快速开始

框架的使用

让我们从0开始做个示例项目,在此之前先要说明 backtrader 中两个重要概念:

  1. Line

    价格数据(Data Feeds)、技术指标(Indicators)和策略(Strategies)都是 Line

    “Line” 是由一系列的点组成的。典型的价格数据,通常由以下类别的数据组成:

    • 开盘价、最高价、最低价、收盘价、成交量、持仓量

    价格数据中的所有”开盘价”按时间组成一条 Line。所以,一组含有以上6个类别的价格数据,共有6条 Line。

    如果再算上时间(可以看作是一组数据的主键)的话,一共有7条 Line。

  2. 下标0

    当访问一条 Line 的数据时,会默认指向下标为 0 的数据。

    最后一个数据通过下标 -1 来访问。这也设计是为了符合 Python 的迭代器规则(一条 Line 可以被迭代,因此也是iterable)。

假设在创建策略的过程中,我们引入了简单移动平均线:

self.sma = SimpleMovingAverage(.....)

最简便访问移动平均线当前值的方法是:

av = self.sma[0]

在回测过程中,没有必要知道已经回测了多少秒/分钟/天/月了,”0”一直指向当前值。

下面用下标 -1 来访问最后一个值的语句,就是典型的 Python 方式:

previous_value = self.sma[-1]

当然-2、-3下标也是可以照常使用的。

从 0 到 100: 示例

基本步骤

让我们继续。

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt

if __name__ == '__main__':
    cerebro = bt.Cerebro()

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

    cerebro.run()

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

执行后的输出为:

Starting Portfolio Value: 10000.00
Final Portfolio Value: 10000.00

在刚才的例子中:

  • 引入了 backtrader
  • 实例化了 Cerebro 引擎
  • 调用了 cerebro 实例的 run 方法循环处理数据
  • 输出了运行结果

虽然感觉不到,程序还是做了一些事情的:

  • Cerebro 引擎在后台创建了一个 broker 实例
  • 引擎默认给了一些初始金额

引入 broker 经纪人的概念,是为了与事实贴近让用户更易于理解。如果用户没有指定 broker 实例,则会自动创建一个。

默认情况线,系统提供了1万块钱来开始交易。

设置起始金额

在金融行当里,屌丝才会拿1万块钱搞投资。让我们多投点钱再运行一遍。

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt

if __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())

运行后的输出为:

Starting Portfolio Value: 1000000.00
Final Portfolio Value: 1000000.00

任务完成,喝点水就回来。

加载价格数据

现在有钱了,我们的目的是为了根据*价格数据*做出投资决策,将资金翻上几倍。

So … 巧妇难为无米之炊. 让我们给程序喂点数据。

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt

if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values after this date
        todate=datetime.datetime(2000, 12, 31),
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

运行后的输出为:

Starting Portfolio Value: 1000000.00
Final Portfolio Value: 1000000.00

程序在后台做了更多的事,因为我们添加了:

  • 加载了我们的 价格数据 文件
  • 设置了 价格数据 的起止范围

价格数据 被创建并被添加到 cerebro 引擎实例中。

输出结果并没有变,这很正常。

Note

Yahoo 的价格数据非主流,它是以时间倒序排列的。reversed=True 参数将顺序 反转 一次,这样就得到了我们想要的正序数据。

第一个策略

钱已经给了 broker (经纪人),价格数据 也已经载入了。万事俱备,只欠东风。

让我们给策略加点代码,让他打印出每天的”收盘价”。

DataSeries (K线类的父类) 能够直接访问到 OHLC (开盘价、最高价、最低价、收盘价) 数据。这使我们打印数据很方便。

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class 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] dataseries
        self.dataclose = self.datas[0].close

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


if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

运行后的输出结果为:

Starting Portfolio Value: 100000.00
2000-01-03T00:00:00, Close, 27.85
2000-01-04T00:00:00, Close, 25.39
2000-01-05T00:00:00, Close, 24.05
...
...
...
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
Final Portfolio Value: 100000.00

有人说股市有风险,貌似看起来未必。

说下刚才程序的逻辑:

  • 可以通过 cerebro.adddata(data) 加载多条价格数据。在策略类中的 __init__ 方法中,可以通过 self.datas 访问到所有的价格数据。

    self.datas 是一个标准的 Python 列表,内容就是加载的一条或多条价格数据,列表的顺序就是价格数据加载的顺序。

    self.datas[0] 即是加载的第一条价格数据,它被框架默认使用。

  • 由于只需访问收盘价数据,于是使用 self.dataclose = self.datas[0].close 将第一条价格数据的 收盘价 Line 赋值给新变量。

  • 现在开始循环处理价格数据,当经过一个K线柱的时候 next() 方法就会被调用一次。当然中间也会处理 技术指标 相关逻辑,后边会谈到。

给策略加点逻辑

让我们观察一下图线,做点实际交易吧。

  • 如果价格三连跌的话,买买买!
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
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):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

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

        if self.dataclose[0] < self.dataclose[-1]:
            # current close less than previous close

            if 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 entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

运行之后的输出是:

Starting Portfolio Value: 100000.00
2000-01-03, Close, 27.85
2000-01-04, Close, 25.39
2000-01-05, Close, 24.05
2000-01-05, BUY CREATE, 24.05
2000-01-06, Close, 22.63
2000-01-06, BUY CREATE, 22.63
2000-01-07, Close, 24.37
...
...
...
2000-12-20, BUY CREATE, 26.88
2000-12-21, Close, 27.82
2000-12-22, Close, 30.06
2000-12-26, Close, 29.17
2000-12-27, Close, 28.94
2000-12-27, BUY CREATE, 28.94
2000-12-28, Close, 29.29
2000-12-29, Close, 27.41
Final Portfolio Value: 99725.08

若干个买入操作被执行,我们的余额也在减少。一些重要的事情似乎还不清楚。

  • 交易在何时以什么价格执行的?

    在下一个例子中,将会打印执行的结果。

细心的读者可能会问,买了多少?买的什么?订单怎么被执行的?框架替我们做了这些事:

  • 如果没有指定的话,self.datas[0] (即主价格数据) 即是标的物
  • 交易数量由 仓位数量 参数默认指定了,默认指定为”1”了,后面例子我们会修改
  • 订单被以”市价”成交了。 Broker (经纪人,之前提到过)使用了下一个K线柱的开盘价,因为你在当前柱的收盘提交的订单,下一柱的开盘价是他接触到的第一个价格
  • 没有为订单设置后续费,后边会加上

不止买入,还要卖出

知道了如何买入,就需要说一下卖出了。

  • Strategy 类有一个变量 position 保存当前持有的资产数量
  • buy()sell() 会返回 被创建的订单 (还未被执行)
  • 订单状态改变后将会通知 Strategy 实例的 notify() 方法

“卖出” 逻辑也很简单:

  • 5个柱之后(在第6个时候执行)不管涨跌都卖

    请注意,这里没有指定具体时间,而是指定的柱的数量。一个柱可能代表1分钟、1小时、1天、1星期等等,这取决于你价格数据文件里一条数据代表的周期。

    虽然我们心里知道每个柱代表一天,但策略不知道也不关心。

还有一条:

  • 当还有头寸的时候,不再买入

Note

没有柱的下标传给 next() 方法,那它是怎么知道已经经过了5个柱了呢? 这里用了一个很 Python 的方式:调用 len() 获取它 Line 的长度。 交易发生时记下它的长度,后边都比较大小,看是否经过了5个柱。

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
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):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders
        self.order = None

    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, %.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 order
        self.order = None

    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.dataclose[0] < self.dataclose[-1]:
                    # current close less than previous close

                    if self.dataclose[-1] < self.dataclose[-2]:
                        # previous close less than the previous close

                        # BUY, BUY, BUY!!! (with 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:

            # Already in the market ... we might sell
            if len(self) >= (self.bar_executed + 5):
                # 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__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

运行之后的输出为:

Starting Portfolio Value: 100000.00
2000-01-03T00:00:00, Close, 27.85
2000-01-04T00:00:00, Close, 25.39
2000-01-05T00:00:00, Close, 24.05
2000-01-05T00:00:00, BUY CREATE, 24.05
2000-01-06T00:00:00, BUY EXECUTED, 23.61
2000-01-06T00:00:00, Close, 22.63
2000-01-07T00:00:00, Close, 24.37
2000-01-10T00:00:00, Close, 27.29
2000-01-11T00:00:00, Close, 26.49
2000-01-12T00:00:00, Close, 24.90
2000-01-13T00:00:00, Close, 24.77
2000-01-13T00:00:00, SELL CREATE, 24.77
2000-01-14T00:00:00, SELL EXECUTED, 25.70
2000-01-14T00:00:00, Close, 25.18
...
...
...
2000-12-15T00:00:00, SELL CREATE, 26.93
2000-12-18T00:00:00, SELL EXECUTED, 28.29
2000-12-18T00:00:00, Close, 30.18
2000-12-19T00:00:00, Close, 28.88
2000-12-20T00:00:00, Close, 26.88
2000-12-20T00:00:00, BUY CREATE, 26.88
2000-12-21T00:00:00, BUY EXECUTED, 26.23
2000-12-21T00:00:00, Close, 27.82
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 100018.53

哇!系统竟然盈利了。

经纪人发话:手续费呢!

经纪人喊你来交手续费了。

让我们设定一个常见的 0.1% 的费率,买卖都要收(经纪人就是这么贪)。

一行代码就能搞定:

cerebro.broker.setcommission(commission=0.001) # 0.001 即是 0.1%

我们想看看,加和不加手续费,结果有什么区别。

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
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):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None

    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')

        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.dataclose[0] < self.dataclose[-1]:
                    # current close less than previous close

                    if self.dataclose[-1] < self.dataclose[-2]:
                        # previous close less than the previous close

                        # BUY, BUY, BUY!!! (with 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:

            # Already in the market ... we might sell
            if len(self) >= (self.bar_executed + 5):
                # 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__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Set the commission - 0.1% ... divide by 100 to remove the %
    cerebro.broker.setcommission(commission=0.001)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

运行后的结果为:

Starting Portfolio Value: 100000.00
2000-01-03T00:00:00, Close, 27.85
2000-01-04T00:00:00, Close, 25.39
2000-01-05T00:00:00, Close, 24.05
2000-01-05T00:00:00, BUY CREATE, 24.05
2000-01-06T00:00:00, BUY EXECUTED, Price: 23.61, Cost: 23.61, Commission 0.02
2000-01-06T00:00:00, Close, 22.63
2000-01-07T00:00:00, Close, 24.37
2000-01-10T00:00:00, Close, 27.29
2000-01-11T00:00:00, Close, 26.49
2000-01-12T00:00:00, Close, 24.90
2000-01-13T00:00:00, Close, 24.77
2000-01-13T00:00:00, SELL CREATE, 24.77
2000-01-14T00:00:00, SELL EXECUTED, Price: 25.70, Cost: 25.70, Commission 0.03
2000-01-14T00:00:00, OPERATION PROFIT, GROSS 2.09, NET 2.04
2000-01-14T00:00:00, Close, 25.18
...
...
...
2000-12-15T00:00:00, SELL CREATE, 26.93
2000-12-18T00:00:00, SELL EXECUTED, Price: 28.29, Cost: 28.29, Commission 0.03
2000-12-18T00:00:00, OPERATION PROFIT, GROSS -0.06, NET -0.12
2000-12-18T00:00:00, Close, 30.18
2000-12-19T00:00:00, Close, 28.88
2000-12-20T00:00:00, Close, 26.88
2000-12-20T00:00:00, BUY CREATE, 26.88
2000-12-21T00:00:00, BUY EXECUTED, Price: 26.23, Cost: 26.23, Commission 0.03
2000-12-21T00:00:00, Close, 27.82
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 100016.98

天呐!竟然还盈利了。

在继续之前,让我们看看这些带有盈亏的操作:

2000-01-14T00:00:00, OPERATION PROFIT, GROSS 2.09, NET 2.04
2000-02-07T00:00:00, OPERATION PROFIT, GROSS 3.68, NET 3.63
2000-02-28T00:00:00, OPERATION PROFIT, GROSS 4.48, NET 4.42
2000-03-13T00:00:00, OPERATION PROFIT, GROSS 3.48, NET 3.41
2000-03-22T00:00:00, OPERATION PROFIT, GROSS -0.41, NET -0.49
2000-04-07T00:00:00, OPERATION PROFIT, GROSS 2.45, NET 2.37
2000-04-20T00:00:00, OPERATION PROFIT, GROSS -1.95, NET -2.02
2000-05-02T00:00:00, OPERATION PROFIT, GROSS 5.46, NET 5.39
2000-05-11T00:00:00, OPERATION PROFIT, GROSS -3.74, NET -3.81
2000-05-30T00:00:00, OPERATION PROFIT, GROSS -1.46, NET -1.53
2000-07-05T00:00:00, OPERATION PROFIT, GROSS -1.62, NET -1.69
2000-07-14T00:00:00, OPERATION PROFIT, GROSS 2.08, NET 2.01
2000-07-28T00:00:00, OPERATION PROFIT, GROSS 0.14, NET 0.07
2000-08-08T00:00:00, OPERATION PROFIT, GROSS 4.36, NET 4.29
2000-08-21T00:00:00, OPERATION PROFIT, GROSS 1.03, NET 0.95
2000-09-15T00:00:00, OPERATION PROFIT, GROSS -4.26, NET -4.34
2000-09-27T00:00:00, OPERATION PROFIT, GROSS 1.29, NET 1.22
2000-10-13T00:00:00, OPERATION PROFIT, GROSS -2.98, NET -3.04
2000-10-26T00:00:00, OPERATION PROFIT, GROSS 3.01, NET 2.95
2000-11-06T00:00:00, OPERATION PROFIT, GROSS -3.59, NET -3.65
2000-11-16T00:00:00, OPERATION PROFIT, GROSS 1.28, NET 1.23
2000-12-01T00:00:00, OPERATION PROFIT, GROSS 2.59, NET 2.54
2000-12-18T00:00:00, OPERATION PROFIT, GROSS -0.06, NET -0.12

“NET” 那列的净收益加起来是:

15.83

但系统最后的余额是:

2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 100016.98

很明显 15.83 不等于 16.98。其实没发生什么错误,净收益 “NET” 指的是已经落到口袋里的钱。

造成差别的原因是,最后一天还持有头寸。其实卖单已经发出去了,但还没来得及执行…

Broker 版本的净收益率是按照2000-12-29收盘价算的。实际应该按下一个交易日2001-01-02价格算:

2001-01-02T00:00:00, SELL EXECUTED, Price: 27.87, Cost: 27.87, Commission 0.03
2001-01-02T00:00:00, OPERATION PROFIT, GROSS 1.64, NET 1.59
2001-01-02T00:00:00, Close, 24.87
2001-01-02T00:00:00, BUY CREATE, 24.87
Final Portfolio Value: 100017.41

加起来之前的净收益:

15.83 + 1.59 = 17.42

这个净收益率17.42和最后的余额100017.41就对上了(忽略小数点误差)。

自定义策略:技术指标参数

在实践中,一般不将参数硬编码到策略中。Parameters(参数) 就是用来处理这个的。

参数的定义像这样:

params = (('myparam', 27), ('exitbars', 5),)

这个 tuple 嵌套看着不方便,格式化一下:

params = (
    ('myparam', 27),
    ('exitbars', 5),
)

将策略添加到引擎的时候,可以指定刚才定义的参数:

# 添加策略
cerebro.addstrategy(TestStrategy, myparam=20, exitbars=7)

Note

下面的 setsizing 方法已经被弃用。这里还保留是因为还有一些老示例在用。方法已改为下面这种:

cerebro.addsizer(bt.sizers.FixedSize, stake=10)

请参考 sizers 章节。

在策略类中使用买卖数量参数很容易,它们被保存在 “params” 参数里。例如,参数已经传入,在策略类里的 __init__ 方法中这样调用就可以了:

# 根据传入的参加设置买卖数量
self.sizer.setsizing(self.params.stake)

也可以直接将买卖数量传入 buysell 方法。

卖出的逻辑改为:

# 已经持有,可以卖出了
if len(self) >= (self.bar_executed + self.params.exitbars):

代码修改为:

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('exitbars', 5),
    )

    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] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None

    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')

        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.dataclose[0] < self.dataclose[-1]:
                    # current close less than previous close

                    if self.dataclose[-1] < self.dataclose[-2]:
                        # previous close less than the previous close

                        # BUY, BUY, BUY!!! (with 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:

            # Already in the market ... we might sell
            if len(self) >= (self.bar_executed + self.params.exitbars):
                # 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__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=10)

    # Set the commission - 0.1% ... divide by 100 to remove the %
    cerebro.broker.setcommission(commission=0.001)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

运行后的输出为:

Starting Portfolio Value: 100000.00
2000-01-03T00:00:00, Close, 27.85
2000-01-04T00:00:00, Close, 25.39
2000-01-05T00:00:00, Close, 24.05
2000-01-05T00:00:00, BUY CREATE, 24.05
2000-01-06T00:00:00, BUY EXECUTED, Size 10, Price: 23.61, Cost: 236.10, Commission 0.24
2000-01-06T00:00:00, Close, 22.63
...
...
...
2000-12-20T00:00:00, BUY CREATE, 26.88
2000-12-21T00:00:00, BUY EXECUTED, Size 10, Price: 26.23, Cost: 262.30, Commission 0.26
2000-12-21T00:00:00, Close, 27.82
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 100169.80

为了显示改变已生效,输出中显示了买卖数量。

买卖数量改为了原来的10倍,盈亏也变为了原来的10倍,变为了 169.80

添加技术指标

之前我提到过 indicators(技术指标) ,下一步就该添加他们了,要做的肯定比前边 “三连跌” 这种复杂点。

借用 PyAlgoTrade 这个框架的一个使用移动平均线的例子:

  • 收盘价高于平均价的时候,以市价买入
  • 持有仓位的时候,如果收盘价低于平均价,卖出
  • 只有一个待执行的订单

大多数代码不用改变,在 __init__ 方法中加入移动平均的实例化:

self.sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.maperiod)

当然买入卖出的逻辑依赖平均价,具体代码如下。

Note

起始金额为1000元,无手续费,这和 PyAlgoTrade 保持一致。

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('maperiod', 15),
    )

    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] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None

        # Add a MovingAverageSimple indicator
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.maperiod)

    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')

        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.dataclose[0] > self.sma[0]:

                # 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.dataclose[0] < self.sma[0]:
                # 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__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(1000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=10)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

让我们 仔细地 看一下出现在下面日志中的第一条记录:

  • 不再是新千年的第一个交易日 2000-01-03 了。

    变成了 2000-01-24 ,怎么回事呢?

是因为,框架根据新代码做出了改变: The missing days are not missing. The platform has adapted to the new circumstances:

  • 在策略中我们加入了移动平均技术指标。
  • 移动平均需要有个均线周期参数,程序根据这个参数回看计算前边的 X 条价格数据然后进行开仓判断,例子中周期是15
  • 2000-01-24 就是第15天

backtrader 框架假定策略加入这个技术指标是有正当理由的,比如 做开平仓的决策 。框架不会在数据没到位的时候就进行下一步。

  • 在技术指标产生第一条数据之后,next 方法第一个被调用
  • 在示例中只有一个技术指标,其实策略支持添加多个技术指标

运行后的输出为:

Starting Portfolio Value: 1000.00
2000-01-24T00:00:00, Close, 25.55
2000-01-25T00:00:00, Close, 26.61
2000-01-25T00:00:00, BUY CREATE, 26.61
2000-01-26T00:00:00, BUY EXECUTED, Size 10, Price: 26.76, Cost: 267.60, Commission 0.00
2000-01-26T00:00:00, Close, 25.96
2000-01-27T00:00:00, Close, 24.43
2000-01-27T00:00:00, SELL CREATE, 24.43
2000-01-28T00:00:00, SELL EXECUTED, Size 10, Price: 24.28, Cost: 242.80, Commission 0.00
2000-01-28T00:00:00, OPERATION PROFIT, GROSS -24.80, NET -24.80
2000-01-28T00:00:00, Close, 22.34
2000-01-31T00:00:00, Close, 23.55
2000-02-01T00:00:00, Close, 25.46
2000-02-02T00:00:00, Close, 25.61
2000-02-02T00:00:00, BUY CREATE, 25.61
2000-02-03T00:00:00, BUY EXECUTED, Size 10, Price: 26.11, Cost: 261.10, Commission 0.00
...
...
...
2000-12-20T00:00:00, SELL CREATE, 26.88
2000-12-21T00:00:00, SELL EXECUTED, Size 10, Price: 26.23, Cost: 262.30, Commission 0.00
2000-12-21T00:00:00, OPERATION PROFIT, GROSS -20.60, NET -20.60
2000-12-21T00:00:00, Close, 27.82
2000-12-21T00:00:00, BUY CREATE, 27.82
2000-12-22T00:00:00, BUY EXECUTED, Size 10, Price: 28.65, Cost: 286.50, Commission 0.00
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 973.90

一个盈利系统被改变之后开始亏损了…还是在手续费率设置为0的情况下。看来像他们说的 简单 添加一个 技术指标 并不是万能的。

Note

同样的交易逻辑和数据,和 PyAlgoTrade 输出的结果并不完全一致,当然只是轻微不一致。最可疑的原因是因为:小数点

处理”调整后价格”(分红、拆股后调整)时,PyAlgoTrade 并不对小数点进行四舍五入。

在对价格进行调整后, backtrader 的数据引擎将 Yahoo 价格数据的价格小数点缩减到2位。虽然输出看起来差不多,但积少成多结果就不同了。

将价格小数点缩减到2位是合理的,一般交易所只允许价格保留小数点后面2位。

Note

1.8.11.99 版本开始,backtrader 的 Yahoo 数据引擎可以设置是否做小数点位数保留,还可以设置保留多少位。

可视化:绘图

文字日志虽然能看到细节,但人们还是喜欢看可视化的东西,所以有必要将结果绘制成图表。

Note

如果要使用绘图功能,需要安装 matplotlib

绘图很容易使用,只需添加一行代码:

cerebro.plot()

这行代码要放在 cerebro.run() 之后。

为方便使用,框架做了下面这些自动化的事情:

  • 将添加第二条指数移动平均线,默认将使用数据进行绘制(就像第1条)。
  • 将添加第三条加权移动平均线,在单独区域绘制(也许看起来不合理)
  • 将添加一条 Stochastic (慢) ,使用默认参数。
  • 将添加一条 MACD ,使用默认参数。
  • 将添加一条 RSI 指标,使用默认参数。
  • 将添加一条 RSI 指标的简单移动平均线,使用默认参数(将和 RSI 一起被绘制)。
  • 将添加一条 ATR 指标,修改了默认参数以避免被绘制。

上面添加的这些指标,等于在策略类的 __init__ 方法中添加了以下语句:

# 需要绘制的指标
bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
bt.indicators.WeightedMovingAverage(self.datas[0], period=25).subplot = True
bt.indicators.StochasticSlow(self.datas[0])
bt.indicators.MACDHisto(self.datas[0])
rsi = bt.indicators.RSI(self.datas[0])
bt.indicators.SmoothedMovingAverage(rsi, period=10)
bt.indicators.ATR(self.datas[0]).plot = False

Note

即使 指标 没有被显式地声明为成员变量(如 self.sma = MovingAverageSimple…), 它们还是会被自动注册到策略类中,并影响开始执行 next 的最小周期,而且会被绘制。

在例子中,只有 RSI 的指标被赋予了一个 rsi 的变量,供后边为它创建移动平均线使用。

The example now:

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('maperiod', 15),
    )

    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] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None

        # Add a MovingAverageSimple indicator
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.maperiod)

        # Indicators for the plotting show
        bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
        bt.indicators.WeightedMovingAverage(self.datas[0], period=25,
                                            subplot=True)
        bt.indicators.StochasticSlow(self.datas[0])
        bt.indicators.MACDHisto(self.datas[0])
        rsi = bt.indicators.RSI(self.datas[0])
        bt.indicators.SmoothedMovingAverage(rsi, period=10)
        bt.indicators.ATR(self.datas[0], plot=False)

    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.dataclose[0] > self.sma[0]:

                # 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.dataclose[0] < self.sma[0]:
                # 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__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(1000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=10)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Plot the result
    cerebro.plot()

执行后的输出结果为:

Starting Portfolio Value: 1000.00
2000-02-18T00:00:00, Close, 27.61
2000-02-22T00:00:00, Close, 27.97
2000-02-22T00:00:00, BUY CREATE, 27.97
2000-02-23T00:00:00, BUY EXECUTED, Size 10, Price: 28.38, Cost: 283.80, Commission 0.00
2000-02-23T00:00:00, Close, 29.73
...
...
...
2000-12-21T00:00:00, BUY CREATE, 27.82
2000-12-22T00:00:00, BUY EXECUTED, Size 10, Price: 28.65, Cost: 286.50, Commission 0.00
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 981.00

虽然策略逻辑没有变,但回测结果却变了。这是由于执行的 bar 的数量发生了变化。

Note

前面提到过,框架会等待所有指标数据到位之后,才会运行 next 函数。 在上例中, MACD 是最后一个数据到位的指标(它的3条线都完成了输出)。 所以第一笔下单已经不是2000年1月份了,而是2000年2月份末。

图表如下:

参数调优

许多交易书籍都会说每个市场、每只股票(或期货等等)都有不同的节奏,也就是说没有一个参数能吃遍天。

在之前的例子里,策略里使用的默认参数是15。这个参数可以被更换并进行测试,以评估什么值更适合于市场。

Note

大量文献讨论了关于优化的优缺点。一般建议都会指向同一方向:不要过度优化。如果策略不理想, 而在拟合上下功夫,则可能产生一个在回测数据上非常优秀的参数,但这个参数在将来表现可能并不好。

修改了代码,以测试移动平均线的最优周期参数。为清楚起见,删除了任何买入、卖出订单的输出。

修改后的例子:

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])


# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('maperiod', 15),
        ('printlog', False),
    )

    def log(self, txt, dt=None, doprint=False):
        ''' Logging function fot this strategy'''
        if self.params.printlog or doprint:
            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] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None

        # Add a MovingAverageSimple indicator
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.maperiod)

    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.dataclose[0] > self.sma[0]:

                # 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.dataclose[0] < self.sma[0]:
                # 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()

    def stop(self):
        self.log('(MA Period %2d) Ending Value %.2f' %
                 (self.params.maperiod, self.broker.getvalue()), doprint=True)


if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    strats = cerebro.optstrategy(
        TestStrategy,
        maperiod=range(10, 31))

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(1000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=10)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0)

    # Run over everything
    cerebro.run()

这次没有调用 addstrategy ,而是用 optstrategy 函数将策略添加到 Cerebro 。 传入的也不是单个值,而是要测试的一系列值。

在策略类中添加了 stop 方法,它将在每轮回测之后被调用。 我们用它来打印回测结束之后的资产余额(之前在 Cerebro 做的)。

框架将为策略测试每个参数值,下面是输出结果:

2000-12-29, (MA Period 10) Ending Value 880.30
2000-12-29, (MA Period 11) Ending Value 880.00
2000-12-29, (MA Period 12) Ending Value 830.30
2000-12-29, (MA Period 13) Ending Value 893.90
2000-12-29, (MA Period 14) Ending Value 896.90
2000-12-29, (MA Period 15) Ending Value 973.90
2000-12-29, (MA Period 16) Ending Value 959.40
2000-12-29, (MA Period 17) Ending Value 949.80
2000-12-29, (MA Period 18) Ending Value 1011.90
2000-12-29, (MA Period 19) Ending Value 1041.90
2000-12-29, (MA Period 20) Ending Value 1078.00
2000-12-29, (MA Period 21) Ending Value 1058.80
2000-12-29, (MA Period 22) Ending Value 1061.50
2000-12-29, (MA Period 23) Ending Value 1023.00
2000-12-29, (MA Period 24) Ending Value 1020.10
2000-12-29, (MA Period 25) Ending Value 1013.30
2000-12-29, (MA Period 26) Ending Value 998.30
2000-12-29, (MA Period 27) Ending Value 982.20
2000-12-29, (MA Period 28) Ending Value 975.70
2000-12-29, (MA Period 29) Ending Value 983.30
2000-12-29, (MA Period 30) Ending Value 979.80

结果:

  • 周期参数在18以下的亏损(在没有手续费的情况下)。
  • 周期参数在18至26之间的盈利。
  • 周期参数大于26的又会亏损。

对这个策略来说,最优的参数是:

  • 回看周期20,本金1000,盈利78元,收益率7.8%。

Note

在上例中,移除了多余的用来绘图的指标,数据到位开始回测的时间仅取决于我们添加的 简单移动平均线。所以周期为15的回测结果和之前的有轻微不同。

总结

上面的教程,我们从一个骨架开始,一步步搭建了一个能运行的回测系统,并且具备绘制结果和优化参数功能。

除此之外,还能做一些提供胜率的事情:

  • 自定义指标

    创建自定义指标很容易,绘制它们同样简单

  • 下单数量

    资金管理是交易成功的关键之一

  • 委托单类型(限价单、止损单、限价止损单)

  • 等等

请阅读其他章节,获取相关功能的介绍。

请查看目录,继续阅读和开发吧。

祝君好运~