出函数，self.log(),我们选择输出收盘价。代码
import datetime # For datetime objectsimport os.path # To manage pathsimport sys # To find out the script name (in argv[0])# Import the backtrader platformimport backtrader as bt# Create a Strateyclass TestStrategy(bt.Strategy):def log(self, txt, dt=None):''' Logging function for this strategy'''dt = dt or self.datas[0].datetime.date(0)print('%s, %s' % (dt.isoformat(), txt))def __init__(self):# Keep a reference to the "close" line in the data[0] dataseriesself.dataclose = self.datas[0].closedef next(self):# Simply log the closing price of the series from the referenceself.log('Close, %.2f' % self.dataclose[0])if __name__ == '__main__':# Create a cerebro entitycerebro = bt.Cerebro()# Add a strategycerebro.addstrategy(TestStrategy)# Datas are in a subfolder of the samples. Need to find where the script is# because it could have been called from anywheremodpath = os.path.dirname(os.path.abspath(sys.argv[0]))datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')# Create a Data Feeddata = bt.feeds.YahooFinanceCSVData(dataname=datapath,# Do not pass values before this datefromdate=datetime.datetime(2000, 1, 1),# Do not pass values before this datetodate=datetime.datetime(2000, 12, 31),# Do not pass values after this datereverse=False)# Add the Data Feed to Cerebrocerebro.adddata(data)# Set our desired cash startcerebro.broker.setcash(100000.0)# Print out the starting conditionsprint('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())# Run over everythingcerebro.run()# Print out the final resultprint('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
我们已经有数据的输入了，但是我们没有交易策略运行数据下面我们看交易策略编写，我们在函数next下面写交易策略，我们的交易策略为上涨3天卖出，连续下跌3天买入。下面为官网的代码，也可以参考我的代码
import datetime # For datetime objectsimport os.path # To manage pathsimport sys # To find out the script name (in argv[0])# Import the backtrader platformimport backtrader as bt# Create a Strateyclass 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):# Keep a reference to the "close" line in the data[0] dataseriesself.dataclose = self.datas[0].closedef next(self):# Simply log the closing price of the series from the referenceself.log('Close, %.2f' % self.dataclose[0])if self.dataclose[0] < self.dataclose[-1]:# current close less than previous closeif self.dataclose[-1] < self.dataclose[-2]:# previous close less than the previous close# BUY, BUY, BUY!!! (with all possible default parameters)self.log('BUY CREATE, %.2f' % self.dataclose[0])self.buy()if __name__ == '__main__':# Create a cerebro entitycerebro = bt.Cerebro()# Add a strategycerebro.addstrategy(TestStrategy)# Datas are in a subfolder of the samples. Need to find where the script is# because it could have been called from anywheremodpath = os.path.dirname(os.path.abspath(sys.argv[0]))datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')# Create a Data Feeddata = bt.feeds.YahooFinanceCSVData(dataname=datapath,# Do not pass values before this datefromdate=datetime.datetime(2000, 1, 1),# Do not pass values before this datetodate=datetime.datetime(2000, 12, 31),# Do not pass values after this datereverse=False)# Add the Data Feed to Cerebrocerebro.adddata(data)# Set our desired cash startcerebro.broker.setcash(100000.0)# Print out the starting conditionsprint('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())# Run over everythingcerebro.run()# Print out the final resultprint('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
下面为输出的结果
Starting Portfolio Value: 100000.002000-01-03, Close, 27.852000-01-04, Close, 25.392000-01-05, Close, 24.052000-01-05, BUY CREATE, 24.052000-01-06, Close, 22.632000-01-06, BUY CREATE, 22.632000-01-07, Close, 24.37......2000-12-20, BUY CREATE, 26.882000-12-21, Close, 27.822000-12-22, Close, 30.062000-12-26, Close, 29.172000-12-27, Close, 28.942000-12-27, BUY CREATE, 28.942000-12-28, Close, 29.292000-12-29, Close, 27.41Final Portfolio Value: 99725.08
我们知道交易策略，但是我们不知道买卖点，什么时候买的所以我们需要定义交易状态检测函数如下
def notify_order(self, order):if order.status in [order.Submitted, order.Accepted]:# Buy/Sell order submitted/accepted to/by broker - Nothing to doreturn# Check if an order has been completed# Attention: broker could reject order if not enough cashif order.status in [order.Completed]:if order.isbuy():self.log('BUY EXECUTED, %.2f' % order.executed.price)elif order.issell():self.log('SELL EXECUTED, %.2f' % order.executed.price)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 orderself.order = None
下面为我写的基础使用完整的代码，供参考学习
#交易策略为连续下跌3天买入，连续上涨3天卖出import pandas as pdimport matplotlib.pyplot as pltimport backtrader as btimport backtrader.feeds as btfeedimport backtrader.indicators as btindfrom backtrader import analyzersimport pyalgotradeimport PySimpleGUI as sgimport akshare as akimport pandas as pdimport datetimestock=sg.popup_get_file('输入股票代码比如sz002466')start_date=sg.popup_get_file('输入数据开始时间比如20200101')start_cash=sg.popup_get_file('输入开始资金比如1000000')#定义测试类class testStrategy(bt.Strategy):#定义记录函数def log(self,txt,dt=None):dt=dt or self.datas[0].datetime.date(0)print('%s, %s' % (dt.isoformat(), txt))#初始化数据def __init__(self):self.dataclose=self.datas[0].close#定义交易状态检测函数，交易前def notify_order(self,order):#如果交易提交/接受if order.status in [order.Submitted,order.Accepted]:print('交易提交，交易接受')#如果交易完成if order.status in [order.Completed]:#如果交易类型是买if order.isbuy():self.log('买的价格， %.2f' % order.executed.price)#如果交易类型是卖elif order.issell():self.log('卖的价格, %.2f' % order.executed.price)self.bar_executed=len(self)#如果交易交易取消，保证金不足，交易拒绝elif order.status in [order.Canceled,order.Margin,order.Rejected]:self.log('交易取消/保证金不足/交易拒绝')self.order=None#主交易函数def next(self):self.log('收盘价,%.2f' % self.dataclose[0])#写交易函数，连续下跌3天买入,连续上涨3天卖出#如果今天的价格小于昨天的价格if self.dataclose[0]<self.dataclose[-1]:#如果昨天的价格小于前天的价格if self.dataclose[-1]<self.dataclose[-2]:#记录买卖的价格self.log('买的价格 %.2f' % self.dataclose[0])self.buy(size=200)#单位为股，代表1手elif self.dataclose[0]>self.dataclose[-1]:if self.dataclose[-1]>self.dataclose[-2]:self.log('卖出的价格 %.2f' %self.dataclose[0])self.sell(size=100)#简单例子if __name__ == "__main__":#将大脑实例化cerebro=bt.Cerebro()cerebro.addstrategy(testStrategy)#加入数据df=ak.stock_zh_a_daily(symbol=stock,start_date=start_date)df.index=pd.to_datetime(df['date'])data=btfeed.PandasData(dataname=df)cerebro.adddata(data=data)#设置开始资金cerebro.broker.set_cash(int(start_cash))#设置交易费用cerebro.broker.setcommission(0.003)print('开始值{}'.format(cerebro.broker.getvalue()))cerebro.run()print('最终值{}'.format(cerebro.broker.getvalue()))cerebro.plot(style='candle')plt.show()
